Cognitive Science and Psychology

The protocol algorithm abstracted from a human cognizer's own narrative in the course of doing a cognitive task is an explanation of the corresponding mental activity in Pylyshyn's (1984) virtual machine model of mind. Strong equivalence between an analytic algorithm and the protocol algorithm is an index of validity of the explanatory model. Cognitive psychologists may not find the index strong equivalence useful as a means to ensure that a theory is not circular because (a) research data are also used as foundation data, (b) there is no justification for the relationship between a to﷓be﷓validated theory and its criterion of validity, and (c) foundation data, validation criterion and to﷓be﷓validated theory are not independent in cognitive science. There is also the difficulty with not knowing what psychological primitives are

Topological Foundations of Cognitive Science

A collection of papers presented at the First International Summer Institute in Cognitive Science, University at Buffalo, July 1994, including the following papers: ** Topological Foundations of Cognitive Science, Barry Smith ** The Bounds of Axiomatisation, Graham White ** Rethinking Boundaries, Wojciech Zelaniec ** Sheaf Mereology and Space Cognition, Jean Petitot ** A Mereotopological Definition of 'Point', Carola Eschenbach ** Discreteness, Finiteness, and the Structure of Topological Spaces, Christopher Habel ** Mass Reference and the Geometry of Solids, Almerindo E. Ojeda ** Defining a 'Doughnut' Made Difficult, N .M. Gotts ** A Theory of Spatial Regions with Indeterminate Boundaries, A.G. Cohn and N.M. Gotts ** Mereotopological Construction of Time from Events, Fabio Pianesi and Achille C. Varzi ** Computational Mereology: A Study of Part-of Relations for Multi-media Indexing, Wlodek Zadrozny and Michelle Ki

From cognitive science to cognitive neuroscience to neuroeconomics

As an emerging discipline, neuroeconomics faces considerable methodological and practical challenges. In this paper, I suggest that these challenges can be understood by exploring the similarities and dissimilarities between the emergence of neuroeconomics and the emergence of cognitive and computational neuroscience two decades ago. From these parallels, I suggest the major challenge facing theory formation in the neural and behavioural sciences is that of being under-constrained by data, making a detailed understanding of physical implementation necessary for theory construction in neuroeconomics. Rather than following a top-down strategy, neuroeconomists should be pragmatic in the use of available data from animal models, information regarding neural pathways and projections, computational models of neural function, functional imaging and behavioural data. By providing convergent evidence across multiple levels of organization, neuroeconomics will have its most promising prospects of success

Why it is important to build robots capable of doing science

Science, like any other cognitive activity, is grounded in the sensorimotor interaction of our bodies with the environment. Human embodiment thus constrains the class of scientific concepts and theories which are accessible to us. The paper explores the possibility of doing science with artificial cognitive agents, in the framework of an interactivist-constructivist cognitive model of science. Intelligent robots, by virtue of having different sensorimotor capabilities, may overcome the fundamental limitations of human science and provide important technological innovations. Mathematics and nanophysics are prime candidates for being studied by artificial scientists

Cognitive science and epistemic openness

Recent findings in cognitive science suggest that the epistemic subject is more complex and epistemically porous than is generally pictured. Human knowers are open to the world via multiple channels, each operating for particular purposes and according to its own logic. These findings need to be understood and addressed by the philosophical community. The current essay argues that one consequence of the new findings is to invalidate certain arguments for epistemic anti-realism

Unification Strategies in Cognitive Science

Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead to unification are analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models

Renewing the link between cognitive archeology and cognitive science

In cognitive archeology, theories of cognition are used to guide interpretation of archeological evidence. This process provides useful feedback on the theories themselves. The attempt to accommodate archeological data helps shape ideas about how human cognition has evolved and thus—by extension—how the modern form functions. But the implications that archeology has for cognitive science particularly relate to traditional proposals from the field involving modular decomposition, symbolic thought and the mediating role of language. There is a need to make a connection with more recent approaches, which more strongly emphasize information, probabilistic reasoning and exploitation of embodiment. Proposals from cognitive archeology, in which evolution of cognition is seen to involve a transition to symbolic thought need to be realigned with theories from cognitive science that no longer give symbolic reasoning a central role. The present paper develops an informational approach, in which the transition is understood to involve cumulative development of information-rich generalizations

Hume's Legacy: A Cognitive Science Perspective

Hume is an experimental philosopher who attempts to understand why we think, feel, and act as we do. But how should we evaluate the adequacy of his proposals? This chapter examines Hume’s account from the perspective of interdisciplinary work in cognitive science

Leveling the Field: Talking Levels in Cognitive Science

Talk of levels is everywhere in cognitive science. Whether it is in terms of adjudicating longstanding debates or motivating foundational concepts, one cannot go far without hearing about the need to talk at different ‘levels’. Yet in spite of its widespread application and use, the concept of levels has received little sustained attention within cognitive science. This paper provides an analysis of the various ways the notion of levels has been deployed within cognitive science. The paper begins by introducing and motivating discussion via four representative accounts of levels. It then turns to outlining and relating the four accounts using two dimensions of comparison. The result is the creation of a conceptual framework that maps the logical space of levels talk, which offers an important step toward making sense of levels talk within cognitive science