Deborah Hanus

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Quantifying Error Distributions in Crowding

Hanus, D. & Vul E. (2013) Quantifying Error Distributions in Crowding, Journal of Vision, 13(4):17, 1-27 [pdf]

When multiple objects are in close proximity, people have diffculty identifying them individually. Two classes of theories aim to account for this "crowding" phenomenon: spatial pooling and spatial substitution. Variations of these accounts predict different patterns of errors in crowded displays. Here we aim to characterize the kinds of errors that people make during crowding by comparing a number of error models across three experiments in which we manipulate flanker spacing, display eccentricity, and pre-cueing duration. We find that both spatial intrusions and individual letter confusions play a considerable role in errors. Moreover, we find no evidence that a naive pooling model that predicts errors based on a non-additive combination of target and flankers explains errors better than an independent intrusion model (indeed, in our data, an independent intrusion model is slightly, but significantly, better). Finally, we find that manipulating trial diffculty in any way (spacing, eccentricity, or pre-cueing) produces homogenous changes in error distributions. Together, these results provide quantitative baselines for predictive models of crowding errors, suggest that "pooling" and "spatial substitution" models are difficult to tease apart, and imply that manipulations of crowding all influence a common mechanism that impacts subject performance.

Attention as Inference

Vul E., Hanus D. & Kanwisher N. (2009) Attention as inference: Selection is probabilistic; Responses are all-or-none samples, Journal of Experimental Psychology: General, 138(4), 540-560 [pdf]

Theories of probabilistic cognition postulate that internal representations are made up of multiple simultaneously held hypotheses, each with its own probability of being correct (henceforth, probability distributions). However, subjects make discrete responses and report the phenomenal contents of their mind to be all-or-none states rather than graded probabilities. How can these 2 positions be reconciled? Selective attention tasks, such as those used to study crowding, the attentional blink, rapid serial visual, and so forth, were recast as probabilistic inference problems and used to assess how graded, probabilistic representations may produce discrete subjective states. The authors asked subjects to make multiple guesses per trial and used 2nd-order statistics to show that (a) visual selective attention operates in a graded fashion in time and space, selecting multiple targets to varying degrees on any given trial; and (b) responses are generated by a process of sampling from the probabilistic states that result from graded selection. The authors concluded that although people represent probability distributions, their discrete responses and conscious states are products of a process that samples from these probabilistic representations.

Delay of Selective Attention in the Attentional Blink

Vul E., Hanus D. & Kanwisher N. (2008) Delay of selective attention during the attentional blink, Vision Research, 48(18), 1902-1909 [pdf]

The attentional blink is the inability to report the second of two targets in an RSVP stream when they are separated by 200-500 ms. Recent evidence shows that this failure results from three dissociable changes to the properties of temporal selective attention. During the attentional blink, selection is suppressed (items are selected less effectively, resulting in greater levels of random guessing), diffused (more letters around the target are selected), and delayed (the items that are selected tend to be later in the RSVP stream relative to the cue). We assessed the properties of the delay in selection and evaluate how the delay contributes to the attentional blink. First, by pre-cueing, we manipulate the delay of selective attention and show that neither delay nor suppression alone is sufficient to account for the failure to report the second target; thus both play a role in the usual attentional blink. Second, we explore the persistence of the delay effect over much longer T1-T2 SOAs and show that the effect remains strong at lags of 1400 ms and appears to subside with a timeconstant of roughly 500 ms. Third, we manipulate RSVP rate and find that the "delay" of selection is a delay in time, independent of the number of items.