Temporal Stability of Bayesian Belief Updating in Perceptual Decision Making

Abstract

Bayesian inference suggests that perception is inferred from a weighted integration of prior contextual beliefs with current sensory evidence (likelihood) about the world around us. The perceived precision or uncertainty associated with prior and likelihood information is used to guide perceptual decision making, such that more weight is placed on the source of information with greater precision. This provides a framework for understanding a spectrum of clinical transdiagnostic symptoms associated with aberrant perception, as well as individual differences in the general population. Whilst behavioural paradigms are commonly used to characterise individual differences in perception as a trait-like characteristic, measurement reliability in these behavioural tasks is rarely assessed. To remedy this gap, we empirically evaluate the reliability of a perceptual decision-making task that quantifies individual differences in Bayesian belief updating in terms of the relative precision weighting afforded to prior and likelihood information (i.e., sensory weight). We analysed data from participants (n = 37) who performed this task twice. We found that the precision afforded to prior and likelihood information showed high internal consistency and good test re-test reliability (ICC = 0.86, 95%CI[0.72, 0.93]) when averaged across participants, as well as at the individual level using hierarchical modelling. Our results provide support for the assumption that Bayesian belief updating operates as a stable, trait-like characteristic in perceptual decision making. We discuss the utility and applicability of reliable perceptual decision-making paradigms as a measure of individual differences in the general population, as well as a diagnostic tool in psychiatric research.

Are we really Bayesian? Probabilistic inference shows sub-optimal knowledge transfer

“…Firstly, we calculated s 2 Li , the variance of estimates of the mean (μ Lest ) relative to the true mean of the splashes (μ L ) in the likelihood-only task [43,49,50]…”