Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data

Abstract

Observers in perceptual tasks are often reported to combine multiple sensory cues in a weighted average that improves precision – in some studies, approaching statistically-optimal (Bayesian) weighting, but in others departing from optimality, or not benefitting from combined cues at all. To correctly conclude which combination rules observers use, it is crucial to have accurate measures of their sensory precision and cue weighting. Here, we present a new approach for accurately recovering these parameters in perceptual tasks with continuous responses. Continuous responses have many advantages, but are susceptible to a central tendency bias, where responses are biased towards the central stimulus value. We show such biases lead to inaccuracies in estimating both precision gains and cue weightings, two key measures used to assess sensory cue combination. We introduce a method that estimates sensory precision by regressing continuous responses on targets and dividing the variance of the residuals by the squared slope of the regression line, correcting-out the error introduced by the central bias and increasing statistical power. We also suggest a complementary analysis that recovers the sensory cue weights. Using both simulations and empirical data, we show that the proposed methods can accurately estimate sensory precision and cue weightings in the presence of central tendency biases. We conclude that central tendency biases should be (and can easily be) accounted for to consistently capture Bayesian cue combination in continuous response data.

Merging familiar and new senses to perceive and act in space

“…For example, there is initial evidence that within ten training sessions, and with another visual cue with a more natural form of noise (uncertainty), participants still do not attain Bayes-optimal performance; however, the skill enhances speed (as well as accuracy) of responses and resists verbal interference (Negen et al 2021 ). Sensitive model-based tests of some of these abilities are assisted by analysis methods beyond those in the classic cue combination literature (Aston et al 2021 ). Key future directions include investigating extended training , neural substrates (using fMRI), motor/action tasks , and other perceptual problem domains (e.g.…”