Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this “hierarchical prediction machine” approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

Key words: action, attention, bayesian brain, expectation, generative model, hierarchy, perception, precision, predictive coding, prediction, prediction error, top-down processing.

This commentary makes three points: (1) There may be no clear-cut distinction between emotion and appraisal “constituents” at neural and psychological levels. (2) The microdevelopment of an emotional interpretation contains a complex microdevelopment of affect. (3) Neurophenomenology is a promising research program for testing Lewis’s hypotheses about the neurodynamics of emotion-appraisal amalgams.

We address the claim that nonhuman animals do not represent unobservable states, based on studies of physical cognition by rooks and social cognition by scrub-jays. In both cases, the most parsimonious explanation for the results is counter to the reinterpretation hypothesis. We suggest that imagination and prospection can be investigated in animals and included in models of cognitive architecture.

The paper explores the distinction between two doctrines, both of which inform theory construction in much of modern cognitive psychology: the representational theory of mind and the computational theory of mind. According to the former, propositional attitudes are to be construed as relations that organisms bear to mental representations. According to the latter, mental processes have access only to formal (nonsemantic) properties of the mental representations over which they are defined. The following claims are defended: (1) That the traditional dispute between “rational” and “naturalistic” psychology is plausibly viewed as an argument about the status of the computational theory of mind. Rational psychologists accept a formality condition on the specification of mental processes; naturalists do not. (2) That to accept the formality condition is to endorse a version of methodological solipsism. (3) That the acceptance of some such condition is warranted, at least for that part of psychology which concerns itself with theories of the mental causation of behavior. This is because: (4) such theories require nontransparent taxonomies of mental states; and (5) nontransparent taxonomies individuate mental states without reference to their semantic properties. Equivalently, (6) nontransparent taxonomies respect the way that the organism represents the object of its propositional attitudes to itself, and it is this representation which functions in the causation of behavior. The final section of the paper considers the prospect for a naturalistic psychology: one which defines its generalizations over relations between mental representations and their environmental causes, thus seeking to account for the semantic properties of propositional attitudes. Two related arguments are proposed, both leading to the conclusion that no such research strategy is likely to prove fruitful.

The notion of an enactive system requires thinking about the brain in a way that is different from the standard computationalrepresentational models. In evolutionary terms, the brain does what it does and is the way that it is, across some scale of variations, because it is part of a living body with hands that can reach and grasp in certain limited ways, eyes structured to focus, an autonomic system, an upright posture, etc. coping with specific kinds of environments, and with other people. Changes to any of the bodily, environmental, or intersubjective conditions elicit responses from the system as a whole. On this view, rather than representing or computing information, the brain is better conceived as participating in the action.

Bindra’s proposal is still an attempt at “minor revision” that does not get away from the traditional tenet that living organisms are passive receivers of stimuli to which they react according to pseudo-mechanical principles A detailed analysis of what is covered by the term “gnostic organization” would, I believe, reveal the indispensability of induction and of inductively built-up representations of desirable and undesirable states and events that can then serve as goals in the assembly of adaptively modifiable behaviors

Key words: cognition, cybernetics, feedback

Key words: cognition

Excerpt: A theoretical clarification of terms in the area of numerical competence has been lacking for quite some time. The ascendency of formalism in mathematics during the last half century, which propagated the notion that if learns to manipulate the symbols the concepts will look after themselves (Hilbert 1899; Peano 1894; Whitehead & Russell 1927), was of little help, not only in education but also in number-oriented studies with animals. When researchers use terms such as number and counting, with all their ordinary-language ambiguities, it is not surprising that claims as well as theories turn out to be incompatible with one another. Davis & Perusse’s (D & P’s) target article does a welcome sorting job and, although there will certainly be dissidents, it prepares the ground for productive communication. My comments, therefore, should be taken as an attempt at expansion rather than disagreement.

Recent work in rational probabilistic modeling suggests that a kind of propositional reasoning is ubiquitous in cognition and especially in cognitive development. However, there is no reason to believe that this type of computation is necessarily conscious or resource-intensive.

While color realism is endorsed, Byrne & Hilbert’s (B&H’s) case for it stretches the notion of “physical property” beyond acceptable bounds. It is argued that a satisfactory account of color should do much more to respond to antirealist intuitions that flow from the specificity of color experience, and a pointer to an approach that does so is provided.