Michel Denis. The International Handbook of Psychology. Editor: Kurt Pawlik & Mark R Rosenzweig. Sage Publications. 2000.
Introduction: Three Levels of Cross-Disciplinary Interactions
Psychology belongs to a rich family of disciplines that includes biology and the life sciences, as well as the human and social sciences, and it has recently been extended to the engineering sciences. Psychology has always been in contact with other disciplines, first with those from which it developed historically, such as philosophy, and then with those whose links are immediately apparent, such as psychiatry, as a practice and a science devoted to psychological dysfunctioning. Other links have always been active in the domains of learning and education.
As both a basic science and an applications-oriented science, psychology is naturally in touch with the scientists and practitioners devoted to remediation and increasing human potential. Even in the basic research contexts of psychophysiology and more recently neuroscience, the background idea remains that elaborating fundamental knowledge is likely to serve intervention and remediation.
As a scientific discipline, psychology has forged its identity during the rather short century of its existence. This chapter explores the idea that psychology, like any other discipline, derives its existence and identity not only from the features that are specific to it (that is, a set of domains and a set of methods), but also from the topics that it shares with other disciplines. Thus, psychology depends for part of its existence on its relations with its neighbor disciplines. This interaction with other disciplines has resulted in some concepts related to cross-disciplinary exchange becoming more prominent in the last decades than they were before. This is also true for the whole field of science. It is useful to clarify the terminology here, and to distinguish among several possible levels of interactions that may occur between a discipline (here, psychology) and one or several others.
The reference to pluridisciplinary research is frequently found in these contexts. In general, the term merely refers to situations or contexts involving more than one discipline. The use of this term implies nothing about the form of the joint involvement, or the degree of its reciprocity, or whether the joint venture serves truly common objectives. It simply delineates a domain of research and the several disciplines that provide their respective points of view on the domain. A plurality of methods is implemented to attack the issue under investigation, at different levels of analysis.
In some cases, one may wish to refer to the fact that not only are several disciplines involved in a project, but that they are engaged in a more integrated cooperation to solve a common problem. Here, the underlying (or, preferably, explicit) idea is that of a common enterprise, in which not only several disciplines share their resources, but that the final outcome essentially depends on this cooperation. The trend, then, is to use the concept of interdisciplinary research to express such stronger integration and convergence on the part of the disciplines involved. However, each discipline maintains its identity in the partnership.
In interdisciplinary projects, a new question may at some point be considered. The issue is whether this converging process is intended to maintain each discipline as a separate entity, or whether the joint efforts reflect a new broader approach, which integrates the capacities of the individual sciences involved in the project. The concept of a transdisciplinary approach may emerge in such contexts. It is a more ambitious concept, maybe also more difficult to circumscribe. The idea is that the work developed in an interdisciplinary project may force the partners to ‘cross the divide’ between disciplines and delineate a new domain that encompasses several disciplines (or parts of them). A common language, which is not the original language of each individual partner, is to be forged, going ‘beyond’ individual disciplines, perhaps to found a new science.
Whatever the degree of integration of projects involving the cooperation of psychology with other disciplines, it is important to clarify whether pluridisciplinarity (or one of its more integrated versions) is an intrinsic goal. While some scientists favor cooperation among disciplines in principle, an alternate view is that there is no a priori virtue in pluridisciplinarity. Its virtue depends on the relevance of making disciplines converge to resolve a specific issue that a single discipline cannot. Pluridisciplinarity is thus to be seen as a sort of ‘metamethod,’ that is to be used simply to answer research questions that require this approach. Actually, some of the questions that psychologists face are intrinsic to their own endeavor and do not need any special contribution from other disciplines. This, for instance, is the case when psychology assigns to itself the task of finding an adequate method for measuring intelligence, or for describing a child’ behavior. In some other cases, a psychologist may wish to develop a model of a cognitive function, while taking care that the model is realistic, that is, compatible with what is currently known of brain functioning. This requires cooperation with neuroscience, and this cooperation must be specified in such a way that the partners approach the same level of a scientific reality, or close enough levels. Similarly, when engineers have to design software that is to be used by ordinary people, the role of psychology is to make sure that the level of cognitive processing required by the software is compatible with human cognitive capacities. This latter example has the additional advantage of revealing that pluridisciplinary or interdisciplinary projects may respond to a plurality of scientific motivations. While the engineer may simply feel committed to designing artificial functional systems, without being interested in human cognition itself, the aim of the psychologist is to provide accounts of the cognitive functions, as a domain of natural sciences, some of these functions being likely to be involved when a person interacts with artificial systems. Psychological science may thus contribute to a joint effort that goes beyond the establishment of scientific knowledge per se. It takes part in a broader concert that aims to improve the conditions in which human beings interact with their surrounding world.
Psychology and Neuroscience
Most of the disciplines gathered under the generic label of neuroscience take account of the research and concepts of psychology, and the reciprocal is true as well. Cognitive functions can well be analyzed at a rather abstract level, independently of the physical reality of the brain that supports their functioning. This approach, isolated from any reference to the brain, is useful for understanding the algorithms that underlie the functioning of any ‘cognitive agent,’ whether its support is biological or arte-factual. However, there has been a strong tendency in recent years to claim that researchers must at some point take into account the constraints imposed by the biological systems underlying cognitive functions. Neuroscience is assigned the task of identifying the biological infrastructures of these functions (cf. Kosslyn & Andersen, 1992).
The progress of research in neuroscience has been rapid and considerable over the last two decades. Part of this progress can be attributed to the enormous effort devoted to the comprehension of neurological disease and the invention of efficient treatments. This concern was also connected to the investigation of the effects of aging on neurological and psychological functions. A major goal for a growing number of neuroscientists is to understand the neurological basis of cognitive functions (perception, attention, memory, action), which includes how they are implemented in the architecture of the brain (cf. Kosslyn & Koenig, 1992).
The rapid progress made in this domain is undoubtedly due to the increased emphasis on model building, and the development of highly sophisticated techniques, in particular those based on functional neuroimaging. For instance, the positron emission tomography (PET) first offered rich information on the cerebral regions involved in psychiatric syndromes (such as depression or anxiety). But the greatest value of neuroimaging techniques is their use to examine normal people. It is then possible to identify the cerebral structures activated during the execution of behavioral or cognitive tasks, such as in visual attention, mental imagery, language comprehension, motor activity, or decision processes. New techniques, such as functional magnetic resonance imaging (fMRI) and mag-netoencephalography (MEG), are also used with normal people, and make it possible to overcome the current limitations of PET in temporal accuracy (cf. Toga & Mazziotta, 1996).
Visual attention, visual perception, and motor control, are all examples of domains where cerebral mechanisms are also quite usefully studied by recording neuron activity in awake animals. Crucial to the understanding of psychological functions, some studies have shown that the cell responses in some highly integrated cortical regions may reflect great selectivity, that is, specific classes of complex stimuli (for instance, faces) can activate specific neurons. Our understanding of cognitive functions has also benefited from new developments in anatomical techniques and knowledge of the biochemical mechanisms involved in synaptic plasticity and long-term potentiation.
The growing effort of neuroscience to understand cerebral functioning in terms of coordinated information-processing systems is essential to the development of psychological theory. Computational neuroscience is orienting itself towards analyzing the coding of sensory and motor information in the brain. The information gained from this approach has important implications for researchers attempting to design information-processing systems directly inspired by the concepts established by neuroscience. This is the case of the connectionist (or neuro-mimetic) systems, to which more and more psychologists are contributing. The concept of a ‘neuronal computer’ has become common in this community. This is probably a domain in which interdisciplinary integration is especially advanced, and is on its way to producing some form of transdisciplinarity, attempting to establish connectionism as a field and discipline on its own, ‘beyond’ the individual contributions of the disciplines involved in the process (cf. Churchland & Sejnowski, 1992).
One of the most remarkable trends in neuroscience is that it is more and more involved in attempts to understand cognitive behavior that implies a high degree of integration. This is important for psychology, and may explain the success of initiatives to establish long-term sustained cooperation between psychologists and neuroscientists. Hence, a growing number of psychologists tend to describe themselves as cognitive neuroscientists. It is worth stressing that the majority of the members of the Society of Cognitive Neuroscience, which was founded a few years ago in the United States, are psychologists and neuropsychologists. The domains covered by this community are the effects of brain lesions in humans and animals, electro-physiological recordings in animals, and neuro-imaging in normal humans. The picture that emerges is that of a genuine cooperation among scientists of different backgrounds to link brain, mind, and behavior. This trio, after all, sounds like a good description of the objectives of psychology in general. The same set of psychological functions form the program of psychology and neuroscience, thus justifying the launching of integrated actions. This kind of integration probably reflects genuine interdisci-plinarity, in that the same concepts are investigated at two distinct levels of their implementation, that is, cognitive and neuronal. Each discipline keeps control and use of its methods in this joint approach, but the models of cognition and the experimental designs are elaborated jointly. Each discipline attacks the phenomena at the level for which it is specialized and methodologically equipped, but without ignoring that other levels of description are relevant from the alternate perspective. This balance is very valuable, and it is important to encourage it, inasmuch as it contributes to defining the field of investigation in both biological and psychological terms.
There are also more recent trends. For instance, it is well established that the internal chemical environment of a person shapes his/her nervous states and behavior. The external chemical environment (such as olfactory signals) also influences individual and group behavior. Promising collaborations have started with biochemistry, that is, the study of the chemistry specific to biological systems, and biological chemistry, with the creation of synthetic molecules that control biological activities. Psychology has obviously much to contribute to this kind of cooperative research. Lastly, there are links between psychology and neuropharmacology. The perspective here is to produce molecules likely to repair altered cognitive functions (in particular, in the domain of memory).
Psychology, Computer Science, and Artificial Intelligence
Human cognition expresses itself in natural contexts (such as when people solve problems in natural environments or social contexts), but it also has many opportunities to reveal its capacities when interacting with human-made devices or artefacts. Artificial systems that are capable of generating ‘intelligent’ outputs are becoming more and more available to everyone. Psychology must obviously interact with the ‘sciences of the artificial,’ first of all computer science and artificial intelligence (AI). This is because it has become important for engineers to create artificial systems which behave like cognitive agents endowed with rational capacities. These systems must also contain representations and processing modes that are functionally compatible with those of human beings (cf. Osherson, Stob, & Weinstein, 1986; Russell & Norvig, 1995; Winston, 1984).
The human mind is frequently confronted with incomplete, uncertain, or partly contradictory information. Nevertheless, it is capable of conducting a variety of modes of reasoning in order to solve problems. The mechanisms by which the human mind achieves such processing and adapts to an uncertain world are typically opaque to people. Psychological science attempts to discover the algorithms underlying reasoning processes, whether they are productive of optimal solutions or not. The objective of AI is to formalize reasoning in a rigorous, efficient manner, so that computers can reason and produce outputs that satisfy explicit rational criteria. Complementarily, by relying on formal models of reasoning, psychologists try to understand how people reason, including an analysis of their ‘errors’ and ‘contradictions’ in scientific accounts. This is considered to be a crucial component in the process of developing human—machine communication systems, as well as computer-based instructional devices. Although computer science may not have been originally prepared to value the analysis of the errors produced by an intelligent system, it has now developed a more explicit interest in the mechanisms which cause errors in human reasoning.
Not only has AI come to value the data collected and models proposed by psychologists, but the results obtained by AI for formalizing reasoning are of great interest for psychologists themselves. This is a good context for the development of theoretical frameworks encompassing the variety of modes of reasoning, but also for providing a more solid epistemo-logical basis for the analysis of natural reasoning and argumentation.
An important domain in this cross-disciplinary context is the automatic processing of natural language. Originally, this domain was considered to be just part of computer science. Nowadays, it is agreed by both psychologists and computer scientists that understanding how humans process natural language is a prerequisite for building systems capable of producing similar behavior. In particular, language-processing systems can only be efficient if they produce appropriate inferences based on the inputs provided to them. Similarly, the ‘behavior’ of the systems must offer outputs that make it possible for their users to perform the same type of inferences as those they make when processing natural language. Psychologists, and more specifically psycholinguists, play an important role in this endeavor. They contribute to the view that, beyond superficial comprehension, it is crucial for any intelligent system to access the meaning and deep implications of the message, including the goals, intentions, and strategies of its author.
The most fruitful relationships between psychology and AI are those which take place in the framework of ‘cognitive science’ (CS), that is the project interrelating the family of disciplines concerned by the ways in which intelligent systems or organisms acquire, store, and make use of knowledge for adaptive purposes (cf. Posner, 1989; Stillings et al., 1987). Artificial systems are intended to produce intelligent behavior, not necessarily by mimicking the processes believed to occur in a human mind. However, when AI researchers become engaged in interdisciplinary projects, then psychology is more than likely to be a key participant. One reason is that the human mind is the first (and probably most sophisticated) cognitive system that has ever been approached as a scientific object. Psychology is not only greatly concerned in most current programs in CS, but it is quite central to them, especially in the projects that develop computational models in the areas of perception, language comprehension, memory, and problem solving. Such programs may also combine psychology and computer science with physical and life sciences in the area of neural networks (cf. Bechtel & Graham, 1998; Sun & Bookman, 1994).
The importance of maintaining close contacts between psychology and AI in the domain of CS is justified by the capacity of psychology to address issues at both the theoretical and the empirical level. There are good epistemological reasons for the involvement of psychology. Psychology investigates human cognitive functions and provides data for modeling processes that are relevant to AI. In addition, human cognition is by far the most well-developed example of cognitive function, which makes it possible to provide models that inform computer science. Another set of reasons is the methodological expertise of psychologists in collecting and analyzing empirical data. Psychological researchers are recognized by AI researchers as rigorous and well-trained research methodologists. Thus, because it provides both the core theoretical and empirical basis for advancing the field, psychology is a prerequisite for any program in which AI will attempt to build intelligent artificial systems, especially if these systems are intended to mimic some of the human cognitive capacities (cf. Denis, 1998).
Psychology is biased to human forms of cognition and intelligence by its very nature, and requires little computational expertise. However, the computational approach is gaining popularity and consideration in contemporary cognitive psychology (which may be a result of a growing interaction with AI). Psychology may contribute its basic experimental nature and strong research methodology to joint projects with computer science, but its great strength is its ability to model mental processes at different levels of abstraction. In this respect, any common project may benefit from the unique integrative capacity of psychology and its care in maintaining the place of the human being at the center of CS. Despite the development of computers and information technology, or perhaps because this development, it is clear that the human being remains central in CS, thanks to the involvement of psychology.
Psychology, Ergonomics, and Human—Machine Communication
Improving communication between humans and artificial devices is an important task. The researchers whose aim is to endow machines with faculties similar to (or compatible with) human faculties have to deal with a number of problems, such as the automatic processing of speech, written language, visual information, or gesture. The current trend in human-machine communication (HMC) research is to consider these different modes of communication in an integrated fashion (rather than independently). This tendency is favored by the fact that quite similar methods for shape recognition can be applied to these different domains. Furthermore, a growing number of interactive systems actually call upon multiple modes of communication (such as vision + language, or speech + pointing, etc.) (cf. Maybury, 1993).
HMC should not be seen as a discipline, but rather as a set of problems. It is an extremely interdisciplinary field, which has to take into account three terms of a situation: a user, a machine, and the object of the user’ activity. The aim of HMC researchers is to specify the constraints on the interactions between a user and a machine, given the assigned task or activity. The growing need for new software and interfaces in HMC offers a number of opportunities for psychology to identify the cognitive difficulties likely to be engendered by such systems, and to propose ways of circumventing these difficulties. Knowledge based on ergonomics is of unique value here, as it has to solve problems at the very junction of cognition and communication. There is an important social aspect of this issue, given the key position of computers in new professional environments. This situation creates an opportunity for using knowledge acquired in cognitive psychology to design devices that enhance the quality of working conditions and increase the reliability of human-machine systems. Psychology helps HMC to identify a key issue, that is, the compatibility between the languages used in the interactions between the user and the machine, and the representational and processing capacities of the users’ cognitive system (cf. Card, Moran, & Newell, 1983; Carroll, 1991).
Early research in ergonomics extensively investigated the motor components of working situations. When ergonomics turned towards more cognitive issues, the researchers mainly focused on training in professional contexts and on the development of software suited to the users’ cognitive capacities. This evolution paralleled a shift in perspective, moving from an approach mainly concerned with the repairing of ill-adjusted situations to a perspective where ergonomics plays a role in the very conception and design of systems. Cognitive psychology and ergonomics also have an important role in assessing the relevance of the results collected in ergonomic research to real work situations. Another important part of cognitive ergonomics is interested in knowledge contents, such as scientific knowledge in education or pragmatic knowledge in professional contexts. There, it is highly important to verify the compatibility between the designers’ and the users’ knowledge. A related field is that of instructional devices for professional activities, where it is important to account not only for public or explicit expert knowledge, but also for private or implicit procedural knowledge (cf. Rasmussen, 1986; Wickens, 1984).
Psychology is becoming more and more involved in applications that require consideration of the expectations or queries of the industry. Hence, human factors and ergonomics are essential issues in research and development. Psychologists are involved in conducting user-oriented surveys and evaluating systems. Cognitive psychologists may also prove to be especially valuable in refining the human—system interaction aspect of system design. The difference between cognitive psychologists and engineers in refining the human—system features of a design is that the latter usually rely on user feedback and trial and error. In contrast, cognitive psychologists are armed with psychological principles to guide the design and reduce the risk of errors by avoiding potential pitfalls they have learned through experimental studies.
This is, in fact, a feature that is of growing importance, as a contrast to the classical methods used in the design of human—machine systems. The former methods tended to emphasize the technical aspects (such as the electronics of the system), giving secondary importance to the perceptual, motor, and cognitive characteristics of the user. It rapidly became apparent that an approach focusing primarily on the technological aspects is limited. Many iterations are needed to produce really functional systems, due to the neglect of including both the user and the task from the outset of the designing process. The new trend is to explicitly include the constraints of the user and the task early in the design, that is, to adapt the systems to their future users. But this does not imply that the psychologist should ignore the economical, technical, and temporal constraints that the designers must take into account (cf. Helander, 1988; Norman & Draper, 1986).
In conjunction with the system designer, the psychologist works to elaborate a ‘model of the user,’ which offers an abstract description of the actual functioning of the cognitive system in the considered task. A ‘model of the task’ is also constructed, based on detailed analysis of the objective of the user. These preliminary formulations force the designer and the psychologist to make explicit the steps to be followed by the users when they process information and respond to the system. The combination of the two models provides a basis for designing the optimal configuration of the information to be displayed by the system (which pieces of information to deliver, which media to use, which modes to call upon jointly, in which temporal sequence). The articulation of the two models also makes it possible to formulate predictions of any incident likely to occur during processing, whether the incident concerns the user or the task.
To summarize, in the many cases where psychology is invited to contribute to technology-oriented research, its first role is to provide theories and tools for modeling a user’ knowledge and ways of handling a task. Then, psychology contributes to design and development by helping engineers design more user-centered systems. It may also study the organizational impact of new systems (for instance, the impact of a new system on the users and on the organization as a whole).
Psychology and Sciences of Language
Language is a major domain in which psychology interacts with other disciplines, the first of which is linguistics. One good reason for placing language at the core of interdisciplinary research involving psychology is its status of a specific human activity. As a consequence, many disciplines are likely to converge on this very particular object. For instance, lexical processing is more and more approached by interdisciplinary programs that combine behavioral analysis, electrophysiological methods, and con-nectionist modeling. At a higher level of complexity, language comprehension and production are approached as complementary aspects of a common underlying system, on the elucidation of which linguistics (including computational linguistics) and several branches of psychology may share interests. Some specialization normally takes place in such converging processes.
While linguists describe the product, psycholinguists attempt to specify how language is generated and understood by the brain and why it has evolved in the way it has. Computer scientists, for their part, build systems that are intended to process natural language by using mechanisms that mimic the human mind, in addition to mechanisms of their own (cf. Gleitman & Liberman, 1995).
Two broad perspectives are usually contrasted in the sciences of language. On the one hand, language is mainly considered to be a symbolic activity that occurs in contexts where it is important to take into account the speaker’ perceptual—cognitive system and the social interactions in which the speaker is involved. This perspective is characteristic of the theories dedicated to the elaboration of ‘cognitive grammars’ (cf. Langacker, 1987-1991). On the other hand, emphasis is placed on identifying the formal properties of natural languages. One possible extension of this approach is to implement these properties in computers in order to simulate comprehension processes (cf. Kintsch, 1998). These two perspectives correspond to two aspects of an interdisciplinary approach to language, namely, the analysis of cognitive and socio-cognitive representations involved in the relationships among language, perception, and action, and the modeling of processes underlying language production and comprehension. The two perspectives are closely knit in a number of applied domains (such as expert systems, databases, hypertexts, speech synthesis, etc.).
Psychology is making special contributions to a number of fields in the domain of language. One area is the ontegenetic development of language, and the way it is related to cognitive development in general (cf. Fletcher & MacWhinney, 1995). Cross-linguistic studies are also developing well in psychology, with the aim of revealing the basic cognitive processes that underlie the discourse structures of different languages and the semantic representations that are associated with them. Another domain is the study of the disorders of language, in which neuropsychology and neurology are closely interacting with psychology. Still another important domain is that of the comparative approach and the investigation of language capacities in animals.
The contribution of psychology in interdisciplinary linguistic research is to consider language as a reflection of human intelligence and cognition. In the analysis and modeling of perception, memory, and learning, it is crucial to consider that quite large parts of cognition depend on language, and that language is also a privileged mode for expressing thought in social contexts. The cognitive status of language is thus a primary fact that provides guidelines for its approach by scientists taking part in cognitive science projects. A cognitive approach to language cannot be based solely on models restricted to grammar and syntactic regulations and that ignore the relationships of the speakers with meaning. At the same time, by placing emphasis on the semantic components of language, this does not mean that the syntactic constraints are only of secondary importance in the construction of meaningful statements. Lastly, it is important to acknowledge that the intricate relationships between syntax and semantics take place in pragmatic contexts, that is, they are dependent on concrete situations and on the speakers’ intention to adjust their messages to these situations (cf. Garnham, 1985; Levelt, 1989).
Accounting for the cognitive status of language implies two correlated sets of operations on the part of psychologists and linguists. The first set consists of modeling the processes by which language creates and communicates knowledge. The second set of operations consists of identifying the processes by which language helps a person to build and organize his/ her own knowledge (which includes knowledge shared with other members of a community as well as idiosyncratic knowledge). These processes are essential for the interactions of speakers with their environment. The major point is the intricate relationship between the two facets of language, as a system of communication and as a system of symbolic representation of objects, events, and actions. Language is probably the most sophisticated system used to serve these two purposes, which justifies the special consideration it requires from the cognitive sciences.
An important objective in which linguistics and psychology cooperate is the definition and classification of the functions of language, and the sequence in which these functions appear during language acquisition. These functions include instrumental ones, in which language is used to obtain some form of satisfaction (for instance, obtain a desired object). Other functions are linked to communicating, that is, establishing and maintaining interactions with other people in a social environment. Still other functions of language are the acquisition and manipulation of knowledge, through exploring the physical environment or creating imaginary worlds. More sophisticated cognitive operations are those by which speakers not only declare representational contents, but also express their position relative to these contents (such as in argumentation).
Another special contribution of psychology is to develop models of the cognitive architecture that include non-linguistic modes of symbolic representation, endowed with distinct functional properties. This is the case of the models including analogue representational systems in addition to the linguistic system. Psychology, then, has to include in the models adequate translation procedures that make it possible for speakers and listeners to perform complex tasks, such as generating linguistic outputs that describe multidimensional configurations, or construct visuo-spatial images from verbal descriptions. Such articulations between differently structured internal representations are also an important issue in AI, when a computer is required to perform translations between linguistic and perceptual inputs.
Psychology, Philosophy, and Logic
Psychology and philosophy obviously share a number of notions and concepts, although the corresponding objects are approached by the two disciplines with rather different methods. When scientific psychology became autonomous with respect to philosophy, by developing its own methodological tools and proof-based argumentation, philosophy pursued its investigation of crucial issues, such as the nature of the human mind and the functioning of thinking. This created a context favorable to the maintenance of interdisciplinary relationships between the two disciplines. During the first century of its existence as a scientific discipline, psychology has built a body of knowledge based on systematic observation of behavior in natural and experimental settings. However, philosophers have only fairly recently become aware of the scientific knowledge established by psychologists and reformulated some of their issues by taking into account psychological data obtained by empirical methods, and not solely by intuitive or introspective approaches.
To take an example, an issue of interest for both psychology and philosophy is the nature of mental activity. Philosophy tends to take two main properties into account when dealing with this issue, consciousness and intentionality. A mental state is described as being specific to a conscious subject; it is also an intentional state in that it bears upon things and evokes them in the form of representations (cf. Baars, 1988; Dretske, 1988). Psychology and psychophysiology have considerable information on consciousness and the measurement of its varying states. They also have methods for dealing with the study of mental representations, not by ‘capturing’ them as observable entities, but as constructs inferred from the covariations between situations and responses. Cognitive psychology postulates that such internal representations have a functional role in behavior, although there seems to be no intrinsic need for them to be conscious in order to have behavioral consequences. For instance, priming effects obtained when stimuli are presented below the perceptual threshold generate new questions for philosophy and, more broadly, the disciplines which aim at elucidating what is a mental state and how mental representations determine behavior.
One of the most important tenets of the so-called ‘philosophy of the mind,’ which has considerable relevance for cognitive psychology, is that mental phenomena should be considered from a ‘functional’ perspective, that is, they should be accounted for essentially by the functions they serve in a mental system. According to this approach, mental states are causal in shaping behavioral outcomes, although they cannot be viewed solely as ‘precursors’ of behavior. They must also be analyzed for their own properties and functional characteristics, in line with the view that more important than behavior itself are the mechanisms by which internal states articulate to one another to trigger behavioral responses. This concept creates an epistemological context that favors an attempt to analyze causal relations among mental states. A consequence of that approach is that the relevance of a psychological level of explanation is recognized, distinct from a neurobiological level, without denying that the two levels certainly involve causal relationships. The functional perspective thus promotes the idea that psychological phenomena do have neurobiological counterparts, but cannot be reduced to them in principle (cf. Rey, 1996). This approach departs from the former ‘physicalist’ philosophical perspective, according to which mental states should simply be identified with cerebral states. Obviously, through these discussions, philosophical concepts have had a direct impact on issues that were in the scope of scientific psychology.
The early steps in the development of cognitive science were marked by the postulate that there is a ‘language of thought’ (cf. Fodor, 1975). This concept was used by both psychologists and philosophers to account for the way humans create and use internal representations of the world. The dominant conception favored a view of the language of thought as being encoded in the human brain just like a formal language is encoded in a computer. This approach involved efforts to specify the syntactic structure of that language, by defining predicates, quantifiers, logical connectors, etc., that should be combined to form higher-order, more complex entities, equivalent to sentences, but expressed in propositional terms. The propositional format of the language of thought is generally claimed to have a greater expressive power than other representational formats, in particular mental images. On the other hand, the analog structure of images gives them a power that propositions do not have, in particular when continuous dimensions of the world are represented by the mind.
Whereas philosophy covers a wide spectrum of mental phenomena, from sensory to higher cognitive processes, logic essentially focuses on reasoning (cf. Overton, 1990). Logicians are interested in systems based on three main components: a language (typically, propositional), a deductive device applying inference rules to formulas of that language, and a set of procedures computing the truth value of these formulas. Logic and psychology were bound to develop mutual relationships because of the common objectives of the operations undertaken by logic systems and human reasoners, that is, to produce valid conclusions from combining informational inputs in an appropriate manner. Cognitivism has employed the concept of the brain as a deductive system governed by logical principles from its very outset.
As a science of formal reasoning, logic traditionally excludes from its scope any reference to psychological processes. However, the development of scientific psychology has created a new context, in which the mind is assessed for its deductive capacities, and thus compared with a computer. Experimental psychology also pointed to behavioral features suggesting that the human mind performs sub-optimally according to the rules of logic. In short, ordinary people are not intuitive logicians. Systematic errors and biases in deductive and probabilistic reasoning forced researchers to acknowledge that only a limited part of human reasoning is governed by the principles of logic. The theory of the ‘mental logic’ was thus faced with the somewhat provocative view that some forms of reasoning at least can be performed without any recourse to formal logic. The concept of a ‘mental model’ was proposed to account for the actual deductive capacities of ordinary people when they do not apply the formal rules of inference (cf. Johnson-Laird & Byrne, 1991). Advances in psychological accounts of natural reasoning were thus very dependent on the capacity of psychologists to exchange concepts with logicians.
Psychology, Sociology, Political Science, and Economics
There are many opportunities for human beings to become part of social groups. In fact, they are generally obliged to do so. It has long been acknowledged that the study of human cognition and behavior must take into account the social dimension of the contexts where they take place. Contemporary social psychology illustrates the need to consider the contextual and social factors in any approach to behavior and its underlying cognitive mechanisms.
An individual’ personal identity is regulated by social factors. Autobiographical memory is closely connected to the personal experience of individuals as members of groups. Also important is the social regulation of inter-group perceptions and relationships. The organization, values, and modes of functioning of socio-economic, national, or ethnic groups are studied by social sciences, mainly sociology. The fact that people usually belong to groups has psychological consequences that are the main object of investigation by social psychology. For instance, people tend to classify other people as members of social categories. The consequence is that when a person interacts with another one (even remotely, such as when a judgment is expressed on that person), the alleged characteristics of that person’ group or social category are as much considered as his/her personal characteristics. Furthermore, a person’ perception or judgment of other people is to some extent shaped by that person’ belonging to a social category. These factors have an important impact on the mutual representations of social groups. Psychosocial research assesses the representational dimension of groups, which is likely to enrich the models developed in sociology.
One interesting example of a domain shared by sociology and psychology is ethnocentrism, which implies that a person attributes different features to the group he/she belongs to and to other groups. The social status of a group relative to others is also thought to affect the rate of ethnocentrism of that group, as well as its perception of the homogeneity of other groups. The relations among groups are governed by asymmetries in their status and powership in natural social contexts. The feeling of belonging to one’ group is more marked in groups of low social status, whereas members of high-status groups tend to perceive more distinct features among themselves. Sociological research has much to do in delineating the macro-features that affect the perception of groups by individual members of these groups (such as socio-economic variables), and consequently the likelihood that institutions that rule a society formally recognize the existence of these groups. Psychology provides its unique expertise in making evident the perceptual biases or stereotypes that may affect the judgments of members of one group on members of other groups. Such powerful psychosocial mechanisms may have crucial consequences in the social transmission of inter-group perceptions and judgments (cf. Devine, Ostrom, & Hamilton, 1994; Mackie & Hamilton, 1993; Tajfel, 1982).
The cooperation of psychology with other social sciences is of special significance in several applied domains. One example is work organization and its effects on productivity. Productive work is a work in which people cooperate. It is important for psychology and sociology to better understand the way to improve mutual perception of co-workers and to promote interand intra-group comprehension, and to reduce resistance to change. This joint approach may shed light on the relationships among productivity, work satisfaction, and work organization. In industrial societies, not only are automated production systems developing rapidly, but dramatic changes also take place in patterns of work organization. Work is a domain where communication patterns and authority relationships are changing; workers’ motivation and satisfaction are also changing. Behavioral and social science research can help identify what motivates productivity for individuals and for work groups, and more generally provide information on the relationships among productivity, work satisfaction, and work organization.
Another domain where psychologists and social scientists may profitably address common issues is the domain of political science. There is a growing interest for assessing the situations in which groups with divergent interests or values are required to interact or reach compromises. Psychology may help political scientists by attracting their attention on factors that hinder communication and make mutual perception more difficult, especially in tense contexts. This occurs when leaders or other agents responsible for making decisions must simultaneously process a cognitive task (e.g., compare a variety of options, evaluate risks) and an emotional dimension (which, in fact, is part of the perception of most social situations). The classic studies on predictive reasoning are an example of empirical facts that should demonstrate to political scientists the susceptibility of decision making to undesirable reasoning biases (cf. Kahne-man, Slovic, & Tversky, 1982). Such factors are particularly relevant in situations of negotiation and conflict resolution.
The domain of economics is also open to interdisciplinary exchange with cognitive psychology and, more generally, cognitive science. Both economists and ordinary people must make economic decisions, most frequently in complex, uncertain environments. The issue is whether ordinary people perform rationally when making decisions, by systematically seeking to maximize rewards and minimize losses. This strategy is expected of rational economic agents. Psychological research shows that people, in fact, exhibit limited rationality in this domain and frequently fail when confronted with quite simple decision-making tasks. Irrational decision making occurs because people’ reasoning is biased by whether descriptions of the choices to be made draw their attention to positive or negative outcomes. Highly salient, but irrelevant information also affects decision making, since the limited processing capacities of the human mind prevent people considering less salient, but relevant information. The value of understanding the cognitive processes involved in decision making is that it allows researchers to introduce the human factor into their accounts of economic mechanisms. Applications can then be considered by developing interactive systems to help decision making that simulate human behavior in various types of economic situations (cf. Hoffman, McCabe, & Smith, 1995; Simon, 1983).
Psychology, Anthropology, and Geography
Psychology and anthropology are both concerned with the human mind. While psychology primarily investigates human mental capacities through their individual manifestations, anthropology devotes its efforts to cultures, as manifestations of these capacities in communities of people. The data collected by ethnography have long been valued for their capacity to reveal the variety of human experience, but anthropology is engaged in a distinct program, to account for the variation of such experience from a theoretical point of view.
Although the projects of psychology and anthropology are distinct from each other, they converge on a number of points. Anthropology aims to construct a science of the specificities of humans, both their universality and their variations. Psychology is similarly concerned by the universal (to establish general laws of psychological function) and the individual (to account for the variations in human capacities and behavior). After a long period during which anthropology remained isolated from psychology, the dominant branch of the discipline, ‘cultural anthropology,’ is increasingly taking into account those issues related to human cognition and the variety of its expressions. Based on the premises that cognition is always culturally situated, investigations of new populations are intended to understand their perception of the world and also their perception of their world.
Nowadays, the objects studied by cognitive anthropology (such as perception, thinking, and the construction of knowledge) are quite similar to those of cognitive psychology. These domains are mainly approached by anthropologists in natural settings, but the fact that their description may require observations that meet the standards of empirical methodology is fully accepted, the objective being to evaluate the degree to which culture influences the expression of human cognition. For instance, people in populations that rely heavily on an oral tradition are better at remembering verbal information than those in other cultures. This is restricted, however, to their memory of narratives (and not other verbal materials). This suggests that differences among populations with different traditions reflect cultural rather than psychological differences.
One of the most extensively investigated domains of cognition is categorization. To take a famous example, the classification of colors varies considerably among languages and cultures. The fact that each language segments the color continuum in a specific way has long been taken as supporting the relativistic view that no universal constraints weigh on color perception. However, anthropologists have also considered the hypothesis that perception is based on a small number of universal, fundamental concepts. The experiments carried out by psychologists in collaboration with anthropologists have confirmed that there is a universal set of focal colors underlying every lexicon of colors, beyond their diversity (cf. Berlin & Kay, 1969; Rosch & Lloyd, 1978).
The cognitive structures shaped by culture include ‘mental schemas’ or ‘models of the world.’ These models are knowledge structures that are taken for granted and shared by the members of a society. They have a great adaptive value, in that they help generate inferences in situations that contain only partial information. Such models play a major role in people’ understanding of the world and generating behavior. The relevant point for both psychologists and anthropologists is that schemas may vary greatly among cultures, but observations converge on the universal availability of such schemas in all cultures (cf. Holland & Quinn, 1987).
Cultural models are closely related to the domain of beliefs. A belief that is considered to be rational in a given culture (and is thus a candidate for the status of ‘knowledge’) may seem irrational in another. Anthropological research has forced us to consider that the criteria of rationality applied to beliefs vary widely among cultures. In some cultures, it suffices for a belief to have internal coherence and to be coherent with the other beliefs of the person to be considered ‘rational.’ Cognitive psychology provides conceptual guidelines for differentiating between the concepts of ‘belief’ and ‘knowledge,’ and to account for the situations in which they interact. Anthropology documents the difficulties arising when people are confronted with the beliefs held by people from another culture.
The concept of variability is also important in other social sciences, in particular geography. Beyond the description of geographic objects, geographers are paying more and more attention to people’ interactions with their environments. The development of ‘cognitive geography’ is correlated with an increase in interest in human activities in these environments, and in the way people create representations and communicate with each other about these environments. These issues are most relevant for psychologists, whose aim is to account for the mechanisms by which people generate and use internal representations (or ‘cognitive maps’) of the external world. Geography is a special field of application for the study of mental representations of space.
Psychologists interested in spatial cognition have considered the various contexts in which learning of spatial environments takes place. Navigation is the primary and most common source of spatial knowledge for most species. Humans also rely on symbolic substitutes for spatial environments, such as maps. They also acquire spatial knowledge from verbal descriptions. Psychology develops empirical methods to assess the specific merits of each type of learning, but also to reveal their common features. In particular, research in this domain considers the capacity of representational subsystems endowed with different functional characteristics to cooperate within a single cognitive system (cf. Bloom, Peterson, Nadel, & Garrett, 1996; Portugali, 1996).
Psychologists and geographers also cooperate in the design of artefacts used to represent space and assist people’ navigation in unfamiliar environments. For instance, cartographers may profit from empirical investigations into the formats of map representations that are the easiest to process and memorize. With additional input from computer scientists, these investigations have direct applications in the design of geographic information systems and on-board navigational aid systems (cf. Barfield & Dingus, 1998; Freksa & Mark, 1999). Geography offers a wide range of other subjects for collaboration with psychology and other social sciences, such as the physical and psychological effects of natural hazards, the organization of industrial space, the perception of the safety of living environments, and the human factors involved in urban development and urban life.
This chapter has reviewed a number of scientific domains in which psychology is cooperating with other sciences. There are probably research domains that have not been mentioned and deserve consideration for the development of joint efforts between psychology and other disciplines. For the time being, the wide spectrum of domains in which psychology interacts in order to solve problems shared with other disciplines is impressive. This is probably due to one important feature of psychology, its diversity and the number of branches that have developed within it. This situation enables psychology to attack problems at different levels of analysis, from microlevels to highly integrated levels. It also provides a unique opportunity for psychology to develop a wide range of interactions with other disciplines.
Most of the examples of interactions cited above may be labeled as pluridisciplinary or interdisciplinary. Psychology clearly has a great capacity to become involved in true scientific interactions with other disciplines. This contrasts with the long-term projects aiming at establishing genuinely transdisciplinary collaborations. The most frequent forms of interactions in current research illustrate the benefits resulting from sharing the exploration of well-specified fields, and in some cases sharing the concepts related to these fields with other disciplines. To be productive in both the short and long terms, this converging process must respect the other disciplines, their specific aims, concepts, and methods.