Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON, M5S 3G3, Canada.
Psychon Bull Rev. 2021 Oct;28(5):1638-1647. doi: 10.3758/s13423-021-01939-4. Epub 2021 May 7.
Successful categorization requires a careful coordination of attention, representation, and decision making. Comprehensive theories that span levels of analysis are key to understanding the computational and neural dynamics of categorization. Here, we build on recent work linking neural representations of category learning to computational models to investigate how category decision making is driven by neural signals across the brain. We uniquely combine functional magnetic resonance imaging with drift diffusion and exemplar-based categorization models to show that trial-by-trial fluctuations in neural activation from regions of occipital, cingulate, and lateral prefrontal cortices are linked to category decisions. Notably, only lateral prefrontal cortex activation was associated with exemplar-based model predictions of trial-by-trial category evidence. We propose that these brain regions underlie distinct functions that contribute to successful category learning.
成功的分类需要注意力、表示和决策的精心协调。涵盖分析层次的综合理论是理解分类的计算和神经动力学的关键。在这里，我们基于最近将类别学习的神经表示与计算模型联系起来的工作，研究了大脑中神经信号如何驱动类别决策。我们独特地将功能磁共振成像与漂移扩散和基于范例的分类模型相结合，表明来自枕叶、扣带回和外侧前额叶皮质的神经激活的逐次试验波动与类别决策有关。值得注意的是，只有外侧前额叶皮质的激活与基于范例的模型对逐次类别证据的预测相关。我们提出这些大脑区域是成功的类别学习的基础，它们具有不同的功能。
