Improving saliency models’ predictions of the next fixation with humans’ intrinsic cost of gaze shifts

Kadner, Florian; Thomas, Tobias; Hoppe, David; Rothkopf, Constantin A.

doi:10.1109/wacv56688.2023.00214

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…The presence of these biases suggests that additional factors must contribute to the decision of where to saccade, here referred to as ‘saccade selection’. Recent evidence suggests that the effort involved with planning and executing (eye) movements may be one crucial factor [22–25]. Effort is thought to be minimized whenever possible [26, 27], likely because it is costly to spend inherently limited cognitive resources [22, 28].…”

Section: Mainmentioning

confidence: 99%

Effort Drives Saccade Selection

Koevoet,

Van Zantwijk,

Naber

et al. 2024

Preprint

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What determines where to move the eyes? We recently showed that pupil size, a marker of noradrenaline release, reflects the effort associated with making a saccade (’saccade costs’). Here we demonstrate saccade costs to critically drive saccade selection: when choosing between any two saccade directions, the least costly direction was consistently preferred. Strikingly, this principle even held during search in natural scenes in two additional experiments. When increasing cognitive demand experimentally through an auditory counting task, participants made fewer saccades and especially cut costly directions. This suggests that the eye-movement system and other cognitive operations consume similar resources that are flexibly allocated among each other as cognitive demand changes. Together, we argue that eye-movement behavior is not only determined by physical saliency, goals, and selection history, but also by a qualitatively distinct and equally fundamental factor: effort.

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Section: Mainmentioning

confidence: 99%

Effort Drives Saccade Selection

Koevoet,

Van Zantwijk,

Naber

et al. 2024

Preprint

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“…Furthermore, eye movements have been proposed to be closely related to the planning horizon since they can be understood as information sampling in the visual environment [18] and have indeed been shown to be planned ahead [19]. Furthermore, the planning horizon and strategy can differ dynamically within a task and between subjects [20,21] not the least by the simple fact that human scan paths are not independent of individual behavioral preferences in gaze selection [22,23]. Recent work suggests that humans balance depth and breadth searches [24] and prune decision trees related to their plans when encountering large losses [25] in sequential decision making tasks different from mazes.…”

Section: Introductionmentioning

confidence: 99%

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

Kadner¹,

Willkomm²,

Ibs³

et al. 2023

Preprint

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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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Improving saliency models’ predictions of the next fixation with humans’ intrinsic cost of gaze shifts

Cited by 2 publications

References 54 publications

Effort Drives Saccade Selection

Effort Drives Saccade Selection

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

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