State estimation bias induced by optimization under uncertainty and error cost asymmetry is likely reflected in perception

Abstract

It is well known from numerous studies that perception can be significantly affected by intended action in many everyday situations, indicating that perception and related decision-making is not a simple, one-way sequence, but a complex iterative cognitive process. However, the underlying functional mechanisms are yet unclear. Based on an optimality approach, a quantitative computational model of one such mechanism has been developed in this study. It is assumed in the model that significant uncertainty about task-related parameters of the environment results in parameter estimation errors and an optimal control system should minimize the cost of such errors in terms of the optimality criterion. It is demonstrated that, if the cost of a parameter estimation error is significantly asymmetrical with respect to error direction, the tendency to minimize error cost creates a systematic deviation of the optimal parameter estimate from its maximum likelihood value. Consequently, optimization of parameter estimate and optimization of control action cannot be performed separately from each other under parameter uncertainty combined with asymmetry of estimation error cost, thus making the certainty equivalence principle non-applicable under those conditions. A hypothesis that not only the action, but also perception itself is biased by the above deviation of parameter estimate is supported by ample experimental evidence. The results provide important insights into the cognitive mechanisms of interaction between sensory perception and planning an action under realistic conditions. Implications for understanding related functional mechanisms of optimal control in the CNS are discussed.

Action Effects on Visual Perception of Distances: A Multilevel Bayesian Meta-Analysis

“…Because most studies included in our meta-analysis were not designed to address this question, the debate will continue. Nevertheless, even if action-constraint effects are postperceptual, they might be worth studying if they are, for instance, memory effects (e.g., Cooper, Sterling, Bacon, & Bridgeman, 2012) or adaptive-judgment biases (e.g., Haselton et al, 2009; for a computational model, see Shimansky, 2011) rather than mere experimental demand biases.…”

Action Effects on Visual Perception of Distances: A Multilevel Bayesian Meta-Analysis

“…As most studies included in our meta-analysis were not designed to address this question, the debate will continue. Nevertheless, even if action constraint effects are post-perceptual, it might be worth studying if they are, for instance, memory effects (e.g., Cooper, Sterling, Bacon, & Bridgeman, 2012) or adaptive judgement biases (e.g., Haselton et al, 2009; for a computational model, see Shimansky, 2011) rather than mere experimental demand biases.…”

Inclusion of neural effort in cost function can explain perceptual decision suboptimality

“…An example can be jumping over an obstacle under poor visibility conditions. As Shimansky (2011) demonstrated, an optimality approach predicts a tendency to overestimate the size of the obstacle under these conditions.…”

“…Experimentally observed perceptual biases may also be consistent with perceptual decision optimality. Shimansky (2011) used an optimality approach to predict perceptual biases in experimental conditions that included a combination of perceptual uncertainty with loss asymmetry with respect to the direction of decision error. An example can be jumping over an obstacle under poor visibility conditions.…”