Nine-month-old infants update their predictive models of a changing environment.

Humans generate internal models of their environment to predict events in the world. As the environments change, our brains adjust to these changes by updating their internal models. Here, we investigated whether and how 9-month-old infants differentially update their models to represent a dynamic environment. Infants observed a predictable sequence of stimuli, which were interrupted by two types of cues. Following the update cue, the pattern was altered, thus, infants were expected to update their predictions for the upcoming stimuli. Because the pattern remained the same after the no-update cue, no subsequent updating was required. Infants showed an amplified negative central (Nc) response when the predictable sequence was interrupted. Late components such as the PSW were also evoked in response to unexpected stimuli; however, we found no evidence for a differential response to the informational value of surprising cues at later stages of processing. Infants rather learned that surprising cues always signal a change in the environment that requires updating. Interestingly, infants responded with an amplified neural response to the absence of an expected change, suggesting a top-down modulation of early sensory processing in infants. Our findings corroborate emerging evidence showing that infants build predictive models early in life.

Infants differentially update their internal models of a dynamic environment.

Unexpected events provide us with opportunities for learning about what to expect from the world around us. Using a saccadic-planning paradigm, we investigated whether and how infants and adults represent the statistics of a changing environment (i.e. build an internal model of the environment). Participants observed differently colored bees that appeared at an unexpected location every few trials. The color cues indicated whether the subsequent bees would appear at this new location (i.e. update trials) or at the same location as previously (i.e. no-update trials). Infants learned the predictive value of the color cues and updated their internal models when necessary. Unlike infants, adults had a tendency to update their models each time they observed a change in the structure. We argue that infants are open to learning from current evidence due to being less influenced by their prior knowledge. This is an advantageous learning strategy to form accurate representations in dynamic environments, which is fundamental for successful adaptation.

Social network size affects neural circuits in macaques.

It has been suggested that variation in brain structure correlates with the sizes of individuals' social networks. Whether variation in social network size causes variation in brain structure, however, is unknown. To address this question, we neuroimaged 23 monkeys that had been living in social groups set to different sizes. Subject comparison revealed that living in larger groups caused increases in gray matter in mid-superior temporal sulcus and rostral prefrontal cortex and increased coupling of activity in frontal and temporal cortex. Social network size, therefore, contributes to changes both in brain structure and function. The changes have potential implications for an animal's success in a social context; gray matter differences in similar areas were also correlated with each animal's dominance within its social network.