Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task

Li, Jason; Watters, Nicholas; Sohn, Hansem; Jazayeri, Mehrdad

doi:10.32470/ccn.2022.1291-0

Cited by 2 publications

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“…We also analyzed participants' eye movements to examine the strategy they used to perform the task. Previous studies have shown that the pattern of eye movements during spatial reasoning tasks can reveal behavioral strategy (Gerstenberg et al, 2017;Hayhoe & Ballard, 2005;Levy et al, 2009;Li et al, 2023;Liversedge & Findlay, 2000;Spering, 2022). Accordingly, we reasoned that shifts in gaze position over the H-maze would serve as another clue in deciphering participants' strategy.…”

Section: Humans Use Hierarchical Reasoning and Counterfactuals To Sol...mentioning

confidence: 91%

Computational basis of hierarchical and counterfactual information processing

Ramadan,

Tang,

Watters

et al. 2024

Preprint

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Cognitive theories attribute humans' unparalleled capacity in solving complex multistage decision problems to distinctive hierarchical and counterfactual reasoning strategies. Here, we used a combination of human psychophysics and behaviorally-constrained neural network modeling to understand the computational basis of these cognitive strategies. We first developed a multi-stage decision-making task that humans solve using a combination of hierarchical and counterfactual processing. We then used a series of hypothesis-driven behavioral experiments to systematically dissect the potential computational constraints that underlie these strategies. One experiment revealed that humans have limited capacity for parallel processing. Another indicated that counterfactuals do not fully compensate for this limitation because of working memory limits. A third experiment revealed that the degree to which humans use counterfactuals depends on the fidelity of their working memory. Next, we asked whether the strategies humans adopt are computationally rational; i.e., optimal under these constraints. To do so, we analyzed the behavior of a battery of task-optimized recurrent neural networks (RNNs) that were subjected to one or more of these constraints. Remarkably, only RNNs that were subjected to all these constraints behaved similarly to humans. Further analysis of the RNNs revealed that what cognitive theories posit as distinctive strategies such as hierarchical and counterfactual are subdivisions in a continuum of computationally rational solutions that includes optimal, counterfactual, postdictive, and hierarchical.

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Section: Humans Use Hierarchical Reasoning and Counterfactuals To Sol...mentioning

confidence: 91%

Computational basis of hierarchical and counterfactual information processing

Ramadan,

Tang,

Watters

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Eye movements have been investigated in previous studies involving mazes to gain insight into human maze-solving strategies, particularly related to planning. Previous studies suggest that gaze reflects a mental simulation process during mazesolving and is, therefore, reflective of the maze and its solution path structure [12,13,26]. [27] found that gaze patterns during maze-solving can be differentiated into those that subserve exploration and those that aid in motor guidance.…”

Section: Introductionmentioning

confidence: 99%

Finding your Way Out: Planning Strategies in Human Maze-Solving Behavior

Kadner¹,

Willkomm²,

Ibs³

et al. 2023

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In many situations encountered in our daily lives where we have several options to choose from, we need to balance the amount of planning into the future with the number of alternatives we want to consider to achieve our long-term goals. A popular way to study these planning problems in controlled environments is maze-solving tasks, since they can be precisely defined and controlled in terms of their topology. In our study, participants solved mazes that differed systematically in topological properties regulating the number of alternatives and depth of paths. Replicating previous results, we show the influence of these spatial features on performance and stopping times. Longer and more branched solution paths lead to more planning effort and longer solution times. Additionally, we measured subjects’ eye movements to investigate their planning horizon. Our results suggest that people decrease their planning depth with increasing number of alternatives.

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Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task

Cited by 2 publications

References 0 publications

Computational basis of hierarchical and counterfactual information processing

Computational basis of hierarchical and counterfactual information processing

Finding your Way Out: Planning Strategies in Human Maze-Solving Behavior

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