An Attention-Driven Computational Model of Human Causal Reasoning
An Attention-Driven Computational Model of Human Causal Reasoning
复制标题
人类因果推理的注意力驱动计算模型
DOI：
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发表时间：
2018
期刊：
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通讯作者：
Kevin O’Neill
中科院分区：
文献类型：
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作者：
P. Bello;A. Lovett;Gordon Briggs;Kevin O’Neill
在这里，我们描述CRAMM，通过注意力和心理模型的因果推理框架。CRAMM开发和扩展了先前开发的人类因果判断的反事实模拟模型所做的假设。我们实现CRAMM计算，并展示它如何强大地捕捉人类的因果判断简单的两个对象的相互作用的基础认知和感知过程的水平，包括眼动的数据，作为因果判断中的反事实的作用的直接证据。
Herein we describe CRAMM, a framework for Causal Reasoning via Attention and Mental Models. CRAMM develops and extends assumptions made by a previously developed coun-terfactual simulation model of human causal judgment. We implement CRAMM computationally and demonstrate how it robustly captures human causal judgments about simple two-object interactions at the level of underlying cognitive and perceptual processes, including data on eye-movements that serve as direct evidence for the role of counterfactuals in causal judgment.