An explanation of the familiarity-to-novelty-shift in infant habituation

Abstract

Event Abstract Back to Event An explanation of the familiarity-to-novelty-shift in infant habituation Quan Wang1* and Jochen Triesch1 1 Frankfurt Institute for Advanced Studies, Germany Habituation is generally defined as a reversible decrement of response to repeated stimulation. It is considered one of the simplest and most fundamental forms of learning. As such it has been studied at the neurophysiological, behavioral and computational levels in species ranging from invertebrates to humans. Habituation is of particular importance for the study of cognitive development in human infants, since habituation paradigms like ‘violation of expectation’ use it to infer infants’ perceptual and cognitive abilities [1]. Current models of infant habituation typically assume that the infant is constructing an internal model of the stimulus. The accuracy of this internal model is interpreted as a predictor of the infant’s interest in or attention towards the stimulus. In the early phase of habituation, infants look longer or react more to a stimulus, because they have not yet learned an accurate model for it yet. As their internal model improves, their interest in the stimulus decreases. This explains why novel stimuli tend to be preferred over familiar ones. Importantly, however, such models do not account for the so-called familiarity-to-novelty-shift, the finding that infants often transiently prefer a familiar stimulus over a novel one, given sufficient complexity of both stimuli [2]. We propose a new account of infant habituation in which the infant’s interest in a stimulus is related to the infant’s learning progress, i.e. the improvement of the infant’s internal model [3]. As a consequence, infants prefer stimuli for which their learning progress is maximal. Specifically, we describe the infant’s interest in a stimulus or its degree of attention as the time derivative of the infant’s learning curve for that stimulus. We consider two kinds of idealized learning curves with exponential and sigmoidal shape, corresponding to simpler and more complex stimuli, respectively. The first kind of learning curve has an exponentially decreasing time derivative, matching the most well-known habituation characteristic. For sigmoidal learning curves, however, the time derivative has a bell shaped form, as supported by experimental evidence [4]. This bell-shaped form naturally explains the presence of a familiarity-to-novelty-shift if, say, a second (novel) stimulus is introduced when learning progress for a first (familiar) stimulus is currently maximal. Thus, our model predicts that the familiarity-to-novelty-shift emerges for stimuli that produce sigmoidal but not exponential learning curves.Using the derivative of performance as a predictor of attention, our model proposes a dynamic familiarity-to-novelty shift, which depends on both the subject's learning efficiency and the task complexity. We speculate that the anterior cingulate cortex may contribute to estimating the learning progress, since it has been reported that it is activated by change of error rate but not by error per se [5].