DeStefano, Oey, Brockbank, and Vul explore interdisciplinary collaboration using data‐driven measures of research topics and co‐authorship, constructed from a rich dataset of over 11,000 Cogsci conference papers. Findings suggest the cognitive science research community has become increasingly integrated in the last 19 years.

Context: Cognitive science, as an interdisciplinary research endeavour, poses challenges for teaching and learning insofar as the integration of various participating disciplines requires a reflective approach, considering and making explicit different epistemological attitudes and hidden assumptions and premises. Only few curricula in cognitive science face this integrative challenge. Problem: The lack of integrative activities might result from different challenges for people involved in truly interdisciplinary efforts, such as discussing issues on a conceptual level, negotiating colliding frameworks or sets of premises, (...) asking profound questions challenging one’s own paradigm, and differences in terminologies, as well as from the “personal” challenge of realising one’s own limits of knowledge and, hence, the need to trust in another person’s expertise. This implies that the proposed curriculum structure provides an “epistemic laboratory”: a space for experiencing and negotiating, as well as constructing different viewpoints in a trustful setting. Approach: A newly-designed interdisciplinary cognitive science curriculum is presented that is based on a constructivist epistemology. We suggest that a careful construction of the learning space is a necessary requirement. The MEi:CogSci curriculum is designed and structured in such a way that enables didactical measures that allow for collaborative construction of meaning by discussing concepts, methods and terminologies and also hidden assumptions. Findings: The experience with four cohorts of students has shown that a truly interdisciplinary approach to cognitive science demands a different attitude towards knowledge as well as towards teaching and learning on both sides: the teacher and the student. The research orientation promotes an understanding of knowledge as something that is actively constructed, rendering the role of the teacher that of a co-learner rather than a transmitter of knowledge, thereby also changing the responsibility of students. (shrink)

This systematic investigation of computation and mental phenomena by a noted psychologist and computer scientist argues that cognition is a form of computation, that the semantic contents of mental states are encoded in the same general way as computer representations are encoded. It is a rich and sustained investigation of the assumptions underlying the directions cognitive science research is taking. 1 The Explanatory Vocabulary of Cognition 2 The Explanatory Role of Representations 3 The Relevance of Computation 4 The Psychological Reality (...) of Programs: Strong Equivalence 5 Constraining Functional Architecture 6 The Bridge from Physical to Symbolic: Transduction 7 Functional Architecture and Analogue Processes 8 Mental Imagery and Functional Architecture 9 Epilogue: What is Cognitive Science the Science of? (shrink)

These papers are based on a Symposium at the COGSCI Conference in 2010. 1. Naturalizing the Mammalian Mind 2. Modularity in Cognitive Psychology and Affective Neuroscience 3. Affective Neuroscience and the Philosophy of Self 4. Affective Neuroscience and Law.

This paper presents a practical case study showing how, despite the nowadays limited collaboration between AI and Cognitive Science (CogSci), cognitive research can still have an important role in the development of novel AI technologies. After a brief historical introduction about the reasons of the divorce between AI and CogSci research agendas (happened in the mid’80s of the last century), we try to provide evidence of a renewed collaboration by showing a recent case study on a commonsense reasoning (...) system, built by using insights from cognitive semantics. (shrink)

Fifty years before the present volume appeared, artificial intelligence (AI) and cognitive science (Cogsci) emerged from a couple of small-scale academic encounters on the East Coast of the United States. Wedded together like Siamese twins, these nascent research programs appeared to rest on some general assumptions regarding the human mind, and closely connected methodological principles, which set them at such a distance from phenomenology that no contact between the two approaches seemed conceivable. Soon however contact was made, in the (...) form of a head-on critique of the AI/Cogsci project mostly inspired by arguments from phenomenology. For a while, it seemed like nothing would come of it: AI/Cogsci bloomed while the small troop of critical phenomenologists kept objecting. Then AI and Cogsci went their separate ways. AI underwent a deep transformation and all but surrendered to the phenomenological critique. Cogsci meanwhile pursued the initial program with a far richer collection of problems, concepts and methods, and was for a long time quite unconcerned by suggestions and objections from phenomenology. The last decade and half has seen a remarkable reversal: on the one hand, a few cognitive scientists have been actively pursuing the goal of reconciliating Cogsci, whether empirically or foundationally, with some of the insights procured by phenomenology; on the other, many cognitive scientists and philosophers of mind who think of themselves as, respectively, mainstream and analytic, and have no or little acquaintance with, and often little sympathy for, phenomenology, have been actively pursuing research programs geared toward some of the key issues identified by phenomenological critics of early AI/Cogsci. It might seem then as if those critics were now vindicated. But while these new directions are undoubtedly promising, it is not yet clear that phenomenology and Cogsci can be truly reconciled. Some suspect that Cogsci must distort beyond recognition those phenomenological themes it means to weave into its fabric, while phenomenology may be losing touch with its roots by tuning onto the logic of Cogsci which is, after all, an empirical science. To the present writer, it is far too early for anything like a verdict, as the task of clarifying the issues and gaining a much deeper understanding of the issues on both sides has barely begun. But whatever emerges from this exploration will probably have deep consequences for both Cogsci and philosophy.. (shrink)

Conditionals pervade every aspect of our thinking, from the mundane and everyday such as ‘if you eat too much cheese, you will have nightmares’ to the most fundamental concerns as in ‘if global warming isn’t halted, sea levels will rise dramatically’. Many decades of research have focussed on the semantics of conditionals and how people reason from conditionals in everyday life. Here it has been rather overlooked how we come to such conditionals in the first place. In many cases, they (...) are learned through testimony: someone warns us about the ill-effects of cheese. Any full account of the conditional must consequently incorporate such learning. Here, we provide a new formal account of belief change in response to a testimonial conditional. (shrink)

A consistent finding in research on conditional reasoning is that individuals are more likely to endorse the valid modus ponens (MP) inference than the equally valid modus tollens (MT) inference. This pattern holds for both abstract task and probabilistic task. The existing explanation for this phenomenon within a Bayesian framework (e.g., Oaksford & Chater, 2008) accounts for this asymmetry by assuming separate probability distributions for both MP and MT. We propose a novel explanation within a computational-level Bayesian account of reasoning (...) according to which “argumentation is learning”. We show that the asymmetry must appear for certain prior probability distributions, under the assumption that the conditional inference provides the agent with new information that is integrated into the existing knowledge by minimizing the Kullback-Leibler divergence between the posterior and prior probability distribution. We also show under which conditions we would expect the opposite pattern, an MT-MP asymmetry. (shrink)

In this paper we address the issue of grounding for experiential concepts. Given that perceptual demonstratives are a basic form of such concepts, we examine ways of fixing the referents of such demonstratives. To avoid ‘encodingism’, that is, relating representations to representations, we postulate that the process of reference fixing must be bottom-up and nonconceptual, so that it can break the circle of conceptual content and touch the world. For that purpose, an appropriate causal relation between representations and the world (...) is needed. We claim that this relation is provided by spatial and object-centered attention that leads to the formation of object files through the function of deictic acts. This entire causal process takes place at a pre-conceptual level, meeting the requirement for a solution to the grounding problem. Finally we claim that our account captures fundamental insights in Putnam’s and Kripke’s work on “new” reference. (shrink)

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.

Reasoning about what other people know is an important cognitive ability, known as epistemic reasoning, which has fascinated psychologists, economists, and logicians. In this paper, we propose a computational model of humans’ epistemic reasoning, including higher-order epistemic reasoning—reasoning about what one person knows about another person’s knowledge—that we test in an experiment using a deductive card game called “Aces and Eights”. Our starting point is the model of perfect higher-order epistemic reasoners given by the framework of dynamic epistemic logic. We (...) modify this idealized model with bounds on the level of feasible epistemic reasoning and stochastic update of a player’s space of possibilities in response to new information. These modifications are crucial for explaining the variation in human performance across different participants and different games in the experiment. Our results demonstrate how research on epistemic logic and cognitive models can inform each other. (shrink)