Page argues convincingly for several important properties of localist representations in connectionist models of cognition. I argue that another important property of localist representations is that they serve as the starting point for connectionist representations of symbolic (relational) structures because they express meaningful properties independent of one another and their relations.

Ramscar and colleagues (2010, this volume) describe the “feature-label-order” (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of “learning elements of a symbol system” (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the sense of “solving the symbol grounding problem.”.

Schyns, Goldstone & Thibaut argue that categorization experience results in the learning of new perceptual features that are not derivable from the learner's existing feature set. We explore the meaning and implications of this “nonderivability” claim and relate it to the question of whether perceptual invariants are learnable, and if so, what might be entailed in learning them.

People are habitual explanation generators. At its most mundane, our propensity to explain allows us to infer that we should not drink milk that smells sour; at the other extreme, it allows us to establish facts (e.g., theorems in mathematical logic) whose truth was not even known prior to the existence of the explanation (proof). What do the cognitive operations underlying the inference that the milk is sour have in common with the proof that, say, the square root of two (...) is irrational? Our ability to generate explanations bears striking similarities to our ability to make analogies. Both reflect a capacity to generate inferences and generalizations that go beyond the featural similarities between a novel problem and familiar problems in terms of which the novel problem may be understood. However, a notable difference between analogy-making and explanation-generation is that the former is a process in which a single source situation is used to reason about a single target, whereas the latter often requires the reasoner to integrate multiple sources of knowledge. This seemingly small difference poses a challenge to the task of marshaling our understanding of analogical reasoning to understanding explanation. We describe a model of explanation, derived from a model of analogy, adapted to permit systematic violations of this one-to-one mapping constraint. Simulation results demonstrate that the resulting model can generate explanations for novel explananda and that, like the explanations generated by human reasoners, these explanations vary in their coherence. (shrink)

Humans, including preschool children, exhibit role-based relational reasoning, of which analogical reasoning is a canonical example. The connectionist model proposed in the target article is only capable of conditional paired-associate learning.

Theories of relational concept acquisition based on structured intersection discovery predict that relational concepts with a probabilistic structure ought to be extremely difficult to learn. We report four experiments testing this prediction by investigating conditions hypothesized to facilitate the learning of such categories. Experiment 1 showed that changing the task from a category-learning task to choosing the “winning” object in each stimulus greatly facilitated participants' ability to learn probabilistic relational categories. Experiments 2 and 3 further investigated the mechanisms underlying this (...) “who's winning” effect. Experiment 4 replicated and generalized the “who's winning” effect with more natural stimuli. Together, our findings suggest that people learn relational concepts by a process of intersection discovery akin to schema induction, and that any task that encourages people to discover a higher order relation that remains invariant over members of a category will facilitate the learning of putatively probabilistic relational concepts. (shrink)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting (...) brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

We are big fans of propositions. But we are not big fans of the proposed by Mitchell et al. The authors ignore the critical role played by implicit, non-inferential processes in biological cognition, overestimate the work that propositions alone can do, and gloss over substantial differences in how different kinds of animals and different kinds of cognitive processes approximate propositional representations.

Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats (...) both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent. (shrink)

Theories of analogical reasoning have assumed that a 1‐to‐1 constraint discourages reasoners from mapping a single element in 1 analog to multiple elements in another. Empirical evidence suggests that reasoners sometimes appear to violate the one‐to‐one constraint when asked to generate mappings, yet virtually never violate it when asked to generate analogical inferences. However, few studies have examined analogical inferences based on nonisomorphic analogs, and their conclusions are suspect due to methodological problems. We sought to elicit mixed inferences that could (...) result from combining information from 2 possible mappings. Participants generated 2‐to‐1 correspondences when asked for explicit mappings, but did not produce mixed inferences. Multiple correspondences appear to arise from multiple isomorphic mappings, rather than from a single homomorphic mapping. (shrink)

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN–human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in (...) the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN–human correspondences in vision and other domains of cognition. We explore all these issues in this response. (shrink)

van der Velde & de Kamps argue for the importance of considering the binding problem in accounts of human mental representation. However, their proposed solution fails as a complete account because it represents the bindings between roles and their fillers through associations (or connections). In addition, many criticisms leveled by the authors towards synchrony-based bindings models do not hold.

Author John E. Rexine expounds on the theologies of the great Roman thinkers Plato and Cicero in this essay.

Les scolastiques indiennes: Genèses, développements, interactions. Edited by Émilie Aussant and Gérard Colas. Études thématiques, vol. 32. Paris: École française d’Extrême-orient, 2020. Pp. 322. €40.

A mutual relation between God and the world provides a crucial distinction between panentheism and both classical theism and pantheism. Several proposals responding to Analytical Theology's challenge to distinguish panentheism from other forms of theism are considered and found inadequate. After defining mutual relation, conceptual evidence and the frequency of descriptions of panentheism that affirm a mutual relation between God and the world provide evidence that a mutual relation is crucial to distinguishing panentheism. Finally, benefits of recognizing a mutual relation (...) between God and the world as crucial to panentheism are described. (shrink)

What can we learn from reading Levinas alongside postcolonial theories of difference? With that question in view, Drabinski undertakes readings of Gayatri Spivak, Homi Bhabha, Edouard Glissant, and Subcommandante Marcos in order to rethink ideas of difference, language, subjectivity, ethics, and politics. Through these philosophical readings, he gives a new perspective on the work of these important postcolonial theorists and helps make Levinas relevant to other disciplines concerned with postcolonialism and ethics.

What were Shakespeare's final thoughts on history, tragedy, and comedy? Shakespeare's Last Plays focuses much needed scholarly attention on Shakespeare's "Late Romances." The work--a collection of newly commissioned essays by leading scholars of classical political philosophy and literature--offers careful textual analysis of Pericles, Prince of Tyre, Cymbeline, The Winter's Tale, The Tempest, All is True, and The Two Noble Kinsmen. The essays reveal how Shakespeare's thought in these final works compliments, challenges, fulfills, or transforms previously held conceptions of the playwright (...) and his political-philosophical views. (shrink)

John E. Smith; XI—Radical Empiricism, Proceedings of the Aristotelian Society, Volume 65, Issue 1, 1 June 1965, Pages 205–218, https://doi.org/10.1093/aristotel.

A nonlocal equation of motion with damping is derived by means of a Mori-Zwanzig renormalization process. The treatment is analogous to that of Mori in deriving the Langevin equation. For the case of electrodynamics, a local approximation yields the Lorentz equation; a relativistic generalization gives the Lorentz-Dirac equation. No self-acceleration or self-mass difficulties occur in the classical treatment, although runaway solutions are not eliminated. The nonrelativistic quantum case does not exhibit runaways, however, provided one remains within a weak damping approximation. (...) The correspondence limit shows that a classical limit may be taken, again within the same approximation. (shrink)