Perception of artificial stereoscopic stimuli from an incorrect viewing point.

The present study investigates the distortions in the perception of artificial stereoscopic displays seen from an inappropriate distance and/or orientation. Stereoscopic displays represent 3-D information correctly, provided they are seen from the correct station point. The viewing point may differ from the correct station point in its distance or in its orientation to the screen. These differences lead to distortions that can be predicted mathematically. However, the perceptual function may be different from the predictions, since people may possibly compensate for the distortions. To test the degree of this compensation, participants saw anaglyphic stereoscopic stimuli that showed angles in the horizontal plane, and their perception of the configuration was tested for various orientations and distances. The estimates were compared with the values predicted from the mathematical functions, and participants' virtual positions were reconstructed via nonlinear regressions. The analyses revealed a moderate compensation for viewing orientations and a systematically overestimation of the viewing distances. These results indicate that people compensate partially for distortions in stereopsis, given that the relevant information is available.

On Learning To Become a Successful Loser: A Comparison of Alternative Abstractions of Learning Processes in the Loss Domain.

One of the main difficulties in the development of descriptive models of learning in repeated choice tasks involves the abstraction of the effect of losses. The present paper explains this difficulty, summarizes its common solutions, and presents an experiment that was designed to compare the descriptive power of the specific quantifications of these solutions proposed in recent research. The experiment utilized a probability learning task. In each of the experiment's 500 trials participants were asked to predict the appearance of one of two colors. The probabilities of appearance of the colors were different but fixed during the entire experiment. The experimental manipulation involved an addition of a constant to the payoffs. The results demonstrate that learning in the loss domain can be faster than learning in the gain domain; adding a constant to the payoff matrix can affect the learning process. These results are consistent with Erev & Roth's (1996) adjustable reference point abstraction of the effect of losses, and violate all other models. Copyright 1998 Academic Press.