通过结合自然主义范例、深度采样和预测计算模型,实现逆向工程现实世界泛化的路线图

P. Herholz, Eddy Fortier, Mariya Toneva, Nicolas Farrugia, Leila Wehbe, V. Borghesani
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引用次数: 0

摘要

现实世界的泛化,例如,决定接近从未见过的动物,依赖于上下文信息和以前的经验。这样一个看似简单的行为选择需要多种神经机制的相互作用,从整合编码到基于类别的推理,根据环境的不同加权。在此,我们认为现实世界泛化的神经认知基础的综合理论将极大地受益于三个关键要素的实证研究。首先,多模态、自然主义范式提供的生态有效性。二是深度采样所提供的模型稳定性。最后,通过预测建模和计算控制获得的统计严谨性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models
Real-world generalization, e.g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences. Such a seemingly easy behavioral choice requires the interplay of multiple neural mechanisms, from integrative encoding to category-based inference, weighted differently according to the circumstances. Here, we argue that a comprehensive theory of the neuro-cognitive substrates of real-world generalization will greatly benefit from empirical research with three key elements. First, the ecological validity provided by multimodal, naturalistic paradigms. Second, the model stability afforded by deep sampling. Finally, the statistical rigor granted by predictive modeling and computational controls.
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