Bekkering, H. | Search bioRxiv

bioRxiv2024

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↗

bioRxiv2018

Young Children Integrate Current Observations, Priors and Agent Information to Build Predictive Models of Others’ Actions

From early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agents preferences from systematic violations of random sampling. We investigated how young children build and update models of an agents sampling actions over time, and whether a computational model based on the causal Bayesian network formalization of predictive processing can explain this process.\n\nWe formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to build predictive models of others actions. We measured pupillary responses as a behavioral marker of prediction errors (i.e., the perceived mismatch between what ones model of an agent predicts and what the agent actually does), as described in the predictive processing framework. Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to generate predictive models of agents and their actions.\n\nThese findings shed light on the mechanisms behind toddlers inferences about agent-caused events. To our knowledge, this is the first study in which young childrens pupillary responses are used as markers of prediction errors, and explained by a computational model based on the causal Bayesian network formalization of predictive processing. We argue that the predictive processing framework provides a promising explanation of the way in which young children process other persons actions.\n\nHighlightsO_LIWe present three formalized hypotheses on how young children generate predictive models of others sampling actions.\nC_LIO_LIWe measured pupillary responses of children as a behavioral marker of prediction errors as described in the predictive processing framework.\nC_LIO_LIResults showed that young children integrated information about current observations, prior probabilities and agents to generate predictive models about others actions.\nC_LIO_LIA computational model based on the causal Bayesian network formalization of predictive processing can explain this process.\nC_LI

neuroscience↗