Statistical processing: computing the average size in perceptual groups

Chong, Sang Chul; Treisman, Anne

doi:10.1016/j.visres.2004.10.004

Cited by 303 publications

(405 citation statements)

References 16 publications

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“…In general, others have referred to these features as global features (Navon, 1977;Oliva & Torralba, 2001), holistic features (Kimchi, 1992), or sets (Ariely, 2001;Chong & Treisman, 2003;Chong & Treisman, 2005b). We refer to each of these types of features under the umbrella term "ensemble visual features."…”

Section: Discussionmentioning

confidence: 99%

“…For example, the size or the location of an individual object would be a local visual feature. In contrast, there are a variety of statistical summary features that represent information at a more abstract level, collapsing across local details (Ariely, 2001;Chong & Treisman, 2003;Chong & Treisman, 2005b). For the present study, we will focus on relatively simple summary features, such as the center of mass of a collection of objects (henceforth the "centroid"), which is essentially the mean position of the group.…”

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The Representation of Simple Ensemble Visual Features Outside the Focus of Attention

2008

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The representation of visual information inside the focus of attention is precise relative to the representation of information outside the focus of attention. We find that the visual system can compensate for the cost of withdrawing attention by pooling noisy local features and computing summary statistics. The location of an individual object is a local feature, while the center of mass of several objects (centroid) is a summary feature representing the mean object location. Three Experiments showed that withdrawing attention degraded the representation of individual positions more than the representation of the centroid. It appears that information outside the focus of attention can be represented at an abstract level which lacks local detail, but nevertheless carries a precise statistical summary of the scene. The term "ensemble features" refers to a broad class of statistical summary features, which we propose collectively comprise the representation of information outside the focus of attention.

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Section: Discussionmentioning

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The Representation of Simple Ensemble Visual Features Outside the Focus of Attention

2008

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“…Empirically, there are various experimental results indicating that the visual system can rapidly access substantial amounts of information without focused attention, such as scene gist (Potter & Levy, 1969;Potter, 1976;Schyns & Oliva, 1994;Oliva, 2005), statistical properties in a scene (e.g., Parkes, Lund, Angelucci, Solomon & Morgan, 2001;Chong & Treisman, 2005a, 2005bHaberman & Whitney, 2009), and some basic categorical information of objects (Li, VanRullen, Koch, & Perona, 2002;Li, Iyer, Koch & Perona, 2007). Such processing power must be based on this parallel processing stage, of which relatively little has been learned.…”

Section: Parallel Processing In Visual Searchmentioning

confidence: 99%

Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects

Wang

Buetti

Lleras

2017

Collabra: Psychology

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Previous work in our lab has demonstrated that efficient visual search with a fixed target has a reaction time by set size function that is best characterized by logarithmic curves. Further, the steepness of these logarithmic curves is determined by the similarity between target and distractor items (Buetti et al., 2016). A theoretical account of these findings was proposed, namely that a parallel, unlimited capacity, exhaustive processing architecture is underlying such data. Here, we conducted two experiments to expand these findings to a set of real-world stimuli, in both homogeneous and heterogeneous search displays. We used computational simulations of this architecture to identify a way to predict RT performance in heterogeneous search using parameters estimated from homogeneous search data. Further, by examining the systematic deviation from our predictions in the observed data, we found evidence that early visual processing for individual items is not independent. Instead, items in homogeneous displays seemed to facilitate each other's processing by a multiplicative factor. These results challenge previous accounts of heterogeneity effects in visual search, and demonstrate the explanatory and predictive power of an approach that combines computational simulations and behavioral data to better understand performance in visual search.

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“…Mean judgment accuracy also remains good under difficult perceptual conditions, such as brief set exposure duration, or the insertion of a delay between two sets, the means of which need to be compared. Increasing the number and density of the elements in a set also does not lead to a deterioration in mean judgment (Ariely, 2001;Chong & Treisman, 2005b).…”

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Attention modulates set representation by statistical properties

Fockert

Marchant

2008

Perception & Psychophysics

100

128

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Recent reports have claimed that observers show accurate knowledge of the mean size of a group of similar objects, a finding that has been interpreted to suggest that sets of multiple objects are represented in terms of their statistical properties, such as mean size (Ariely, 2001;Chong & Treisman, 2003, 2005a, 2005b. In the present study, we directed visual attention to a single set member and found that mean estimations were modulated according to the size of the attended item, regardless of whether size was the relevant search criterion (Experiment 1) or not (Experiment 2). These findings suggest that observers do not always accurately average together the entire set, and that instead the average is either biased by the features of the attended item, or based on a short-cut strategy of extracting the mean of a smaller subset.

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Statistical processing: computing the average size in perceptual groups

Cited by 303 publications

References 16 publications

The Representation of Simple Ensemble Visual Features Outside the Focus of Attention

The Representation of Simple Ensemble Visual Features Outside the Focus of Attention

Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects

Attention modulates set representation by statistical properties

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