Abstract
Prior expectations shape neural responses in sensory regions of the brain, consistent with a Bayesian predictive coding account of perception. Yet, it remains unclear whether such a mechanism is already functional during early stages of development. To address this issue, we study how the infant brain responds to prediction violations using a cross-modal cueing paradigm. We record electroencephalographic responses to expected and unexpected visual events preceded by auditory cues in 12-month-old infants. We find an increased response for unexpected events. However, this effect of prediction error is only observed during late processing stages associated with conscious access mechanisms. In contrast, early perceptual components reveal an amplification of neural responses for predicted relative to surprising events, suggesting that selective attention enhances perceptual processing for expected events. Taken together, these results demonstrate that cross-modal statistical regularities are used to generate predictions that differentially influence early and late neural responses in infants.
Highlights
Prior expectations shape neural responses in sensory regions of the brain, consistent with a Bayesian predictive coding account of perception
Our results reveal that the infant brain relies on two complimentary systems for prediction: first, an early attentional amplification of neural activity to visual events that confirm prior expectations, and a late neural amplification to surprising, unexpected events
In the remaining onethird of the trials, we used a baseline ‘no-cue’ condition in which visual targets were presented without a preceding sound. This condition allowed us to address whether any modulations of sensory signals by predictive mechanisms were driven by expected events or rather by surprising events
Summary
Prior expectations shape neural responses in sensory regions of the brain, consistent with a Bayesian predictive coding account of perception. These findings are accounted for by predictive coding theories[6,7,8], which make the strong assumption that the brain is primarily meant to detect violations of expectations, resulting in an increased propagation of prediction error signals for unexpected events In this framework, neural systems learn the statistical regularities inherent in the natural world and reduce redundancy by removing the predictable components of the input, transmitting only ‘surprise’ (that is, prediction error). The use of an arbitrary, cross-modal mapping allowed us to ensure that infants’, expectations would be driven by top-down mechanisms rather than the processing of local, low-level regularities, or adaptation effects[15,16,17] (see Discussion) This design allowed us to address whether prior expectations about upcoming visual categories would impact early or late modulations of EEG components associated with visual responses (that is, in occipitotemporal electrodes). Our results reveal that the infant brain relies on two complimentary systems for prediction: first, an early attentional amplification of neural activity to visual events that confirm prior expectations, and a late neural amplification to surprising, unexpected events
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