Reasoning, learning, and creativity: frontal lobe function and human decision-making.
Reasoning, learning, and creativity: frontal lobe function and human decision-making.
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推理，学习和创造力：额叶功能和人类决策。
DOI：
10.1371/journal.pbio.1001293
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发表时间：
2012
期刊：
影响因子：
9.8
通讯作者：
Koechlin E
中科院分区：
文献类型：
--
作者：
Collins A;Koechlin E
计算模型和行为实验表明，人类额叶功能能够同时监测三到四种行为策略，以便在决策过程中选择最合适的行为策略。额叶为决策和执行控制服务，即目标导向行为的选择和协调。然而，目前的额叶执行功能模型并不能解释人类在日常环境中的决策，这些环境具有不确定性、变化性，尤其是开放式的情况。在这里，我们提出了一个人类执行功能的计算模型来澄清这个问题。通过行为实验，我们表明，与其他模型不同，我们提出的模型预测了人类的决策及其在自然情境下个体之间的差异。该模型显示，对于驱动行为，人类额叶功能监控多达三/四种并发的行为策略，并在线推断其预测行为结果的能力：每当一种策略看起来比不可靠更可靠时，就选择这种策略来指导选择和学习最大化回报的行为。否则，一种新的行为策略就会暂时形成，部分是由储存在长期记忆中的策略形成的，然后进行探索，如果竞争得到证实，就会随后推动行动。因此，人类执行功能的监控能力仅限于三到四种行为策略。执行控制的二元结构弥补了这种限制，在模棱两可和未知的情况下，它促进了新的行为策略的探索和创造。研究结果支持了一种人类额叶功能模型，该模型将推理、学习和创造能力整合在决策和适应性行为中。推理、学习和创造是人类智慧的标志。这些能力涉及到大脑的额叶，但目前还不清楚额叶在不确定或开放式的情况下如何发挥作用。我们在此提出了一个人类执行功能的计算模型，该模型集成了决策过程中的多个过程，如不确定性预期、任务切换和强化学习。该模型在行为实验中进行了测试，并解释了人类的决策及其在个体之间的差异。该模型揭示了执行功能能够同时监控三到四个行为策略，并推断出在线策略预测行动结果的能力。如果一种策略似乎可以可靠地预测行动结果，那么人们就会选择它，并可能对其进行调整；否则，就会暂时形成、探索并选择新的策略。因此，人类额叶功能的监控能力仅限于三到四种行为策略。研究结果支持额叶执行功能模型，该模型解释了人类推理、学习和创造能力在决策和适应行为中的作用和局限性。
Computational modeling and behavioral experimentation suggest that human frontal lobe function is capable of monitoring three or four concurrent behavioral strategies in order to select the most suitable one during decision-making. The frontal lobes subserve decision-making and executive control—that is, the selection and coordination of goal-directed behaviors. Current models of frontal executive function, however, do not explain human decision-making in everyday environments featuring uncertain, changing, and especially open-ended situations. Here, we propose a computational model of human executive function that clarifies this issue. Using behavioral experiments, we show that unlike others, the proposed model predicts human decisions and their variations across individuals in naturalistic situations. The model reveals that for driving action, the human frontal function monitors up to three/four concurrent behavioral strategies and infers online their ability to predict action outcomes: whenever one appears more reliable than unreliable, this strategy is chosen to guide the selection and learning of actions that maximize rewards. Otherwise, a new behavioral strategy is tentatively formed, partly from those stored in long-term memory, then probed, and if competitive confirmed to subsequently drive action. Thus, the human executive function has a monitoring capacity limited to three or four behavioral strategies. This limitation is compensated by the binary structure of executive control that in ambiguous and unknown situations promotes the exploration and creation of new behavioral strategies. The results support a model of human frontal function that integrates reasoning, learning, and creative abilities in the service of decision-making and adaptive behavior. Reasoning, learning, and creativity are hallmarks of human intelligence. These abilities involve the frontal lobe of the brain, but it remains unclear how the frontal lobes function in uncertain or open-ended situations. We propose here a computational model of human executive function that integrates multiple processes during decision-making, such as expectedness of uncertainty, task switching, and reinforcement learning. The model was tested in behavioral experiments and accounts for human decisions and their variations across individuals. The model reveals that executive function is capable of monitoring three or four concurrent behavioral strategies and infers online strategies' ability to predict action outcomes. If one strategy appears to reliably predict action outcomes, then it is chosen and possibly adjusted; otherwise a new strategy is tentatively formed, probed, and chosen instead. Thus, human frontal function has a monitoring capacity limited to three or four behavioral strategies. The results support a model of frontal executive function that explains the role and limitations of human reasoning, learning, and creative abilities in decision-making and adaptive behavior.
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影响因子：
64.8
作者：
Daw, Nathaniel D.;O'Doherty, John P.;Dayan, Peter;Seymour, Ben;Dolan, Raymond J.
通讯作者：
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影响因子：
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作者：
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Niv, Yael
DOI：
10.1098/rstb.2007.2098
发表时间：
2007-05-29
影响因子：
6.3
作者：
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通讯作者：
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影响因子：
16.2
作者：
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通讯作者：
Rushworth, Matthew F. S.
DOI：
10.1073/pnas.0404965101
发表时间：
2004-09-07
影响因子：
11.1
作者：
Gallistel, CR;Fairhurst, S;Balsam, P
通讯作者：
Balsam, P