Laboratory of Computational Embodied Neuroscience, Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche, Roma, Italy.
Psychol Rev. 2010 Oct;117(4):1188-228. doi: 10.1037/a0020887.
Perceiving objects activates the representation of their affordances. For example, experiments on compatibility effects showed that categorizing objects by producing certain handgrips (power or precision) is faster if the requested responses are compatible with the affordance elicited by the size of objects (e.g., small or large). The article presents a neural-network architecture that provides a general framework to account for compatibility effects. The model was designed with a methodological approach (computational embodied neuroscience) that aims to provide increasingly general accounts of brain and behavior (4 sources of constraints are used: neuroscientific data, behavioral data, embodied systems, reproduction of learning processes). The model is based on 4 principles of brain organization that we claim underlie most compatibility effects. First, visual perception and action are organized in the brain along a dorsal neural pathway encoding affordances and a ventral pathway encoding goals. Second, the prefrontal cortex within the ventral pathway gives a top-down bias to action selection by integrating information on stimuli, context, and goals. Third, reaction times depend on dynamic neural competitions for action selection that integrate bottom-up and top-down information. The congruence or incongruence between affordances and goals explains the different reaction times found in the experiments. Fourth, as words trigger internal simulations of their referents, they can cause compatibility effects as objects do. We validated the model by reproducing and explaining 3 types of compatibility effects and showed its heuristic power by producing 2 testable predictions. We also assessed the explicative power of the model by comparing it with related models and showed how it can be extended to account for other compatibility effects.
感知物体激活了它们的可提供性的表示。例如，在兼容性效应的实验中表明，如果请求的响应与物体大小引发的可提供性相匹配（例如，小或大），那么通过产生特定的手柄（力量或精度）对物体进行分类会更快。本文提出了一种神经网络架构，为兼容性效应提供了一个通用框架。该模型是使用一种方法论方法（计算体现神经科学）设计的，旨在为大脑和行为提供越来越普遍的解释（使用了 4 种约束源：神经科学数据、行为数据、体现系统、学习过程的再现）。该模型基于我们认为是大多数兼容性效应基础的大脑组织的 4 个原则。首先，视觉感知和动作沿着编码可提供性的背侧神经通路和编码目标的腹侧神经通路在大脑中组织。其次，腹侧通路中的前额叶皮层通过整合关于刺激、上下文和目标的信息，为动作选择提供自上而下的偏见。第三，反应时间取决于用于动作选择的动态神经竞争，该竞争整合了自下而上和自上而下的信息。可提供性和目标之间的一致性或不一致性解释了实验中发现的不同反应时间。第四，由于单词触发了对其指代物的内部模拟，因此它们可以像物体一样引起兼容性效应。我们通过再现和解释 3 种兼容性效应来验证该模型，并通过产生 2 个可测试的预测来展示其启发式力量。我们还通过将其与相关模型进行比较来评估模型的解释力，并展示了如何扩展它以解释其他兼容性效应。
