Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America.
Neurosciences Graduate Program, University of California San Diego, La Jolla, California, United States of America.
PLoS Comput Biol. 2023 Nov 20;19(11):e1011618. doi: 10.1371/journal.pcbi.1011618. eCollection 2023 Nov.
Animal models are used to understand principles of human biology. Within cognitive neuroscience, non-human primates are considered the premier model for studying decision-making behaviors in which direct manipulation experiments are still possible. Some prominent studies have brought to light major discrepancies between monkey and human cognition, highlighting problems with unverified extrapolation from monkey to human. Here, we use a parallel model system-artificial neural networks (ANNs)-to investigate a well-established discrepancy identified between monkeys and humans with a working memory task, in which monkeys appear to use a recency-based strategy while humans use a target-selective strategy. We find that ANNs trained on the same task exhibit a progression of behavior from random behavior (untrained) to recency-like behavior (partially trained) and finally to selective behavior (further trained), suggesting monkeys and humans may occupy different points in the same overall learning progression. Surprisingly, what appears to be recency-like behavior in the ANN, is in fact an emergent non-recency-based property of the organization of the neural network's state space during its development through training. We find that explicit encouragement of recency behavior during training has a dual effect, not only causing an accentuated recency-like behavior, but also speeding up the learning process altogether, resulting in an efficient shaping mechanism to achieve the optimal strategy. Our results suggest a new explanation for the discrepency observed between monkeys and humans and reveal that what can appear to be a recency-based strategy in some cases may not be recency at all.
动物模型被用于理解人类生物学原理。在认知神经科学中，非人类灵长类动物被认为是研究决策行为的首选模型，因为在这些模型中仍然可以进行直接操纵实验。一些著名的研究揭示了猴子和人类认知之间的主要差异，强调了未经证实的从猴子到人类的推断存在问题。在这里，我们使用平行模型系统——人工神经网络 (ANN)——来研究猴子和人类在工作记忆任务中存在的一个已确立的差异，在这个任务中，猴子似乎使用基于最近的策略，而人类使用目标选择性策略。我们发现，在相同任务上训练的 ANN 表现出从随机行为（未训练）到最近似行为（部分训练）再到选择性行为（进一步训练）的行为进展，这表明猴子和人类可能处于同一总体学习进展的不同位置。令人惊讶的是，ANN 中似乎是最近似的行为，实际上是神经网络状态空间组织在训练过程中的一个非基于最近的属性的涌现。我们发现，在训练过程中明确鼓励最近似行为具有双重效果，不仅会导致更明显的最近似行为，而且还会加快整个学习过程，从而形成一种有效的塑造机制，以达到最佳策略。我们的结果为猴子和人类之间观察到的差异提供了一个新的解释，并揭示了在某些情况下看似基于最近似的策略可能根本不是最近似。
