Emily R. Thomas’s research while affiliated with Birkbeck, University of London and other places

Forming expectations about what we are likely to perceive often facilitates perception. We forge such expectations on the basis of strong statistical relationships between events in our environment. However, due to our ever-changing world these relationships often subsequently degrade or even disappear, yet it is unclear how these altered statistics influence perceptual expectations. We examined this question across two studies by training participants in perfect relationships between actions (index or little finger abductions) and outcomes (clockwise or counter-clockwise gratings), before degrading the predictive relationship in a test phase – such that ‘expected’ events followed actions on 50–75% of trials and ‘unexpected’ events ensued on the remainder. Perceptual decisions about outcomes were faster and less error prone on expected than unexpected trials when predictive relationships remained high and reduced as the relationship diminished. Drift diffusion modelling indicated that these effects are explained by shifting the starting point in the evidence accumulation process as well as biasing the rate of evidence accumulation – with the former reflecting biases from statistics within the training session and the latter those of the test session. These findings demonstrate how perceptual expectations are updated as statistical certainty diminishes, with interacting influences speculatively dependent upon learning consolidation. We discuss how underlying mechanisms optimise the interaction between learning and perception – allowing our experiences to reflect a nuanced, ever-changing environment.

Forming expectations about what we are likely to perceive often facilitates perception. We forge such expectations on the basis of strong statistical relationships between events in our environment. However, due to our ever-changing world these relationships often subsequently degrade or even disappear, yet it is unclear how these altered statistics influence perceptual expectations. We examined this question across three studies by training participants in perfect relationships between actions (index or little finger abductions) and outcomes (clockwise or counter-clockwise gratings), before degrading the predictive relationship in a test phase – such that ‘expected’ events followed actions on 50-85% of trials and ‘unexpected’ events ensued on the remainder. Perceptual decisions about outcomes were faster and less error prone on expected than unexpected trials when predictive relationships remained high and reduced as the relationship diminished. Drift diffusion modelling indicated that these effects are explained by shifting the starting point in the evidence accumulation process as well as biasing the rate of evidence accumulation – with the former reflecting biases from statistics within the training session and the latter those of the test session. These findings demonstrate how perceptual expectations are updated as statistical certainty diminishes, with interacting influences speculatively dependent upon learning consolidation. We discuss how underlying mechanisms optimise the interaction between learning and perception – allowing our experiences to reflect a nuanced, ever-changing environment.

It is widely believed that predicted tactile action outcomes are perceptually attenuated. The present experiments determined whether predictive mechanisms necessarily generate attenuation or, instead, can enhance perception—as typically observed in sensory cognition domains outside of action. We manipulated probabilistic expectations in a paradigm often used to demonstrate tactile attenuation. Adult participants produced actions and subsequently rated the intensity of forces on a static finger. Experiment 1 confirmed previous findings that action outcomes are perceived less intensely than passive stimulation but demonstrated more intense perception when active finger stimulation was removed. Experiments 2 and 3 manipulated prediction explicitly and found that expected touch during action is perceived more intensely than unexpected touch. Computational modeling suggested that expectations increase the gain afforded to expected tactile signals. These findings challenge a central tenet of prominent motor control theories and demonstrate that sensorimotor predictions do not exhibit a qualitatively distinct influence on tactile perception.