摘要翻译：
选择了简单的记忆任务，如矩阵上的二进制代码。在建立适当的程序后，将汇编好的矩阵单独提交给150名大学生，他们必须记住这些矩阵。类似任务的计算机模拟是可用的，它使用感知器，在感知器上实现了允许某种程度的全局性（在通常的玻尔兹曼-吉布斯统计力学的当前推广中，技术上称为熵非扩展性）的算法。我们的主要观察是，对于我们在这里关注的非常具体的学习任务，人类的表现类似于稍微非广泛的感知器。
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英文标题：
《Human and computer learning: An experimental study》
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作者：
Alexandra C. Tsallis, Constantino Tsallis, Aglae C.N. de Magalhaes,
  Francisco A. Tamarit
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最新提交年份：
2004
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Simple memorizing tasks have been chosen such as a binary code on a matrix. After the establishment of an appropriate protocol, the codified matrices were individually presented to 150 university students who had to memorize them. A computer simulation for a similar task is available which uses a perceptron on which an algorithm was implemented allowing for some degree of globality (technically referred to as entropic nonextensivity within a current generalization of the usual, Boltzmann-Gibbs, statistical mechanics). Our main observation is that, for the very specific learning task on which we focus here, humans perform similarly to slightly nonextensive perceptrons.
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PDF链接：
https://arxiv.org/pdf/q-bio/0410005