Le Pelley, M. E. | Search bioRxiv

bioRxiv2018

Learned predictiveness acquired through experience prevails over the influence of conflicting verbal instructions in rapid selective attention

Previous studies have provided evidence that selective attention tends to prioritize the processing of stimuli that are good predictors of upcoming events over nonpredictive stimuli. In the present study we explored whether the mechanism responsible for this effect critically reflects the influence of prior experience of predictiveness (history of attentional selection of predictive stimuli), or whether it reflects a more flexible process that can be adapted to new verbally acquired knowledge. Our experiment manipulated participants experience of the predictiveness of different stimuli over the course of trial-by-trial training; we then provided explicit verbal instructions regarding stimulus predictiveness that were designed to be either consistent or inconsistent with the previously established learned predictiveness. The effects of training and instruction on attention to stimuli were measured using a dot probe task. Results revealed a rapid attentional bias towards stimuli experienced as predictive (versus those experienced as nonpredictive), that was completely unaffected by verbal instructions. This was not due to participants failure to recall or use instructions appropriately, as revealed by analyses of their learning about stimuli, and their memory for instructions. Overall, these findings suggest that stimuli experienced as predictive through trial-by-trial training produce a relatively inflexible attentional bias based on prior selection history, which is not (always) easily altered through instructions.

animal behavior and cognition↗

bioRxiv2017

The Algorithmic Neuroanatomy Of Action-Outcome Learning

Although it is well known that animals can encode the consequences of their actions and can use this information to control action selection and evaluation, it is not known what learning rules control action-outcome (AO) learning. Here we trained participants to encode specific AO associations whilst undergoing functional imaging (fMRI) and used computational modelling to evaluate competing models. This analysis revealed that a Kalman filter, which learned the unique causal effect of each action, best characterized AO learning and found the medial prefrontal cortex differentiated the unique effect of actions from background effects. We subsequently extended these findings to show that mPFC participated in a circuit with parietal cortex and caudate nucleus to segregate distinct contributions to AO learning. The results extend our understanding of goal-directed learning and demonstrate that sensitivity to the causal relationship between actions and outcomes guides goal-directed learning rather than contiguous state-action relations.

neuroscience↗