Artificial Intelligence (AI) is a core area of Cognitive Science, yet today few AI researchers attend the Cognitive Science Society meetings. This essay examines why, how AI has changed over the last 30 years, and some emerging areas of potential interest where AI and the Society can go together in the next 30 years, if they choose.

Although philosophy has been only a minor contributor to cognitive science to date, this paper describes two projects in naturalistic philosophy of mind and one in naturalistic philosophy of science that have been pursued during the past 30 years and that can make theoretical and methodological contributions to cognitive science. First, stances on the mind–body problem (identity theory, functionalism, and heuristic identity theory) are relevant to cognitive science as it negotiates its relation to neuroscience and cognitive neuroscience. Second, analyses of (...) mental representations address both their vehicles and their contents; new approaches to characterizing how representations have content are particularly relevant to understanding the relation of cognitive agents to their environments. Third, the recently formulated accounts of mechanistic explanation in philosophy of science both provide perspective on the explanatory project of cognitive science and may offer normative guidance to cognitive science (e.g., by providing perspective on how multiple disciplinary perspectives can be integrated in understanding a given mechanism). (shrink)

In the beginning, there was the DN (Deductive Nomological) model of explanation, articulated by Hempel and Oppenheim (1948). According to DN, scientific explanation is subsumption under natural law. Individual events are explained by deducing them from laws together with initial conditions (or boundary conditions), and laws are explained by deriving them from other more fundamental laws, as, for example, the simple pendulum law is derived from Newton's laws of motion.

A recent article (Núñez et al., 2019) claims that cognitive science, while starting off as a multidisciplinary enterprise, has “failed to transition to a mature inter‐disciplinary coherent field.” Two indicators reported in support of this claim target one of the two journals of the Cognitive Science Society, Cognitive Science, depicting cognitive science as an increasingly monodisciplinary subfield which is dominated by psychology. With a focus on the society's other journal, Topics in Cognitive Science, the present commentary reveals a greater degree (...) of interdisciplinarity and discusses the relative values of diversity and integration for the field. (shrink)

Cognitive science is a child of the 1950s, the product of a time when psychology, anthropology and linguistics were redefining themselves and computer science and neuroscience as disciplines were coming into existence. Psychology could not participate in the cognitive revolution until it had freed itself from behaviorism, thus restoring cognition to scientific respectability. By then, it was becoming clear in several disciplines that the solution to some of their problems depended crucially on solving problems traditionally allocated to other disciplines. Collaboration (...) was called for: this is a personal account of how it came about. (shrink)

In a case study of the growth of cognitive science, we analyzed the activities of the Cognitive Science Society with a particular emphasis on the multidisciplinary nature of the field. Analyses of departmental affiliations, training back‐grounds, research methodology, and paper citations suggest that the journal Cognitive Science and the Annual Meeting of the Cognitive Science Society are dominated by cognitive psychology and computer science, rather than being an equal division among the constituent disciplines of cognitive science. However, at many levels, (...) a growing percentage of work was found to involve a conjunction of multiple disciplines, such that approximately 30–50% of recent work in the Cognitive Science Society is multidisciplinary. In a questionnaire study of cognitive scientists involved in collaborative research, multidisciplinarity was found to shape the research process and affect the factors associated with successful research. (shrink)

Núñez et al. (2019) argue that cognitive science has failed either “to transition to a mature inter‐disciplinary coherent field” (p. 782) or “to generate a successful [Lakatosian] research program” (p. 789). We argue that the former was never the intention of many early researchers within the field, while the latter is an inappropriate criterion by which to judge an entire discipline. However, we concur with Núñez et al. (2019) that the individual disciplinary balance within cognitive science has changed over time. (...) Of particular concern is the fact that the use of computational methods appears to be on the wane. (shrink)

Contrary to common views that philosophy is extraneous to cognitive science, this paper argues that philosophy has a crucial role to play in cognitive science with respect to generality and normativity. General questions include the nature of theories and explanations, the role of computer simulation in cognitive theorizing, and the relations among the different fields of cognitive science. Normative questions include whether human thinking should be Bayesian, whether decision making should maximize expected utility, and how norms should be established. These (...) kinds of general and normative questions make philosophical reflection an important part of progress in cognitive science. Philosophy operates best, however, not with a priori reasoning or conceptual analysis, but rather with empirically informed reflection on a wide range of findings in cognitive science. (shrink)

Cognitive science has converged in many ways with cognitive psychology, but while also maintaining a distinctive interdisciplinary nature. Here we further characterize this existing state of the field before proposing how it might be reconceptualized toward a broader and more distinct, and thus more stable, position in the realm of sciences.

This paper considers the past and future of Psychology within Cognitive Science. In the history section, I focus on three questions: (a) how has the position of Psychology evolved within Cognitive Science, relative to the other disciplines that make up Cognitive Science; (b) how have particular Cognitive Science areas within Psychology waxed or waned; and (c) what have we gained and lost. After discussing what’s happened since the late 1970s, when the Society and the journal began, I speculate about where (...) the field is going. (shrink)

Anthropology and the other cognitive science (CS) subdisciplines currently maintain a troubled relationship. With a debate in topiCS we aim at exploring the prospects for improving this relationship, and our introduction is intended as a catalyst for this debate. In order to encourage a frank sharing of perspectives, our comments will be deliberately provocative. Several challenges for a successful rapprochement are identified, encompassing the diverging paths that CS and anthropology have taken in the past, the degree of compatibility between (1) (...) CS and (2) anthropology with regard to methodology and (3) research strategies, (4) the importance of anthropology for CS, and (5) the need for disciplinary diversity. Given this set of challenges, a reconciliation seems unlikely to follow on the heels of good intentions alone. (shrink)

Psychology is a broad field that endeavors to develop explanatory theories of human capacities and behaviors based on a wide variety of methodologies and dependent measures. Here we argue that whether or not researchers choose to employ modeling is one of the most important and divisive factors in our field. Modeling is underdiscussed and underemployed, yet, in our view, holds integrative promise for advancing the goals of psychological science. The inherent demands of computational modeling oer invaluable momentum towards a better, (...) and more open, psychological science. These demands force the scientist to conceptually analyze, specify, and ideally, formalise intuitions and ideas which would otherwise remain implicit or unexamined — something we propose should be called “open theory”. Constraining our inference process through specification and modeling is what will enable us as a field to meaningfully interpret data, and to build theories that explain and predict. In this piece, we present scientific inference in psychology as a path function, where each step shapes the next. Computational modeling can constrain the steps in the path, and has the potential to advance scientific inference over and above the stewardship of the experimental practice. If as a field we continue to eschew, inadvertently avoid, or remain ignorant of formal and computational modeling, we set ourselves up for a persistent lack of replicability and, moreover, for failure at coherent theory-building that includes explanatory force. We explain how the basic steps in the modeling process can be accomplished and we touch on the cultural and practical issues that need to be faced therein, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all. The process of computational modeling promotes transparent theorising; “open science” should include open theory alongside, e.g., open data and open source code. (shrink)