Learning cue validity through performance feedback

Droll, Jason A.; Abbey, Craig K.; Eckstein, Miguel P.

doi:10.1167/9.2.18

Cited by 38 publications

(40 citation statements)

References 103 publications

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“…The findings are consistent with studies showing the benefits of learning eye movement strategies on perceptual performance in visual search tasks (Chukoskie et al, 2013; Droll et al, 2009; Koehler, Akbas, Peterson, & Eckstein, 2012). Yet, the importance or role of eye movements outside the scope of visual search has not been demonstrated.…”

Section: Discussionsupporting

confidence: 91%

“…Perceptual learning has been shown to change which stimulus features and spatial locations the visual system selects for task-specific processing (Ahissar & Hochstein, 1993; Dosher & Lu, 1998; Droll et al, 2009; Eckstein et al, 2004; Peterson et al, 2009). In this work, we showed that humans are able to adjust their eye movements to optimize the sampling of a novel visual stimulus, with these modulations contributing significantly to task performance maximization beyond the contributions of covert mechanisms alone.…”

Section: Discussionmentioning

confidence: 99%

“…Although these studies have increased our understanding of the mechanisms mediating perceptual learning, most investigations have not considered the role active vision, and specifically eye movements, plays in perceptual learning (but see Chukoskie, Snider, Mozer, Krauzlis, & Sejnowski, 2013; Droll, Abbey, & Eckstein, 2009; Holm, Engel, & Schrater, 2012 for exceptions). This would seem to be an important factor to explore, as the inhomogeneity in visual of processing across the visual field suggests that during active vision, familiarization with a perceptual environment might lead to changes in saccade strategies and contribute to performance improvements.…”

Section: Introductionmentioning

confidence: 99%

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Learning optimal eye movements to unusual faces

Peterson

Eckstein

2014

Vision Research

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Eye movements, which guide the fovea’s high resolution and computational power to relevant areas of the visual scene, are integral to efficient, successful completion of many visual tasks. How humans modify their eye movements through experience with their perceptual environments, and its functional role in learning new tasks, has not been fully investigated. Here, we used a face identification task where only the mouth discriminated exemplars to assess if, how, and when eye movement modulation may mediate learning. By interleaving trials of unconstrained eye movements with trials of forced fixation, we attempted to separate the contributions of eye movements and covert mechanisms to performance improvements. Without instruction, a majority of observers substantially increased accuracy and learned to direct their initial eye movements towards the optimal fixation point. The proximity of an observer’s default face identification eye movement behavior to the new optimal fixation point and the observer’s peripheral processing ability were predictive of performance gains and eye movement learning. After practice in a subsequent condition in which observers were directed to fixate different locations along the face, including the relevant mouth region, all observers learned to make eye movements to the optimal fixation point. In this fully learned state, augmented fixation strategy accounted for 43% of total efficiency improvements while covert mechanisms accounted for the remaining 57%. The findings suggest a critical role for eye movement planning to perceptual learning, and elucidate factors that can predict when and how well an observer can learn a new task with unusual exemplars.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning optimal eye movements to unusual faces

Peterson

Eckstein

2014

Vision Research

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“…We found a strong modulation of attentional capture, which we relate to statistical learning (e.g., Chun & Jiang, 1998;Droll, Abbey, & Eckstein, 2009;Fiser & Aslin, 2001, 2002Rosenthal, Fusi, & Hochstein, 2001;Saffran, Aslin, & Newport, 1996;Saffran, Newport, Aslin, Tunick, & Barrueco, 1997;Turk-Brown, Jungé, & Scholl, 2005). The higher the rate of the onset distractors, the stronger they were suppressed.…”

Section: Introductionmentioning

confidence: 84%

Display probability modulates attentional capture by onset distractors

Sayim

Grubert

Herzog

et al. 2010

JOV

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Attention can be stimulus-driven and bottom-up or goal-driven and top-down. Bottom-up attention and, particularly, attentional capture are often thought to be strongly automatic, i.e., not modulable. For example, in visual search, it has been shown that salient distractors strongly attract attention even though observers were instructed to ignore them. However, it was also shown that the strength of distraction can be modulated by the display probabilities of the distractors. Hence, bottom-up attention seems not to be completely automatic. In these studies, the distractors were salient by color differences to the other items in the display. Such color distractors, however, do not necessarily trigger bottom-up attention. Here, we presented onset distractors, that is, distractors displayed after the onset of the other search items, which are thought to strongly elicit bottom-up attention and to capture eye movements. Varying the display probabilities of the onset distractors strongly modulated attentional capture. We suggest that modulation was due to statistical learning. This study adds further evidence that bottom-up processes are not completely automatic.

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“…In 20% of randomly interleaved so-226 called catch trials none of the four possible target X stimuli dimmed 227 and the trial ended after 3.5 s. These catch trials were discarded from 228 all further analysis. The task also included a hidden schedule of proba-229 bilities changing rapidly every 25 correct trials (probability that a 230 dimming event was linked to a specific color, see Droll et al, 2009 Fig. 1.…”

mentioning

confidence: 99%