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ABSTRACT Metacognition, or monitoring and controlling one's knowledge, is a key feature of human cognition. Accumulating evidence shows that foundational forms of metacognition are already present in young infants and then scaffold later‐emerging skills. Although many animals exhibit cognitive processes relevant to metacognition, it is unclear if other species share the developmental trajectories seen in humans. Here, we examine the emergence of metacognitive information‐seeking in rhesus monkeys (Macaca mulatta). We presented a large sample of semi‐free‐ranging monkeys, ranging from juvenility to adulthood, with a one‐shot task where they could seek information about a food reward by bending down to peer into a center vantage point in an array of tubes. In thehiddencondition, information‐seeking was necessary as no food was visible on the apparatus, whereas in thevisiblecontrol, condition information‐seeking was not necessary to detect the location of the reward. Monkeys sought information at the center vantage point more often when it was necessary than in the control condition, and younger monkeys already showed competency similar to adults. We also tracked additional monkeys who voluntarily chose not to approach to assess monkeys’ ability to actively infer opportunities for information‐seeking, and again found similar performance in juveniles and adults. Finally, we found that monkeys were overall slower to make metacognitive inferences than to approach known reward, and that younger monkeys were specifically slower to detect opportunities for information‐seeking compared to adults. These results indicate that many features of mature metacognition are already detectable in young monkeys, paralleling evidence for “core metacognition” in infant humans.
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Computational metacognition
Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning.
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- Award ID(s):
- 1849131
- PAR ID:
- 10352557
- Date Published:
- Journal Name:
- Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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