ArticleOpen mind : discoveries in cognitive science2024
A Deep Learning Approach to Analyzing Continuous-Time Cognitive Processes.
Article in Open mind : discoveries in cognitive science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Research on the business performance evaluation method for small and medium-sized enterprises in cross-border e-commerce based on artificial bee colony optimized LSTM model.Scientific reports · 2025Article
- Large-scale evidence for logarithmic effects of word predictability on reading time.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Word Frequency and Predictability Dissociate in Naturalistic Reading.Open mind : discoveries in cognitive science · 2024Article
- The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing.Open mind : discoveries in cognitive science · 2023Article
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2 authors.
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Abstract
The dynamics of the mind are complex. Mental processes unfold continuously in time and may be sensitive to a myriad of interacting variables, especially in naturalistic settings. But statistical models used to analyze data from cognitive experiments often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to simulations of dynamical cognitive processes, including speech comprehension, visual perception, and goal-directed behavior. But due to poor interpretability, deep learning is generally not used for scientific analysis. Here, we bridge this gap by showing that deep learning can be used, not just to imitate, but to
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