强化学习和工作记忆系统的双重过程损伤是生理焦虑中学习缺陷的基础。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-09-26 eCollection Date: 2025-09-01 DOI:10.1371/journal.pcbi.1012872
Jennifer D Senta, Sonia J Bishop, Anne G E Collins
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引用次数: 0

摘要

焦虑与包括工作记忆(WM)和注意力控制过程在内的额叶执行功能缺陷密切相关。然而,尽管焦虑也与学习任务的表现受损有关,但对焦虑中强化学习(RL)损伤的计算研究得出了不同的结果。已知WM过程与RL过程并行促进学习行为,并将有效学习率调节为负载的函数。然而,WM过程通常没有在焦虑和RL的调查中建模。在当前的研究中,我们利用了一个实验范式(RLWM)来操纵WM和RL过程在使用多个刺激集大小的强化学习和保留任务中的相对贡献。利用RL和WM交互过程的计算模型,我们研究了生理或认知焦虑的个体差异是否通过RL或WM缺陷影响任务表现。在所有规模的学习和记忆测试中,生理而非认知焦虑分数的升高与较差的表现密切相关。计算结果表明,较高的生理焦虑得分与学习率降低和WM衰减率增加显著相关。为了强调建模WM对学习的贡献的重要性,我们考虑了拟合没有WM模块的强化学习模型对数据的影响。在这里,我们发现,在10个只考虑rl的模型中,有9个模型中,较高生理焦虑导致的学习成绩下降至少部分归因于随机决策噪声。这些发现揭示了焦虑中学习的双重过程损伤,这与生理上的而不是认知上的焦虑表型有关。更广泛地说,这项工作也指出了在调查精神病理学相关的学习缺陷时,考虑WM对RL的贡献的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety.

Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety.

Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety.

Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety.

Anxiety has been robustly linked to deficits in frontal executive function including working memory (WM) and attentional control processes. However, although anxiety has also been associated with impaired performance on learning tasks, computational investigations of reinforcement learning (RL) impairment in anxiety have yielded mixed results. WM processes are known to contribute to learning behavior in parallel to RL processes and to modulate the effective learning rate as a function of load. However, WM processes have typically not been modeled in investigations of anxiety and RL. In the current study, we leveraged an experimental paradigm (RLWM) which manipulates the relative contributions of WM and RL processes in a reinforcement learning and retention task using multiple stimulus set sizes. Using a computational model of interactive RL and WM processes, we investigated whether individual differences in physiological or cognitive anxiety impacted task performance via deficits in RL or WM. Elevated physiological, but not cognitive, anxiety scores were strongly associated with worse performance during learning and retention testing across all set sizes. Computationally, higher physiological anxiety scores were significantly related to reduced learning rate and increased rate of WM decay. To highlight the importance of modeling WM contributions to learning, we considered the effect of fitting RL models without WM modules to the data. Here we found that reduced learning performance for higher physiological anxiety was at least partially misattributed to stochastic decision noise in 9 out of 10 RL-only models considered. These findings reveal a dual-process impairment in learning in anxiety that is linked to a more physiological than cognitive anxiety phenotype. More broadly, this work also points to the importance of accounting for the contribution of WM to RL when investigating psychopathology-related deficits in learning.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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