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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Non-human primates are key models for decision-making research in cognitive neuroscience.
  • Discrepancies exist between monkey and human cognition, raising concerns about extrapolating findings.
  • Working memory tasks reveal monkeys use recency strategies, while humans use target-selective strategies.

Purpose of the Study:

  • To investigate the cognitive discrepancy between monkeys and humans using artificial neural networks (ANNs).
  • To explore the learning progression in ANNs on a working memory task.
  • To understand the underlying mechanisms of apparent recency-based strategies.

Main Methods:

  • Utilized artificial neural networks (ANNs) as a parallel model system.
  • Trained ANNs on a working memory task that differentiates monkey and human strategies.
  • Analyzed behavioral progression and internal network states during training.

Main Results:

  • ANNs demonstrated a behavioral progression from random to recency-like, and finally to selective strategies.
  • Apparent recency-like behavior in ANNs emerged as a non-recency-based property of network organization.
  • Encouraging recency behavior accelerated learning and enhanced the optimal strategy.

Conclusions:

  • Monkeys and humans may represent different stages in the same cognitive learning progression.
  • Apparent recency strategies can be emergent properties, not necessarily true recency-based decision-making.
  • ANNs offer insights into cognitive discrepancies and can serve as efficient training models.