Abstract:A model that is capable of maintaining the identities of individuated elements as they move is described. It solves a particular problem of underdetermination, the motion correspondence problem, by simultaneously applying 3 constraints: the nearest neighbor principle, the relative velocity principle, and the element integrity principle. The model generates the same correspondence solutions as does the human visual system for a variety of displays, and many of its properties are consistent with what is known about the physiological mechanisms underlying human motion perception. The model can also be viewed as a proposal of how the identities of attentional tags are maintained by visual cognition, and thus it can be differentiated from a system that serves merely to detect movement. PDF Image: PDF Full Text Database: PsycARTICLESThe How Nevin-Meadows, Don Schopflocher, and Nancy Digdon at the University of Alberta. Comments from Dennis Proffitt and an anonymous reviewer on an earlier version of the manuscript were also extremely useful. I would like to acknowledge Brian Harder, who died during the past year. He began work on this research with me and performed numerous simulation runs and prepared many of the figures.Correspondence may be addressed to: Michael R. W. Dawson, Department of Psychology, University of Alberta, Edmonton, Alberta T6G 2E9 Canada. Electronic mail may be sent to mike@psych.ualberta.ca.Many researchers have described the goal of visual perception as the construction of useful representations about the world (e. g. , Horn, 1986;Marr, 1976Marr, , 1982Ullman, 1979). These representations are derived from the information projected from a three-dimensional visual world (the distal stimulus) onto an essentially twodimensional surface of light receptors in the eyes. The interpretation of the distal stimulus must be determined from the resulting pattern of retinal stimulation (the proximal stimulus).However, the information represented in the proximal stimulus cannot, by itself, completely determine the nature of the distal stimulus. This is because the proximal stimulus does not preserve the full dimensionality of the physical world. The mapping from three-dimensional patterns to two-dimensional patterns is a many-to-one mapping and is not uniquely invertible (see Gregory, 1970;Horn, 1986;Marr, 1982;Richards, 1988). Retinal stimulation geometrically underdetermines interpretations of the physical world. 1Underdetermination can also result because the information available from local measurements of the proximal stimulus is consistent with a large number of different global interpretations. Alone, the local measurements are not sufficient to determine which global interpretation is correct. One example of this is the aperture problem: local measurements of a contour's movement do not by themselves specify the contour's true velocity (e. g. , Hildreth, 1983;Marr & Ullman, 1981). Another example is the stereo correspondence problem: local measurements do not by themselves specify which proxim...

Neurons in the accessory optic system (AOS) and pretectum are involved in the analysis of optic flow and the generation of the optokinetic response. Previous studies found that neurons in the pretectum and AOS exhibit direction selectivity in response to large-field motion and are tuned in the spatiotemporal domain. Furthermore, it has been emphasized that pretectal and AOS neurons are tuned to a particular temporal frequency, consistent with the "correlation" model of motion detection. We examined the responses of neurons in the nucleus of the basal optic root (nBOR) of the AOS in pigeons to large-field drifting sine wave gratings of varying spatial (SF) and temporal frequencies (TF). nBOR neurons clustered into two categories: "Fast" neurons preferred low SFs and high TFs, and "Slow" neurons preferred high SFs and low TFs. The fast neurons were tuned for TF, but the slow nBOR neurons had spatiotemporally oriented peaks that suggested velocity tuning (TF/SF). However, the peak response was not independent of SF; thus we refer to the tuning as "apparent velocity tuning" or "velocity-like tuning." Some neurons showed peaks in both the fast and slow regions. These neurons were TF-tuned at low SFs, and showed velocity-like tuning at high SFs. We used computer simulations of the response of an elaborated Reichardt detector to show that both the TF-tuning and velocity-like tuning shown by the fast and slow neurons, respectively, may be explained by modified versions of the correlation model of motion detection.

Two programs that can be used to determine the probability distributions of reaction times are detailed. The first program takes rank-ordered reaction times as input and outputs a file of quantized data. The second program uses a simplex procedure to estimate the parameters of the ex-Gaussian equation that provides the best description of the quantized data. The advantages of this type of data analysis are also discussed.Reaction time is one of the most common dependent measures used to study cognition and perception. Typically, researchers attempt to determine the effect of manipulations by analyzing differences between average response times. However, other types of analyses are possible, and in many instances are preferable (e.g., Luce, 1986;McGill, 1963;Townsend & Ashby, 1983). For example, one may investigate effects of manipulations on the probability distributions of observed reaction times. This requires that reaction time distributions be described with mathematical equations.The analysis of reaction time distributions is recommended on several grounds. First, response latencies are intrinsically variable, and probability distributions provide precise descriptions of this variability. Second, the shape of reaction time distributions can be used to make inferences about the identity of underlying psychological processes (McGill, 1963). Third, analyses of reaction time distributions can falsify models that are consistent with the mean and variance of response latencies (Ratcliff & Murdock, 1976).Although the analysis of reaction time distributions is clearly desirable, a survey of the literature provides few demonstrations of how this analysis is accomplished. The purpose of this paper is to describe two computer programs that can be used to fit a particular mathematical equation to reaction time data. These programs are written in BASIC for the Commodore 64/128, but can easily be modified for use with other machines. The first program uses 8,612 bytes of RAM, whereas the second uses 10,422 bytes. The first program summarizes and condenses a large number of reaction times into a much smaller set of numbers to input to the second program. As a result, very large data sets can be analyzed. For example, Dawson and Conforto (1987) used these programs to analyze the data from 3 subjects, who had each generated 960 reaction time responses.

The purpose of this monograph is to examine the relationship between a particular artificial neural network, the perceptron, and the Rescorla-Wagner model of learning. It is shown that in spite of the fact that there is a formal equivalence between the two, they can make different predictions about the outcomes of a number of classical conditioning experiments. It is argued that this is due to algorithmic differences between the two, differences which are separate from their computational equivalence.A peer-reviewed monograph published by Comparative Cognition and Behavior Reviews on behalf of the Comparative Cognition Society.

Many studies have examined how humans and other animals reestablish a sense of direction following disorientation in enclosed environments. Results showing that geometric shape of an enclosure is typically encoded, sometimes to the exclusion of featural cues, have led to suggestions that geometry might be encoded in a dedicated geometric module. Recently, Miller and Shettleworth (2007) proposed that the reorientation task be viewed as an operant task and they presented an associative operant model that appears to account for many empirical findings from reorientation studies. In this paper we show that, although Miller and Shettleworth's insights into the operant nature of the reorientation task may be sound, their mathematical model has a serious flaw. We present simulations to illustrate the implications of the flaw. We also propose that the output of a simple neural network, the perceptron, can be used to conduct operant learning within the reorientation task and can solve the problem in Miller and Shettleworth's model.