一刀切:成语计算模型揭示学习和元学习策略的个体差异

IF 2.9 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Theodros M. Haile, Chantel S. Prat, Andrea Stocco
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引用次数: 0

摘要

复杂技能的学习取决于多个相互作用系统的共同贡献:工作记忆(WM)、陈述性长期记忆(LTM)和强化学习(RL)。本研究旨在了解学习过程中这些系统相对贡献的个体差异。我们针对刺激-反应学习、强化学习工作记忆任务的表现建立了四种ACT-R模型。该任务由短的 3 个图像和长的 6 个图像组成,以反馈为基础。学习后进行无反馈测试,在学习和测试之间插入干扰任务。我们的四个模型包括两个单一机制的 RL 和 LTM 模型,以及两个综合的 RL-LTM 模型:(a)基于 RL 的元学习,即根据最近的成功案例选择 RL 或 LTM 进行学习;(b)参数化的 RL-LTM 选择模型,其固定比例与学习成功与否无关。对于我们的学习者中的一部分人来说,每种模型都是最合适的(LTM:68.7%,RL:4.8%,Meta-RL:13.25%,bias-RL:13.25%),这表明个体部署基本学习机制的方式存在根本差异,即使对于简单的刺激-反应任务也是如此。最后,无论图块长度如何(3 个图块与 6 个图块),长期陈述性记忆似乎都是这项任务的首选学习策略,这一点可以从大量受试者的学习特征被仅有的 LTM 模型所最好地捕捉到,以及由于我们的成因分析方法的优势,在我们的两个综合模型中,LTM 比 RL 更受青睐这两点得到证实。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
One Size Does Not Fit All: Idiographic Computational Models Reveal Individual Differences in Learning and Meta‐Learning Strategies
Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long‐term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT‐R models of performance on the stimulus‐response learning, Reinforcement Learning Working Memory task. The task consisted of short 3‐image, and long 6‐image, feedback‐based learning blocks. A no‐feedback test phase was administered after learning, with an interfering task inserted between learning and test. Our four models included two single‐mechanism RL and LTM models, and two integrated RL‐LTM models: (a) RL‐based meta‐learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL‐LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta‐RL: 13.25%, bias‐RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus‐response task. Finally, long‐term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3‐ vs 6‐image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated‐models, owing to the strength of our idiographic approach.
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来源期刊
Topics in Cognitive Science
Topics in Cognitive Science PSYCHOLOGY, EXPERIMENTAL-
CiteScore
8.50
自引率
10.00%
发文量
52
期刊介绍: Topics in Cognitive Science (topiCS) is an innovative new journal that covers all areas of cognitive science including cognitive modeling, cognitive neuroscience, cognitive anthropology, and cognitive science and philosophy. topiCS aims to provide a forum for: -New communities of researchers- New controversies in established areas- Debates and commentaries- Reflections and integration The publication features multiple scholarly papers dedicated to a single topic. Some of these topics will appear together in one issue, but others may appear across several issues or develop into a regular feature. Controversies or debates started in one issue may be followed up by commentaries in a later issue, etc. However, the format and origin of the topics will vary greatly.
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