The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...) these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. (shrink)

When people interact with social robots, they treat them as real social agents. How people depict robots is fun to consider, but when people are confronted with embodied entities that move and talk – whether humans or robots – they interact with them as authentic social agents with minds, and not as mere representations.

The considerable agreement across languages in the way they categorize the color domain, despite independent historical development, demands an explanation. One option is to postulate a universal innate representation of the color categories, 'hardwired' into each observer's brain. An alternative is that observers internalize their color categories through a process of cultural transmission, constrained by some kind of 'optimality hypothesis' to account for the cross-language agreement. A number of optimality hypotheses are reviewed. It is tempting to believe that the vivid (...) experiential quality of the categories can only be explained if they are determined by innate representations rather than by linguistic imprinting. However, linguistic transmission of color categories – perturbed from their optimal boundaries by special circumstances – fits best with the experience of dichromats. Even for the 'primary hue' categories, where the case for innate representations should be strongest, the evidence is far from convincing. (shrink)

The universality, invariance, and elegance of principles governing the universe may be reflected in principles of the minds that have evolved in that universe – provided that the mental principles are formulated with respect to the abstract spaces appropriate for the representation of biologically significant objects and their properties. (1) Positions and motions of objects conserve their shapes in the geometrically fullest and simplest way when represented as points and connecting geodesic paths in the six-dimensional manifold jointly determined by the (...) Euclidean group of three-dimensional space and the symmetry group of each object. (2) Colors of objects attain constancy when represented as points in a three-dimensional vector space in which each variation in natural illumination is canceled by application of its inverse from the three-dimensional linear group of terrestrial transformations of the invariant solar source. (3) Kinds of objects support optimal generalization and categorization when represented, in an evolutionarily-shaped space of possible objects, as connected regions with associated weights determined by Bayesian revision of maximum-entropy priors. Key Words: apparent motion; Bayesian inference; cognition; color constancy; generalization; mental rotation; perception; psychological laws; psychological space; universal laws. (shrink)

The aim of the present paper is to examine the contribution of evaluative conditioning (EC) to attitude formation theory in social psychology. This aim is pursued on two fronts. First, evaluative conditioning is analysed for its relevance to social psychological research. We show that conditioned attitudes can be acquired through simple co‐occurrences of a neutral and a valenced stimulus. Moreover, we argue that conditioned attitudes are not confined to direct contact with a valenced stimulus, but can be formed and dynamically (...) reformed indirectly, through association chains. Second, social research is examined in an effort to identify evaluative learning mechanisms. We suggest that several important phenomena in social psychology (e.g., ingroup favouritism, prejudice, name letter effect) are at least partly due to simple mechanisms of evaluative learning. The implications for attitude formation theory and for applied settings are discussed. (shrink)