When it pays off to take a look: Infants learn to follow an object’s motion with their gaze — Especially if it features eyes

Social cues and instrumental learning are two aspects potentially fostering early gaze following. We systematically investigated the influence of social features (schematic eyes vs. reverse-contrast eyes) and gaze-contingent reinforcement (elicited vs. not elicited) on 4-month-olds' learning to attend to gaze-cued objects. In 4 experiments, we tested infants' (N = 74) gaze following of a turning block with schematic or reverse-contrast eyes. In Experiments 1 and 2, infants could elicit an attractive animation in a training phase via interactive eye tracking by following the turning of the block. Experiments 3 and 4 were yoked controls without contingent reinforcement. Infants did not spontaneously follow the motion of the block. Four-month-olds always followed the block after training when it featured schematic eyes. When the block featured reverse-contrast eyes, the training phase only affected infants' looking behavior without reinforcement. While speaking to a certain degree of plasticity, findings stress the importance of eyes for guiding infants' attention

A Neural Model of Biased Oscillations in Aplysia Head-Waving Behavior

A long-term bias in the exploratory head-waving behavior of Aplysia can be induced using bright lights as an aversive stimulus: coupling onset of the lights with head movements to one side results in a bias away from that side (Cook & Carew, 1986). This bias has been interpreted as a form of operant conditioning, and has previously been simulated with a neural network model based on associative synaptic facilitation (Raymond, Baxter, Buonomano, & Byrne, 1992). In this article we simulate the head-waving behavior using a recurrent gated dipole, a nonlinear dynamical neural model that has previously been used to explain various data including oscillatory behavior in biological pacemakers. Within the recurrent gated dipole, two channels operate antagonistically to generate oscillations, which drive the side-to-side head waving. The frequency of oscillations depends on transmitter mobilization dynamics, which exhibit both short- and long-term adaptation. We assume that light onset results in a nonspecific increase in arousal to both channels of the dipole. Repeated pairing of arousal increments with activation of one channel (the "reinforced" channel) of the dipole leads to a bias in transmitter dynamics, which causes the oscillation to last a shorter time on the reinforced channel than on the non-reinforced channel. Our model provides a parsimonious explanation of the observed behavior, and it avoids some of the unexpected results obtained with the Raymond et al. model. In addition, our model makes predictions concerning the rate of onset and extinction of the biases, and it suggests new lines of experimentation to test the nature of the head-waving behavior.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100, N0014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); A.P. Sloan Foundation (BR-3122

Connectionist simulation of attitude learning: Asymmetries in the acquisition of positive and negative evaluations

Connectionist computer simulation was employed to explore the notion that, if attitudes guide approach and avoidance behaviors, false negative beliefs are likely to remain uncorrected for longer than false positive beliefs. In Study 1, the authors trained a three-layer neural network to discriminate "good" and "bad" inputs distributed across a two-dimensional space. "Full feedback" training, whereby connection weights were modified to reduce error after every trial, resulted in perfect discrimination. "Contingent feedback," whereby connection weights were only updated following outputs representing approach behavior, led to several false negative errors (good inputs misclassified as bad). In Study 2, the network was redesigned to distinguish a system for learning evaluations from a mechanism for selecting actions. Biasing action selection toward approach eliminated the asymmetry between learning of good and bad inputs under contingent feedback. Implications for various attitudinal phenomena and biases in social cognition are discussed

Learning salience amoung [sic] features through contingency in the CEL framework

Determining which features in an environment are salient given a task, salience assignment, is a central problem in Machine Learning. A related phenomenon, contingency (the conditions under which relative salience among environmental features is acquired), is central to learning and memory in animal psychology. This paper presents an analysis of a set of empirical data on contingency and an algorithm for the salience assignment problem. The algorithm presented is implemented in a working computer program which interacts with a simulated environment to produce contingent associative learning corresponding to relevant behavioral data. The model also makes specific empirical predictions that can be experimentally tested

Learning in Economics: Where Do We Stand?

This paper briefly reviews the current literature on learning in economics from a behavioral point of view. It critically compares theory with aspects of learning in real-life and with evidence from laboratory experiments, and argues that most customary approaches lack criteria for their applicability. Hence, there is a need for a theory that includes criteria when to employ which theory or which element(s) of existing theories contingent on the situation or environment in question. A discussion of several unsolved issues in economic learning stresses the fundamental role of learning conditions that have be neglected in the literature, but are accounted for in behavioral approaches such as "contingent learning".economic learning, behavioral economics, experiments, game theory, information feedback, contingent learning