Chatham Christopher H, Yerys Benjamin E, Munakata Yuko
Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence RI, USA.
Department of Neurosciences, Children's National Medical Center, George Washington University School of Medicine, Washington DC, USA.
Cogn Dev. 2012 Oct 1;27(4):349-366. doi: 10.1016/j.cogdev.2012.07.003.
Computational models are powerful tools - too powerful, according to some. We argue that the idea that models can "do anything" is wrong, and describe how their failures have been informative. We present new work showing surprising diversity in the effects of feedback on children's task-switching, such that some children perseverate despite this feedback, other children switch as instructed, and yet others play an "opposites" game without truly switching to the newly-instructed task. We present simulations that demonstrate the failure of an otherwise-successful neural network model to capture this failure of children. Simulating this pattern motivates the inclusion of updating mechanisms that make contact with a growing literature on frontostriatal function, despite their absence in extant theories of the development of cognitive flexibility. We argue from this and other examples that computational models are more constrained than is typically acknowledged, and that their resulting failures can be theoretically illuminating.
计算模型是强大的工具——有些人认为过于强大了。我们认为模型可以“无所不能”的观点是错误的,并描述了它们的失败如何具有启发性。我们展示了新的研究成果,表明反馈对儿童任务切换的影响存在惊人的多样性,即有些儿童尽管有这种反馈仍会固执己见,其他儿童会按指示进行切换,还有些儿童会玩“反着来”的游戏,却没有真正切换到新指示的任务。我们进行了模拟,结果表明一个原本成功的神经网络模型无法捕捉到儿童的这种失败情况。模拟这种模式促使我们纳入更新机制,这与关于额纹状体功能的不断增加的文献相关,尽管现有认知灵活性发展理论中并未提及这些机制。基于此及其他例子,我们认为计算模型比通常所认为的受到更多限制,并且它们产生的失败情况在理论上可能具有启发性。