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Statistical Learning Signals in Macaque Inferior Temporal Cortex
被引:53
作者:
Kaposvari, Peter
[1
,2
]
Kumar, Susheel
[1
]
Vogels, Rufin
[1
]
机构:
[1] Dept Neurosci, Lab Neuroen Psychofysiol, Campus Gasthuisberg, B-3000 Leuven, Belgium
[2] Univ Szeged, Dept Med Physiol, Dom Sq 10, H-6720 Szeged, Hungary
关键词:
inferior temporal cortex;
macaques;
predictions;
statistical learning;
temporal regularities;
MONKEY INFEROTEMPORAL CORTEX;
INVARIANT OBJECT REPRESENTATION;
VENTRAL VISUAL PATHWAY;
LONG-TERM-MEMORY;
REPETITION SUPPRESSION;
CEREBRAL-CORTEX;
NEURONS;
PREDICTION;
SEQUENCES;
RESPONSES;
D O I:
10.1093/cercor/bhw374
中图分类号:
Q189 [神经科学];
学科分类号:
071006 ;
摘要:
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.
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页码:250 / 266
页数:17
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