Multiple-object working memory - A model for behavioral performance
Multiple-object working memory - A model for behavioral performance
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DOI：
10.1093/cercor/13.5.435
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发表时间：
2003-05-01
期刊：
影响因子：
3.7
通讯作者：
Yakovlev, V
中科院分区：
文献类型：
--
作者：
Amit, DJ;Bernacchia, A;Yakovlev, V
在一个心理物理学实验中，猴子被展示了一系列2到8个图像，从16个图像中随机选择，每个图像后面都有一个延迟间隔，序列中的最后一个图像是序列中任何一个图像的重复。猴子学会了识别图像的重复。的性能水平进行了研究，作为一个功能的图像分离线索（图像将被重复）从匹配不同的序列长度，以及在固定的线索匹配分离与序列长度。这些实验结果被解释为特征的多项目工作记忆的框架内的递归神经网络。结果表明，模型网络可以维持多项目工作记忆。由于网络的有限大小的波动，加上一个单一的额外成分，与预期的奖励，占依赖的性能上的线索位置，以及依赖的序列长度固定的线索匹配分离的性能。
In a psychophysics experiment, monkeys were shown a sequence of two to eight images, randomly chosen out of a set of 16, each image followed by a delay interval, the last image in the sequence being a repetition of any (one) of the images shown in the sequence. The monkeys learned to recognize the repetition of an image. The performance level was studied as a function of the number of images separating cue (image that will be repeated) from match for different sequence lengths, as well as at fixed cue-match separation versus length of sequence. These experimental results are interpreted as features of multi-item working memory in the framework of a recurrent neural network. It is shown that a model network can sustain multi-item working memory. Fluctuations due to the finite size of the network, together with a single extra ingredient, related to expectation of reward, account for the dependence of the performance on the cue-position, as well as for the dependence of performance on sequence length for fixed cue-match separation.