Personality and Individual Differences 30 (2001) 353±362 www.elsevier.com/locate/paid Reply Personality, biology and cognitive science: a reply to Corr (2001) Gerald Matthews a,*, Kirby Gilliland b b a Department of Psychology, University of Cincinnati, Cincinnati, OH 45221, USA Department of Psychology, University of Oklahoma, 455 West Lindsay, Norman, OK 73019, USA Received 1 November 1999; received in revised form 19 November 1999 Abstract Corr's (2000) commentary on the Matthews and Gilliland (1999) review article provides a useful account of the current status of Gray's personality theory, and the prospects for theory development. In this reply, we ®nd some common ground with Corr (2000). We agree that it is important to articulate and test biological models of personality. Likewise, the moderator e€ects of reinforcement factors, which may be controlled by brain motivation systems, are an important focus for empirical study. We di€er from Corr (2000), at least in emphasis, in two respects. First, Gray's Reinforcement Sensitivity Theory does not seem to have accommodated the multiplicity of brain systems which may relate to personality, including attentional systems which may modulate motivation. Second, the evidence from studies of personality and performance, suggests that cognitive models of trait action are often more successful in explaining behavioral data than theories of Gray and Eysenck. Cognitive and biological approaches may be integrated within a cognitive science framework which distinguishes multiple, complementary levels of explanation. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Personality; Motivation; Arousal; Neuroscience; Cognitive science; Extraversion; Anxiety Corr's (2000) commentary on our review of the biological theories of H.J. Eysenck and J.A. Gray (Matthews & Gilliland, 1999) provides some valuable insights into the current status of Gray's (1991) Reinforcement Sensitivity Theory (RST). We agree that further development of RST is needed, and we broadly endorse Corr's analysis of the various obstacles to theory-testing. However, Corr's article raises two general issues that require further discussion. The ®rst issue is the best research strategy for improving biological personality theory. The obstacle here is the complexity of the experimental data and the diculty of obtaining replicable results. We will * Corresponding author. Tel.: +1-513-556-0954; fax: +1-513-556-1904. E-mail address: matthegd@email.uc.edu (G. Matthews). 0191-8869/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved. PII: S0191-8869(00)00029-5 354 G. Matthews, K. Gilliland / Personality and Individual Di€erences 30 (2001) 353±362 argue that biological theories have neglected the increasing evidence that personality traits relate to multiple, independent brain systems. The second issue is the role that individual di€erences in cognition should play in trait theory. We will argue that some phenomena may be amenable to integrated biocognitive explanations, whereas others are best explained in terms of cognitive constructs without direct reference to neural processes. We are concerned not so much with the detail of individual studies, but with the broad conceptual issues of theory development, i.e. the scope of biological theories, and the circumstances under which cognitive explanations are to be preferred to neurological ones. 1. Conceptual issues in trait theory We begin with a recapitulation of the points on which most trait researchers can agree. Eysenck's (1967) original theoretical stance is strongly supported in several respects. Evidence from behavior genetics and the more consistent psychophysiological paradigms eliminates the possibility that traits are social constructions with no biological component. Matthews and Gilliland (1999) argued that data on psychophysiological response, sensory thresholds and eyelid conditioning supported Eysenck's view that extraversion relates to lower cortico-reticular arousability. The generalization of interactive e€ects of extraversion and stimulus intensity on phasic response across di€erent paradigms requires some mechanism of this kind, although the neural systems supporting response are often not well-speci®ed. Researchers may also agree that motivational factors are important moderators of personality e€ects on physiological and behavioral response, but, in this case, the evidence is open to various interpretations. We agree with Corr (2000) that Gray's (1991) Reinforcement Sensitivity Theory (RST) has some attractive features and merits further investigation. However, the evidence that personalityreinforcement interactions directly re¯ect individual di€erences in neural function is not yet compelling. Importantly, Gray and his colleagues have yet to identify a psychophysiological paradigm which provides results of comparable replicability to paradigms linking extraversion to arousability, such as phasic EDA (Smith, Concannon, Campbell, Bozman & Kline, 1990) and certain evoked potential paradigms (Stelmack & Houlihan, 1995). Our review pointed out instances of motivational moderation of personality e€ects on psychophysiological response, especially in evoked potential studies, but, as we also indicated, these e€ects seem unstable across similar studies (Bartussek, Becker, Diedrich, Naumann & Maier, 1996). The case for RST depends more on behavioral evidence from the learning and performance tasks discussed by Corr (2000). As Corr points out, response on these tasks may often be in¯uenced by cognitive factors such as expectancies, even in simple conditioning paradigms. Personality factors correlate with an extensive range of cognitive variables (Matthews et al., 2000), and so we cannot exclude the possibility that personality e€ects on behavior are mediated by cognitive processes that do not relate in any simple way to subcortical brain systems. Trivially, all behavior depends on the brain, and so all behavior is biological. However, the more important issue is whether theory succeeds as better science when the psychological constructs explaining personality expression are described neurologically or cognitively. Conceivably, cognitive constructs may be more readily operationalized and measured than neurological constructs, and allow more accurate prediction of outcomes in experiments. Sperry (1993) has argued G. Matthews, K. Gilliland / Personality and Individual Di€erences 30 (2001) 353±362 355 that cognitive constructs may be as `real' as biological ones, and equally capable of supporting causal explanation. Beyond the consensus that biology plays some role in personality, further progress requires methods and theories which will permit researchers to discriminate and partition biological and cognitive components of personality, much as testing genetic models requires discrimination of environmental e€ects (Mann, 1994). Next, we discuss approaches to handling complexity in personality data within the boundaries of biological models, and reasons for complementing or supplementing biological explanations with cognitive ones. We also discuss the implications of these conceptual issues for testing biological models and for theory development. 2. Handling data complexity within biological models Current biological theories have yet to decide how to conceptualize the complexity evident in the empirical data. The position taken by Corr and others (Pickering et al., 1997) is to emphasize interaction between systems (the `joint subsystems' approach). In fact, system interaction has always been part of theory (see Eysenck's, 1967, discussion of EN interactions), although researchers have sometimes ignored it. Such interaction is plausible, but complexity of outcome may re¯ect other factors. In particular, traits may be distributed across multiple brain systems, such as the `cortico-reticular' and `dopaminergic' aspects of extraversion tentatively identi®ed by Matthews and Gilliland (1999). Although Corr (2000) acknowledges that arousal and reinforcement sensitivity accounts of personality may be complementary, RST seems to underestimate the multiplicity of possible biological sources of personality. There is a good case that relationships between E and wave V of the auditory brainstem evoked response re¯ect c.n.s. arousal (Bullock & Gilliland, 1993), but E might independently relate to more peripheral neural mechanisms (Stelmack, Campbell & Bell, 1993). Recent work on linguistic di€erences between extraverts and introverts (Dewaele & Furnham, 1999, 2000) suggests that personality may be linked to the `language centers' of the brain. It remains to be seen whether e€ects of E on response speed (Doucet & Stelmack, 1997) should be attributed to dopaminergic facilitation or motor response execution per se. Similarly, anxiety/neuroticism may relate to systems other than motivational ones. On the basis of performance studies, Derryberry and Reed (1997) link anxiety to speci®c subcomponents of the multiple attention systems identi®ed by Posner and Rothbart (1991), such as systems for disengagement from threat and focusing on ®ne detail. In Derryberry's conception, anxiety relates more to attentional modulation of motivation than motivation per se. Personality may also relate to circulating hormones, as well as to discrete brain areas (Zuckerman, 1991). Trait theorists often assume an orderly hierarchy of systems contributing to personality. We might imagine that each trait is supported by a number of brain systems, such as the multiple systems said to support incentive motivation (Depue & Collins, 1999). Within such a hierarchy, facets of E and N corresponding to speci®c systems might be identi®ed, facilitating behavioral prediction. However, the assumption may be incorrect. Zuckerman (1991) argues against any simple isomorphism between traits and brain systems: one system may contribute to several traits, each of which re¯ects several systems. At a more ®ne-grained level, similar considerations may limit the impact of molecular genetic studies. Relationships between genes and brain systems may be equally non-isomorphic. If so, we would expect to ®nd only small associations between 356 G. Matthews, K. Gilliland / Personality and Individual Di€erences 30 (2001) 353±362 speci®c gene loci and traits, the picture emerging so far from molecular genetic studies (Plomin & Caspi, 1998). In sum, stimuli used as reinforcement signals in human experiments may activate brain systems related to peripheral processes, nonspeci®c arousal, language and attention. RST has yet to suggest how motivational e€ects may be distinguished from these other sources of individual di€erences. Even if we had some means of attributing e€ects to brain motivation systems, there may be no simple mappings between motivational subsystems and traits. 3. Implications for methods One of the main themes of Corr's (2000) commentary is the diculty of operationalizing the constructs of Gray's theory. The list of uncertainties is formidable: the exact personality dimensions to be used, the motivational consequences of reinforcement manipulations, temporal qualities of reinforcement, empirical confounding of motivation and arousal, and interactions between systems. Corr (2000) advocates dealing with these problems by developing behavioral indices of the processes involved in positive and negative incentive motivation, but his article does not specify how cognitive and biological control of these various processes might be distinguished. There may also be serious diculties in developing a biobehavioral theory of motivation through experimental studies alone. The example of arousal theory is instructive. Studies attempting to infer arousal from behavioral consequences of experimental manipulations have fallen into disrepute because they tend to encourage arbitrary post hoc explanation and circular reasoning (Hockey, 1984). Use of arousal as an explanatory construct has only been successful when hypothesized arousal change can be validated psychophysiologically, or, in recent work, through self-report (Thayer, 1996). It would be a pity if motivation theorists were to repeat the methodological errors of arousal theorists. Corr (2000) rightly emphasizes the importance of motivational state. It appears, therefore, that RST urgently requires measurement techniques that will obviate the need for post hoc rationalization. It seems psychophysiological researchers have some work to do here. For example, Fowles' (1980) identi®cation of electrodermal activity with the BIS and heart rate with the BAS has not provided clear support for Gray's theory. As in the case of arousal, there may be scope for developing self-report measures of motivational state. Our recent work (Matthews et al., 1999) shows that self-report task motivation is distinct from Thayer's (1996) arousal dimensions in psychometric and experimental studies, although, in general, it correlates with neither E nor N. More radically, we might question why biological theorists should be focusing on self-report measures of personality at all, given the weaknesses of these measures described by Corr (2000). It would appear more rational to develop a behavioral or psychophysiological measure of reinforcement sensitivity, and explore its nomological network, much as is done for di€erent rat strains in animal studies. 4. Cognitive explanations for trait expression Thus far, we have argued that even if we accept their underlying assumptions, current biological theories underestimate the diculty of establishing neurological explanations for personality G. Matthews, K. Gilliland / Personality and Individual Di€erences 30 (2001) 353±362 357 expression. One of these assumptions is that cognitive processes are readily reducible to neural processes. For example, Corr (2000) conceptualizes `expectancies' as outputs of a septo-hippocampal comparator (SHS: Gray, 1982). If this assumption is incorrect, further diculties ensue, as we now discuss. Advances in cognitive neuroscience demonstrate the biological underpinnings of cognition. Nevertheless, cognitive science develops reasons for preferring, on occasion, cognitive to neural explanations. One of us has discussed the application of cognitive science to personality research at greater length elsewhere (Matthews, 1997a; Matthews, Derryberry & Siegle, 2000a). In brief, cognitive theory (Newell, 1982; Pylyshyn, 1984) suggests that di€erent behavioral phenomena may require di€erent levels of explanation. The brain may be envisaged as a physical computational device running `programs' whose operation is controlled by formal rules of computation, operating on symbols. It may be dicult to describe the computational `software' in terms of the hardware, because (yet again) there is no simple mapping between software and hardware constructs. By analogy, explaining how the word processor on a PC operates is more easily e€ected by describing software operations than by describing how electrons ¯ow through silicon. (Pylyshyn, 1984, argues that it may actually be impossible to describe software symbols in hardware terms, although this view is controversial: Bickle, 1998). The distinction between neural ('hardware') and cognitive-architectural ('software') explanations is directly relevant to personality theory, because of the extensive evidence that personality e€ects on performance vary with the information-processing demands of the task (Revelle, 1993). For example, extraversion e€ects appear to be moderated by factors such as attentional resource demands (Necka, 1997), semantic priming and word-nonword confusability (Harley & Matthews, 1992), linguistic formality and complexity (Dewaele & Furnham, 1999) and dominance of category exemplar (Eysenck, 1981b). Interactions between extraversion and arousal/stress factors are similarly contingent upon processing factors (Matthews & Dorn, 1995). Theories of the cognitive architecture provide immediate explanations for such e€ects. For example, Neely (1991) identi®es three mechanisms for semantic priming: parametric studies of extraversion e€ects on priming allow them to be related to a speci®c mechanism (spreading activation between lexical units). Such hypotheses make no direct reference to biology but they are scienti®cally valuable because they a€ord the prediction of individual di€erences in performance. Biological theories of personality have notably struggled to explain how personality e€ects vary with task demands: simple generalities such as the Yerkes-Dodson Law break down when scrutinized in detail (Matthews, 1992). Cognitive science also distinguishes two di€erent types of `cognitive' explanation. The cognitive-architectural explanation just described refers to inbuilt constraints on processing functions. By contrast, `knowledge' explanations (Newell, 1982) refer to intentionality, and the persons' beliefs about how to attain personal goals. This level of explanation corresponds to self-regulative theories of personality (Matthews et al., 2000b). Empirically, as Matthews, Schwean et al. discuss, traits such as E and N correlate extensively with self-regulative constructs such as self-perceptions, metacognitions and coping. These constructs explain much of the shared variance in personality and a€ect, and may help to explain performance correlates of personality (Matthews, 1999). For example, the bias towards threat stimuli on tasks such as the emotional Stroop shown by anxious individuals super®cially corresponds to the attentional output of Gray's (1982) Behavioral Inhibition System (BIS). However, studies of the bias e€ect show that it is far from 358 G. Matthews, K. Gilliland / Personality and Individual Di€erences 30 (2001) 353±362 automatic, varying with information-processing parameters such as temporal lags between stimuli, trial-blocking, and other contextual factors (see Matthews & Wells, 1999, for a review). A more satisfactory explanation is that attentional bias is a consequence of strategic monitoring for threat, a conclusion also supported by connectionist simulation data (Matthews & Harley, 1996). In other words, performance di€erences re¯ect di€ering options for evaluating and managing environmental demands, or, in Lazarus' (1991) terms, di€ering person-environment transactions. In studies of motivation and personality, behavioral e€ects may re¯ect individual di€erences in goals, expectations of success, and beliefs about goal attainment, as explored in cognitive theories of achievement motivation (Weiner, 1985). Cognitive science provides a theoretical rationale for preferring cognitive-architectural and knowledge level explanations for certain types of behavioral phenomenon. Cognitive explanations appear to be more successful in predicting how e€ects of E and N on performance vary with task demands than any biological theory yet advanced (Matthews, 1997b, 1999). Similarly, they o€er empirically-supported explanations for relationships between personality and stress vulnerability (Matthews et al., 2000b). We can link traits to a nomological network of informationprocessing and cognitive constructs established by mainstream psychological research, which provides an imperfect but promising basis for predicting how personality traits will relate to behaviors in novel experimental paradigms and real-world settings. 5. Integration of cognitive and biological models Developing motivational theories of personality requires better integration of cognitive and neural constructs. Gray's (1982) theory refers to `cognitive' constructs such as expectancies, but, as Corr (2000) discusses, `expectancy' describes a prediction of reinforcement value, or of some other simple stimulus attribute such as novelty. However, Gray's view of cognition is too simplistic to apply to anxiety in humans (Eysenck, 1992). Speci®cally, it fails to recognize qualitatively di€erent types of expectancy, such as Bandura's (1977) distinction between behavioroutcome (`will the behavior produce a speci®c outcome?') and ecacy-outcome (`can I personally execute the behavior successfully?') expectancies. It fails to accommodate cognitive biases in prediction such as those demonstrated in decision-making studies (Tversky & Kahneman, 1981). It has no means to describe the distortions of language-based, propositional reasoning about the future which contribute to pathological emotion (Wells & Matthews, 1994). Perhaps the various computations of expectancy-related constructs serve functionally to spit out a predicted reinforcement value for the SHS, and no more. However, this view of cognition is so much at variance with the contemporary psychology of expectancy (Kirsch, 1999), that the onus is on RST theorists to demonstrate its applicability to humans. The required evidence has not so far been forthcoming. One promising approach towards biocognitive integration is the use of connectionist models, which represent processing-level constructs as patterns of activation within networks of neuronlike units. For example, Siegle (1999) and Siegle and Ingram (1997) have developed a model of feedback between units representing semantic constructs and units representing emotions which simulates aspects of depressive cognition, corresponding to emotional processing in amygdala, hippocampus and frontal lobes. Similarly, Matthews and Harley (1993) simulated extraversion and arousal e€ects on word recognition, although without commitment to any particular brain G. Matthews, K. Gilliland / Personality and Individual Di€erences 30 (2001) 353±362 359 area. Simulation data might prove useful in modeling e€ects of motivational systems on learning and performance, although such a project would require better speci®cation of information-processing mechanisms than RST currently provides. There may be other aspects of cognition that cannot be captured by biological models in suf®cient detail to a€ord successful prediction of results. To many, trait theory is interesting to the extent it explains expressions of personality in real-world contexts such as life stresses and occupational choice and performance (Furnham, 1992). Contemporary biological models seem to have retreated from the ambitious inclusiveness evident in the early writings of Gray and Eysenck, to a search for speci®c experimental paradigms that give coherent and replicable ®ndings. In fact, much current work on personality and real-world behavior deals with `knowledgelevel' constructs such as coping and self-regulation (Carver & Scheier, 1990), and traits such as E and N overlap with self-referent traits such as self-esteem, self-ecacy and self-consciousness (Matthews et al., 2000b). Matthews (1999) suggests that personality relates to availability of skills for dealing with particular kinds of challenge, such that individual di€erence in skill acquisition re¯ects both cognitive-architectural attributes of the trait, and the person's voluntary choice of interests and exposure to learning opportunities. For example, extraverts may have better social skills both because of facilitation of aspects of verbal information-processing, and because they choose to enter social situations which require those skills. In the case of neuroticism/anxiety, the trait is strongly related to heightened awareness of cognition (metacognition) and the contents of cognition, such as worries (Wells, 1994). Models based on such constructs provide a powerful means for conceptualizing personality traits as styles of self-regulation, and, empirically, explain much of the common variation in personality and a€ect (Matthews et al., 2000b). It is dicult to see how animal models can accommodate the role of self-referent processing (Eysenck, 1992), and verbal mediation of behavior (Wells & Matthews, 1994) in anxiety. More generally, personality seems to re¯ect both `feed-forward' from inherent neural and cognitive-architecture qualities, and `feed-back' as the individual learns how to handle the adaptive challenges arising from their life circumstances, and traits relate to both aspects (Matthews, 1999). Clearly, biological models contribute to understanding how genes and early learning jointly in¯uence brain development and eventual adult behavior. However, it remains to be seen how well such models can explain dynamic person-environment interaction across the lifespan. 6. Conclusions We agree with Corr (2000) that individual di€erences in brain function are an essential aspect of personality traits, and that further work on brain motivational systems is highly desirable. However, we consider also that Gray's RST has su€ered signi®cant setbacks since emerging as a theory distinct from Eysenck's, as evidenced by Gray's retreat from prediction of associative learning e€ects (Matthews & Gilliland, 1999), and the various modi®cations to theory introduced by Gray's colleagues (Corr, 2000; Pickering et al., 1997). Eysenck (1981a) discussed how scienti®c `paradigms' handle the inevitable problem of discon®rmatory ®ndings. The danger is that modi®cations to theory, like Ptolemy's epicycles, deal with existing problems, but increase complexity, and fail to enhance prediction of new ®ndings. It would be premature to label Gray's theory as a `degenerating paradigm', but our impression is that it has some ground to make up. 360 G. Matthews, K. Gilliland / Personality and Individual Di€erences 30 (2001) 353±362 Corr (2000) takes issue with the view expressed by Matthews and Gilliland (1999) that `cognitive constructs may be more appropriate than biological ones for explaining the majority of behaviours, so that explanations of the kind o€ered by the Eysenck and Gray theories are relevant to a restricted range of phenomena only'. We do not wish to discourage attempts to link individual di€erences in behavior directly to brain functions, as RST attempts to do. Evidently, personality theory can only be strengthened by a diversity of rigorous, evidence-based approaches to explaining individual di€erences, and our di€erences with Corr's (2000) position are to some extent di€erences in emphasis. However, our reading of the available evidence is that, thus far, cognitive constructs linked to traits have proved to be generally more predictive of laboratory performance and real-world outcomes than neural-level constructs indexed psychophysiologically. Similarly, use of cognitive rather than biological constructs currently provides a more coherent picture of how trait e€ects are moderated by environmental and intrapersonal factors (Matthews, 1997b). Some versions of cognitive science theory (Pylyshyn, 1984) suggests that biological-level theories such as RST may be fundamentally incapable of conceptualizing processing and intentional constructs central to personality. Another view is that connectionist modelling may reinstate neural reductionism of cognitive processes (Matthews et al., 2000a). We have proposed also that some modi®cations in methods and theoretical stance might improve the likelihood of eventual success for RST. Methodologically, the theory may require some means of assessing state activity of BIS and BAS for it to be tested satisfactorily. It would also be desirable to work forward from individual di€erences in biological constructs, as well as backwards from questionnaire measures. Perhaps advances in molecular genetics will allow researchers to compare individuals of di€erent genotype directly. Conceptually, we have raised various issues, relating both to the improvement of theory as biological theory, and to its alignment with increasingly successful cognitive accounts of traits. RST might be a better biological theory if it accommodated the multiple sources of variation in personality suggested by research, such as the attention systems described by Derryberry and Reed (1997). Personality may relate to the specialized brain systems identi®ed by contemporary cognitive neuroscience, as well as to nonspeci®c arousal and motivational systems. 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