Seeing faces in the noise: Stochastic activity in perceptual regions of the brain may influence the perception of ambiguous stimuli

Wild, Heather A.; Busey, Thomas A.

doi:10.3758/bf03196598

Cited by 25 publications

(30 citation statements)

References 21 publications

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“…There is a trend for larger N170 magnitudes with increasing certainty of the presence of a face in the noise over both hemispheres, indicating that the N170 component was likely involved in the percept of a face signal in the noise. Specifically, for each participant, the correlation between the magnitude of the N170 and the behavioral response was: "BN", left: r = −.81, right: r = −.60; "SN", left: r = −.84, right: r = −.64; "MVM", right: r = − .98, left: r = −.82, and is consistent with previous reports (with respect to the right hemisphere) using EEG methodology and white noise as stimuli (Wild and Busey, 2004), however, here we see the relationship in both hemispheres. In addition, the overall magnitude of the N170 is greater over the right hemisphere than the left.…”

Section: Data and Analysissupporting

confidence: 91%

“…Motivated by previous studies employing fMRI (Zhang et al, 2008) and ERPs (Wild and Busey, 2004) which showed modulation of brain signals associated with face processing when participants reported the percept of faces-in-white noise (when no faces were actually present), we sought to bypass the aforementioned behavioral measures and derive whole face representations directly from neural activity in the human brain using EEG. That is, instead of conducting a classification image analysis using behavioral responses, we sought to derive CIs based on ambiguous noise (i.e., fractal noise) images that "triggered" strong negative EEG amplitudes on a trial-by-trial basis within the 150 to 200 ms range corresponding to the range the N170 is typically observed in ERPs.…”

Section: Introductionmentioning

confidence: 99%

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Extracting the internal representation of faces from human brain activity: An analogue to reverse correlation

Hansen¹,

Thompson²,

Hess³

et al. 2010

NeuroImage

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Section: Data and Analysissupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Extracting the internal representation of faces from human brain activity: An analogue to reverse correlation

Hansen¹,

Thompson²,

Hess³

et al. 2010

NeuroImage

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“…Critically, and consistent with other recent reports in the literature (e.g., Wild & Busey, 2004), practice on contrast detection produces both reliable decreases in detection thresholds and reliable increases in false alarm rates. Critically, these increases in false alarm rates with practice occur in the absence of any changes in identification preferences and occur even when observers do not practice the task by making explicit present/absent decisions.…”

Section: Discussionsupporting

confidence: 90%

“…a modified version of the classic (Fechner, 1860(Fechner, /1966 method of constant stimuli (MCS), and similar results have been reported by other investigators (e.g., Wild & Busey, 2004).…”

supporting

confidence: 85%

“…This evidence, along with data suggesting that perceptual practice can produce reliable liberal shifts in detection responses (Seitz, Nanez, Holloway, Koyama, & Watanabe, 2005;Wenger & Rasche, 2006;Wild & Busey, 2004), motivates the two central hypotheses of the present effort, which we tested in the context of perceptual learning for the detection of grayscale contrast. The first hypothesis is that practice can lead to appreciable liberal shifts in response bias for detection without any concomitant changes in response bias for identification.…”

mentioning

confidence: 52%

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Evidence for criterion shifts in visual perceptual learning: Data and implications

Wenger

Copeland

Bittner

et al. 2008

Perception & Psychophysics

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) documents the range of tasks in which practice on elementary visual judgments can produce dramatic improvements in performance. This behavioral evidence has been used to motivate the development of a range of models for the mechanisms of perceptual learning (e.g., Adini & Sagi, 2001;Adini, Sagi, & Tsodyks, 2002;Gold, Bennett, & Sekuler, 1999;Gold, Sekuler, & Bennett, 2004;Petrov, Dosher, & Lu, 2005;Yu, Klein, & Levi, 2004), with the majority of these models focused on how perceptual sensitivity to either the presence of or a variation in a defining characteristic changes with experience. The work presented here explores the possibility that the changes observed in visual perceptual learning may also reflect changes in response (decisional) criteria for both detection and identification, in addition to or instead of changes in perceptual sensitivity.In order to ask these questions, it is critical to consider the extent to which the behavioral evidence available in any study of perceptual learning can speak in a reasonably restrictive manner to changes in perceptual sensitivity (Fahle & Poggio, 2002;Sagi & Tanne, 1994;Zenger & Sagi, 2002). If that evidence can reflect changes due to factors other than changes in sensitivity-such as changes in biases for detection, identification, response selection, or all three-inferences regarding the underlying mechanisms may be in error. Consider, in particular, the possibility that experience may produce changes in both sensitivity and bias. The latter could suggest important roles for selective attention, memory, or other influences, such as might be available by way of back-projections within the visual system (e.g., Di Lollo, Enns, & Rensink, 2000;Lamme & Roelfsema, 2000;Li, Piëch, & Gilbert, 2004;Ling & Carrasco, 2006;Sigman & Gilbert, 2000;Sigman et al., 2005). However, if the experimental approach does not provide the data necessary to measure both types of potential changes, or if it does not adequately control for the possible effects of different types of response bias, any inferences regarding any single basis for change is potentially suspect.In a large number of the studies on perceptual learning, an experimental approach-an adaptive version of a Work on visual perceptual learning for contrast detection has shown that reliable decreases in detection thresholds are accompanied by reliable increases in false alarm rates (Wenger & Rasche, 2006). The present study assesses the robustness and replicability of these changes, demonstrating that they are independent of a variety of task demands (i.e., the specific method used for perceptual practice and threshold estimation) and the presence or absence of trial-by-trial feedback and that the source of the increases can be found in shifts in changes in sensitivity and in bias for detection, identification, or both. Although the increase in false alarm rates suggests a strategic shift in response criteria for detection, we demonstrate that there are multiple potential explanations, including explanations that do not requi...

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Internal representations for face detection: An application of noise‐based image classification to BOLD responses

Nestor

Vettel

Tarr

2012

Human Brain Mapping

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What basic visual structures underlie human face detection and how can we extract such structures directly from the amplitude of neural responses elicited by face processing? Here, we address these issues by investigating an extension of noise-based image classification to BOLD responses recorded in high-level visual areas. First, we assess the applicability of this classification method to such data and, second, we explore its results in connection with the neural processing of faces. To this end, we construct luminance templates from white noise fields based on the response of face-selective areas in the human ventral cortex. Using behaviorally and neurally-derived classification images, our results reveal a family of simple but robust image structures subserving face representation and detection. Thus, we confirm the role played by classical face selective regions in face detection and we help clarify the representational basis of this perceptual function. From a theory standpoint, our findings support the idea of simple but highly diagnostic neurally-coded features for face detection. At the same time, from a methodological perspective, our work demonstrates the ability of noise-based image classification in conjunction with fMRI to help uncover the structure of high-level perceptual representations.

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Seeing faces in the noise: Stochastic activity in perceptual regions of the brain may influence the perception of ambiguous stimuli

Cited by 25 publications

References 21 publications

Extracting the internal representation of faces from human brain activity: An analogue to reverse correlation

Extracting the internal representation of faces from human brain activity: An analogue to reverse correlation

Evidence for criterion shifts in visual perceptual learning: Data and implications

Internal representations for face detection: An application of noise‐based image classification to BOLD responses

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