This paper serves to introduce the papers from the symposium by the same title, by describing the sort of work done in philosophy of psychology conceived as a branch of the philosophy of science, distinguishing it from other discussions of psychology in philosophy, and criticizing the claims to set limits on scientific psychology in the largely psychologically uninformed literatures concerning "folk psychology' and "wide" and "narrow" content. Philosophy of psychology as philosophy of science takes seriously and analyzes the explanatory structures, (...) conceptual problems, and evidentiary practices of extant scientific psychology. (shrink)

Central to contemporary cognitive science is the notion that mental processes involve computations defined over internal representations. This view stands in sharp contrast to the to visual perception and cognition, whose most prominent proponent has been J.J. Gibson. In the direct theory, perception does not involve computations of any sort; it is the result of the direct pickup of available information. The publication of Gibson's recent book (Gibson 1979) offers an opportunity to examine his approach, and, more generally, to contrast (...) the theory of direct perception with the computational/representational view. In the first part of the present article (Sections 2direct perceptioncase study”: the problem of perceiving the three-dimensional shape of moving objects is examined. This problem, which has been extensively studied within the immediate perception framework, serves to illustrate some of the inherent shortcomings of that approach. Finally, in Section 5, an attempt is made to place the theory of direct perception in perspective by embedding it in a more comprehensive framework. (shrink)

We examine a number of investigations of perceptual economy or, more specifically, of minimum tendencies and minimum principles in the visual perception of form, depth, and motion. A minimum tendency is a psychophysical finding that perception tends toward simplicity, as measured in accordance with a specified metric. A minimum principle is a theoretical construct imputed to the visual system to explain minimum tendencies. After examining a number of studies of perceptual economy, we embark on a systematic analysis of this notion. (...) We examine the notion that simple perceptual representations must be defined within the "geometric constraints" provided by proximal stimulation. We then take up metrics of simplicity. Any study of perceptual economy must use a metric of simplicity; the choice of metric may be seen as a matter of convention, or it may have deep theoretical and empirical implications. We evaluate several answers to the question of why the visual system might favor economical representations. Finally, we examine several accounts of the process for achieving perceptual economy, concluding that those which favor massively parallel processing are the most plausible. (shrink)

The Gestalt principle of isomorphism reveals the primacy of subjective experience as a valid source of evidence for the information encoded neurophysiologically. This theory invalidates the abstractionist view that the neurophysiological representation can be of lower dimensionality than the percept to which it gives rise.

We address the problem of predicting how people will spontaneously divide into groups a set of novel items. This is a process akin to perceptual organization. We therefore employ the simplicity principle from perceptual organization to propose a simplicity model of unconstrained spontaneous grouping. The simplicity model predicts that people would prefer the categories for a set of novel items that provide the simplest encoding of these items. Classification predictions are derived from the model without information either about the number (...) of categories sought or information about the distributional properties of the objects to be classified. These features of the simplicity model distinguish it from other models in unsupervised categorization (where, for example, the number of categories sought is determined via a free parameter), and we discuss how these computational differences are related to differences in modeling objectives. The predictions of the simplicity model are validated in four experiments. We also discuss the significance of simplicity in cognitive modeling more generally. (shrink)

Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...) a residual role for logic in understanding reasoning; and others put forward alternative formalisms for uncertain reasoning, or raise specific technical, methodological, or empirical challenges. In responding to these points, we aim to clarify the scope and limits of probability and logic in cognitive science; explore the meaning of the explanation of cognition; and re-evaluate the empirical case for Bayesian rationality. (shrink)

Computational theories of vision typically rely on the analysis of two aspects of human visual function: (1) object and shape recognition (2) co-calibration of sensory measurements. Both these approaches are usually based on an inverse-optics model, where visual perception is viewed as a process of inference from a 2D retinal projection to a 3D percept within a Euclidean space schema. This paradigm has had great success in certain areas of vision science, but has been relatively less successful in understanding perceptual (...) representation, namely, the nature of the perceptual encoding. One of the drawbacks of inverse-optics approaches has been the difficulty in defining the constraints needed to make the inference computationally tractable (e.g. regularity assumptions, Bayesian priors, etc.). These constraints, thought to be learned assumptions about the nature of the physical and optical structures of the external world, have to be incorporated into any workable computational model in the inverse-optics paradigm. But inference models that employ an inverse optics plus structural assumptions approach inevitably result in a na. (shrink)

Summary In recent years, the concept of simplicity in perception has acquired a leading role, above all thanks to scholars linked to Bayesian modeling and to theories like structural information theory derived from information theory. Unfortunately, two misleading ideas made their way into the discussion: that in perception, simplicity is equivalent to Prägnanz and that Occam’s razor plays a role in the simplicity of percepts. Here it is shown that in Gestalt theory, simplicity is only one of the factors of (...) Prägnanz and that the use of Occam’s razor is improper, because it applies only to the theories that generate, in this case, a percept, and not to the product of the theory. (shrink)

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g., ), it is argued that some universal principles may be attainable in cognitive science. Here, 2 examples are proposed: the simplicity (...) principle (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale‐invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. This article illustrates how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (), and briefly mentions some extensions to models of identification and categorization. This article also considers the scope and limitations of universal laws in cognitive science. (shrink)

Children learn their native language by exposure to their linguistic and communicative environment, but apparently without requiring that their mistakes be corrected. Such learning from “positive evidence” has been viewed as raising “logical” problems for language acquisition. In particular, without correction, how is the child to recover from conjecturing an over-general grammar, which will be consistent with any sentence that the child hears? There have been many proposals concerning how this “logical problem” can be dissolved. In this study, we review (...) recent formal results showing that the learner has sufficient data to learn successfully from positive evidence, if it favors the simplest encoding of the linguistic input. Results include the learnability of linguistic prediction, grammaticality judgments, language production, and form-meaning mappings. The simplicity approach can also be “scaled down” to analyze the learnability of specific linguistic constructions, and it is amenable to empirical testing as a framework for describing human language acquisition. (shrink)

Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.