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Shared cognitive biases influence numerical judgments in macaques and crows.

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Cognitive biases in numerical estimation are evolutionarily widespread, observed in both humans and animals like monkeys and crows. These biases stem from integrating recent experiences and uncertainty, suggesting shared cognitive mechanisms.

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Area of Science:

  • Cognitive science
  • Comparative psychology
  • Neuroscience

Background:

  • Cognitive biases arise from mental shortcuts, affecting magnitude estimations in humans.
  • The evolutionary origins and cross-species prevalence of these biases, particularly in numerical estimation, are not well understood.
  • Nonhuman animals, including primates and corvids, possess a nonsymbolic number sense, raising questions about shared biases.

Purpose of the Study:

  • To investigate the presence and nature of magnitude estimation biases in nonhuman animals.
  • To determine if cognitive biases in numerical tasks are evolutionarily conserved across distantly related species.
  • To explore the underlying mechanisms, such as Bayesian-like processing, that may explain these biases.

Main Methods:

  • A delayed match-to-numerosity task using dot arrays was administered to macaque monkeys and carrion crows.
  • Behavioral data were analyzed to identify patterns such as scalar variability, regression to the mean, and sequential effects.
  • A Bayesian computational model incorporating dynamic priors was used to account for observed biases.

Main Results:

  • Both macaque monkeys and carrion crows exhibited key human-like biases in numerical estimation.
  • Observed biases included scalar variability, regression to the mean, and sequential effects.
  • A Bayesian model effectively explained the observed behavioral patterns by integrating recent experiences.

Conclusions:

  • Cognitive biases in magnitude estimation are not unique to humans and appear to be evolutionarily widespread.
  • Numerical judgments in both species are influenced by uncertainty and the integration of recent trial information.
  • Shared Bayesian-like mechanisms for cognitive biases suggest common evolutionary pathways for numerical cognition across diverse animal groups.