Human search for a target on a textured background is consistent with a stochastic model

Clarke, Alasdair; Green, Patrick Richey; Chantler, Mike J.; Hunt, Amelia R.

doi:10.1167/16.7.4

Cited by 28 publications

(46 citation statements)

References 37 publications

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“…Whereas it is neither possible nor desirable to control eye movements of observers while they perform the task, our gazecontingent target presentation paradigm allowed us to fully control the reinforcement schedule. Our results provide direct evidence that complex eye movement behavior is sensitive to reinforcement by visual information gain, as hypothesized by earlier investigators (Clarke et al, 2016;Jiang et al, 2013).…”

Section: Discussionsupporting

confidence: 89%

“…Overall, the picture emerges that eye movements for visual search are optimal or close to optimal under some conditions (Ackermann & Landy, 2013;Clarke, Green, Chantler, & Hunt, 2016;Droll, Abbey, & Eckstein, 2009;Eckstein et al, 2015;Najemnik & Geisler, 2005, but not others (Morvan & Maloney, 2012;Verghese, 2010). The reason for the different outcomes is unclear at present.…”

Section: Introductionmentioning

confidence: 97%

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Visual reinforcement shapes eye movements in visual search

Schütz

Gegenfurtner

2016

Journal of Vision

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We use eye movements to gain information about our visual environment; this information can indirectly be used to affect the environment. Whereas eye movements are affected by explicit rewards such as points or money, it is not clear whether the information gained by finding a hidden target has a similar reward value. Here we tested whether finding a visual target can reinforce eye movements in visual search performed in a noise background, which conforms to natural scene statistics and contains a large number of possible target locations. First we tested whether presenting the target more often in one specific quadrant would modify eye movement search behavior. Surprisingly, participants did not learn to search for the target more often in high probability areas. Presumably, participants could not learn the reward structure of the environment. In two subsequent experiments we used a gaze-contingent display to gain full control over the reinforcement schedule. The target was presented more often after saccades into a specific quadrant or a specific direction. The proportions of saccades meeting the reinforcement criteria increased considerably, and participants matched their search behavior to the relative reinforcement rates of targets. Reinforcement learning seems to serve as the mechanism to optimize search behavior with respect to the statistics of the task.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 97%

Visual reinforcement shapes eye movements in visual search

Schütz

Gegenfurtner

2016

Journal of Vision

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“…It is important to note that biases and heuristics can boost performance above a completely random baseline, but without the computations required for computing an optimal strategy. This idea is formalised in a model of eye movements during visual search (Clarke, Green, Chantler, & Hunt, 2016), in which the sequence of eye movements was selected at random, but from a population of eye movements participants made from that region of the search area (see also Clarke, Stainer, Tatler, & Hunt, 2017). This random walk incorporates natural tendencies of the saccade system to, for example, make eye movements of a particular size and angle, and to saccade toward the centre more than other regions.…”

Section: Discussionmentioning

confidence: 99%

Decision making in slow and rapid reaching: Sacrificing success to minimize effort

Hesse¹,

Kangur²,

Hunt³

2019

Preprint

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Current studies on visuomotor decision making come to inconsistent conclusions regarding the optimality with which these decisions are made. When executing rapid reaching movements under uncertainty, humans seem to automatically select optimal movement paths that take into account the position of all potential targets (spatial averaging). In contrast, humans rarely employ optimal strategies when making decisions on whether to pursue two action goals simultaneously or prioritise one goal over another. Here, we manipulated whether spatial averaging or pre-selection of a single target would provide the optimal strategy by varying the spatial separation between two potential movement targets as well as the time available for movement execution. Experiment 1 provided a generous amount of time to reach the final target. Participants tended to pre-select the target that was easiest to reach and correct their movement path in-flight if required. In Experiment 2, a strict time limit was set, such that the optimal strategy depended on the separation between the potential targets: for small separations, there was sufficient time to employ averaging strategies, but higher success for larger separations required pre-selecting the final target instead. None of our participants adjusted their movement strategies with spatial separation, however. In Experiment 3, we confirm the bias towards targets that are easiest to reach and show that this comes at the expense of overall task success. The results suggest a strong tendency to minimize immediate movement effort, and a failure to flexibly adjust movement strategies to maximize the probability of success.

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“…Importantly, neither of our searcher models included an explicit fixation selection strategy, instead relying on human fixations. While the choice of fixation strategy can dramatically influence search performance, there is an ongoing debate regarding the optimality of human fixation strategies, with evidence for near-optimal fixation selection in some search tasks (e.g., Michel & Geisler, 2009;Najemnik & Geisler, 2005, 2008 and evidence for markedly suboptimal fixation selection in others (e.g., Ackermann & Landy, 2013;Clarke, Green, Chantler, & Hunt, 2016;Morvan & Maloney, 2012;Nowakowska, Clarke, & Hunt, 2017;Paulun, Schütz, Michel, Geisler, & Gegenfurtner, 2015;Verghese, 2012). Because the purpose of the current study is to determine the role that IPU plays in search, we do not attempt to engage this debate.…”

Section: Searcher Modelsmentioning

confidence: 99%

Intrinsic position uncertainty impairs overt search performance

Semizer

Michel

2017

Journal of Vision

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Uncertainty regarding the position of the search target is a fundamental component of visual search. However, due to perceptual limitations of the human visual system, this uncertainty can arise from intrinsic, as well as extrinsic, sources. The current study sought to characterize the role of intrinsic position uncertainty (IPU) in overt visual search and to determine whether it significantly limits human search performance. After completing a preliminary detection experiment to characterize sensitivity as a function of visual field position, observers completed a search task that required localizing a Gabor target within a field of synthetic luminance noise. The search experiment included two clutter conditions designed to modulate the effect of IPU across search displays of varying set size. In the Cluttered condition, the display was tiled uniformly with feature clutter to maximize the effects of IPU. In the Uncluttered condition, the clutter at irrelevant locations was removed to attenuate the effects of IPU. Finally, we derived an IPU-constrained ideal searcher model, limited by the IPU measured in human observers. Ideal searchers were simulated based on the detection sensitivity and fixation sequences measured for individual human observers. The IPU-constrained ideal searcher predicted performance trends similar to those exhibited by the human observers. In the Uncluttered condition, performance decreased steeply as a function of increasing set size. However, in the Cluttered condition, the effect of IPU dominated and performance was approximately constant as a function of set size. Our findings suggest that IPU substantially limits overt search performance, especially in crowded displays.

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Human search for a target on a textured background is consistent with a stochastic model

Cited by 28 publications

References 37 publications

Visual reinforcement shapes eye movements in visual search

Visual reinforcement shapes eye movements in visual search

Decision making in slow and rapid reaching: Sacrificing success to minimize effort

Intrinsic position uncertainty impairs overt search performance

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