Probability Matching as a Computational Strategy Used in Perception

Abstract

The question of which strategy is employed in human decision making has been studied extensively in the context of cognitive tasks; however, this question has not been investigated systematically in the context of perceptual tasks. The goal of this study was to gain insight into the decision-making strategy used by human observers in a low-level perceptual task. Data from more than 100 individuals who participated in an auditory-visual spatial localization task was evaluated to examine which of three plausible strategies could account for each observer's behavior the best. This task is very suitable for exploring this question because it involves an implicit inference about whether the auditory and visual stimuli were caused by the same object or independent objects, and provides different strategies of how using the inference about causes can lead to distinctly different spatial estimates and response patterns. For example, employing the commonly used cost function of minimizing the mean squared error of spatial estimates would result in a weighted averaging of estimates corresponding to different causal structures. A strategy that would minimize the error in the inferred causal structure would result in the selection of the most likely causal structure and sticking with it in the subsequent inference of location—“model selection.” A third strategy is one that selects a causal structure in proportion to its probability, thus attempting to match the probability of the inferred causal structure. This type of probability matching strategy has been reported to be used by participants predominantly in cognitive tasks. Comparing these three strategies, the behavior of the vast majority of observers in this perceptual task was most consistent with probability matching. While this appears to be a suboptimal strategy and hence a surprising choice for the perceptual system to adopt, we discuss potential advantages of such a strategy for perception.

Multisensory Encoding Improves Auditory Recognition

“…Assuming the causal inference model of multisensory processing (Kording et al, 2007;Shams et al, 2005;Wozny et al, 2010), the largest cross-modal facilitation should be obtained when the image and the sound are highly congruent (e.g., sound and image of a barking dog), as this would result in perception of a common cause for the two and integration of the two signals which will in turn lead to the updating of unisensory posterior by the bisensory posterior. If the image and sound are highly incongruent (e.g., sound of a dog bark paired with the image of a flower), then the brain would likely infer independent causes.…”

Behavioral, Neural, and Computational Principles of Bodily Self-Consciousness

“…These models have been applied to visuo-tactile integration (Ernst and Banks, 2002;Ernst and Bü lthoff, 2004) to explain object perception during haptic manipulation and to audiovisual integration to explain phenomena such as the ventriloquism effect or sound-induced visual illusions (Alais and Burr, 2004;Magnotti et al, 2013;Wozny et al, 2010;Wozny and Shams, 2011). The integration of visuo-vestibular stimuli for translational and rotational self-motion perception has also been studied by several investigators (Fetsch et al, 2012(Fetsch et al, , 2013Prsa et al, 2012Prsa et al, , 2015.…”

Heterogeneous Preference and Local Nonlinearity in Consensus Decision Making

“…Beyond altering the coarsening dynamics [27,28], the social nonlinearity can qualitatively change the equilibrium magnetization of the system. First, in contrast with the mean-field model, the boundary between the þ1 or −1 dominated regions of the parameter space depends on the concentration of unbiased individuals.…”