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Rutar, D.

Publications and source records attributed to Rutar, D..

2 recordsLinked to original sources

Pupil dilation offers a time-window on prediction error

Task-evoked pupil dilation has been linked to many cognitive variables, perhaps most notably unexpected events. Zenon (2019) proposed a unifying framework stating that pupil dilation related to cognition should be considered from an information-theory perspective. In the current study, we investigated whether the pupils response to decision outcome in the context of associative learning reflects a prediction error signal defined operationally as an interaction between stimulus-pair frequency and accuracy, while also exploring the time course of this prediction error signal. Thereafter, we tested whether these prediction error signals correlated with information gain, defined formally as the KL divergence between posterior and prior belief distributions of the ideal observer. We reasoned that information gain should be proportional to the (precision-weighted) prediction error signals potentially arising from neuromodulatory arousal networks. To do so, we adapted a simple model of trial-by-trial learning of stimulus probabilities based on information theory from previous literature. We analyzed two data sets in which participants performed perceptual decision-making tasks that required associative learning while pupil dilation was recorded. Our findings consistently showed that a significant proportion of variability in the post-feedback pupil response during decision-making can be explained by a formal quantification of information gain shortly after feedback presentation in both task contexts. In the later time window, the relationship between information-theoretic variables and the post-feedback pupil response differed per task. For the first time, we present evidence that whether the post-feedback pupil dilates or constricts along with information gain was context dependent. This study offers empirical evidence showcasing how the pupils response can offer valuable insights into the process of model updating during learning, highlighting the promising utility of this readily accessible physiological indicator for investigating internal belief states.

neuroscience↗

Differentiating Bayesian model updating and model revision based on their prediction error dynamics

Within predictive processing learning is construed as Bayesian model updating with the degree of certainty for different existing hypotheses changing in light of new evidence. Bayesian model updating, however, cannot explain how new hypotheses are added to a model. Model revision, unlike model updating, makes structural changes to a generative model by altering its causal connections or adding or removing hypotheses. Whilst model updating and model revision have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between model updating and revision on the basis of how they affect prediction errors and predictions over time. To study this, participants took part in a within-subject computer-based learning experiment with two phases: updating and revision. In the updating phase, participants had to predict the relationship between cues and target stimuli and in the revision phase, they had to correctly predict a change in the said relationship. Based on previous research, phasic pupil dilation was taken as a proxy for prediction error. During model updating, we expected that the prediction errors over trials would be gradually decreasing as a reflection of the continuous integration of new evidence. During model revision, in contrast, prediction errors over trials were expected to show an abrupt decrease following the successful integration of a new hypothesis within the existing model. The opposite results were expected for predictions. Our results show that the learning dynamics as reflected in pupil and accuracy data are indeed qualitatively different between the revision and the updating phase, however in the opposite direction as expected. Participants were learning more gradually in the revision phase compared to the updating phase. This could imply that participants first built multiple models from scratch in the updating phase and updated them in the revision phase.

neuroscience↗