Unexpected changes in direction of motion attract attention

Howard, Christopher; Holcombe, Alex O.

doi:10.3758/app.72.8.2087

Cited by 7 publications

(12 citation statements)

References 30 publications

(35 reference statements)

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“…Clair et al, 2010;Tripathy & Barrett, 2004) and that unexpected changes in the trajectory of an object can attract attention (Howard & Holcombe, 2010;Pratt et al, 2010). However, no studies have directly examined the effects of unexpected changes in trajectory on MOT performance while also controlling and/or measuring other factors that could impact MOT performance.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, changes in trajectory did not interact with distance traveled, demonstrating that it is the total number of changes, not the rate at which the changes occur, that increases the probability of errors in the tracking task. Because tracking performance relies on attention (Alvarez & Franconeri, 2007;Howe et al, 2010) and changes in trajectory attract attention (Howard & Holcombe, 2010;Pratt et al, 2010), it is likely that changes in trajectory increase tracking errors because they momentarily attract attention away from the nonchanging targets. The advantage of using PMT instead of the traditional MOT task is the control of target-distractor proximity, which is imperative to being able to reliably test the effects of distance traveled and number of trajectory changes.…”

Section: Discussionmentioning

confidence: 99%

“…Given that tracking performance relies on trajectory information and that there are limited resources available for representing trajectory information, unexpected changes in a target's trajectory may disrupt tracking performance by attracting attention away from other targets (Howard & Holcombe, 2010;Pratt et al, 2010). As evidence that changes in trajectory attract attention, Howard and Holcombe reported that participants were worse at reporting the orientation of a rotating grating on the surface of the target item if a nonqueried target had recently changed trajectory.…”

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confidence: 99%

“…Furthermore, as was demonstrated by Howard and Holcombe (2010), although attention may initially be distributed to all the targets, attention may be attracted toward a target item that has recently changed direction and moved away from the remaining target items. Attentional resources would need to be directed to the changed target to update its trajectory representation.…”

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confidence: 99%

“…Several factors are important to MOT performance, such as speed (Alvarez & Franconeri, 2007), number of distractors (Bettencourt & Somers, 2009), and the proximity of objects (Franconeri, Jonathon, & Scimeca, 2010;Pylyshyn, 2004). Recent research suggests that trajectory information is also important for MOT performance: Participants can represent trajectory information (Fencsik, Klieger, & Horowitz, 2007;Iordanescu, Grabowecky, & Suzuki, 2009), they can detect changes in trajectory (Narasimhan, Tripathy, & Barrett, 2009;Pratt, Radulescu, Guo, & Abrams, 2010;Tripathy & Barrett, 2004), and when trajectory information is difficult to discern, MOT performance suffers (Howard & Holcombe, 2010;St. Clair, Huff, & Seiffert, 2010).…”

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confidence: 99%

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Changing target trajectories influences tracking performance

Ericson

Beck

2013

Psychon Bull Rev

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Changing target trajectories influences tracking performance

Ericson

Beck

2013

Psychon Bull Rev

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Spatially uninformative sounds increase sensitivity for visual motion change

2011

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It has recently been shown that spatially uninformative sounds can cause a visual stimulus to pop out from an array of similar distractor stimuli when that sound is presented in temporal proximity to a feature change in the visual stimulus. Until now, this effect has predominantly been demonstrated by using stationary stimuli. Here, we extended these results by showing that auditory stimuli can also improve the sensitivity of visual motion change detection. To accomplish this, we presented moving visual stimuli (small dots) on a computer screen. At a random moment during a trial, one of these stimuli could abruptly move in an orthogonal direction. Participants’ task was to indicate whether such an abrupt motion change occurred or not by making a corresponding button press. If a sound (a short 1,000 Hz tone pip) co-occurred with the abrupt motion change, participants were able to detect this motion change more frequently than when the sound was not present. Using measures derived from signal detection theory, we were able to demonstrate that the effect on accuracy was due to increased sensitivity rather than to changes in response bias.

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Statistical learning of visual transitions in monkey inferotemporal cortex

Meyer

Olson

2011

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

270

302

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One of the most fundamental functions of the brain is to predict upcoming events on the basis of the recent past. A closely related function is to signal when a prediction has been violated. The identity of the brain regions that mediate these functions is not known. We set out to determine whether they are implemented at the level of single neurons in the visual system. We gave monkeys prolonged exposure to pairs of images presented in fixed sequence so that each leading image became a strong predictor for the corresponding trailing image. We then monitored the responses of neurons in the inferotemporal cortex to image sequences that obeyed or violated the transitional rules imposed during training. Inferotemporal neurons exhibited a transitional surprise effect, responding much more strongly to unpredicted transitions than to predicted transitions. Thus, neurons even in the visual system make experience-based predictions and react when they fail.macaque | vision | plasticity T he inferotemporal cortex (ITC), the terminus of the ventral stream of visual areas (1), plays a critical role in object vision (2, 3). ITC neurons respond with individual patterns of selectivity to complex images (4). Training monkeys to discriminate between images (5-7), categorize them (8, 9), or form associations between them (10-12) induces functional changes among neurons in the ITC which have the effect of strengthening the representation of image attributes relevant to task performance. Even passive viewing causes changes in neuronal visual responsiveness. Repeated viewing of a single image leads to a weakening of responses to it (13,14). Repeated viewing of two images close together in time leads to pair coding: a tendency for neurons responsive to one image also to respond to the other (15-17). The effects of passive viewing, because they do not depend on task demands, fall into the category of unsupervised statistical learning.An important form of unsupervised learning not previously studied at the level of single neurons concerns transitional statistics. The learning of transitional statistics has been the focus of much behavioral study in humans because it is thought to underlie the development during infancy of the ability to perceive event boundaries including word boundaries in speech (18,19). Human infants passively exposed to a stimulus stream in which certain visual images always follow certain others automatically register the transitional rules as evidenced by their orienting preferentially to a test stream containing novel transitions (20). The adult human brain is sensitive to transitional probabilities, as evidenced by its generating strong responses to improbable transitions at the level of scalp potential and blood oxygenation measures (21-27). Monkeys, like human infants, have been reported to learn transitional probabilities and to orient preferentially to improbable transitions (28). No effort has been made as yet to characterize the underlying neuronal mechanisms (29). We hypothesized that neurons in the ITC ...

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Unexpected changes in direction of motion attract attention

Cited by 7 publications

References 30 publications

Changing target trajectories influences tracking performance

Changing target trajectories influences tracking performance

Spatially uninformative sounds increase sensitivity for visual motion change

Statistical learning of visual transitions in monkey inferotemporal cortex

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