Computational models can replicate the capacity of human recognition memory
Computational models can replicate the capacity of human recognition memory
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DOI：
10.1080/09548980802412638
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发表时间：
2008-01-01
影响因子：
7.8
通讯作者：
Brown, Malcolm W.
中科院分区：
文献类型：
--
作者：
Androulidakis, Zacharias;Lulham, Andrew;Brown, Malcolm W.
斯坦丁研究了人类识别记忆的能力，他向几组参与者展示了不同数量的图片（从20张到1万张），随后测试了他们区分先前展示的图片和新图片的能力。将不同组在识别记忆中保留的图片估计数量绘制为呈现图片数量的对数函数，形成一条直线，表示幂律关系。在这里，我们调查是否已发表的熟悉度歧视模型可以复制站立的结果。我们首先考虑一个简化的假设，即视觉刺激是由提供熟悉辨别网络输入的视觉神经元的不相关放电模式来表示的。我们证明了在这种情况下，三个模型（基于能量的熟悉度判别（FamE）， Anti-Hebbian和Info-max）可以在适当初始化它们的突触权重时再现所观察到的幂律关系。对于刺激的神经表征更为现实的假设，FamE模型在模拟中不再能够再现幂律关系，而Anti-Hebbian和Info-max可以再现幂律关系。然而，在所有模拟中，由模型产生的幂律关系的斜率与斯坦丁观察到的不同。我们讨论了这种差异的可能原因，包括熟悉度和回忆过程的不同贡献，并根据我们的分析描述了实验可测试的预测。
The capacity of human recognition memory was investigated by Standing, who presented several groups of participants with different numbers of pictures (from 20 to 10 000), and subsequently tested their ability to distinguish between previously presented and novel pictures. The estimated number of pictures retained in recognition memory by different groups when plotted as a logarithmic function of the number of pictures presented formed a straight line, representing a power-law relationship. Here, we investigate if published models of familiarity discrimination can replicate Standing's results. We first consider a simplified assumption that visual stimuli are represented by uncorrelated patterns of firing of visual neurons providing input to the familiarity discrimination network. We show that for this case three models (Familiarity discrimination based on Energy (FamE), Anti-Hebbian and Info-max) can reproduce the observed power-law relationship when their synaptic weights are appropriately initialized. For more realistic assumptions on neural representation of stimuli, the FamE model is no longer able to reproduce the power-law relationship in simulations, while the Anti-Hebbian and Info-max can reproduce it. Nevertheless, the slopes of the power-law relationships produced by the models in all simulations differ from that observed by Standing. We discuss possible reasons for this difference, including separate contributions of familiarity and recollection processes, and describe experimentally testable predictions based on our analysis.