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元认知在“选择退出”范式中何时发展?

When does metacognition evolve in the opt-out paradigm?

机构信息

School of Human Evolution and Social Change, Arizona State University, 900 South Cady Mall, Tempe, AZ, 85287, USA.

Institute of Human Origins, Arizona State University, 777 E University Drive, Tempe, AZ, 85287, USA.

出版信息

Anim Cogn. 2024 Oct 22;27(1):68. doi: 10.1007/s10071-024-01910-5.

Abstract

Metacognition (awareness of one's own knowledge) is taken for granted in humans, but its evolution in non-human animals is not well understood. While there is experimental evidence of seemingly metacognitive judgements across species, studies rarely focus on why metacognition may have evolved. To address this, I present an evolutionary model of the opt-out paradigm, a common experiment used to assess animal's metacognition. Individuals are repeatedly presented with a task or problem and must decide between opting-out and receiving a fixed payoff or opting-in and receiving a larger reward if they successfully solve the task. Two evolving traits - bias and metacognition - jointly determine whether individuals opt-in. The task's reward, the mean probability of success and the variability in success across trials, and the cost of metacognition were varied. Results identify two scenarios where metacognition evolves: (1) environments where success variability is high; and (2) environments where mean success is low, but rewards are high. Overall, the results support predictions implicating uncertainty in the evolution of metacognition but suggest metacognition may also evolve in conditions where metacognition can be used to identify cases where an otherwise inaccessible high payoff is easy to acquire.

摘要

元认知(对自身知识的意识)在人类中是理所当然的,但它在非人类动物中的进化尚不清楚。虽然有实验证据表明在不同物种中存在看似元认知的判断,但研究很少关注元认知为何会进化。为了解决这个问题,我提出了一种“选择退出”范式的进化模型,这是一种常用于评估动物元认知的常见实验。个体反复接受一项任务或问题,并必须在选择退出和获得固定收益或选择进入并在成功解决任务时获得更大奖励之间做出选择。两个进化特征——偏差和元认知——共同决定个体是否选择进入。任务的奖励、成功的平均概率以及成功在不同试验中的变化以及元认知的成本都有所不同。结果确定了元认知进化的两种情况:(1)成功变化率高的环境;(2)平均成功率低但奖励高的环境。总的来说,结果支持了涉及不确定性在元认知进化中的作用的预测,但也表明元认知也可能在可以用来识别无法获得的高收益很容易获得的情况下进化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2506/11496335/ac5d999d7dfa/10071_2024_1910_Fig1_HTML.jpg

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