Understanding Human Cognition Through Computational Modeling.

IF 2.9 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Topics in Cognitive Science Pub Date : 2024-07-01 Epub Date: 2024-05-23 DOI:10.1111/tops.12737
Janet Hui-Wen Hsiao
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引用次数: 0

Abstract

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

通过计算建模了解人类认知。
认知科学的一个重要目标是从表征和计算能力的角度来理解思维,其中计算建模在提供人类行为和心理现象的理论解释和预测方面发挥着至关重要的作用。在我的研究中,我一直在使用计算建模以及行为实验和认知神经科学方法,从知觉表征和注意策略两个方面研究学习和视觉认知的信息处理机制。在知觉表征方面,我利用神经网络模型来理解人类视觉系统的分裂结构如何影响视觉认知,并研究知觉表征的发展是专业知识的结果。在注意力策略方面,我开发了 "隐马尔可夫模型眼动分析 "方法,利用空间和时间信息量化眼动模式和一致性,该方法带来了传统方法无法发现的跨学科新发现。通过将其与深度神经网络(DNN)相结合,我开发了 DNN+HMM 来解释人类视觉认知中的眼动策略学习。通过计算建模理解人类思维也有助于研究人工智能(AI)与人类认知的可比性,进而帮助可解释的人工智能系统推断人类对人工智能操作的信念,并提供以人为本的解释,加强人与人工智能的互动和相互理解。这些都证明了计算建模方法在对人类思维及其与环境和人工智能系统的互动进行理论解释方面的重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Topics in Cognitive Science
Topics in Cognitive Science PSYCHOLOGY, EXPERIMENTAL-
CiteScore
8.50
自引率
10.00%
发文量
52
期刊介绍: Topics in Cognitive Science (topiCS) is an innovative new journal that covers all areas of cognitive science including cognitive modeling, cognitive neuroscience, cognitive anthropology, and cognitive science and philosophy. topiCS aims to provide a forum for: -New communities of researchers- New controversies in established areas- Debates and commentaries- Reflections and integration The publication features multiple scholarly papers dedicated to a single topic. Some of these topics will appear together in one issue, but others may appear across several issues or develop into a regular feature. Controversies or debates started in one issue may be followed up by commentaries in a later issue, etc. However, the format and origin of the topics will vary greatly.
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