Prediction in infants and adults: A pupillometry study. Issue 4 (27th December 2018) - Zhang, Felicia; Jaffe‐Dax, Sagi; Wilson, Robert C.; Emberson, Lauren L.
Abstract: Adults use both bottom‐up sensory inputs and top‐down signals to generate predictions about future sensory inputs. Infants have also been shown to make predictions with simple stimuli and recent work has suggested top‐down processing is available early in infancy. However, it is unknown whether this indicates that top‐down prediction is an ability that is continuous across the lifespan or whether an infant's ability to predict is different from an adult's, qualitatively or quantitatively. We employed pupillometry to provide a direct comparison of prediction abilities across these disparate age groups. Pupil dilation response (PDR) was measured in 6‐month olds and adults as they completed an identical implicit learning task designed to help learn associations between sounds and pictures. We found significantly larger PDR for visual omission trials (i.e. trials that violated participants' predictions without the presentation of new stimuli to control for bottom‐up signals) compared to visual present trials (i.e. trials that confirmed participants' predictions) in both age groups. Furthermore, a computational learning model that is closely linked to prediction error (Rescorla‐Wagner model) demonstrated similar learning trajectories suggesting a continuity of predictive capacity and learning across the two age groups. Abstract : This paper used pupillometry to directly compare prediction abilities between infants and adults as they completed an identical implicit learning task designed to help them learn associations between sounds and pictures. We found significantly larger pupil dilation response for visual omission trials (i.e. trials that violated participants' predictions without the presentation of new stimuli to control for bottom‐up signals) compared to visual present trials (i.e. trials that confirmed participants' predictions) in both age groups. Furthermore, a computational learning model that is closely linked to prediction error (Rescorla‐Wagner model) demonstrated similar learning trajectories, suggesting a continuity of predictive capacity and learning across these ages