Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...) have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)

Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...) the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations. (shrink)

We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...) and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)

People categorize objects slowly when visual input is highly impoverished instead of optimal. While bottom-up models may explain a decision with optimal input, perceptual hypothesis testing (PHT) theories implicate top-down processes with impoverished input. Brain mechanisms and the time course of PHT are largely unknown. This event-related potential study used a neuroimaging paradigm that implicated prefrontal cortex in top-down modulation of occipitotemporal cortex. Subjects categorized more impoverished and less impoverished real and pseudo objects. PHT theories predict larger impoverishment effects for (...) real than pseudo objects because top-down processes modulate knowledge only for real objects, but different PHT variants predict different timing. Consistent with parietal-prefrontal PHT variants, around 250 ms, the earliest impoverished real object interaction started on an N3 complex, which reflects interactive cortical activity for object cognition. N3 impoverishment effects localized to both prefrontal and occipitotemporal cortex for real objects only. The N3 also showed knowledge effects by 230 ms that localized to occipitotemporal cortex. Later effects reflected (a) word meaning in temporal cortex during the N400, (b) internal evaluation of prior decision and memory processes and secondary higher-order memory involving anterotemporal parts of a default mode network during posterior positivity (P600), and (c) response related activity in posterior cingulate during an anterior slow wave (SW) after 700 ms. Finally, response activity in supplementary motor area during a posterior SW after 900 ms showed impoverishment effects that correlated with RTs. Convergent evidence from studies of vision, memory, and mental imagery which reflects purely top-down inputs, indicates that the N3 reflects the critical top-down processes of PHT. A hybrid multiple-state interactive, PHT and decision theory best explains the visual constancy of object cognition. (shrink)

Two agents endowed with different categorisations engage in bargaining to reach an understanding and agree on a common categorisation. We model the process as a simple non-cooperative game and demonstrate three results. When the initial disagreement is focused, the bargaining process has a zero-sum structure. When the disagreement is widespread, the zero-sum structure disappears and the unique equilibrium requires a retraction of consensus: two agents who individually associate a region with the same category end up rebranding it under a different (...) category. Finally, we show that this last equilibrium outcome is Pareto dominated by a cooperative solution that avoids retraction; that is, the unique equilibrium agreement may be inefficient. (shrink)

Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by the same models. We test GMECCS, an implementation of the Configural Cue Model in a Maximum Entropy phonotactic-learning framework with a single free parameter, against the alternative hypothesis that learners (...) seek featurally simple algebraic rules. We study the full typology of patterns introduced by Shepard, Hovland, and Jenkins (), instantiated as both phonotactic patterns and visual analogs, using unsupervised training. Unlike SHJ, Experiments 1 and 2 found that both phonotactic and visual patterns that depended on fewer features could be more difficult than those that depended on more features, as predicted by GMECCS but not by rule-seeking. GMECCS also correctly predicted performance differences between stimulus subclasses within each pattern. A third experiment tried supervised training to elicit simple rule-seeking phonotactic learning, but cue-based behavior persisted. We conclude that similar cue-based cognitive processes are available for phonological and visual concept learning, and hence that studying either kind of learning can lead to significant insights about the other. (shrink)

This paper uses sim-max games to model perceptual categorization with the goal of answering the following question: to what degree should we expect the perceptual categories of biological actors to track properties of the world around them? I will argue that an analysis of these games suggests that the relationship between real-world structure and evolved perceptual categories is mediated by successful action in the sense that organisms evolve to categorize together states of nature for which similar actions lead to similar (...) results. This conclusion indicates that both strongly realist and strongly anti-realist views about perceptual categories are too simple. (shrink)