Statistical Learning Signals in Macaque Inferior Temporal Cortex

Statistical Learning Signals in Macaque Inferior Temporal Cortex
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DOI:
10.1093/cercor/bhw374
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发表时间:
2018-01-01
期刊:
影响因子:
3.7
通讯作者:
Vogels, Rufin
Vogels, Rufin
中科院分区:
医学2区
文献类型:
--
作者:
Kaposvari, Peter;Kumar, Susheel;Vogels, Rufin

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人类对视觉环境中的统计学习信号很敏感,但潜在的神经统计学习信号的性质仍有待澄清。在人类行为和神经影像学研究的统计学习中,我们将恒河猴暴露在连续的图像流中,没有刺激间隔或奖励关联。刺激集由3组组成,每组5个图像(五重图像)。每个五重奏内的刺激顺序是固定的,但五重奏以随机顺序重复呈现而不中断。因此,只有过渡概率定义图像的五重奏。在下颞叶(IT)皮层的postexploration记录显示了增强的反应,违反了暴露序列的刺激。这种增强只被发现的刺激,没有预测的前一个刺激,反映了时间上相邻的刺激关系,刺激顺序敏感。通过比较IT反应与序列与统计和不统计的干扰,我们观察到一个短的潜伏期,短暂的反应抑制序列的刺激与干扰,除了后来的持续反应增强的刺激,违反了序列与干扰。这些发现限制了可预测的时间序列,如预测编码的神经反应的机制模型。
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.