research paper cognitive style

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53 Cognitive Style

Maria Kozhevnikov, Martinos Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, National University of Singapore, Singapore

• Published: 03 June 2013
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This chapter will review research on cognitive style from different traditions in order to revaluate previous and existing theoretical conceptions of cognitive style and to redefine cognitive style in accordance with current cognitive science and neuroscience theories. First, this chapter will review conventional and applied research on cognitive style that introduces the concept of cognitive style as patterns of adaptation to the external world and demonstrate that, although cognitive style develops on the basis of innate abilities, it is modified further as a result of changing environmental demands. Next, we will review the latest trends in cognitive style research that integrate different style dimensions into unifying models as well as recent findings in transcultural neuroscience that have documented the existence of culturally sensitive individual differences in cognition and suggested a close relationship between sociocultural environment and specific neural and cognitive patterns of information processing. Finally, based on our review, we will redefine cognitive style as ontogenetically flexible individual differences representing an individual’s adaptation of innate predisposition to external physical and sociocultural environments and expressing themselves as environmentally and culturally sensitive neural and/or cognitive patterns of information processing.

Historically, the term “cognitive style” refers to consistencies in an individual’s manner of cognitive functioning, particularly in acquiring and processing information (Ausburn & Ausburn, 1978 ). Messick ( 1976 ) defines cognitive styles as stable attitudes, preferences, or habitual strategies that determine individuals’ modes of perception, memory, thought, and problem solving. Witkin, Moore, Goodenough, and Cox ( 1977 ) characterize cognitive style as individual differences in the way people perceive, think, solve problems, learn, and relate to others.

While it seems obvious that there are differences among individuals’ preferred ways of processing information, what these differences mean and how they might be captured is less apparent. Despite being extremely popular throughout the 1950s–1970s, research on cognitive style has lost much of its appeal and has been seriously questioned in recent decades, and currently, many cognitive scientists are on the verge of accepting that cognitive style research has reached a standstill. The main reasons for this decline of interest in cognitive style seem to be the lack of a coherent organizing framework, and the lack of understanding of how cognitive style maps onto other psychological concepts and theories (see Kozhevnikov, 2007 , for a review). According to its definition, cognitive style should refer to the way individuals process information; however, since the vast majority of cognitive style studies were conducted before the rise of cognitive science, the concept of cognitive style has not been integrated with contemporary cognitive science theories, and the relationship between cognitive style’s and cognitive psychology’s approaches to individual differences in cognition has not been established (Kozhevnikov, 2007 , for a review).

Cognitive psychologists and neuroscientists researching individual differences in cognitive functioning have often focused on such basic dimensions of individual differences as speed of processing, working memory capacity (WMC), and general fluid intelligence ( Gf ). Overall, these reflect stationary individual differences in cognition, in the sense that these individual differences are largely genetically predetermined (Ando, Ono, & Wright, 2001 ; Deary, Penke, & Johnson, 2010 ; Friedman et al., 2008 ) and exhibit only limited ontogenetic sensitivity and training-induced plasticity (e.g., Sayala, Sala, & Courtney, 2005 ). Cognitive style researchers, in contrast, originally introduced the concept of cognitive style as specific modes of adjustment to the external world (Klein, 1951 ; Witkin, Dyk, Faterson, Goodenough, & Karp, 1962 ) modifiable by sociocultural and life experiences, and they have been primarily interested in more flexible , ontogenetically malleable individual differences that are shaped as a result of physical and sociocultural influences.

The goal of the current chapter is to incorporate the concept of cognitive style into current cognitive science theories of individual differences by integrating research findings on individual differences in cognition and cognitive styles from three different research perspectives: (1) cognitive style, (2) cognitive psychology and neuroscience, and (3) transcultural psychology and neuroscience. First, this chapter will review conventional research on cognitive style that introduces the concept of cognitive style as patterns of adaptation or specific modes of adjustment to the external world. Next, the chapter will review cognitive style research in applied fields (education, management) demonstrating that, although cognitive style develops on the basis of innate abilities, it is modified further as a result of changing environmental demands and life experiences, and it must thus be thought of not only in terms of innate predispositions but as a flexible construct, in terms of sociocultural interactions regulating an individual’s behavior. Third, we will summarize the latest trends in cognitive style research that have attempted to integrate the variety of cognitive style dimensions into unifying hierarchical models, and we relate these models to information processing theories. Fourth, we will review recent findings in transcultural psychology and neuroscience that have documented the existence of culturally sensitive individual differences in cognition and suggested a close relationship between sociocultural environment and specific neural and cognitive patterns of information processing.

Finally, based on our review, we will suggest a dissociation between (1) stationary individual differences that are determined primarily by genetic factors and exhibit only limited sensitivity to ontogenetic (environmental and sociocultural) factors; and (2) flexible individual differences or cognitive styles , whose formation, although affected by genetic factors, is largely influenced by environmental and sociocultural factors during ontogenetic development. According to the aforementioned approach, we will redefine the concept of cognitive style as ontogenetically flexible individual differences representing an individual’s adaptation of innate predisposition to external physical and sociocultural environments and expressing themselves as environmentally and culturally sensitive neural and/or cognitive patterns of information processing .

“Conventional” Cognitive Style

“Conventional” cognitive style research began in the late 1940s, with experimental research (e.g., Hanfmann, 1941 ; Klein, 1951 ; Klein & Schlesinger, 1951 ; Witkin & Ash, 1948 ) that focused on identifying the existence of consistent individual differences in performance on lower order cognitive tasks (e.g., perception, simple categorization). For example, Hanfmann ( 1941 ) identified two groups of individuals: those who preferred a perceptual approach when grouping blocks, and others who preferred a more conceptual approach. Klein ( 1951 ) identified “sharpeners,” who tended to notice differences between visual stimuli, and “levelers,” who tended to notice similarities.

These individual differences were first conceptualized as cognitive styles in the early 1950s, with Klein ( 1951 ) terming them as “perceptual attitudes.” These perceptual attitudes were defined as patterns of adaptation to the external world that regulate an individual’s cognitive functioning. According to Klein, adaptation requires balancing one’s inner needs with the requirements of the external environment. Klein also reported a relationship between cognitive style and personality; levelers exhibited a “self-inwardness” pattern characterized by “a retreat from objects, avoidance of competition,” while sharpeners were more manipulative and active (Klein, 1951 , p. 339). Klein considered both poles of the leveling/sharpening dimension as equally valid ways for individuals to achieve a satisfactory equilibrium between their inner needs and outer requirements, but different in their repertoire of psychological functions. Several years later, based on Klein’s findings, Holzman and Klein ( 1954 , p. 105) defined cognitive styles as “generic regulatory principles” or “preferred forms of cognitive regulation” in the sense that cognitive styles are an “ organism’s typical means of resolving adaptive requirements posed by certain types of cognitive problems ,” emphasizing the adaptive and flexible nature of cognitive style.

Around the same time, Witkin et al. ( 1954 ) carried out his large-scale experimental study on field dependence/independence, which was central to the further development of cognitive style research. The goal of this study was to investigate individual differences in perception and to associate these differences with particular trends in personality. Subjects were presented with a number of orientation tests aimed at examining their perceptual skills (e.g., Rod-and-Frame Test, in which the subjects determined the upright position of a rod, or Embedded Figure Test [EFT], in which the subjects were asked to find a simple figure inside a complex one) along with various personality measures. Two main groups of subjects were distinguished: field dependent (FD), those who exhibited high dependency on the surrounding field; and field independent (FI), those who displayed low dependency on the field. There was also a significant relationship between subjects’ performance on perceptual tests and their personality characteristics: FD individuals were more attentive to social cues than FI individuals. In contrast, the FI group had a more impersonal orientation than the FD group, exhibiting psychological and physical distancing from others. Witkin concluded that that the “core” of cognitive style is rooted in an individual’s innate predispositions, such as abilities or personality. Furthermore, Witkin explained individual differences in perception as outcomes of different modes of adjustment to the world, concluding that both FD and FI groups have specific components that are adaptive to particular situations. According to Witkin, Dyk, Paterson, Goodenough, and Karp ( 1962 ), field dependence reflects an earlier and less differentiated mode of adjustment to the world, and field independence reflects a later and more differentiated mode. However, although a highly differentiated FI individual could be highly efficient in perceptual and cognitive tasks, he or she may exhibit inappropriate responses to certain situational requirements and be in disharmony with his or her surroundings. Thus, both Klein and Witkin introduced the notion of cognitive style as patterns or modes of adjustment to the world, which appeared to be equal in their adaptive value but different in their level and repertoire of psychological and/or perceptual functions.

In the late 1950s, Klein’s and Witkin’s idea of bipolarity (value-equal poles of cognitive style dimensions in terms of adaptive nature) spawned a great deal of interest. As a result, a tremendous number of studies on “style types” appeared in the literature. The most commonly studied cognitive styles of this period are impulsivity/reflectivity (Kagan, 1966 ), tolerance for instability (Klein & Schlesinger, 1951 ), breadth of categorization (Pettigrew, 1958 ), field articulation (Messick & Fritzky, 1963 ), conceptual articulation (Messick, 1976 ), conceptual complexity (Harvey, Hunt, & Schroder, 1961 ), range of scanning, constricted/flexible controls (Gardner, Holzman, Klein, Linton, & Spence, 1959 ), holist/serialist (Pask, 1972 ), verbalizer/visualizer (Paivio, 1971 ), and locus of control (Rotter, 1966 ). Attempting to organize these numerous dimensions, Messick ( 1976 ) proposed a list of 19 cognitive styles; Keefe ( 1988 ) synthesized a list of 40 separate styles.

One of the serious limitations of conventional cognitive style research was its narrow focus on lower order cognitive tasks, often assessed by performance ability measures (error rate and response time) with simple “right” and “wrong” answers, which is hypothetically more relevant in testing abilities , not styles. Most of the perceptual tasks used as measures of cognitive style were tapping relatively stationary individual differences related to personality or intelligence. Ironically, this fact appears especially clear in the most commonly used instruments to measure cognitive styles, such as Witkin’s EFT (Witkin et al., 1954 ) and Kagan’s Matching Familiar Figures Test (MFFT; Kagan, Rosman, Day, Albert, & Phillips, 1964 ). While these instruments were supposed to measure bipolar dimensions representing two equally efficient ways of solving a task, in reality, one strategy was usually more effective than the other (e.g., FI subjects usually perform better than FD on many spatial tasks). It is not surprising then that many researchers who have investigated the correlation between intelligence tests and conventional measures of field dependence such as the Rod-and-Frame or EFT (e.g., Cooperman, 1980 ; Goodenough & Karp, 1961 ; McKenna, 1984 ) consistently report higher intelligence among individuals with an FI style than among those with an FD style.

Thus, despite that literature of that period has suggested the adaptive nature of cognitive style, and proposed that cognitive style refers to specific modes of adjustment to the external world (Klein, 1951 ; Witkin et al., 1962 ), early research on cognitive styles often used measures of individual differences sensitive mostly to genetic factors, and it did not clearly distinguish those from adaptive, ontogenetically malleable traits. This caused a situation in which the cognitive styles under study closely resembled genetically predetermined cognitive abilities, sparking later debates as to whether cognitive style and ability were indeed the same. Furthermore, since the majority of the aforementioned studies were conducted before the advent of cognitive science, their main problem was the lack of a unifying theoretical approach to information processing, which could lay the foundation for systematizing numerous overlapping cognitive style dimensions (see Kozhevnikov, 2007 for a review). Consequently, the promising benefits of studying cognitive styles were lost amidst the chaos, and the amount of work devoted to the cognitive style construct declined dramatically by the end of the 1970s, ironically, only a few years before information processing and cognitive science stepped into the forefront of contemporary psychology. Thus, although cognitive style refers to ways of processing information, since the majority of interest in cognitive style was abandoned before the rise of the information processing approach, a close relationship between cognitive style and other psychological concepts from contemporary information processing theories was never properly established.

Research in Applied Fields: Sociocultural Components of Cognitive Styles

Despite declining theoretical interest in conventional cognitive styles toward the end of the 1970s, the number of publications on cognitive styles in applied fields has continued to increase, reflecting an assumption of a practical necessity of understanding cognitive styles and their important role in real-life activities. Applied research on cognitive style focused on the existence of styles related to higher order cognitive functioning, such as problem solving, decision making, learning, and explanation of causality, as reviewed next.

Kirton ( 1976 , 1989 ) was the first to consider “decision-making styles” within the cognitive style framework by introducing the adaptor/innovator dimension of managerial style. Kirton defined adaptors as preferring to accept generally recognized policies while proposing ways of “doing things better,” and innovators as those who question the problem itself and propose ways “for doing things differently”; Kirton proposed that these differences were evident in personality as well as creativity and problem-solving strategies. Kirton ( 1989 ) investigated the adaptation/innovation dimension in organizational settings, widening the concept of cognitive style to characterize not only individuals but also the prevailing style in a group situation (called “organizational cognitive climate”). Kirton argued that overall cognitive climate stems from members of a workgroup sharing similar cognitive styles, that is, with all members within one-half standard deviation around the mean for the workgroup. Other studies on managerial decision-making styles were conducted by Agor ( 1984 ), who introduced three broad types of management styles in decision making: intuitive, analytical, and integrated problem-solving styles. Agor ( 1984 ) surveyed 2,000 managers of various occupations and managerial levels, and cultural backgrounds, and although it is not clear whether the differences cited are indeed statistically significant, Agor states that the data showed variation in executives’ dominant styles of management practice by organizational level, service level, and gender and ethnic background (e.g., women are more intuitive than men, managers of Asian background are more intuitive than the average manager). As did Kirton, Agor pointed out that one’s decision-making style not only includes stable individual characteristics but also applies to interpersonal communications and group behavior.

Rowe and Mason ( 1987 ) proposed a model of decision-making styles based on cognitive complexity (i.e., an individual’s tolerance for ambiguity) and environmental complexity (people-oriented vs. task-oriented work environment). The four styles derived from this model are directive (practical, power-oriented), analytical (logical, task-oriented), conceptual (creative, intuitive), and behavioral (people-oriented, supportive). Rowe and Mason stressed the importance of cognitive style in career success. More recent studies on styles in managerial fields have supported similar ideas. First, that cognitive style is a key “determinant of individual and organizational behavior, which manifests itself in both individual workplace actions and in organizational systems, processes, and routines” (Sadler-Smith & Badger, 1998 , p. 247). Second, although tending to be relatively stable, cognitive styles interact with the external environment and can be modified in response to changing situational demands, as well as influenced by life experiences (Allison & Hayes, 1996 ; Hayes & Allinson, 1998 ; Leonard & Straus, 1997 ).

At the same time, by the end of 1970s, a large number of “personal cognitive styles” have arisen in psychotherapy, such as optimistic/pessimistic, explanatory, anxiety-prone, and others (Haeffel et al., 2003 ; Peterson et al., 1982 ; Seligman, Abramson, Semmel, & von Baeyer, 1979 ). One of the first and most elaborated personality-related styles to be used widely in psychotherapy was the explanatory (attributional) style that reflects differences in the manner in which people habitually explain the causes of uncontrollable events (attributing the cause to internal vs. external circumstances). The cognitive component, according to this theory, refers to the ways in which people perceive, explain, and extrapolate events in their lives. Furthermore, the attribution theory suggests that styles are not always inherent to one’s personality and intelligence and, although relatively stable, can be acquired as a result of an individual’s interaction with the external environment (Peterson, Maier, & Seligman, 1993 ). It requires some amount of repetition of life events or observing other people’s behavior to reinforce or inhibit a certain style.

The Myers-Briggs Type Indicator (MBTI) remains the major tool for describing personal styles (Myers, 1976 ; Myers & McCaulley, 1985 ). The MBTI is a self-report instrument, which was developed based on four of Jung’s ( 1923 ) personality dimensions, extraversion/introversion (EI), sensing/intuition (SI), thinking/feeling (TF), and judging/perceiving (JP). Permutations of the four dimensions form 16 psychological types identified by the MBTI. Although evidence supporting the MBTI as a valid measurement of style is inconclusive (see Coffield, Moseley, Hall, & Ecclestone, 2004 , for a review), and there has been considerable controversy regarding its measurement characteristics (Carlson, 1989 ; Healy, 1989 ; McCaulley, 1991 ) and construct validity (e.g., Bess & Harvey, 2002 ; Girelli & Stake, 1993 ), similar to other “applied” approaches, the MBTI assumes close connections between one’s style and professional specialization and that certain professional settings are suited to individuals with different personality profiles.

The applied field that generated the greatest number of studies on styles was education. In education, research on style was aimed at understanding individual differences (preferences) in learning processes, and thus they were called “learning styles.” One of the first models of learning styles was proposed by Kolb ( 1974 , 1984 ), who suggested that the “cycle of learning” involves four adaptive learning modes—two opposing modes of grasping experience: concrete experience (CE) and abstract conceptualization (AC); and two opposing modes of transforming experience: reflective observation (RO) and active experimentation (AE). Kolb suggested the relationship between learning styles and educational or professional specialization, showing that different job requirements might cause changes in learning styles. Other research on learning styles has focused on the development of psychological instruments to assess individual differences in complex classroom situations (e.g., Dunn, Dunn, & Price, 1989 ; Entwistle, 1981 ; Schmeck, 1988 ). These studies all showed a close connection between educational environment and cognitive style. Overall, learning style research establishes the importance of how education might affect cognitive style and also how cognitive style may affect an individual’s preference for certain educational environments.

The main problem with applied research on cognitive style is similar to the problem with conventional cognitive style research: If in the conventional cognitive style research the number of styles was defined by the number of cognitive tasks used as assessors, here the number of styles was defined by the number of applied fields in which styles were studied. As a consequence, the cognitive style construct multiplied, and in addition to conventional cognitive styles, the new terminologies of decision-making styles, learning styles, and personal styles were introduced in the mid-1970s, without clear definitions of what they were or how they differed from conventional cognitive styles. Despite their problems, one of the most significant contributions of the applied studies to cognitive style research is the examination of how external, social-environmental factors affect the formation of cognitive style. Most of the applied studies on cognitive style converged on the conclusion that cognitive styles, although relatively stable, adapt to changing environmental and situational demands and can be modified by life experiences. Furthermore, evidence has accumulated regarding the connection between an individual’s cognitive style and the requirements of different social groups—including parent–child relationships, educational and professional societies, and sociocultural environment. Thus, the early definition of cognitive styles as patterns of adjustment to the world was further specified to include descriptions of particular requirements of social and professional groups on an individual’s cognitive functioning. Cognitive styles became related not only to personality, ability, or cognition but also to social interactions regulating beliefs and value systems.

Another significant contribution by this line of research is that it expanded the concept of cognitive style to include constructs that might operate not only at perceptual or simple cognitive levels but also on complex, higher order cognitive levels (decision making, learning preferences). Overall, the applied studies on cognitive style seem to more apparently reflect the flexible nature of cognitive styles (e.g., interaction and development within professional and educational settings; relevance for sociocultural interactions and overall sociocultural context).

Recent Developments in Cognitive Style Research

Since the 1970s, conventional cognitive style and applied cognitive style studies have been joined by new trends in cognitive style research, which can be divided into three rough categories. The first includes studies that suggest the existence of cognitive styles (e.g., mobility-fixity), or “metastyles,” that operate on the metacognitive level. The second category contains studies that attempt to unite existing models of cognitive style into a unifying theory with a limited number of central dimensions, culminating in a few theoretical studies that aim to build multilevel hierarchical models of cognitive styles.

The Mobility/Fixity Dimension: “Metastyle”

Studies of the mobility/fixity (also called flexibility/rigidity) dimension attempted to address contradictory results from previous research on conventional cognitive styles, namely, the mobility of cognitive style. Witkin was the first to point out that there might be “mobile” individuals who possess both FD and FI characteristics, and can employ one style or the other depending on the situation (Witkin, 1965 ; Witkin et al., 1962 ). According to Witkin, while FI individuals as a group tend to be creative, FI individuals who also possess FD characteristics may be even more creative, since such mobility signifies greater diversity in functioning and is more adaptive than fixed use of a single style.

Furthermore, Niaz ( 1987 ) administered the ETF and Raven’s Standard Progressive Matrices Test to a group of college freshmen to assess their field dependence/independence and intelligence level. In addition, participants received the Figural Intersection Test to measure mobility/fixity. According to their results on the EFT and Figural Intersection Test, four groups of participants were identified: mobile FI, mobile FD, fixed FI, and fixed FD. Niaz reported that the fixed FI group of students received the highest intelligence scores among all the groups on the Raven’s Matrices Test, while mobile individuals (both FD and FI) performed significantly better than all other groups in college chemistry, mathematics, and biology classes. Niaz ( 1987 , p. 755) concluded that “mobile subjects are those who have available to them both a developmentally advanced mode of functioning (field-independence) and a developmentally earlier mode (field-dependence).” Furthermore, she also concluded that in mature individuals, fixed functioning implies a certain degree of inflexibility and inability to regress to earlier perceptual modes.

Furthermore, Russian psychologist Kholodnaya ( 2002 ) suggested a quadripolar model of several conventional cognitive styles: field dependence/independence, wide/narrow categorization, constricted/flexible cognitive control, and impulsivity/reflectivity. Participants were administered a number of different cognitive style and intelligence tests (EFT, the MFFT, Raven’s Matrices, Stroop task) and a word sorting task. Using cluster analysis, four clusters in the field-dependency/independency dimension were identified. One seemed to represent fixed FI individuals, who demonstrated high scores on EFT; however, they showed high interference and longer response times in the Stroop task, as well as low concept formation ability (as measured by the word sorting task). In contrast, another cluster, representing mobile field independence , included individuals who, along with high EFT scores, showed relatively high performance on the word sorting task, lower conflict on the Stroop task, and higher ability to integrate sensory information with context. The other two clusters, fixed FD and mobile FD , were similar in their relatively poor response on the EFT. However, in contrast to fixed FD, mobile FD individuals exhibited low cognitive conflict in the Stroop task and better ability to coordinate their verbal responses with presented sensory information. Kholodnaya found similar patterns for each of the following dimensions: constricted/flexible cognitive control, impulsivity/reflexivity, and narrow/wide range of equivalence. She concluded that mobile individuals can spontaneously regulate their intellectual activities and effectively resolve cognitive conflicts. In contrast, fixed individuals are unable to adapt their strategies to the situation and exhibit difficulties in monitoring their intellectual activity. Thus, according to Kholodnaya, cognitive style represents the extent to which the metacognitive self-monitoring mechanisms are formed in a particular individual, and in the case of a fixed individual, it is more appropriate to talk about a cognitive deficit rather than a cognitive style.

The important contribution of this research is that it introduces the notion of metacognition to the field of cognitive style. However, there is little support for the conclusions that fixed individuals exhibit cognitive deficits, or that cognitive style can be reduced to metacognition, or that the majority of the cognitive styles are quadripolar dimensions. Rather, the results seem to suggest that individuals are different in the extent to which they exhibit flexibility and their degrees of self-monitoring in their choices of different cognitive styles. Mobility/fixity may be better viewed as a metastyle representing the level of flexibility with which an individual applies a particular style in a particular situation. More recently, Kozhevnikov ( 2007 ) suggested that the mobility/fixity dimension represents a superordinate metastyle dimension, which serves as a control structure for other subordinate cognitive styles. That is, metastyle represents the developmental level of an individual’s metacognitive mechanisms—the ability to consciously control and situationally adapt one’s own active problem-solving strategies to the degree that he or she has a number of available potential solutions and strategies (which may involve opposing cognitive styles) and can select the most appropriate one for any given task.

Toward Hierarchical Models of Cognitive Style

The unifying trend emerged in the 1990s as a general response to vagueness in the cognitive style field, and it aimed to unite and systematize multiple cognitive style dimensions. For example, Hayes and Allinson ( 1994 ) proposed that the different dimensions of cognitive style can be considered as variations of an overarching analytical-intuitive dimension. Others characterize cognitive style as consisting of two orthogonal dimensions, such as holistic-analytical versus visualizer-verbalizer (Hodgkinson & Sadler-Smith, 2003 ; Riding & Cheema, 1991 ). However, these models of cognitive style were too simplistic; they tried to reduce cognitive style to a limited number of dimensions, rather than build a theory that systematizes known styles into a multidimensional structure. Generally, models of cognitive style do not consider cognitive style in the context of information processing theories; they neither attempt to relate cognitive styles to other psychological theories, nor do they fully account for the complexity of the cognitive style construct. Miller ( 1987 ) was the first to consider cognitive style in the context of information processing and proposed that cognitive style consisted of a horizontal analytical-holistic dimension and a vertical dimension representing different stages and levels of information processing, such as perception, memory (representation, organization, and retrieval), and thought. However, Miller’s model has been criticized for its lack of empirical support, and that the placement of cognitive style dimensions into the model was based more on convenience than research evidence or theoretical framework (Messick, 1994 ; Zhang & Sternberg, 2006 ).

There have been few empirical attempts to systematize cognitive style dimensions (e.g., Leonard, Scholl, & Kowalski, 1999 ; Bokoros, Goldstein, & Sweeney, 1992 ). For example, Bokoros et al. ( 1992 ) conducted an empirical study based on the factor analysis of correlations between the various subscales of widely used cognitive style instruments. They identified three factors, to which a variety of cognitive style dimensions could be reduced, which were dubbed as the “information-processing domain,” the “thinking-feeling dimension,” and the “attentional focus dimension.” It is interesting to note that the first and second factors identified empirically by Bokoros et al. closely resemble “conventional” and “applied” styles, respectively. While the first factor comprised cognitive style dimensions that operate at perceptual and low-order cognitive levels, the second factor comprised styles related to individual differences in more complex, higher order cognitive activities. As for the third factor, which is described by Bokoros et al. as “internal and external application of the executive cognitive function,” it closely resembles the mobility/fixity dimension, or metastyle, described in the mobility/fixity lines of research.

Finally, Nosal ( 1990 ) proposed a multidimensional hierarchical model of cognitive style that systematized cognitive style dimensions based on cognitive science theories. Specifically, the model proposes that the variety of cognitive styles can be arranged into a matrix (see Fig. 53.1 ). The horizontal axis of the matrix represents four hierarchical levels of information processing: perception (processing of primary/early perceptual information), concept formation (formation of conceptual representations in the form of symbolic, semantic, and abstract structures), modeling (organizing personal experiences into “schemas,” “models,” or “theories”), and program (goal-directed activity and metacognitive approaches used for complex decision-making tasks).

Cognitive styles in relation to levels of information processing and cross-dimensions, according to Nosal’s theory. 1 = field dependence-independence; 2 = field articulation; 3 = breadth of categorization; 4 = range of equivalence; 5 = articulation of conceptual structure; 6 = tolerance for unrealistic experience; 7 = leveling-sharpening; 8 = range of scanning; 9 = reflectivity-impulsivity; 10 = rigidity-flexibility; 11 = locus of control; 12 = time orientation.

After positioning different conventional styles into these four levels of information processing, Nosal identified a number of vertical cross-dimensions, which he described as “modules of information processing” that encompass all the variety of cognitive styles. These stylistic cross-dimensions, according to Nosal, reflect regulatory mechanisms responsible for generating four qualitatively different bipolar cognitive style dimensions: (1) field structuring ( context dependent vs. context independent ), which describes a tendency to shift attention to perceiving events as separate versus inseparable from their context; (2) field scanning ( rule-driven vs. intuitive ), which describes a tendency for directed, driven by rules, versus aleatoric, driven by salient stimuli, information scanning; (3) control allocation ( internal vs. external locus of processing ), which describes ways of locating criteria for processing at the internal versus external center; and (4) equivalence range ( compartmentalization vs. integration ), which represents a tendency to process and output information globally versus sequentially. The model allows detecting some gaps in the area of cognitive style and identifying yet unknown cognitive styles dimensions in the cells of the matrix. For instance, field-structuring cross-dimension has been studied so far on the basis of Witkin’s field dependence/independence cognitive style, which operates mostly on the perceptual level, so context-dependent/independent styles operating at higher levels of cognitive processing have yet to be identified.

It is interesting to note that the cross-dimensions that Nosal derived from his model resemble the metacomponents suggested by Sternberg’s componential theory of intelligence (selection of low-order components, selection of representation or organization of information, and selection of a strategy for combining lower order components), which are defined as the “specific realization of control processes…sometimes collectively (and loosely) referred to as the executive” (Sternberg, 1985 , p. 99). Thus, the four major cross-dimensions identified by Nosal seem to reflect four different types of executive functions or cognitive control processes that regulate an individual’s perception, thoughts, and actions, and generate four qualitatively different bipolar cognitive style dimensions (i.e., context dependent vs. context independent; rule-driven vs. stimulus-driven information scanning; internal vs. external locus of control; and holistic vs. sequential processing). In this view, any given cognitive style can be viewed as an expression of a particular executive function from these four cross-dimensions, operating at a particular level of information processing. Thus, according to Nosal’s categorization, the number of cognitive styles is finite and unknown styles could be predicted and placed into the cells of the matrix.

In summary, the recent studies on cognitive style endeavored to systematize the variety of cognitive styles and establish a possible structural relationship among them. These studies cast serious doubt on the unitary nature of cognitive style and provided evidence for the hierarchical organization of cognitive style dimensions operating at different levels of information processing (from perceptual to metacognitive). Furthermore, Nosal’s model allows for mapping existing models of cognitive style onto information processing theories, taking into account the complex structure and multidimensionality of the cognitive style construct. Furthermore, many of the aforementioned studies pointed out the regulatory function of cognitive styles. Nosal’s model, in particular, suggested that all the variety of cognitive style dimensions might be clustered around a limited number of stylistic cross-dimensions, related to specific executive functions.

Perspectives From Cognitive Sciences and Neuroscience

Recent cognitive science and neuroscience studies provided new evidence that shed light on the nature of cognitive style and its relation to other basic psychological constructs and processes. In this section, we will review two categories of recent cognitive science and neuroscience studies.

The first line of research contains a few recent cognitive neuroscience studies that attempted to demonstrate that cognitive style may be more accurately represented by specific patterns of neural activity, and not only by differences in performance on behavioral measures. Gevins and Smith ( 2000 ) examined differences between subjects with verbal versus nonverbal cognitive styles by recoding their electroencephalograms (EEGs) while the subjects performed a spatial working memory task. The results showed that although the subjects did not significantly differ in task performance, subjects with a verbal cognitive style tended to make greater use of the left parietal region, whereas subjects with a nonverbal style tended to make greater use of the right parietal region. Furthermore, functional magnetic resonance imaging (fMRI) experiments have revealed that, while performing the EFT (which can be solved with either visual-object or visual-spatial strategies), in the absence of significant behavioral differences in task performance, spatial visualizers (i.e., individuals who prefer to process information spatially in terms of spatial relations and locations) showed greater activation in left occipito-temporal areas, while the object visualizers (i.e., individuals who prefer to process information visually in terms of color, shape, and detail) showed greater activation in the bilateral occipito-parietal junction (Motes, Malach, & Kozhevnikov, 2008 ), supporting the relationship between individual differences in visual cognitive style and differential use of regions in the dorsal and ventral visual processing streams. The importance of these studies is that they indicate that individuals with different cognitive styles may exhibit different patterns of neural activity, even though their behavioral performance may not significantly differ. That is, the findings imply that the differences underlying individuals’ cognitive styles can be associated with different patterns of neural activity in the brain, in addition to the ability to perform a particular task.

The second line of studies is related to the most recent cognitive and cultural neuroscience studies that demonstrated that culture-specific experiences may afford distinct patterns of information processing. Surprisingly, these culture-sensitive patterns of information processing were indentified not only at cognitive levels but also at the neural and perceptual levels, suggesting that sociocultural experiences may affect neural pathways and also shape perception (e.g., Han & Northoff, 2008 ). For instance, several studies have shown that members of Eastern cultures exhibit more holistic and field-dependent rather than analytic and field-independent perceptual affordances (e.g., Miyamoto, Nisbett, & Masuda, 2006 ;Nisbett & Masuda, 2003 ). On a change blindness task, East Asians detected more changes in background context, whereas North Americans detected more changes in foreground objects (Nisbett & Masuda, 2003 ). Kitayama, Duffy, Kawamura, and Larsen ( 2003 ) also found that while North Americans were more accurate in an “absolute task” (drawing a line that was identical to the first line in absolute length), Japanese people were more accurate in a “relative task” (drawing a line that was identical to the first in relation to the surrounding frame), suggesting that the Japanese participants paid more attention to the frame (context) than did the North Americans and were more field dependent. Other studies reported that North Americans recognized previously seen objects in changed contexts better than Asians did, due to their increased focus on objects’ features independent of context (Chua, Boland, & Nisbett, 2005 ). Gutchess, Welsh, Boduroglu, and Park ( 2006 ) evaluated neural bases for these cultural differences in fMRI, concluding that cultural experiences subtly direct neural activity, particularly for focal objects in early visual processing.

Differences between members of different cultures were also reported on lower order and higher order cognitive tasks, as well as on tasks that require metacognitive processing. For instance, Chinese participants organized objects more relationally (e.g., grouping “monkey” and “banana” together because monkeys eat bananas) and less categorically (e.g., grouping “panda” and “monkey” together because both are animals) than Westerners (Nisbett, 2003 ), reflecting differences in field scanning. Significant differences between Easterners and Westerners have also been found in decision making. Kume ( 1985 ) discovered that, when making decisions, Easterners adopt an indirect, agreement-centered approach, based on intuition, while Westerners favor a direct, confrontational strategy using rational criteria. There are also cross-cultural differences at the neural level in language processing; English speakers reading English words activate superior temporal gyrus, but Chinese speakers reading Chinese characters activate inferior parietal lobe (Tan, Laird, Li, & Fox, 2005 ), which might indicate differences in global versus sequential processing.

Furthermore, research also identified self-construal differences: Westerners characterize the self as independent and have self-focused attention, while East Asians emphasize interdependence and social context (Nisbett, Choi, Peng, & Norenzayan, 2001 ). Also, Americans believe that they have control over events to the extent that they often fail to distinguish between objectively controllable events and uncontrolled ones. In contrast, East Asians are not susceptible to this illusion (Glass & Singer, 1973 ), reflecting differences in the locus of control dimension.

Overall, the reported cultural differences were identified at all levels of information processing (from perceptual to higher order cognitive reasoning) and can be generally described as tendencies of East Asian people (1) to engage in context-dependent cognitive processes, while Westerners, who tend to think about the environment analytically, engage in context-independent cognitive processes (Goh et al., 2007 ; Miyamoto et al., 2006 ); (2) to seek intuitive instantaneous understanding through direct perception, while Westerners favor more logic and abstract principles (Nakamura, 1985 ); (3) to exhibit more external locus of control in contrast to Westerners, who have stronger internal locus (Glass & Singer, 1973 ; Nisbett et al., 2001 ); and (4) to have tendencies to perceive and think about the environment more holistically and globally, in contrast to Westerners, who engage in more sequential processing (Goh et al., 2007 ).

Interestingly, all the reported culture-sensitive individual difference can be described by Nosal’s four style cross-dimensions (executive functions), field scanning, organization, locus of control, and equivalence range, and can therefore be positioned into the cells of Nosal’s matrix of cognitive style dimensions, according to the specific executive function they perform and the dominant level of information processing involved in a given task. Thus, culture-sensitive individual differences reported in transcultural psychology and neuroscience seem to represent different dimensions of cognitive style described in the cognitive style literature, and yet unidentified, culture-sensitive individual differences in cognition might be predicted from the Nosal’s matrix.

While cognitive psychology research provides evidence that some components of executive functions (e.g., updating working memory representations, shifting between task sets) are entirely genetic in origin (Friedman et al., 2008 ), social-constructivist research argues for the sociocultural origin of executive functioning, suggesting its flexible nature (e.g., Ardila, 2008 ; Vygotsky, 1984 ). In light of the proposed framework distinguishing between stationary and flexible individual differences, as well as on the basis of the review of culture-sensitive individual differences, we suggest that the four executive functions derived from Nosal’s theory represent “flexible” components of executive functioning, which are shaped and mediated by sociocultural environment. On the basis of this approach, cognitive style research can contribute to transcultural psychology and neuroscience research by helping to organize and predict different dimensions of culture-sensitive individual differences.

Conclusions and Future Directions

The current review attempts to bridge the gap between the large body of traditional cognitive style concepts, cognitive neuroscience, and transcultural psychology and neuroscience research, using an organizing framework that distinguishes between relatively stationary (i.e., abilities, personality traits) and ontogenetically flexible (cognitive styles) individual differences in cognition. As demonstrated throughout the review, the lack of discrimination between stationary and flexible individual differences, as well as the absence of a common theoretical framework for mapping the cognitive style concept onto existing cognitive science and neuroscience research, has led to misinterpretation and underestimation of the cognitive style concept.

The reviewed literature on the state of affairs in the aforementioned three research traditions suggests that the concept of cognitive style has a place in, and should be integrated into, mainstream current cognitive science and neuroscience theories. One of the possible approaches to integrate cognitive style into contemporary cognitive science theories can be based on Nosal’s ( 1990 ) model, which proposes that the variety of cognitive styles could be structuralized as the elements of a matrix, with the horizontal axis representing different levels of information processing (from perception to metacognition) and the vertical axis representing four major types of stylistic cross-dimensions that reflect specific executive functions responsible for generating four qualitatively different bipolar cognitive style dimensions (i.e., context dependent vs. context independent; rule-driven vs. stimulus-driven information scanning; internal vs. external locus of control; holistic vs. sequential processing). Nosal’s model takes into account the complex structure and multidimensionality of the cognitive style construct, and it allows for predicting the existence of other, yet unidentified, styles.

Based on our review, we suggest redefining cognitive style as ontogenetically flexible individual differences representing an individual’s adaptation of innate predisposition to external physical and sociocultural environments and expressing themselves as environmentally and culturally sensitive neural and/or cognitive patterns of information processing. To an extent far greater than that seen in other animals, who are born in a given environment and bound for generations to specific environmental conditions and thus might exhibit numerous fixed inborn patterns of behavior that result from long-term evolutionary processes, humans are much less restricted by fixed innate mechanisms suited for specific environmental conditions. This places more importance on the role of postnatal development, which is largely based on social interactions, concepts, and cultural means of learning, and takes place in ever-expanding and changing environments throughout the life span. Thus, the inborn capacities of humans allow for a wide range of possibilities for their future expression and development. Recent evidence from neuroscience indicates that neurogenesis and neural plasticity are affected by social environments (Lu et al., 2003 ). Research in evolutionary genetics consistently shows evidence of the neural plasticity of human behavior in relation to sociocultural environment, and the coevolution of genes, cognition, and culture (see Li, 2003 for review).

The proposed view on individual differences in cognition is reflected in the model presented in Figure 53.2 . The core is formed by the individual’s innate predispositions and personality traits, which reflect stationary individual differences. This core

Layers constituting individual differences in cognition. (See color insert.)

is surrounded by cognitive style, reflecting flexible individual differences. The development of cognitive style occurs on the basis of these innate core traits and is shaped through interaction with the surrounding environment. The first environmental layer represents the individual’s immediate familial and physical environment, which influences early cognitive development and reinforces certain innate characteristics, while suppressing others. At the next level lies the educational layer, in which the individual progresses through school systems and develops certain problem-solving strategies. The next layer is the professional layer, in which individuals’ ways of thinking are sharpened and become more distinct. In the professional layer, an individual’s cognitive style is affected by both mediated information contained in the professional media, as well as personal interactions with peers. Surrounding all of these is the final, cultural layer, reflecting mental, behavioral and cognitive processing patterns common to a specific cultural group. All these sociocultural layers affect each other and together shape the different layers of information processing and behavioral patterns of an individual. Metacognitive processes can possibly affect all the subordinate layers of information processing; a person with highly developed metacognitive processes would be aware of his or her preferred style, and, when presented with tasks or situations that require use of a different style, would flexibly adapt his or her strategies. The development of flexible metatstyles would allow an individual to switch between preferred styles. Possible reasons for the formation of flexible metastyles could be experiencing different situational contexts (such as different professional, educational, and cultural settings), changing professional field, or changing cultural context (native language, traditions). Indeed, Bagley and Mallick ( 1998 ) indicated malleability of cognitive styles in migrant children and suggested that concept of cognitive style can be deployed as an indicator of process and change in migration and multicultural education, rather than as a description of basic cognitive processes.

The current organizing framework that distinguishes between stationary and ontogenetically flexible individual differences in cognition helps to bridge the gap between the large body of traditional cognitive style concepts, cognitive neuroscience, and transcultural psychology and neuroscience research. We argue for the importance of such a framework for cognitive psychology and neuroscience, which still lacks a coherent framework of individual differences. Moreover, such an organizing framework will be crucial for helping transcultural psychology and neuroscience identify the causes of found cross-cultural individual differences (e.g., whether the identified differences are due to long-term evolutionary processes or ontogenetic development) and assign relative weight to such causes (e.g. whether within-culture differences, such as in educational and/or professional contexts, might overshadow the global cultural effect). Finally, such a framework will aid in understanding the relation between cognitive style and other cognitive science concepts, suggesting that cognitive style can be a valuable concept beyond the largely abandoned filed of cognitive style research, and can bring new insights in understanding the individual differences in humans’ cognitive functioning.

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OPINION article

Cognitive style: time to experiment.

• Directorate of Psychology and Public Health, School of Health Sciences, University of Salford, Salford, UK

Evidence exists that individuals possess habitual ways of approaching tasks and situations associated with particular patterns in cognitive processes including decision making, problem solving, perception, and attention. Such approaches are conceptualized as cognitive style , a concept first formally introduced by Allport almost eight decades ago and defined as an individual's typical or habitual mode of problem solving, thinking, perceiving, and remembering ( Allport, 1937 ). The popularity of the concept has since continued to grow, leading to a profusion of applied research and commercial applications in such areas as business, management, and education. Such levels of activity have led to the emergence of more than 70 identifiable models and measures of cognitive (and learning) style ( Coffield et al., 2004 ) and a plethora of related terminology, constructs, and measures of style. Consequently, the field of style has become wildly confusing to both researchers and practitioners, and, perhaps justifiably, has received weighty criticism, most notably from Coffield et al. (2004) . Following a broad and detailed systematic review of the most popular models and construct measures, Coffield et al. (2004) , together with others (e.g., Curry, 1987 ; Cassidy, 2004 ), issued a damning critique of style, noting the failure of the field to offer a consensus on definitions and terminology, construct models and underlying theory, and valid and reliable construct measurement. Such concerns present a major obstacle for continued research and practice in the field.

Cognitive style focuses on the tradition of identification of styles based on individual differences in cognitive and perceptual functioning ( Grigorenko and Sternberg, 1995 ). As is common in many areas of psychology where there is a need and desire to measure unobservable latent constructs, the majority of style assessment methods rely on self-report measures rather than direct objective observation of style-related behavior. The limitations of self-report questionnaire-based measures are well-documented (e.g., Rayner and Riding, 1997 ) and are particularly pertinent to cognitive style where the prevalent approach to measurement remains single method self-report questionnaires ( Cools, 2009 ). Study designs that utilize multiple methods, including psychometric measures of style and more direct measures of style-based behavior, offer greater potential for validating existing style constructs and construct measures ( Cassidy, 2012 ). However, although the application of a mixed methods approach has the potential to allay some of the limitations associated with self-report questionnaires ( Spratt et al., 2004 ), this approach has been largely overlooked in cognitive styles research ( Cools, 2009 ). One promising area is cognitive neuroscience, with initial findings providing evidence suggesting that cognitive style is directly linked to brain function and behavior. As cognitive style is assumed to reflect underlying cognitive function, evidence linking specific patterns of neural activity to self-report measures of cognitive style would support the validity of such psychometric instruments.

One of the first studies to provide evidence of such a link demonstrated that preferences for visual or verbal cognitive styles were correlated with activity in anatomically and functionally distinct brain regions associated with encoding pictorial (fusiform gyrus) and phonological (supramarginal gyrus; SMG) stimuli, respectively ( Kraemer et al., 2009 ). These findings suggest that individuals who prefer to adopt a visual cognitive style engage in mental imagery of word-based stimuli, and those with a preference for a verbal style show a tendency to verbally encode stimuli even when presented with pictorial information. Further, the results suggest that modality-specific cortical activity underlies processing in visual and verbal cognitive styles.

In a more recent neuroimaging study, Shin and Kim (2015) adopted a modified Stroop task ( Stroop, 1935 ) to investigate whether individual differences in cognitive style influence, through differential responding to distracting information, increases in neural conflict adaptation in brain regions associated with cognitive control. It was evident that the greater the preference for a verbal cognitive style, the greater the conflict adaptation effect. This was especially true for congruent trial types. Furthermore, functional magnetic resonance imaging indicated increased neural conflict adaptation effects in task-relevant brain networks as the preference for a verbal cognitive style increased, suggesting that flexible cognitive control is associated with an individuals' preference for cognitive style ( Shin and Kim, 2015 ).

Whilst these neuroimaging studies are among the first to provide evidence linking preferences in cognitive style to differing patterns of neural activity, they have adopted the visualizer–verbalizer dimension to characterize cognitive and perceptual processing. Research focusing on this characterization of style does not take account of other approaches to cognitive and perceptual processing that reflect the second superordinate orthogonal dimension of cognitive style, wholist-analyst , proposed by Riding and Cheema (1991) , that includes field-dependent/field-independent ( Witkin, 1962 ) and intuition-analysis ( Allinson and Hayes, 1996 ) approaches. Differences in preferences along these dimensions may impact aspects of cognition including visual attention such that eye-tracking and visual search experiments may offer an additional avenue for styles research.

Eye-tracking provides insight into the spatial and temporal allocation of visual attention and thus holds promise for (1) assessing how cognitive style may relate to which information is prioritized during a visual task, and (2) how cognitive style influences the moment-by-moment process of task completion. Tsianos et al. (2009) demonstrated that visualizers looked more at images whilst verbalizers focussed more on text. Mawad et al. (2015) found that field-independent and field-dependant scores related to which details were prioritized when inspecting food labels. Such studies provide useful behavioral validation for different models of cognitive style in relation to attentional focus. However, we propose that greater insight can be gained by assessing the location and temporal order of eye fixations during task completion, as these can reveal how strategy unfolds over time. This is possible because evidence shows that eye fixations pick up information as and when it is used for task completion ( Hayhoe and Ballard, 2005 ). Within cognitive science many studies have applied eye-tracking to understand strategy across a range of tasks, including mental rotation ( Just and Carpenter, 1976 ), visual search ( Zelinsky et al., 1997 ), and comparative visual search ( Galpin and Underwood, 2005 ). However, the focus of this work has been on general patterns in strategy aggregated across participants, rather than individual differences. For example, Galpin and Underwood (2005) demonstrated that observers searched for differences between two pictures by making frequent point-by-point comparisons until detecting a difference, upon which the focus of attention narrowed and fixation durations increased. However, no attempt was made to assess how this strategy varied across participants. We therefore propose that a fruitful line of enquiry would be to assess how such strategies vary in accord with models of cognitive style.

The possibility of combining neuroimaging and/or eye-tracking with visual search paradigms offers a promising avenue for cognitive style research. Visual search tasks can investigate the allocation of attention during task completion (i.e., Galpin and Underwood, 2005 ; Bendall and Thompson, 2015 ) and can be combined with neuroimaging techniques ( Bendall and Thompson, 2016 ). Novel non-invasive neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) have been successfully utilized in a range of cognitive science disciplines (e.g., emotion science; Bendall et al., 2016 ), and offer a number of advantages including reduced cost, the ability to be employed in a wide range of tasks (e.g., during exercise; Lucas et al., 2012 ) and enabling data collection from groups otherwise difficult to access such as infants ( Franceschini et al., 2007 ) and clinical populations ( Matsubara et al., 2014 ). These benefits allow for a greater range of tasks to be investigated including those taking place outside of the laboratory. Cognitive styles may be more evident during natural behavior than laboratory tasks, thus portable eye-trackers and fNIRS offer great scope for future research. Further, techniques that do not rely on verbal report could better reveal the development of styles through childhood. Adopting such mixed methods approaches utilizing visual search tasks, eye-tracking, as well as neuroimaging and electrophysiological approaches allows the simultaneous investigation of both overt strategy measures and underlying neural processing, and will aid in revealing the contributions of both strategy and information preference in determining task performance. For instance, it has been argued that the use of event-related potentials can help to reveal the precise information relating to the time course of mental processing that occurs immediately after stimulus (or task) onset ( Vanlessen et al., 2016 ).

We also argue that future cognitive styles research would benefit from not only adopting a mixed method experimental approach, but also from investigating other dimensions of cognitive style beyond the visualizer–verbalizer dimension. For instance, it has been shown that individual differences in brain structure and function are related to preferences in field-dependence/field-independence ( Hao et al., 2013 ) and that field-dependence/field-independence is related to the type of information that is prioritized ( Mawad et al., 2015 ). However, research adopting mixed methods to investigate wholist-analytic dimension of cognitive style is limited.

Whilst some authors argue that cognitive styles are more dynamic than static, so can change or alter ( Zang, 2013 ), others have presented evidence suggesting longer term stability and resistance to modification ( Clapp, 1993 ). Thus, the question of how flexibly a style can be adapted if it is not working, or if a particular mode of task performance is prevented, is not fully resolved. For instance, what if preference for an analytic approach to visual search is discouraged or leads to poorer performance? We argue that an understanding of the underlying neural activity and overt attentional activity will allow the development of paradigms to disrupt preferred cognitive styles and thus assess their flexibility. Initial work in this area has begun to demonstrate that disruption of cognitive style-related brain activity can impact behavior. Targeted transcranial stimulation of the SMG was able to impair performance on a task requiring verbal processing where the scale of this effect was predicted by an individuals' level of verbal cognitive style ( Kraemer et al., 2014 ). One outcome of this line of enquiry may be that, for most people in many scenarios, cognitive styles are habitual modes of processing which can be adapted or over-ridden depending on context. The ultimate aim of validation work in the area of cognitive styles should be to measure behavior in ecologically meaningful activities and settings. This is important as it is plausible that abstract laboratory tasks may encourage participants to focus unnaturally on their own performance leading to artificial behavior that masks habitual cognitive style. Fortunately, “in-the-field” studies are becoming more possible due to advances in technology such as portable fNIRS equipment or unobtrusive and head-mounted eye-tracking equipment. A fully-rounded field of cognitive styles will therefore achieve an understanding of their habitual manifestation, their flexibility and the importance of context in their use. This is only possible through mixed methods research.

A decade has passed since Coffield et al.'s (2004) heavy criticism of the field of cognitive style, based—mainly—on the questionable reliability and validity of self-report psychometric construct measures so often utilized in the field. Despite this, research adopting mixed measures remains scarce. Recently a small number of studies have begun to adopt a neuroscientific approach revealing important findings about behavioral and neural correlates of cognitive style. However, additional mixed methods experimentation is needed to validate the construct of cognitive style, focusing only on those construct measures that are considered valid and reliable, such as the Cognitive Styles Index ( Allinson and Hayes, 1996 ). Additionally, the field stands to benefit from combining various methodologies including neuroimaging and electrophysiology, visual search paradigms, and eye-tracking, whereby information about underlying processing and strategy can be gathered simultaneously. We propose a particularly beneficial avenue for future research moving beyond correlational designs and toward causal experimental designs where disruptions to strategy and processing can be investigated. Whilst mixed-methods afford greater scientific understanding of cognitive styles, it is important to appreciate the practical application of cognitive styles measures in areas in which the need for efficient administration of measurement tools may preclude complex techniques. We are therefore not suggesting the adoption of in the field eye-tracking or neuroimaging by practitioners. Rather, we offer these techniques in response to previous research indicating the need for further work in the area to validate psychometric measures of cognitive style. Adopting the suggested multi-source, multi-method approaches proposed here will provide a valuable contribution in the field of cognitive style measurement.

Author Contributions

RB, opinion concept, main conclusions, article drafting; AG, LM, and SC verification of opinion concept, main conclusions, article drafting.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: cognitive style, cognition, visual attention, eye-tracking, neuroimaging, functional near-infrared spectroscopy

Citation: Bendall RCA, Galpin A, Marrow LP and Cassidy S (2016) Cognitive Style: Time to Experiment. Front. Psychol . 7:1786. doi: 10.3389/fpsyg.2016.01786

Received: 08 August 2016; Accepted: 31 October 2016; Published: 15 November 2016.

Reviewed by:

Copyright © 2016 Bendall, Galpin, Marrow and Cassidy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Robert C. A. Bendall, [email protected] Simon Cassidy, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Cognitive style: The role of personality and need for cognition in younger and older adults

• Published: 03 August 2019
• Volume 40 , pages 4460–4467, ( 2021 )

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• Andrea Vranic   ORCID: orcid.org/0000-0002-4235-8014 1 ,
• Blaz Rebernjak 1 &
• Marina Martincevic 1  

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Cognitive style seems to influence cognitive activities in many important ways. A recently proposed Cognitive style indicator (CoSI) operationalizes three cognitive styles: knowing, planning and creating style. This study was designed to investigate the relation of five factor personality traits and Need for Cognition (NFC) with regard to a preference towards a certain cognitive style, depending on the age of participants. A sample of students ( n  = 108) and middle-aged employed adults ( n  = 115) completed CoSI, Rational-Experiential Inventory (REI-10) and Ten Item Personality Inventory (TIPI). The results of exploratory and confirmatory factor analysis have validated CoSI on an independent sample and confirmed its originally proposed 3-factor structure. Furthermore, the mediation model with multigroup structure for two age cohorts highlighted several significant connections between personality traits, NFC and three hypothesized cognitive styles. Results suggest that the relations between personality traits and cognitive style differ in different age groups, and are partially or totally mediated by NFC.

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Vranic, A., Rebernjak, B. & Martincevic, M. Cognitive style: The role of personality and need for cognition in younger and older adults. Curr Psychol 40 , 4460–4467 (2021). https://doi.org/10.1007/s12144-019-00388-6

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Cognitive Psychology Research Paper Topics

This page provides a comprehensive list of cognitive psychology research paper topics , curated to inspire and assist students in their exploration of how humans perceive, remember, think, speak, and solve problems. Cognitive psychology, a discipline pivotal to understanding the intricacies of the human mind, encompasses a wide array of fascinating topics that delve into the mental processes underlying our daily functioning and well-being. From investigating the mechanisms of memory and the complexities of language acquisition to exploring the influence of emotion on cognition and the application of cognitive principles in technology, these topics offer students a rich terrain for academic inquiry. Designed to cater to a broad spectrum of interests and academic objectives, this list serves as a starting point for students aiming to contribute meaningful insights into the cognitive processes that define human experience.

100 Cognitive Psychology Research Paper Topics

Cognitive psychology stands at the forefront of exploring the vast capabilities and intricacies of the human mind, offering profound insights into our thoughts, emotions, and behaviors. This branch of psychology delves into how people understand, diagnose, and interact with the world around them, influencing various aspects of human functioning and societal development. The research topics within cognitive psychology are as varied as they are dynamic, reflecting the continuous evolution of the field in response to new scientific discoveries and technological advancements. From the fundamental processes of perception and memory to the complex interplay between emotion and cognition, these topics not only contribute to our scientific knowledge but also have practical applications in education, mental health, artificial intelligence, and beyond.

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• The psychology of visual illusions
• Cross-modal perception and sensory integration
• The impact of aging on sensory processing
• Auditory perception and its cognitive implications
• The role of attention in shaping perception
• Taste, smell, and flavor perception
• Sensory deprivation and its effects on cognition
• Perception of pain and its cognitive modulation
• The neuroscience of touch
• Multisensory experiences and their cognitive effects
• Short-term versus long-term memory processes
• The effects of sleep on memory consolidation
• Autobiographical memory and self-identity
• Cognitive strategies to enhance memory retention
• The role of emotion in memory formation and recall
• False memories and their implications
• The cognitive neuroscience of working memory
• Memory disorders and cognitive rehabilitation
• The impact of technology on memory skills
• Eyewitness memory and cognitive psychology
• Models of attention and cognitive processing
• The impact of multitasking on cognitive performance
• Attentional biases and their psychological implications
• Cognitive load theory and information processing
• The role of attention in learning and memory
• Neural mechanisms underlying attention
• Distraction and cognitive control mechanisms
• The psychology of vigilance and sustained attention
• Attention deficits and hyperactivity disorders
• Selective attention and perceptual filtering
• The cognitive basis of language development
• Bilingualism and cognitive flexibility
• Language disorders and cognitive psychology
• The relationship between thought and language
• Cognitive neuroscience of reading and literacy
• Language processing in the brain
• Pragmatics and cognitive implications of language use
• The role of language in categorization and concept formation
• Sign language and cognitive processing
• Cognitive aspects of language evolution
• Cognitive strategies in problem-solving
• Decision-making processes and biases
• The psychology of judgment and choice
• Heuristics and cognitive shortcuts
• The role of intuition in decision-making
• Problem-solving in groups versus individually
• Cognitive biases and their impact on decision quality
• Risk assessment and decision-making under uncertainty
• The neuroscience of decision-making
• Creativity and cognitive processes in problem-solving
• Stages of cognitive development in children
• Cognitive theories of learning and instruction
• The role of play in cognitive development
• Adolescent cognitive development and risk-taking behavior
• Adult learning and cognitive change
• The impact of cognitive styles on learning outcomes
• Cognitive development in aging populations
• The role of technology in cognitive learning processes
• Cognitive enhancers and their impact on learning
• Metacognition and self-regulated learning
• Cognitive aspects of Alzheimer’s disease
• The neuropsychology of Parkinson’s disease
• Cognitive impairments in traumatic brain injury
• Neurocognitive deficits in schizophrenia
• Attention deficit hyperactivity disorder (ADHD) in adults
• Autism spectrum disorders and cognitive functioning
• The impact of stroke on cognitive functions
• Dementia and cognitive interventions
• Mild cognitive impairment and its progression
• Cognitive rehabilitation techniques for neurocognitive disorders
• The influence of emotion on cognitive processes
• Cognitive appraisal theories of emotion
• The role of cognition in emotional regulation
• Emotional intelligence and cognitive abilities
• The neuroscience of emotions and feelings
• Mood disorders and cognitive functioning
• The impact of stress on cognitive performance
• Emotion-cognition interactions in decision-making
• The cognitive psychology of happiness and well-being
• Emotional memory and its persistence
• Cognitive biases in social judgment and perception
• Theory of mind and perspective-taking
• Social cognition in interpersonal relationships
• The role of stereotypes in cognitive processing
• Cognitive underpinnings of prejudice and discrimination
• Social identity and cognition
• Moral reasoning and cognitive psychology
• The cognitive basis of empathy and altruism
• Social cognition and group dynamics
• Cognitive approaches to understanding social influence
• Cognitive psychology in human-computer interaction
• Virtual reality and its cognitive implications
• The impact of social media on cognition and social behavior
• Cognitive psychology principles in user experience design
• Artificial intelligence and cognitive modeling
• Gaming and cognitive skill development
• Cognitive training apps and their effectiveness
• Neurotechnology and cognitive enhancement
• The role of cognitive psychology in digital education
• Wearable technology and cognitive monitoring

The exploration of cognitive psychology research paper topics presents an unparalleled opportunity to delve into the mechanisms that underpin human cognition and behavior. Each category and topic not only contributes to the rich tapestry of cognitive psychology but also holds the potential for groundbreaking research that can influence educational practices, therapeutic approaches, and policy development. Students are encouraged to engage deeply with these topics, leveraging their curiosity and analytical skills to advance the field and contribute valuable insights into the complex world of human cognition.

What is Cognitive Psychology

Cognitive Psychology as a Discipline

The development of cognitive psychology marked a significant shift from the behaviorist perspective that dominated psychology for much of the early 20th century, which largely ignored mental processes. Instead, cognitive psychology focuses on understanding internal mental states and processes, utilizing this understanding to explain behavioral patterns. This focus on the internal workings of the mind has not only expanded the scope of psychological research but has also had practical applications in various fields such as education, mental health, artificial intelligence, and more, demonstrating the discipline’s broad impact.

The Importance of Research in Expanding Our Understanding of Cognitive Processes

Research in cognitive psychology plays a crucial role in expanding our understanding of the human mind and behavior. Through empirical studies, experiments, and longitudinal research, cognitive psychologists seek to build a body of knowledge about how cognitive processes work, how they change over time, and how they can be improved or altered. This research is fundamental to developing new theories of cognition that can explain complex human behaviors and cognitive anomalies.

One of the key contributions of cognitive psychology research is the development of models that describe various cognitive processes. For example, research on memory has led to the formulation of the multi-store model, which outlines how information flows from sensory memory to short-term memory and finally to long-term memory. Similarly, studies on decision-making and problem-solving have introduced several cognitive biases that influence human judgment, such as confirmation bias and availability heuristic. These models and theories are crucial for understanding the limitations and capabilities of human cognition, informing approaches in education, cognitive therapy, and even interface design in technology.

Moreover, cognitive psychology research has a significant impact on diagnosing and treating cognitive disorders. Studies on neurocognitive disorders, such as Alzheimer’s disease and attention deficit hyperactivity disorder (ADHD), provide insights into their cognitive underpinnings, leading to better diagnostic criteria and treatment options. Research in this field also supports the development of cognitive rehabilitation techniques and cognitive-behavioral therapies, demonstrating its vital role in improving mental health and cognitive function.

The Variety of Research Topics within Cognitive Psychology and Their Relevance to Real-World Applications

Cognitive psychology encompasses a wide array of research topics, each with direct implications for real-world applications. For instance, research in perception and sensation enhances our understanding of how sensory information is interpreted by the brain, influencing fields such as marketing, design, and even virtual reality development. Studies on attention and information processing have led to improvements in educational strategies, helping to develop teaching methods that align with cognitive load theory and the attentional needs of students.

Language and cognition research has profound implications for language teaching methodologies, speech therapy, and understanding language disorders. Insights from this research help in designing interventions for individuals with dyslexia or aphasia, facilitating better communication and learning outcomes. Additionally, the study of problem-solving and decision-making is pivotal for the development of artificial intelligence, providing algorithms with models of human cognition that can be simulated in computational systems.

The exploration of memory and recall has applications in legal settings, especially in eyewitness testimony and the reliability of memory. Cognitive psychology’s findings on the malleability of human memory and the conditions under which memories are accurately or inaccurately recalled are crucial for informing judicial processes and policies. Furthermore, the study of social cognition, which examines how individuals perceive, think about, and interact with others, is essential for understanding social behavior, improving interpersonal relationships, and addressing societal issues such as prejudice and discrimination.

Recent Advancements in Cognitive Psychology Research

Recent advancements in cognitive psychology research have been facilitated by technological innovations, allowing for more sophisticated exploration of cognitive processes. Neuroimaging techniques such as fMRI and PET scans have provided insights into the neural substrates of various cognitive functions, bridging the gap between cognitive psychology and neuroscience. These advancements have led to a deeper understanding of how different brain regions are involved in specific cognitive tasks, such as memory recall or language processing.

Additionally, the integration of machine learning and artificial intelligence in cognitive research has opened new avenues for analyzing large datasets, leading to more nuanced understandings of cognitive patterns and anomalies. This intersection of cognitive psychology and computational modeling has also advanced the development of intelligent systems capable of mimicking human cognitive functions, from language understanding to pattern recognition.

Another significant advancement is in the realm of cognitive enhancement, where research is exploring ways to improve cognitive functions through pharmacological means, cognitive training exercises, and even non-invasive brain stimulation techniques. These studies hold the potential for significant impacts on education, mental health treatment, and the general enhancement of cognitive abilities in healthy individuals.

Ethical Issues Inherent in Cognitive Psychology Research

Cognitive psychology research, while offering vast potential for understanding and enhancing human cognition, also presents several ethical considerations. Issues such as informed consent, privacy, and the potential for misuse of cognitive data are paramount concerns. The use of neuroimaging and other biometric data, for instance, raises questions about the privacy of mental states and the potential for such information to be used in ways that could infringe on individual rights or autonomy.

Additionally, the ethical implications of cognitive enhancement and the potential societal impacts of creating disparities between those who have access to cognitive enhancement technologies and those who do not are areas of ongoing debate. Cognitive psychology researchers must navigate these ethical waters carefully, ensuring that their work promotes the welfare and dignity of all individuals while advancing scientific knowledge.

Future Directions for Research in Cognitive Psychology

The future of cognitive psychology research promises further integration with neuroscience, the application of advanced computational models, and the exploration of how cognitive processes evolve in a rapidly changing digital world. An exciting direction for future research is the investigation of how digital technologies, such as smartphones and social media, are affecting cognitive development, attention spans, and social cognition. Understanding these impacts is crucial for developing strategies to mitigate potential negative effects while harnessing technology’s power to enhance cognitive function.

Another area of future research is the exploration of individual differences in cognition, understanding how genetic, environmental, and cultural factors contribute to the diversity of cognitive processes among individuals. This line of research holds the promise of personalizing educational and therapeutic approaches to cater to individual cognitive profiles.

The Transformative Potential of Research in Cognitive Psychology

Research in cognitive psychology holds transformative potential for numerous aspects of human life, from education and mental health to technology and social interaction. By continuing to explore the intricacies of cognitive processes and their neural underpinnings, cognitive psychology can contribute to a deeper understanding of what it means to be human. The ongoing exploration of cognitive phenomena not only enriches our knowledge of the mind but also translates into practical applications that can improve individual well-being and societal health. As cognitive psychology advances, its research continues to shape our world, demonstrating the enduring power of understanding the human mind.

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• Published: 06 May 2024

APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease

• Juan Fortea   ORCID: orcid.org/0000-0002-1340-638X 1 , 2 , 3   na1 ,
• Jordi Pegueroles   ORCID: orcid.org/0000-0002-3554-2446 1 , 2 ,
• Daniel Alcolea   ORCID: orcid.org/0000-0002-3819-3245 1 , 2 ,
• Olivia Belbin   ORCID: orcid.org/0000-0002-6109-6371 1 , 2 ,
• Oriol Dols-Icardo   ORCID: orcid.org/0000-0003-2656-8748 1 , 2 ,
• Lídia Vaqué-Alcázar 1 , 4 ,
• Laura Videla   ORCID: orcid.org/0000-0002-9748-8465 1 , 2 , 3 ,
• Juan Domingo Gispert 5 , 6 , 7 , 8 , 9 ,
• Marc Suárez-Calvet   ORCID: orcid.org/0000-0002-2993-569X 5 , 6 , 7 , 8 , 9 ,
• Sterling C. Johnson   ORCID: orcid.org/0000-0002-8501-545X 10 ,
• Reisa Sperling   ORCID: orcid.org/0000-0003-1535-6133 11 ,
• Alexandre Bejanin   ORCID: orcid.org/0000-0002-9958-0951 1 , 2 ,
• Alberto Lleó   ORCID: orcid.org/0000-0002-2568-5478 1 , 2 &
• Víctor Montal   ORCID: orcid.org/0000-0002-5714-9282 1 , 2 , 12   na1  

Nature Medicine ( 2024 ) Cite this article

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• Alzheimer's disease
• Predictive markers

This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer’s disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer’s Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.

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APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia

Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants

Data availability.

Access to tabular data from ADNI ( https://adni.loni.usc.edu/ ), OASIS ( https://oasis-brains.org/ ), A4 ( https://ida.loni.usc.edu/collaboration/access/appLicense.jsp ) and NACC ( https://naccdata.org/ ) can be requested online, as publicly available databases. All requests will be reviewed by each studyʼs scientific board. Concrete inquiries to access the WRAP ( https://wrap.wisc.edu/data-requests-2/ ) and ALFA + ( https://www.barcelonabeta.org/en/alfa-study/about-the-alfa-study ) cohort data can be directed to each study team for concept approval and feasibility consultation. Requests will be reviewed to verify whether the request is subject to any intellectual property.

Code availability

All statistical analyses and raw figures were generated using R (v.4.2.2). We used the open-sourced R packages of ggplot2 (v.3.4.3), dplyr (v.1.1.3), ggstream (v.0.1.0), ggpubr (v.0.6), ggstatsplot (v.0.12), Rmisc (v.1.5.1), survival (v.3.5), survminer (v.0.4.9), gtsummary (v.1.7), epitools (v.0.5) and statsExpression (v.1.5.1). Rscripts to replicate our findings can be found at https://gitlab.com/vmontalb/apoe4-asdad (ref. 32 ). For neuroimaging analyses, we used Free Surfer (v.6.0) and ANTs (v.2.4.0).

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Acknowledgements

We acknowledge the contributions of several consortia that provided data for this study. We extend our appreciation to the NACC, the Alzheimer’s Disease Neuroimaging Initiative, The A4 Study, the ALFA Study, the Wisconsin Register for Alzheimer’s Prevention and the OASIS3 Project. Without their dedication to advancing Alzheimer’s disease research and their commitment to data sharing, this study would not have been possible. We also thank all the participants and investigators involved in these consortia for their tireless efforts and invaluable contributions to the field. We also thank the institutions that funded this study, the Fondo de Investigaciones Sanitario, Carlos III Health Institute, the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas and the Generalitat de Catalunya and La Caixa Foundation, as well as the NIH, Horizon 2020 and the Alzheimer’s Association, which was crucial for this research. Funding: National Institute on Aging. This study was supported by the Fondo de Investigaciones Sanitario, Carlos III Health Institute (INT21/00073, PI20/01473 and PI23/01786 to J.F., CP20/00038, PI22/00307 to A.B., PI22/00456 to M.S.-C., PI18/00435 to D.A., PI20/01330 to A.L.) and the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas Program 1, partly jointly funded by Fondo Europeo de Desarrollo Regional, Unión Europea, Una Manera de Hacer Europa. This work was also supported by the National Institutes of Health grants (R01 AG056850; R21 AG056974, R01 AG061566, R01 AG081394 and R61AG066543 to J.F., S10 OD025245, P30 AG062715, U54 HD090256, UL1 TR002373, P01 AG036694 and P50 AG005134 to R.S.; R01 AG027161, R01 AG021155, R01 AG037639, R01 AG054059; P50 AG033514 and P30 AG062715 to S.J.) and ADNI (U01 AG024904), the Department de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca I Innovació en Salut (SLT006/17/00119 to J.F.; SLT002/16/00408 to A.L.) and the A4 Study (R01 AG063689, U24 AG057437 to R.A.S). It was also supported by Fundación Tatiana Pérez de Guzmán el Bueno (IIBSP-DOW-2020-151 o J.F.) and Horizon 2020–Research and Innovation Framework Programme from the European Union (H2020-SC1-BHC-2018-2020 to J.F.; 948677 and 847648 to M.S.-C.). La Caixa Foundation (LCF/PR/GN17/50300004 to M.S.-C.) and EIT Digital (Grant 2021 to J.D.G.) also supported this work. The Alzheimer Association also participated in the funding of this work (AARG-22-923680 to A.B.) and A4/LEARN Study AA15-338729 to R.A.S.). O.D.-I. receives funding from the Alzheimer’s Association (AARF-22-924456) and the Jerome Lejeune Foundation postdoctoral fellowship.

Author information

These authors contributed equally: Juan Fortea, Víctor Montal.

Authors and Affiliations

Sant Pau Memory Unit, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Lídia Vaqué-Alcázar, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain

Juan Fortea & Laura Videla

Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

Lídia Vaqué-Alcázar

Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain

Juan Domingo Gispert & Marc Suárez-Calvet

Neurosciences Programme, IMIM - Hospital del Mar Medical Research Institute, Barcelona, Spain

Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain

Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina. Instituto de Salud carlos III, Madrid, Spain

Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA

Sterling C. Johnson

Brigham and Women’s Hospital Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Reisa Sperling

Barcelona Supercomputing Center, Barcelona, Spain

Víctor Montal

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Contributions

J.F. and V.M. conceptualized the research project and drafted the initial manuscript. V.M., J.P. and J.F. conducted data analysis, interpreted statistical findings and created visual representations of the data. O.B. and O.D.-I. provided valuable insights into the genetics of APOE. L.V., A.B. and L.V.-A. meticulously reviewed and edited the manuscript for clarity, accuracy and coherence. J.D.G., M.S.-C., S.J. and R.S. played pivotal roles in data acquisition and securing funding. A.L. and D.A. contributed to the study design, offering guidance and feedback on statistical analyses, and provided critical review of the paper. All authors carefully reviewed the manuscript, offering pertinent feedback that enhanced the study’s quality, and ultimately approved the final version.

Corresponding authors

Correspondence to Juan Fortea or Víctor Montal .

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Competing interests.

S.C.J. has served at scientific advisory boards for ALZPath, Enigma and Roche Diagnostics. M.S.-C. has given lectures in symposia sponsored by Almirall, Eli Lilly, Novo Nordisk, Roche Diagnostics and Roche Farma, received consultancy fees (paid to the institution) from Roche Diagnostics and served on advisory boards of Roche Diagnostics and Grifols. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research & Development and Roche Diagnostics. J.D.G. has served as consultant for Roche Diagnostics, receives research funding from Hoffmann–La Roche, Roche Diagnostics and GE Healthcare, has given lectures in symposia sponsored by Biogen, Philips Nederlands, Esteve and Life Molecular Imaging and serves on an advisory board for Prothena Biosciences. R.S. has received personal consulting fees from Abbvie, AC Immune, Acumen, Alector, Bristol Myers Squibb, Janssen, Genentech, Ionis and Vaxxinity outside the submitted work. O.B. reported receiving personal fees from Adx NeuroSciences outside the submitted work. D.A. reported receiving personal fees for advisory board services and/or speaker honoraria from Fujirebio-Europe, Roche, Nutricia, Krka Farmacéutica and Esteve, outside the submitted work. A.L. has served as a consultant or on advisory boards for Almirall, Fujirebio-Europe, Grifols, Eisai, Lilly, Novartis, Roche, Biogen and Nutricia, outside the submitted work. J.F. reported receiving personal fees for service on the advisory boards, adjudication committees or speaker honoraria from AC Immune, Adamed, Alzheon, Biogen, Eisai, Esteve, Fujirebio, Ionis, Laboratorios Carnot, Life Molecular Imaging, Lilly, Lundbeck, Perha, Roche and outside the submitted work. O.B., D.A., A.L. and J.F. report holding a patent for markers of synaptopathy in neurodegenerative disease (licensed to Adx, EPI8382175.0). The remaining authors declare no competing interests.

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Fortea, J., Pegueroles, J., Alcolea, D. et al. APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease. Nat Med (2024). https://doi.org/10.1038/s41591-024-02931-w

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Cognitive Distortions, Humor Styles, and Depression

Katerina rnic.

a Department of Psychology, The University of Western Ontario, London, Ontario, Canada

David J. A. Dozois

Rod a. martin.

Cognitive distortions are negative biases in thinking that are theorized to represent vulnerability factors for depression and dysphoria. Despite the emphasis placed on cognitive distortions in the context of cognitive behavioural theory and practice, a paucity of research has examined the mechanisms through which they impact depressive symptomatology. Both adaptive and maladaptive styles of humor represent coping strategies that may mediate the relation between cognitive distortions and depressive symptoms. The current study examined the correlations between the frequency and impact of cognitive distortions across both social and achievement-related contexts and types of humor. Cognitive distortions were associated with reduced use of adaptive Affiliative and Self-Enhancing humor styles and increased use of maladaptive Aggressive and Self-Defeating humor. Reduced use of Self-Enhancing humor mediated the relationship between most types of cognitive distortions and depressed mood, indicating that distorted negative thinking may interfere with an individual’s ability to adopt a humorous and cheerful outlook on life (i.e., use Self-Enhancing humor) as a way of regulating emotions and coping with stress, thereby resulting in elevated depressive symptoms. Similarly, Self-Defeating humor mediated the association of the social impact of cognitive distortions with depression, such that this humor style may be used as a coping strategy for dealing with distorted thinking that ultimately backfires and results in increased dysphoria.

Cognitive distortions are negatively biased errors in thinking that are purported to increase vulnerability to depression ( Dozois & Beck, 2008 ). Individuals experience automatic thoughts in response to events, which in turn lead to emotional and behavioral responses. The content of automatic thoughts is typically consistent with an individual’s core beliefs about important aspects of themselves, others, and the world. When negative core beliefs are activated and negative automatic thoughts elicited (comprised of errors in reasoning that are not evidence-based) a negative, neutral or even positive event may influence negative affect and maladaptive behaviours. Overtime, this sequence among thoughts, emotions and behaviours can cause or maintain symptoms of depression.

Cognitive distortions were first listed and described by Beck, Rush, Shaw, and Emery (1979) . Burns (1980) subsequently expanded on their list and identified 10 common depressotypic thinking errors. These include mindreading (i.e., assuming that others are thinking negatively about oneself), catastrophizing (i.e., making negative predictions about the future based on little or no evidence), all-or-nothing-thinking (i.e., viewing something as either-or, without considering the full spectrum and range of possible evaluations), emotional reasoning (i.e., believing something to be true based on emotional responses rather than objective evidence), labeling (i.e., classifying oneself negatively after the occurrence of an adverse event), mental filtering (i.e., focusing on negative information and devaluing positive information), overgeneralization (i.e., assuming that the occurrence of one negative event means that additional bad things will happen), personalization (i.e., assuming that one is the cause of a negative event), should statements (i.e., thinking that things must or should be a certain way), and minimizing or disqualifying the positive (i.e., ignoring or dismissing positive things that have happened). Cognitive errors may occur with differing frequency across social and achievement domains, particularly depending on the content of an individual’s core beliefs, which typically fall into two categories: unlovability/sociotropy/dependency, or helplessness/autonomy/achievement ( Beck, 1995 ; Beck, Epstein, Harrison, & Emery, 1983 ; Clark, Beck, & Alford, 1999 ). Given the importance of the interpersonal context for the onset and course of depression (see Evraire & Dozois, 2011 ; Hammen & Shih, 2014 ), cognitive distortions occurring in the social domain may be most relevant to depressive symptomatology.

Although cognitive distortions figure prominently in cognitive theory and therapy, a dearth of research has examined the mechanisms through which cognitive distortions impact subsequent psychological distress. Humor styles are potential mediators of the association between cognitive and interpersonal vulnerability factors and psychological dysfunction, distress, or poor interpersonal functioning (e.g., Cann, Norman, Welbourne, & Calhoun, 2008 ; Dozois, Martin, & Faulkner, 2013 ; Fitts, Sebby, & Zlokovich, 2009 ; Kazarian, Moghnie, & Martin, 2010 ; Kuiper & McHale, 2009 ). For example, past research has found that various humor styles mediated the relation of early maladaptive schemas (i.e., core beliefs about the self and others) and depressive symptoms ( Dozois, Martin, & Bieling, 2009 ).

Humor styles comprise the ways in which people use humor to cope and to communicate with others. Because humor involves incongruity and can be interpreted in multiple ways, it can be used to shift perspectives regarding a stressful situation and as a way to gain a sense of mastery. Past research has indicated that some humor styles accomplish this in a way that is beneficial (Affiliative and Self-Enhancing humor), whereas other styles are maladaptive (Aggressive and Self-Defeating humor; Martin, Puhlik-Doris, Larsen, Gray, & Weir, 2003 ; Kuiper, Grimshaw, Leite, & Kirsh, 2004 ). Affiliative humor is used to facilitate relationships, amuse others, and minimize social tension through the use of spontaneous jokes, witty banter, and funny anecdotes. Self-Enhancing humor involves a humorous and cheerful outlook in life and a tendency to be amused by incongruities that facilitates emotion regulation and coping with stress and adversity. This type of humor encompasses a style of thinking, and therefore can be conceptualized as a cognitive construct. Aggressive humor is used to posture in a relationship and to demean or manipulate others through the use of sarcasm, teasing, derision, and ridicule. This style of humor involves making disparaging comments and “putting down” others in an effort to enhance one’s self, but at the expense of relationship quality. Self-Defeating humor involves excessive self-disparagement as one says or does funny things at one’s own expense in order to gain approval, amuse others, or to avoid dealing with a problem. This type of humor is ingratiating and includes allowing oneself to be the “butt” of others’ jokes. This type of humor is associated with low self-esteem and is distinct from not taking oneself overly seriously and making light of one’s faults and errors in a self-accepting manner (which would comprise Affiliative humor).

The four styles of humor, as assessed by the Humor Styles Questionnaire ( Martin et al., 2003 ), are differentially correlated with emotional and psychosocial well-being (see Martin, 2007 , for review). Self-Enhancing humor is associated with emotional well-being, including self-esteem, optimism, and positive affect, and negatively associated with depression, anxiety, rumination, perceived stress, and neuroticism. Affiliative humor, in contrast, is more closely associated with relationship variables than with emotional well-being, and is related to intimacy, relationship satisfaction, social support, interpersonal competence, secure attachment, and extraversion. Affiliative humor is inversely related to loneliness and social anxiety. Likewise, Aggressive humor is predominantly associated with relationship variables, and is negatively related to satisfaction, competence, agreeableness, and conscientiousness, and positively associated with hostility and neuroticism. Self-Defeating humor is related to anxiety, depression, anxious attachment, and neuroticism, and negatively associated with self-esteem and optimism. Whereas Self-Enhancing and low Self-Defeating humor are related to emotional well-being, Affiliative and low Aggressive humor are more closely associated with interpersonal functioning.

The aim of the current study is to investigate how both beneficial and detrimental uses of humor influence depressive symptoms. Humor styles are considered to be coping strategies, which may be used to cope with the experience of activated beliefs and distorted thinking. Because cognitive distortions involve negatively biased thinking about self and others, it was hypothesized that individuals who experience cognitive distortions frequently and whose lives are impacted by them would tend to engage in maladaptive humor styles as these would be more congruent with automatic thoughts involving themes of incompetence, worthlessness, unlovability, and assumptions that others are perceiving and thinking negatively about them. Given that these themes are relevant to low self-esteem, which Self-Defeating, not Aggressive, humor is related to, cognitive distortions may be more robustly associated with a Self-Defeating humor style. A negatively distorted type of thinking is likely not conducive to the use of Affiliative and Self-Enhancing humor (particularly in social contexts where humor is more likely to play a role), which require a sense of playfulness, the generation of positive statements, and an intention to connect with others. Furthermore, given that low Self-Enhancing and high Self-Defeating humor have been found to be most closely associated with emotional distress, these variables were hypothesized to mediate the association of cognitive distortions (both frequency and impact) with depressive symptoms. Mediating models were also tested separately for frequency and impact of cognitive distortions in interpersonal and achievement contexts in an exploratory manner.

Participants

Participants were 208 first-year undergraduate psychology students at the University of Western Ontario, who were predominantly female (70% female, 30% male) and White (69% identified as White, 25% as Asian, 2% as Black, 1% as Hispanic, and 3% as ‘other’ or mixed race). The mean age of the sample was 18.46 ( SD = 1.73).

Cognitive Distortions Scale (CDS)

The CDS ( Covin, Dozois, Ogniewicz, & Seeds, 2011 ) is a 20-item self-report measure that assesses the frequency of 10 types of cognitive distortions (mindreading, catastrophizing, all-or-nothing thinking, emotional reasoning, labeling, mental filtering, overgeneralization, personalization, should statements, minimizing or disqualifying the positive) across both social and achievement related (e.g., school or work) situations. Participants are presented with a definition of the distortion (referred to in the questionnaire as a ‘thinking type’ in order to reduce defensiveness) and provided with an example of that distortion in an interpersonal and achievement context. For example, for ‘should statements,’ the following definition is provided: “People sometimes think that things should or must be a certain way” and the example for interpersonal contexts is: “Anne believes that she must be funny and interesting when socializing.” Participants indicate the frequency with which they engage in the type of thinking on a 7-point Likert-type scale (1 = Never, 7 = All the time) in social and achievement situations. Total, social, and achievement scores are obtained by adding items. Past research has indicated that the CDS has good psychometric properties in undergraduate ( Covin, Dozois, Ogniewicz, & Seeds, 2011 ) and clinical samples ( Özdel, Taymur, Guriz, Tulaci, Kuru, & Turkcapar, 2014 ), including internal consistency, test-retest reliability over two weeks, and construct, discriminant, convergent, and divergent validity. In the current study, participants were also asked to rate the impact that cognitive distortions have in social and achievement contexts on a 7-point Likert-type scale (1 = Not at all, 7 = Totally). Internal consistency of each subscale was good (total frequency = .91, social frequency = .81, achievement frequency = .85, total impact = .92, social impact = .85, achievement impact = .86).

Humor Styles Questionnaire (HSQ)

The HSQ ( Martin et al., 2003 ) is a 32-item self-report measure that assess adaptive (Affiliative, Self-Enhancing) and maladaptive (Aggressive, Self-Defeating) styles of humor. Sample items are: “I laugh and joke a lot with my closest friends” (Affiliative humor); “Even when I’m by myself, I’m often amused by the absurdities of life” (Self-Enhancing humor); “If someone makes a mistake, I will often tease them about it” (Aggressive humor); and “I will often get carried away in putting myself down if it makes my family or friends laugh” (Self-defeating humor). Respondents rate items by indicating the extent to which they agree with statements on a 7-point Likert-type scale (1= totally disagree; 7 = totally agree). Higher scores indicate that a particular humor style is descriptive of the participant. Past research has demonstrated good reliability and validity of subscales ( Martin 2007 ; Martin et al., 2003 ), including internal consistency, test-retest reliability over one week, and construct, criterion, discriminant, and convergent validity, as well as a stable factor structure. In the current study, the internal consistency (Cronbach’s alpha) of each of the subscales was good (Affiliative = .81, Self-Enhancing = .82, Aggressive = .68, Self-Defeating = .75).

Beck Depression Inventory-II (BDI-II)

The BDI-II ( Beck, Steer, & Brown, 1996 ) is a 21-item instrument that assesses the presence and severity of unipolar depressive symptoms. Individuals rate each statement on a 0 to 3 scale according to how well it describes how they have felt over the past two weeks. A sample item is “Sadness: 0 = I do not feel sad; 1 = I feel sad much of the time; 2 = I am sad all the time; 3 = I am so sad or unhappy that I can’t stand it.” Total scores are yielded by summing items, with higher scores indicating greater depressive symptoms. The BDI-II has been widely used with adult and undergraduate samples and is recognized for its strong psychometric properties, including internal consistency, test-retest reliability over several months, content, construct, criterion, convergent, and divergent validity (e.g., Dozois, Dobson, & Ahnberg, 1998 ; see Dozois & Covin, 2004 , for a review). In the current study, internal consistency was excellent (Cronbach’s alpha = .92).

Participants completed measures during group testing sessions. Participants completed the CDS, HSQ, and BDI-II, as well as additional measures as part of a larger study, in randomized order. Participants were then debriefed about the nature of the study and compensated with course credit.

Statistical Analyses

Mediation analyses were conducted to test the hypothesis that humor styles mediate the relationship between cognitive distortions and depressive symptoms. Simple correlations between the predictor variables (cognitive distortions), mediator variables (humor styles) and the criterion variable (depressive symptoms) were first examined (see Table 1 ). A prerequisite for mediation is that all correlations between a predictor and mediator, mediator and criterion, and predictor and criterion for a given analysis be significant ( Baron & Kenny, 1986 ). Mediation analyses were conducted only for the cognitive distortions and corresponding mediators that met this requirement. To test for the potential mediating effects of humor styles, the bootstrap sampling procedure developed by Preacher and Hayes (2008) was used. This procedure examines and tests the direct effect of the predictor variable on the criterion variable and the indirect (i.e., mediating) effect through the pathway of the mediator variable. The bootstrap procedure uses sampling with replacement to draw a large number of samples (1,000 in the present study) from the data set, and path coefficients are calculated for each sample. Using estimates based on the 1,000 samples, the mean direct and indirect effects and their confidence intervals (CIs) are computed. These CIs are used to determine whether or not an effect is statistically significant. For each effect, the corresponding Bias Corrected 95% or 99% CI was examined; if the range did not cross zero, the effect was considered significant at the .05 or .01 level, respectively. An advantage of the bootstrap-driven approach is that it does not assume a normal distribution of variables, unlike product-of-coefficient approaches such as the Sobel test.

Note. BDI-II = Beck Depression Inventory-II; CDS = Cognitive Distortions Scale; HSQ = Humor Styles Questionnaire.

* p < .05. ** p < .01. *** p < .001.

All mediation analyses were conducted using the macro provided by Preacher and Hayes (2008) for conducting the bootstrap procedure. Note that in the figures and tables presented, path coefficients and corresponding p -values are based on mediation analyses without bootstrapping, since the bootstrapping procedure only provides Bias Corrected CIs in the output. Because the bootstrapping procedure provides a more robust analysis, the evaluations of significance in the analyses below are based on bootstrapping. All variables in the analysis were standardized ( M = 0, SD = 1.0), to allow for a comparison of results across analyses. Path coefficients can therefore be interpreted in a manner similar to correlation coefficients.

The means and standard deviations for the six CDS subscales, the four HSQ subscales, and the BDI-II are presented in Table 2 for descriptive purposes. Pearson correlations between the CDS scales, HSQ scales, and BDI-II are presented in Table 1 . Cognitive Distortion Frequency was significantly negatively correlated with Affiliative and Self-Enhancing humor. This variable was also positively associated with Aggressive and Self-Defeating humor. The same pattern of correlations was found for Cognitive Distortion Social Frequency. Cognitive distortion Achievement Frequency was significantly and negatively related to Self-Enhancing humor, and positively correlated with Self-Defeating humor. Cognitive Distortion Impact was negatively correlated with Affiliative and Self-Enhancing humor, and positively correlated with Self-Defeating humor. The same pattern of correlations was found for Cognitive Distortion Social and Achievement Impact.

All scales of the CDS were significantly positively correlated with the BDI-II, consistent with the idea that cognitive distortions are vulnerability factors for dysphoria and depression. Furthermore, consistent with past research, the BDI-II was negatively correlated with Self-Enhancing humor, and positively associated and Self-Defeating humor. It was also positively related to Aggressive humor.

Multiple mediation analyses were conducted using the procedure described earlier to examine potential mediating effects of the humor styles on the relationships between each of the CDS subscales and the BDI-II. In the analysis using CDS Frequency as the predictor variable, HSQ Self-Enhancing, Aggressive, and Self-Defeating humor styles were included as potential mediators, as these were the only humor styles correlated with both CDS Frequency and BDI-II scores. Results of this analysis are presented in Figure 1 . A significant mediating effect was found for Self-Enhancing humor ( p < .05), but the mediating effects for Aggressive and Self-Defeating humor were not significant. Therefore, higher scores on CDS Frequency were associated with lower Self-Enhancing humor, which in turn predicted higher BDI-II scores. In addition to the indirect effect of CDS Frequency on dysphoria through Self-Enhancing humor, a direct effect was also found ( p < .01), indicating that Self-Enhancing humor only partially mediated this relationship.

* p < .05. ** p < .01. *** p < .001. ns = Not statistically significant.

In the mediation analysis for CDS Social Frequency, HSQ Self-Enhancing, Aggressive, and Self-Defeating humor styles were again included as potential mediators as determined by the pattern of correlations found earlier. The results are shown in Figure 2 . In this analysis, and similar to the analysis for CDS Frequency, a significant mediating effect was found for Self-Enhancing humor ( p < .05), but the mediating effects for Aggressive and Self-Defeating humor were not significant. Therefore, higher scores on CDS Social Frequency were associated with lower Self-Enhancing humor, which in turn predicted higher BDI-II scores. Furthermore, in addition to the indirect effect of CDS Frequency on dysphoria through Self-Enhancing humor, a direct effect was also found ( p < .01), indicating that Self-Enhancing humor only partially mediated this relationship.

In the analysis using CDS Achievement Frequency as the predictor variable, only HSQ Self-Enhancing and Self-Defeating humor styles were included as potential mediators based on the obtained pattern of correlations. The results are presented in Figure 3 . No significant mediating effects were found, however there was a direct effect of CDS Achievement Frequency on dysphoria ( p < .01).

In the analysis using CDS Impact as the predictor, HSQ Self-Enhancing and Self-Defeating humor styles were included as potential mediators, see Figure 4 . A significant mediating effect was found for Self-Enhancing humor ( p < .05), and the mediating effect for Self-Defeating humor was nonsignificant. Higher scores on CDS Impact predicted lower Self-Enhancing humor, which in turn predicted higher BDI-II scores. The direct effect of CDS Impact on dysphoria was also significant, ( p < .01), indicating a partial mediating effect of Self-Enhancing humor.

Figure 5 shows the results of the analysis for CDS Social Impact, in which we entered Self-Enhancing and Self-Defeating humor as potential mediators as determined by the pattern of simple correlations. In this analysis, significant mediating effects were found for both Self-Enhancing ( p < .05) and Self-Defeating humor ( p < .05). Higher scores on CDS Social impact predicted lower scores on Self-Enhancing and higher scores on Self-Defeating humor, which in turn predicted higher BDI-II scores. The direct effect of CDS Social Impact on dysphoria was significant ( p < .01), indicating that the humor styles were only partial mediators.

Finally, in the analysis for CDS Achievement Impact, Self-Enhancing and Self-Defeating humor were entered as potential mediators. Only Self-Enhancing humor mediated the relation of CDS Achievement Impact with depressive symptoms ( p < .05). No mediating effect was found for Self-Defeating humor, see Figure 6 . Therefore, higher scores on CDS Achievement Impact predicted lower Self-Enhancing humor, which in turn predicted higher BDI-II scores. The direct effect of CDS Achievement Impact on dysphoria was also significant, ( p < .05), indicating that Self-Enhancing humor was only a partial mediator.

The purpose of the current study was to examine the relationships among cognitive distortions, adaptive and maladaptive humor styles, and depressive symptoms. Another objective was to evaluate whether humor styles mediate the relationship between cognitive distortions and dysphoria. As predicted, the frequency and impact of cognitive distortions, as well as frequency and impact ratings of cognitive distortions in both interpersonal and achievement-related contexts, were positively and significantly associated with depressive symptomatology. This finding is consistent with cognitive theory, which posits that cognitive distortions are a form of automatic thoughts related to negative affect and depressed mood. This finding is also supportive of the practice of assessing and altering cognitive distortions as a treatment target in cognitive behavioral therapy for depressed individuals (see Clark, 2014 ).

Consistent with past research, self-reported depressive symptoms were negatively related to use of Self-Enhancing humor and positively associated with Self-Defeating humor. However, inconsistent with past research, dysphoria was not related to Affiliative humor in the present study, although pearson’s r was in in the expected direction (i.e., negative). Past research has found modest negative correlations of Affiliative humor with dysphoria ( Dozois et al., 2009 ; Frewen, Brinker, Martin, & Dozois, 2008 ) using an undergraduate sample that was similar in terms of age and gender distribution. However, the current sample exhibited a smaller mean and standard deviation in BDI-II scores. It is possible that the current sample lacked sufficient severity and variance in self-reported depressive symptoms to detect a true correlation between dysphoria and Affiliative humor. Moreover, Affiliative humor is both theoretically and empirically more pertinent to relational functioning than to emotional well-being ( Martin, 2007 ), so it is not entirely surprising that a significant relationship was not found in the current study. Additionally, and also in contrast with past research, the current study found that dysphoria was associated with an Aggressive humor style (e.g., Dozois et al., 2009 ; Frewen et al., 2008 ; see Martin, 2007 for review). This is consistent with the finding that depression is predictive of greater generation of interpersonal conflict (i.e., interpersonal stress generation; Hammen, 1991 ), such that some of this conflict may arise from the depressed individual’s use of Aggressive humor.

Consistent with hypotheses, cognitive distortions were inversely associated with beneficial styles of humor, and positively correlated with detrimental humor styles. Affiliative humor was significantly and negatively associated with all forms of cognitive distortions, with the exception of frequency of cognitive distortions in achievement situations, which was not associated with Affiliative humor. This is likely because achievement contexts are less relevant for efforts to affiliate with others. Affiliative humor is used to facilitate relationships and reduce tension, and involves a sense of playfulness and spontaneity. Negatively distorted thinking, in contrast, involves a rigid pattern of thinking that lacks the openness, flexibility, and creativity required to generate funny comments and anecdotes. Moreover, when individuals are thinking negatively, they are less able to access and retrieve information and memories that are incongruent with their current negative state ( Bower, 1981 ; Ingram, Steidtmann, & Bistricky, 2008 ). Moreover, depending on the content of negative distortions, individuals may be assuming that others do not think well of them or will not respond favorably to their efforts to be humorous. If the content of their thinking is self-focused, they may lack the self-efficacy and confidence to make a joke in an effort to bond with others or ‘lighten the mood.’ Similarly, all types of cognitive distortions were associated with reduced Self-Enhancing humor. This finding is likely due to similar reasons as above. Furthermore, it is likely difficult to adopt a humorous, cheerful perspective and to feel amusement when the valence and content of one’s thoughts are in direct conflict. As a predominantly cognitive construct, Self-Enhancing humor is unlikely to coincide with negative cognitive distortions. Aggressive humor was only associated with overall frequency and social frequency of cognitive distortions, such that greater frequency of cognitive distortions was related to increased use of Aggressive humor. Cognitive distortions may involve thoughts that others are hostile or have bad intentions, leading the individual to engage in Aggressive humor out of defensiveness or to retaliate for perceived hostility. The finding that Aggressive humor was not associated with other facets of cognitive distortions is consistent with past research that Aggressive humor is not related to self-esteem ( Martin et al., 2003 ), a construct that represents negatively biased views of the self, and therefore has overlap with cognitive distortions. It is possible that use of Aggressive humor is driven more by other variables, such as hostility. Finally, Self-Defeating humor was positively associated with all forms of cognitive distortions. Individuals who engage in biased negative thinking about themselves may attempt to use humor to gain the approval of others to feel better but, because the content of their thoughts is negative, are more likely to retrieve negative self-relevant information and beliefs, and therefore generate humor that is consistent and is therefore self-disparaging.

An additional goal of this study was to examine whether humor styles mediate the relation of cognitive distortions with dysphoria. Humor styles can be conceptualized as emotion regulation, coping, and communication strategies that are likely influenced by the frequency and impact of cognitive distortions and that may in turn influence severity of depression. Multiple mediation analyses indicated that humor styles partially mediated all types of cognitive distortions, with the exception of the frequency of cognitive distortions in achievement-relevant situations. It is possible that other, less socially relevant behaviours play a role in influencing dysphoria in these contexts, such as procrastination, avoidant coping, and rumination. All other cognitive distortions (overall frequency, frequency in social contexts, overall impact, and impact in social and achievement contexts) were mediated by Self-Enhancing humor, such that cognitive distortions predicted reduced use of Self-Enhancing humor, which in turn predicted greater depressive symptoms. This finding suggests that the experience of negative thinking may interfere with an individual’s ability to adopt a humorous and cheerful outlook on life (i.e., use Self-Enhancing humor) as a way of regulating emotions and coping with stress, thereby resulting in elevated depressive symptoms. In addition, use of Self-Defeating humor (along with decreased Self-Enhancing humor), partially mediated the relation of the impact of cognitive distortions in social situations with dysphoria. Therefore, individuals who experience cognitive distortions in social situations may respond to these thoughts by attempting to connect with others by making jokes at their own expense. This strategy backfires, however, and results in increased dysphoria. The use of Self-Defeating humor may reinforce the individual’s negative self-concept (thereby increasing negative affect), especially when others appear to agree with the individual’s humorous actions or statements, or to react to their use of humor in a rejecting manner. Altogether, these findings are consistent with past research, which found that Self-Enhancing and Self-Defeating humor styles are more predictive of emotional well-being and distress, including depression, than are Affiliative and Aggressive humor, variables for which mediation was not found. Among the positive humor styles, Self-Enhancing humor is more relevant to cognition, whereas Affiliative humor is more relevant to relationships, which could explain why Self-Enhancing humor plays a role in the relationship of cognitive distortions and depression. Among the negative humor styles, Self-Defeating humor is related to self-esteem, a variable associated with both cognitive distortions and depression, whereas Aggressive humor is not, which similarly could explain why Self-Defeating humor was the only maladaptive humor style to demonstrate mediating effects.

A limitation of this study was its cross-sectional design. We cannot rule out, based on the current data, whether humor styles actually precede the tendency to engage in distorted thinking, that prior depression predicts cognitive distortions and use of humor styles, or that an unmeasured third variable accounts for both cognitive distortions and humor style. Moreover, current depressive symptomatology may have influenced the use or reporting of humor styles. Although temporal precedence cannot be determined based on the current study, results are consistent with the hypothesis that the association of cognitive distortions and dysphoria is at least partially mediated by reduced use of adaptive humor and, in one case, increased use of maladaptive humor, to cope with stress. In addition, the generalizability of the study is limited by the nature of the sample, which was predominantly comprised of young adult Caucasian females. Whether the same results would be obtained in a clinical sample (e.g., individuals with major depression) with greater endorsement of cognitive distortions and depressive symptoms, or in a sample with a greater proportion of males, is an empirical question for future research. Longitudinal research is needed to examine changes in the relationships between cognitive distortions, humor styles, and depression across the lifespan. Moreover, future research should examine the relation of cognitive distortions with humor styles and depressive symptoms using behavioural and process measures. An examination of whether information processing influences the ability to use various styles of humor when an individual has recently engaged in distorted thinking is another question worthy of further study.

This study demonstrates that cognitive distortions, which represent a cognitive vulnerability to depression, are mediated by low use of an adaptive humor style, Self-Enhancing humor (with the exception of frequency of cognitive distortions in achievement-related contexts) and, in one case (social impact of cognitive distortions), use of a maladaptive humor style (i.e., Self-Defeating humor). Mediating effects were not found for Affiliative and Aggressive humor. Furthermore, these findings add to the already extensive literature that supports the discriminant and construct validity of the four scales of the HSQ ( Martin, 2015 ; see Heintz & Ruch, 2015 ). From a treatment perspective, increasing use of adaptive coping strategies for managing stressful situations (in particular use of Self-Enhancing humor) and decreasing use of maladaptive strategies may be useful for individuals experiencing depressive symptoms. Targeting cognitive distortions themselves in order to shift an individual toward more evidence-based thinking may also be useful in altering use of humor and decreasing depressive symptomatology. However, future research is needed to examine these questions and to determine the specific mechanisms through which cognitive distortions and humor styles confer risk for depression.

Acknowledgments

The authors have no support to report.

Biographies

Katerina Rnic is a PhD candidate in the clinical psychology program at the University of Western Ontario. Her research interests include cognitive vulnerability, stress generation in depression, and how cognitive and behavioural vulnerabilities relate to the generation of and response to depressogenic life events, particularly those involving rejection.

David J. A. Dozois is a Full Professor of Psychology and Director of the Clinical Psychology Graduate Program at the University of Western Ontario. His research focuses on cognitive vulnerability to depression and cognitive-behavioral theory/therapy.

Rod A. Martin is a professor emeritus of clinical psychology at the University of Western Ontario. His research focuses on the conceptualization and measurement of sense of humor, and on the association between humor and psychosocial well-being.

This research was supported in part by a Standard Research Grant from the Social Sciences and Humanities Research Council (SSHRC) and a SSHRC Vanier Canada Graduate Scholarship.

The authors have declared that no competing interests exist.

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

© 2024 MIT Center for Information Systems Research, Wixom and Beath. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

MIT CISR Associate Members

MIT CISR wishes to thank all of our associate members for their support and contributions.

MIT CISR's Mission Expand

MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.