Statistical Learning Signals in Macaque Inferior Temporal Cortex.

Humans are sensitive to statistical regularities in their visual environment, but the nature of the underlying neural statistical learning signals still remains to be clarified. As in human behavioral and neuroimaging studies of statistical learning, we exposed rhesus monkeys to a continuous stream of images, presented without interstimulus interval or reward association. The stimulus set consisted of 3 groups of 5 images each (quintets). The stimulus order within each quintet was fixed, but the quintets were presented repeatedly in a random order without interruption. Thus, only transitional probabilities defined quintets of images. Postexposure recordings in inferior temporal (IT) cortex showed an enhanced response to stimuli that violated the exposed sequence. This enhancement was found only for stimuli that were not predicted by the just preceding stimulus, reflecting a temporally adjacent stimulus relationship, and was sensitive to stimulus order. By comparing IT responses with sequences with and without statistical regularities, we observed a short latency, transient response suppression for stimuli of the sequence with regularities, in addition to a later sustained response enhancement to stimuli that violated the sequence with regularities. These findings constrain models of mechanisms underlying neural responses in predictable temporal sequences, such as predictive coding.

Macaque monkeys and humans sample temporal regularities in the acoustic environment.

Many animal species show comparable abilities to detect basic rhythms and produce rhythmic behavior. Yet, the capacities to process complex rhythms and synchronize rhythmic behavior appear to be species-specific: vocal learning animals can, but some primates might not. This discrepancy is of high interest as there is a putative link between rhythm processing and the development of sophisticated sensorimotor behavior in humans. Do our closest ancestors show comparable endogenous dispositions to sample the acoustic environment in the absence of task instructions and training? We recorded EEG from macaque monkeys and humans while they passively listened to isochronous equitone sequences. Individual- and trial-level analyses showed that macaque monkeys' and humans' delta-band neural oscillations encoded and tracked the timing of auditory events. Further, mu- (8-15 Hz) and beta-band (12-20 Hz) oscillations revealed the superimposition of varied accentuation patterns on a subset of trials. These observations suggest convergence in the encoding and dynamic attending of temporal regularities in the acoustic environment, bridging a gap in the phylogenesis of rhythm cognition.

Temporal regularities allow saving time for maintenance in working memory.

Recent evidence suggests that working memory (WM) performance can be enhanced in the presence of an isochronous rhythm during the retention interval because it improves refreshing. Considering the cognitive load (CL) effect as an indicator of refreshing, the present study investigated whether an isochronous rhythm might benefit memory performance under varying cognitive load. For that goal, the presence of a regular rhythm and the cognitive load of the concurrent task (i.e., reading of digits that were either same or different within a trial) were systematically varied. Recall performance was decreased by high cognitive load compared with low cognitive load but was improved in the regular rhythm condition compared with the silent condition. No interaction between cognitive load and rhythm was observed. The present results suggest that temporal regularities might speed up the reading of the digits rather than improve the efficiency of refreshing, resulting in more time available for refreshing and, consequently, improved memory performance. These findings are interpreted in the framework of the dynamic attending theory and in the scope of recent models of working memory, which are also considering the temporal components of working memory and the importance of the temporal structure of working memory tasks.

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway-the pathway connecting entorhinal cortex directly to region CA1-was able to support statistical learning, while the trisynaptic pathway-connecting entorhinal cortex to CA1 through dentate gyrus and CA3-learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.