•  466
    Symbolic reasoning in spiking neurons: A model of the cortex/basal ganglia/thalamus loop
    with Terrence C. Stewart and Xuan Choo
    In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, Cognitive Science Society. pp. 1100--1105. 2010.
  •  276
    How Neurons Mean: A Neurocomputational Theory of Representational Content
    Dissertation, Washington University in St. Louis. 2000.
    Questions concerning the nature of representation and what representations are about have been a staple of Western philosophy since Aristotle. Recently, these same questions have begun to concern neuroscientists, who have developed new techniques and theories for understanding how the locus of neurobiological representation, the brain, operates. My dissertation draws on philosophy and neuroscience to develop a novel theory of representational content
  •  239
    Is the brain analog or digital?
    Cognitive Science Quarterly 1 (2): 147-170. 2000.
    It will always remain a remarkable phenomenon in the history of philosophy, that there was a time, when even mathematicians, who at the same time were philosophers, began to doubt, not of the accuracy of their geometrical propositions so far as they concerned space, but of their objective validity and the applicability of this concept itself, and of all its corollaries, to nature. They showed much concern whether a line in nature might not consist of physical points, and consequently that true s…Read more
  •  231
    Moving Beyond Metaphors: Understanding the Mind for What It Is
    Journal of Philosophy 100 (10): 493-520. 2003.
  •  186
    How to build a brain: From function to implementation
    Synthese 153 (3): 373-388. 2006.
    To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach to addressing both…Read more
  •  181
    The myth of the Turing machine: The failings of functionalism and related theses
    Journal of Experimental and Theoretical Artificial Intelligence 14 (1): 1-8. 2002.
    The properties of Turing’s famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. Here, I show that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing’s powerful insights regarding computation. In addressing a possible reply to this criticism, I further argue that functionalism is not a useful approach for understanding what it is to have a mind. In particular, I sh…Read more
  •  151
    Computation and dynamical models of mind
    Minds and Machines 7 (4): 531-41. 1997.
      Van Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt governor to fulfill the role of a dynamicist Turing machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Wat…Read more
  •  143
    In a recent series of publications, dynamicist researchers have proposed a new conception of cognitive functioning. This conception is intended to replace the currently dominant theories of connectionism and symbolicism. The dynamicist approach to cognitive modeling employs concepts developed in the mathematical field of dynamical systems theory. They claim that cognitive models should be embedded, low-dimensional, complex, described by coupled differential equations, and non-representational. I…Read more
  •  143
    A Neural Model of Rule Generation in Inductive Reasoning
    with Daniel Rasmussen
    Topics in Cognitive Science 3 (1): 140-153. 2011.
    Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The mod…Read more
  •  121
    Computational neuroscience
    In Paul R. Thagard (ed.), Philosophy of Psychology and Cognitive Science, Elsevier. forthcoming.
    <b>Keywords</b>: computational neuroscience, neural coding, brain function, neural modeling, cognitive modeling, computation, representation, neuroscience, neuropsychology, semantics, theoretical psychology, theoretical neuroscience.
  •  90
    There has been a long-standing debate between symbolicists and connectionists concerning the nature of representation used by human cognizers. In general, symbolicist commitments have allowed them to provide superior models of high-level cognitive function. In contrast, connectionist distributed representations are preferred for providing a description of low-level cognition. The development of Holographic Reduced Representations (HRRs) has opened the possibility of one representational medium u…Read more
  •  82
    The Complex Systems Approach: Rhetoric or Revolution
    Topics in Cognitive Science 4 (1): 72-77. 2012.
    The complex systems approach (CSA) to characterizing cognitive function is purported to underlie a conceptual and methodological revolution by its proponents. I examine one central claim from each of the contributed papers and argue that the provided examples do not justify calls for radical change in how we do cognitive science. Instead, I note how currently available approaches in ‘‘standard’’ cognitive science are adequate (or even more appropriate) for understanding the CSA provided examples
  •  77
  •  73
    I argue that dynamicism does not provide a convincing alternative to currently available cognitive theories. First, I show that the attractor dynamics of dynamicist models are inadequate for accounting for high-level cognition. Second, I argue that dynamicist arguments for the rejection of computation and representation are unsound in light of recent empirical findings. This new evidence provides a basis for questioning the importance of continuity to cognitive function, challenging a central co…Read more
  •  56
    Is the brain a quantum computer?
    with Abninder Litt, Frederick W. Kroon, Steven Weinstein, and Paul Thagard
    Cognitive Science 30 (3): 593-603. 2006.
    We argue that computation via quantum mechanical processes is irrelevant to explaining how brains produce thought, contrary to the ongoing speculations of many theorists. First, quantum effects do not have the temporal properties required for neural information processing. Second, there are substantial physical obstacles to any organic instantiation of quantum computation. Third, there is no psychological evidence that such mental phenomena as consciousness and mathematical thinking require expl…Read more
  •  55
    Dynamical models and Van gelder's dynamicism
    Behavioral and Brain Sciences 21 (5): 639-639. 1998.
    Van Gelder has presented a position which he ties closely to a broad class of models known as dynamical models. While supporting many of his broader claims about the importance of this class (as has been argued by connectionists for quite some time), I note that there are a number of unique characteristics of his brand of dynamicism. I suggest that these characteristics engender difficulties for his view
  •  53
    Quantum probability (QP) theory can be seen as a type of vector symbolic architecture (VSA): mental states are vectors storing structured information and manipulated using algebraic operations. Furthermore, the operations needed by QP match those in other VSAs. This allows existing biologically realistic neural models to be adapted to provide a mechanistic explanation of the cognitive phenomena described in the target article by Pothos &amp; Busemeyer (P&amp;B)
  •  46
    How we ought to describe computation in the brain
    Studies in History and Philosophy of Science Part A 41 (3): 313-320. 2010.
    I argue that of the four kinds of quantitative description relevant for understanding brain function, a control theoretic approach is most appealing. This argument proceeds by comparing computational, dynamical, statistical and control theoretic approaches, and identifying criteria for a good description of brain function. These criteria include providing useful decompositions, simple state mappings, and the ability to account for variability. The criteria are justified by their importance in pr…Read more
  •  46
    Marr's Attacks: On Reductionism and Vagueness
    with Carter Kolbeck
    Topics in Cognitive Science 7 (2): 323-335. 2015.
    It has been suggested that Marr took the three levels he famously identifies to be independent. In this paper, we argue that Marr's view is more nuanced. Specifically, we show that the view explicitly articulated in his work attempts to integrate the levels, and in doing so results in Marr attacking both reductionism and vagueness. The result is a perspective in which both high-level information-processing constraints and low-level implementational constraints play mutually reinforcing and const…Read more
  •  44
    Epistemic Coherence
    with Paul Thagard, Paul Rusnock, and Cameron Shelley
    In R. Elio (ed.), Common sense, reasoning, and rationality. Vancouver Studies in Cognitive Science (Vol. 11), Oxford University Press. pp. 104-131. 2002.
    Many contemporary philosophers favor coherence theories of knowledge (Bender 1989, BonJour 1985, Davidson 1986, Harman 1986, Lehrer 1990). But the nature of coherence is usually left vague, with no method provided for determining whether a belief should be accepted or rejected on the basis of its coherence or incoherence with other beliefs. Haack's (1993) explication of coherence relies largely on an analogy between epistemic justification and crossword puzzles. We show in this paper how epistem…Read more
  •  34
    Waves, particles, and explanatory coherence
    British Journal for the Philosophy of Science 48 (1): 1-19. 1997.
    Peter Achinstein (1990, 1991) analyses the scientific debate that took place in the eighteenth and nineteenth centuries concerning the nature of light. He offers a probabilistic account of the methods employed by both particle theorists and wave theorists, and rejects any analysis of this debate in terms of coherence. He characterizes coherence through reference to William Whewell's writings concerning how "consilience of inductions" establishes an acceptable theory (Whewell, 1847) . Achinstein …Read more
  •  31
    Concepts as Semantic Pointers: A Framework and Computational Model
    with Peter Blouw, Eugene Solodkin, and Paul Thagard
    Cognitive Science 40 (5): 1128-1162. 2016.
    The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental rep…Read more
  •  30
    The Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory
    with Peter Duggins, Terrence C. Stewart, and Xuan Choo
    Topics in Cognitive Science 8 (4): 117-134. 2016.
    We use a spiking neural network model of working memory capable of performing the spatial delayed response task to investigate two drugs that affect WM: guanfacine and phenylephrine. In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and b…Read more
  •  19
    Improving With Practice: A Neural Model of Mathematical Development
    with Sean Aubin and Aaron R. Voelker
    Topics in Cognitive Science 9 (1): 6-20. 2016.
    The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. Although gradual behavioral improvements from practice have been modeled in spiking neural networks, few such models have attempted to explain cognitive development of a task as complex as addition. In this work, we model the progression from a counting-based strategy for addition to a recall-based strategy. The model consists of two networks working in para…Read more
  •  18
  •  14
    Biologically Plausible, Human‐Scale Knowledge Representation
    with Eric Crawford and Matthew Gingerich
    Cognitive Science 40 (4): 782-821. 2016.
    Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony, “mesh” binding, and conjunctive binding. Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate th…Read more