Recent years have seen a Bayesian revolution in cognitive science. This should be of interest to metaphysicians of science, whose naturalist project involves working out the metaphysical implications of our leading scientific accounts, and in advancing our understanding of those accounts by drawing on the metaphysical frameworks developed by philosophers. Toward these ends, in this paper I develop a metaphysics of the Bayesian mind. My central claim is that the Bayesian approach supports a novel empirical argument for normativism, the thesis (...) that belief has a normative essence, with the norms in question being those of rationality. The argument I develop draws in part on the causal powers subset account of realization defended by Wilson and Shoemaker, with the link being that Bayesians often appeal to Marr’s framework of levels of analysis, which carries with it multiple realizability claims that can be understood in terms of the subset model. I then use the view developed, with its appeal to normativism and subset realization, to explain how Bayesians can accommodate empirical findings of irrationality that are sometimes thought to pose a problem for their view. (shrink)

To make progress related to long-standing questions related to the nature of emotion, we offer the Interactive Activation and Competition framework for Emotion. The IAC-E is not another con...

Emotion Review, Volume 14, Issue 2, Page 99-110, April 2022. Researchers often disagree as to whether emotions are largely consistent across people and over time, or whether they are variable. They also disagree as to whether emotions are initiated by appraisals, or whether they may be initiated in diverse ways. We draw upon Parallel-Distributed-Processing to offer an algorithmic account in which features of an emotion instance are bi-directionally connected to each other via conjunction units. We propose that such indirect connections (...) may be innate as well as learned. These ideas lead to the development of the Interactive Activation and Competition framework for Emotion which allows us to specify when emotions are consistent and when they are variable, as well as when they are appraisal-led and when they are input-agnostic. (shrink)

Emotion Review, Volume 14, Issue 2, Page 116-120, April 2022. To make progress related to long-standing questions related to the nature of emotion, we offer the Interactive Activation and Competition framework for Emotion. The IAC-E is not another conventional theory of emotion. Rather, it offers a neural-network-based, algorithmic account of how emotion instances and categories arise. Our approach suggests that there need not be a contradiction between instances of the same emotion being sometimes consistent and sometimes variable. Similarly, there need (...) not be a contradiction between observations of homogeneity and heterogeneity within emotion categories. (shrink)

Phonemes have variant pronunciations depending on context. For instance, in American English, the [t] in pat [pæt] and the [d] in pad [pæd] are both realized with a tap [ɾ] when the –ing suffix is attached, [pæɾɪŋ]. We show that despite greater distributional and acoustic support for the [t]-tap alternation, 12-month-olds successfully relate taps to stems with a perceptually-similar final [d], not the dissimilar final-[t]. Thus, distributional learning of phonological alternations is constrained by infants' preference for the alternation of perceptually-similar (...) segments. Further, the ability to relate variant surface forms emerges between 8- and 12-months. Our findings of biased learning provide further empirical support for a role for perceptual similarity in the acquisition of linguistically-relevant categories. We discuss the implications of our findings for phonological theory, language acquisition and models of the mental lexicon. (shrink)

The brain networks underlying human multiple-cue judgment, the judgment of a continuous criterion based on multiple cues, have been examined in a few recent studies, and the ventral precuneus has been found to be a key region. Specifically, activation differences in ventral precuneus (as measured with functional magnetic resonance imaging, fMRI) has been linked to an exemplar-based judgment process, where judgments are based on memory for previous similar cases. What aspects of exemplar-based judgment ventral precuneus supports is however poorly understood. (...) Ventral precuneus is a brain region important for various episodic memory processes, where increased engagement of this region together with the ventromedial prefrontal cortex (vmPFC) and the medial temporal lobes (MTL) during learning have been linked to retrieval success. Several alternatives are plausible, for example that reduced brain activity in ventral precuneus during learning to make exemplar-based judgments could reflect reduced needs for visuo-spatial attention as a function of learning to master the task. The present study used fMRI during a multiple-cue judgment task to gain novel neurocognitive evidence informative for the link between learning-related activity changes in ventral precuneus and exemplar-based judgment. Participants (N = 27) spontaneously learned to make judgments during fMRI, in a multiple-cue judgment task specifically designed to induce exemplar-based processing. Contrasting brain activity during late learning to early learning indeed revealed higher activity in ventral precuneus, the bilateral MTL and the vmPFC. Activity in the ventral precuneus and the vmPFC was found to parametrically increase between each judgment event, and activity levels in the ventral precuneus predicted performance after learning. These results are interpreted such that the ventral precuneus supports the aspects of exemplar-based processes that are related to episodic memory, tentatively by building and retrieving memory representations for judgment. (shrink)

Learning depends on attention. The processes that cue attention in the moment dynamically integrate learned regularities and immediate contextual cues. This paper reviews the extensive literature on cued attention and attentional learning in the adult literature and proposes that these fundamental processes are likely significant mechanisms of change in cognitive development. The value of this idea is illustrated using phenomena in children's novel word learning.

Words refer to objects in the world, but this correspondence is not one-to-one: Each word has a range of referents that share features on some dimensions but differ on others. This property of language is called underspecification. Parts of the lexicon have characteristic patterns of underspecification; for example, artifact nouns tend to specify shape, but not color, whereas substance nouns specify material but not shape. These regularities in the lexicon enable learners to generalize new words appropriately. How does the lexicon (...) come to have these helpful regularities? We test the hypothesis that systematic backgrounding of some dimensions during learning and use causes language to gradually change, over repeated episodes of transmission, to produce a lexicon with strong patterns of underspecification across these less salient dimensions. This offers a cultural evolutionary mechanism linking individual word learning and generalization to the origin of regularities in the lexicon that help learners generalize words appropriately. (shrink)

While the failure of the so-called classical theory of concepts - according to which definitions are the proper way to characterize concepts - is a consensus, metaphysical philosophy of religion still deals with the concept of God in a predominantly definitional way. It thus seems fair to ask: Does this failure imply that a definitional characterization of the concept of God is equally untenable? The first purpose of this paper is to answer this question. I focus on the representational side (...) of the matter. My goal is to analyze the extent to which the most important problems raised against the classical view of concepts affect a definitional-representational approach to the concept of God. The second purpose of the paper is to deepen into these issues, arguing for a pluralistic view of concepts and outlining a hybrid special theory of concepts, called the theory of ideal concepts. The theory is special because it deals with a special sort of concepts that I call ideal concepts; it is hybrid because, in addition to definitions, it uses another structure in the characterization of concepts: ideals. My goal is to argue that when applied to the concept of God and added to a pluralistic view of concepts, this theory can function as a tenable representational theory of the concept of God. (shrink)

Shin and DuBrow propose that a key principle driving event segmentation relates to causal analyses: specifically, that experiences that are attributed as having the same underlying cause are grouped together into an event. This offers an alternative to accounts of segmentation based on prediction error.

Previous studies have shown that people often use heuristics in making inferences and that subjective memory experiences, such as recognition or familiarity of objects, can be valid cues for inferences. So far, many researchers have used the binary choice task in which two objects are presented as alternatives (e.g., “Which city has the larger population, city A or city B?”). However, objects can be presented not only as alternatives but also in a question (e.g., “Which country is city X in, (...) country A or country B?”). In such a situation, people can make inferences based on the relationship between the object in the question and each object given as an alternative. In the present study, we call this type of task a “relationships‐comparison task.” We modeled the three inference strategies that people could apply to solve it (familiarity‐matching [FM; a new heuristic we propose in this study], familiarity heuristic [FH], and knowledge‐based inference [KI]) to examine people's inference processes. Through Studies 1, 2, and 3, we found that (a) people tended to rely on heuristics, and that FM (inferences based on similarity in familiarity between objects) well explained participants' inference patterns; (b) FM could work as an ecologically rational strategy for the relationships–comparison task since it could effectively reflect environmental structures, and that the use of FM could be highly replicable and robust; and (c) people could sometimes use a decision strategy like FM, even in their daily lives (consumer behaviors). The nature of the relationships–comparison task and human cognitive processes is discussed. (shrink)

Abstract A survey of different senses of the term ?representation? is presented. The presentation is guided by the appraisal that this key term is employed in the cognitive literature in different senses and that the distinction between these is not always explicitly stated or appreciated. Furthermore, the different senses seem to be associated with different rationales for the postulation of representation. Given that there may be a lack of convergence between the various senses of the construct in question and the (...) rationales for its postulation, a clear differentiation of these may be crucial for any evaluation of the role of representations in cognition and in cognitive theory. (shrink)

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning (...) derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, between learning that takes place with versus without concurrent awareness, and between learning that involves the encoding of instances versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, (...) conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial (...) roles of other cognitive processes (such as explanation and credit assignment) are ignored or oversimplified. Learning necessarily involves pragmatic considerations that can only be handled by complex cognitive processes.We provide an alternative framework for learning according to which category definitions must be based on category function. The learning system invokes other cognitive processes to accomplish difficult tasks, makes inferences, analyses and decides among potential features, and specifies how and when categories are to be generated and modified. We also examine the methodological underpinnings of the two approaches and compare their motivations. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming module associates (...) category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming module associates (...) category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)