Modeling Lag‐2 Revisits to Understand Trade‐Offs in Mixed Control of Fixation Termination During Visual Search

Godwin, H. J.; Reichle, Erik D.; Menneer, Tamaryn

doi:10.1111/cogs.12379

Cited by 15 publications

(15 citation statements)

References 63 publications

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“…We additionally analysed lag-2 revisits as a variant of revisits that occur when a stimulus is revisited after a single off-stimulus fixation. Lag-2 revisits presumably reflect ongoing stimulus processing even after the next saccade has already started (Godwin et al, 2017). In our experiment, the probability of lag-2 revisits increased with target-distractor similarity both on targets (.11 vs. .24) and on distractors (.01 vs. .03), ts > 3.17, ps < .009.…”

Section: Implications For Models Of Visual Searchmentioning

confidence: 41%

“…This in turn implies that decision processes do not stop when the next shift of gaze is executed, but rather continue in parallel to the next eye movement. It also suggests that initiating the next shift is partly independent from target identification (see Godwin, Reichle, & Menneer, 2017). Given the substantial amount of revisiting, this variable seems to be underrepresented in theoretical accounts of search efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, rescanning may have a strategic component. For instance, with a dwelling-rescanning trade-off, one might choose for a fast pace in scanning (with short dwell times), at the cost of an increased need to rescan items (Godwin et al, 2017).…”

Section: Explaining the Similarity Effectmentioning

confidence: 99%

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Dwelling, rescanning, and skipping of distractors explain search efficiency in difficult search better than guidance by the target

2017

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Prominent models of overt and covert visual search focus on explaining search efficiency by visual guidance. That some searches are fast whereas others are slow is explained by the ability of the target to guide attention to the target's position. Comparably little attention is given to other variables that might also influence search efficiency, such as dwelling on distractors, skipping distractors, and revisiting distractors. Here, we examine the relative contributions of dwelling, skipping, rescanning, and the use of visual guidance, in explaining visual search times in general, and the similarity effect in particular. The hallmark of the similarity effect is more efficient search for a target that is dissimilar to the distractors compared to a target that is similar to the distractors. In the present experiment, participants have to find an emotional face target among nine neutral face non-targets. In different blocks, the target is either more or less similar to the non-targets. Eye-tracking is used to separately measure selection latency, dwelling on distractors, and skipping and revisiting of distractors. As expected, visual search times show a large similarity effect. Similarity also has strong effects on dwelling, skipping, and revisiting, but only weak effects on visual guidance. Regression analyses show that dwelling, skipping, and revisiting determine search times on trial level. The influence of dwelling and revisiting is stronger in target absent than in target present trials, whereas the opposite is true for skipping. The similarity effect is best explained by dwelling. Additionally, including a measure of guidance does not yield substantial benefits. In sum, results indicate that guidance by the target is not the sole principle behind fast search; rather, distractors are less often skipped, more often visited, and longer dwelled on in slow search conditions.

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Section: Implications For Models Of Visual Searchmentioning

confidence: 41%

Section: Discussionmentioning

confidence: 99%

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Dwelling, rescanning, and skipping of distractors explain search efficiency in difficult search better than guidance by the target

2017

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“…There are also examples of heuristic extensions to grid search for improving the scalability. One such was presented by Godwin, Reichle, and Menneer (), who modeled fixation revisits in visual search, and used successively more constrained grid searches to fit six model parameters. Each successive grid search centered on the best‐fitting parameter values of the previous search, using smaller cell sizes.…”

Section: Probabilistic Inference For Computational Cognitive Modelsmentioning

confidence: 99%

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

et al. 2019

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This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results.

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“…The physical shape, complexity, or familiarity of an object (7)(8)(9)(10) and how it is related to the task (11) significantly influence the amount of time we spend gazing at it. Indeed, a fundamental aspect of models of eye movements in visual search (12,13), scene perception (14,15), and reading (9,16) is the inclusion of an inhibitory action that keeps the eye from moving if the object being foveated is important for the task.…”

mentioning

confidence: 99%

Suppression of frontal eye field neuronal responses with maintained fixation

Mirpour

Bolandnazar

Bisley

2018

Proc. Natl. Acad. Sci. U.S.A.

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The decision of where to make an eye movement is thought to be driven primarily by responses to stimuli in neurons' receptive fields (RFs) in oculomotor areas, including the frontal eye field (FEF) of prefrontal cortex. It is also thought that a saccade may be generated when the accumulation of this activity in favor of one location or another reaches a threshold. However, in the reading and scene perception fields, it is well known that the properties of the stimulus at the fovea often affect when the eyes leave that stimulus. We propose that if FEF plays a role in generating eye movements, then the identity of the stimulus at fixation should affect the FEF responses so as to reduce the probability of making a saccade when fixating an item of interest. Using a visual foraging task in which animals could make multiple eye movements within a single trial, we found that responses were strongly modulated by the identity of the stimulus at the fovea. Specifically, responses to the stimulus in the RF were suppressed when the animal maintained fixation for longer durations on a stimulus that could be associated with a reward. We suggest that this suppression, which was predicted by models of eye movement behavior, could be a mechanism by which FEF can modulate the temporal flow of saccades based on the importance of the stimulus at the fovea.

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Modeling Lag‐2 Revisits to Understand Trade‐Offs in Mixed Control of Fixation Termination During Visual Search

Cited by 15 publications

References 63 publications

Dwelling, rescanning, and skipping of distractors explain search efficiency in difficult search better than guidance by the target

Dwelling, rescanning, and skipping of distractors explain search efficiency in difficult search better than guidance by the target

Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

Suppression of frontal eye field neuronal responses with maintained fixation

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