On the effective number of tracked trajectories in normal human vision

Tripathy, Srimant P.; Narasimhan, S.; Barrett, Brendan T.

doi:10.1167/7.6.2

Cited by 25 publications

(45 citation statements)

References 27 publications

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“…Tripathy and Barrett (Tripathy, 2003;Tripathy & Barnett, 2003 found that splitting attention among targets severely impaired the ability to detect small changes in direction. However, Tripathy and colleagues have shown that when direction changes were larger (as bounces were here, on average), many targets could be monitored successfully for a motion change (Tripathy & Levi, 2008;Tripathy, Narasimhan, & Barrett, 2007). In other words, a larger motion change was needed when fewer attentional resources were available per target.…”

Section: Discussionmentioning

confidence: 96%

Unexpected changes in direction of motion attract attention

Howard

Holcombe

2010

Attention, Perception & Psychophysics

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Under some circumstances, moving objects capture attention. Whether a change in the direction of a moving object attracts attention is still unexplored. We investigated this using a continuous tracking task. In Experiment 1, four grating patches changed smoothly and semirandomly in their positions and orientations, and observers attempted to track the orientations of two of them. After the stimuli disappeared, one of the two target gratings was queried and observers reported its orientation; hence direction of the gratings' motion across the screen was an irrelevant feature. Despite the irrelevance of its motion, when the nonqueried grating had collided with an invisible boundary within the last 200 msec of the trial, accuracy reporting the queried grating was worse than when it had not. Attention was likely drawn by the unexpected nature of these changes in direction of motion, since the effect was eliminated when the boundaries were visible (Experiment 2). This tendency for unexpected motion changes to attract attention has important consequences for the monitoring of objects in everyday environments.

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Section: Discussionmentioning

confidence: 96%

Unexpected changes in direction of motion attract attention

Howard

Holcombe

2010

Attention, Perception & Psychophysics

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“…Effective number tracked was defined as the capacity of a hypothetical limited-capacity observer achieving the measured percentage correct. To the surprise of the investigators, this number was found to depend strongly on deviation magnitude, but only weakly on N and D (Tripathy et al, 2007). In Experiment 5, different values of $ and D were interleaved within a block, making it impossible for subjects to know the difficulty of a trial beforehand.…”

Section: Theory and Methodsmentioning

confidence: 96%

“…In Experiment 3, D = 1 but fixed, suprathreshold deviations of magnitudes $ = 19-, 38-, 76-were used. As N was varied, performance decreased, but the curves for the different angles of deviation were clearly separated (Tripathy et al, 2007). In Experiment 4, the same suprathreshold deviations were used, but N was fixed at 6 or 8, and D was varied between blocks.…”

Section: Theory and Methodsmentioning

confidence: 98%

“…Deviation discrimination data We model published data from a variation of multipleobject tracking that has fewer degrees of freedom and is therefore easier to analyze (Tripathy & Barrett, 2004;Tripathy, Narasimhan, & Barrett, 2007). On each trial, N dots moved from left to right in linear trajectories ( Figure 1).…”

Section: Theory and Methodsmentioning

confidence: 99%

“…In Experiment 5, different values of $ and D were interleaved within a block, making it impossible for subjects to know the difficulty of a trial beforehand. The values N = 10, D = 1, 2, and $ = 19-, 38-, 57-were used (Tripathy et al, 2007). Effective number tracked was again strongly dependent on $, and only weakly on D.…”

Section: Theory and Methodsmentioning

confidence: 99%

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No capacity limit in attentional tracking: Evidence for probabilistic inference under a resource constraint

Wei

2009

Journal of Vision

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Human ability to simultaneously track multiple items declines with set size. This effect is commonly attributed to a fixed limit on the number of items that can be attended to, a notion that is formalized in limited-capacity and slot models. Instead, we propose that observers are constrained by stimulus uncertainty that increases with the number of items but use Bayesian inference to achieve optimal performance. We model five data sets from published deviation discrimination experiments that varied set size, number of deviations, and magnitude of deviation. A constrained Bayesian observer better explains each data set than do the traditional limited-capacity model, the recently proposed slots-plus-averaging model, a fixed-uncertainty Bayesian model, a Bayesian model with capacity limit, and a simple averaging model. This indicates that the notion of limited capacity in attentional tracking needs to be revised. Moreover, it supports the idea that Bayesian optimality of human perception extends to high-level perceptual computations.

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