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)

We explore different ways in which the human visual system can adapt for perceiving and categorizing the environment. There are various accounts of supervised (categorical) and unsupervised perceptual learning, and different perspectives on the functional relationship between perception and categorization. We suggest that common experimental designs are insufficient to differentiate between hypothesized perceptual learning mechanisms and reveal their possible interplay. We propose a relatively underutilized way of studying potential categorical effects on perception, and we test the predictions of different perceptual (...) learning models using a two‐dimensional, interleaved categorization‐plus‐reconstruction task. We find evidence that the human visual system adapts its encodings to the feature structure of the environment, uses categorical expectations for robust reconstruction, allocates encoding resources with respect to categorization utility, and adapts to prevent miscategorizations. (shrink)

Does natural selection favor veridical percepts—those that accurately depict objective reality? Perceptual and cognitive scientists standardly claim that it does. Here we formalize this claim using the tools of evolutionary game theory and Bayesian decision theory. We state and prove the “Fitness-Beats-Truth Theorem” which shows that the claim is false: If one starts with the assumption that perception involves inference to states of the objective world, then the FBT Theorem shows that a strategy that simply seeks to maximize expected-fitness payoff, (...) with no attempt to estimate the “true” world state, does consistently better. More precisely, the FBT Theorem provides a quantitative measure of the extent to which the fitness-only strategy dominates the truth strategy, and of how this dominance increases with the size of the perceptual space. The FBT Theorem supports the Interface Theory of Perception, which proposes that our perceptual systems have evolved to provide a species-specific interface to guide adaptive behavior, and not to provide a veridical representation of objective reality. (shrink)

In the Meno, Phaedo, and Phaedrus, Plato outlines the controversial thesis of a priori knowledge that all learning is a form of recollection—anamnesis. He uses this as an argument for the immortality of the soul via reincarnation. Because of this latter claim, the thesis is widely mocked by contemporary evolutionarily-informed materialists. But we can safely reject the metaphysical claim without abandoning the insight of the epistemological one. And indeed, modern evolutionary theory can explain how learning—at least of the sort that (...) depends on certain a priori concepts—can be a kind of recollection. Through this metaphor, natural selection is a process by which information about the world is transmitted across time. When we learn by reasoning about a priori knowledge, then, we in an important sense rely on information in our genomes—if not our souls—information acquired by the process of natural selection—if not conscious acquisition. Thinking of a priori knowledge with the metaphor of anamnesis elucidates two essential features of the relationship between epistemology and ontology. First, it emphasizes that there is necessarily a time-delay between our a priori knowledge and the universe to which it bears a relationship, if any. Second, it clarifies that a priori knowledge is knowledge that enhances reproductive fitness—which could well be because it reflects ontology faithfully, but could as easily be a kind of innate nominalism. (shrink)

Mathematical models can be profitably used to establish whether our perception of the external world is accurate. Donald Hoffman and his collaborators have developed a promising mathematical framework within which this question can be addressed and which is based on an exhaustive taxonomy of the different possible relations between perceptual representations and the external world. After reformulating their framework by means of an improved formal system, we discuss their application of evolutionary game theory, which appears to show that an essentially (...) anti-realistic perceptual strategy would in the long run biologically outcompete its rivals. We argue that their model does not take the crucial biological significance of environmental changes into due consideration and propose alternative models which do. We conclude that a partially realistic representation would be favoured in our models. (shrink)

The Interface Theory of Perception, as stated by D. Hoffman, says that perceptual experiences do not to approximate properties of an “objective” world; instead, they have evolved to provide a simplified, species-specific, user interface to the world. Conscious Realism states that the objective world consists of ‘conscious agents’ and their experiences. Under these two theses, consciousness creates all objects and properties of the physical world: the problem of explaining this process reverses the mind-body problem. In support of the interface theory (...) I propose that our perceptions have evolved, not to report the truth, but to guide adaptive behaviors. Using evolutionary game theory, I state a theorem asserting that perceptual strategies that see the truth will, under natural selection, be driven to extinction by perceptual strategies of equal complexity but tuned instead to fitness. I then give a minimal mathematical definition of the essential elements of a “conscious agent.” Under the conscious realism thesis, this leads to a non-dualistic, dynamical theory of conscious process in which both observer and observed have the same mathematical structure. The dynamics raises the possibility of emergence of combinations of conscious agents, in whose experiences those of the component agents are entangled. In support of conscious realism, I discuss two more theorems showing that a conscious agent can consistently see geometric and probabilistic structures of space that are not necessarily in the world per se but are properties of the conscious agent itself. The world simply has to be amenable to such a construction on the part of the agent; and different agents may construct different structures as seeming to belong to the world. This again supports the idea that any true structure of the world is likely quite different from what we see. I conclude by observing that these theorems suggest the need for a new theory which resolves the reverse mind-body problem, a good candidate for which is conscious agent theory. (shrink)

One of the most lasting contributions of Marr's posthumous book is his articulation of the different “levels of analysis” that are needed to understand vision. Although a variety of work has examined how these different levels are related, there is comparatively little examination of the assumptions on which his proposed levels rest, or the plausibility of the approach Marr articulated given those assumptions. Marr placed particular significance on computational level theory, which specifies the “goal” of a computation, its appropriateness for (...) solving a particular problem, and the logic by which it can be carried out. The structure of computational level theory is inherently teleological: What the brain does is described in terms of its purpose. I argue that computational level theory, and the reverse-engineering approach it inspires, requires understanding the historical trajectory that gave rise to functional capacities that can be meaningfully attributed with some sense of purpose or goal, that is, a reconstruction of the fitness function on which natural selection acted in shaping our visual abilities. I argue that this reconstruction is required to distinguish abilities shaped by natural selection—“natural tasks” —from evolutionary “by-products”, rather than merely demonstrating that computational goals can be embedded in a Bayesian model that renders a particular behavior or process rational. (shrink)

This article introduces a process-oriented approach for improving present moment conceptualization in psychotherapy that is in alignment with neuroscience: the Temporospatial movements of mind model. We elaborate on seven temporal movements that describe the moment-to-moment morphogenesis of emotional feelings and thoughts from inception to maturity. Temporal refers to the passage of time through which feelings and thoughts develop, and electromagnetic activity, that among other responsibilities, bind information across time. Spatial dynamics extend from an undifferentiated to three dimensional experiences of emotional (...) and cognitive processes. Neurophysiologically, spatial refers to structures within the brain and their varying interactions with one another. This article culminates in the development of an atheoretical temporospatial grid that may help clinicians conceptualize where patients are in their cognitive and emotional development to further guide technique. (shrink)

A widespread assumption in the contemporary discussion of probabilistic models of cognition, often attributed to the Bayesian program, is that inference is optimal when the observer's priors match the true priors in the world—the actual “statistics of the environment.” But in fact the idea of a “true” prior plays no role in traditional Bayesian philosophy, which regards probability as a quantification of belief, not an objective characteristic of the world. In this paper I discuss the significance of the traditional Bayesian (...) epistemic view of probability and its mismatch with the more objectivist assumptions about probability that are widely held in contemporary cognitive science. I then introduce a novel mathematical framework, the observer lattice, that aims to clarify this issue while avoiding philosophically tendentious assumptions. The mathematical argument shows that even if we assume that “ground truth” probabilities actually do exist, there is no objective way to tell what they are. Different observers, conditioning on different information, will inevitably have different probability estimates, and there is no general procedure to determine which one is right. The argument sheds light on the use of probabilistic models in cognitive science, and in particular on what exactly it means for the mind to be “tuned” to its environment. (shrink)