Accurate statistical tests for smooth classification images

Chauvin, Alan; Worsley, Keith J.; Schyns, Philippe G.; Arguin, Martin; Gosselin, Frédéric

doi:10.1167/5.9.1

Cited by 184 publications

(197 citation statements)

References 54 publications

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“…For each observer, we transformed these probabilities into Z-scores, marked with a colour code for the statistically significant (p b 0.05, one tailed) probabilities, revealing the corresponding features used to perform the gender and expressive or not categorisations (see Fig. 1, Behaviour Classification Image, Accuracy; Chauvin et al, 2005;Gosselin and Schyns, 2001, for details). Reaction time: To determine the features discriminating between fast and slow reaction times on correct trials, we derived a classification image by summing the masks leading to reaction times greater than the mean and those lower than the mean (RTs above and below 2.5 std were treated as outliers and removed from the analysis) and computed the difference.…”

Section: Computation: Behavioural Classification Imagementioning

confidence: 99%

“…To establish those features that are significantly correlated with the EEG energy at each point in the time-frequency space, we applied a cluster test (p b 0.001, Chauvin et al, 2005) to each EEG classification image-using the non-diagnostic normalised hairstyle and forehead region as the baseline distribution. Significant regions were then compared (i.e., intersected) with reference templates derived from the diagnostic information of the behaviour classification images (accuracy) of each observer (the left eye, the right eye and the mouth, or ρ( f, ci), for each significant behavioural feature f and Time × Frequency classification image ci) to extract sensitivity matrices to this information (for a total of 2 electrodes (OTR and OTL) × 2 tasks (GENDER and EXNEX) × 3 basic features × 4 observers = 48 sensitivity matrices, not shown).…”

Section: Computation: Time × Frequency Classification Imagesmentioning

confidence: 99%

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From a face to its category via a few information processing states in the brain

2007

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Cognitive neuroscience assumes a correspondence between specific spatio-temporal patterns of neural activity and the states of a mechanism that processes cognitive information. Mechanistic explanations of cognition should therefore translate patterns of neural activity into the components of a formal mechanism: a set of information processing states and their transitions. For the first time, we carried out this research programme with four naive observers instructed to categorise randomly presented face information. With classification image techniques, we revealed the diagnostic features that the brain requires to produce correct behaviour (i.e., two eyes for gender categorisation in one session; the mouth for expression in the other session). With the same techniques applied to brain signals, we revealed the features processing states associated with modulations of oscillatory EEG energy (measured on occipito-temporal face-sensitive electrodes). Here we show how transitions between distinct feature processing states in the theta/alpha [4-12 Hz] oscillatory bands implement two face categorisations. On the left and right occipito-temporal electrodes of each observer, processing of the contra-lateral eye precedes bilateral integration of the features required for behaviour. For the first time, we relate stimulus information to behaviour via sequences of categorisation-specific feature processing states in the brain. © 2007 Elsevier Inc. All rights reserved.To study face categorisation mechanisms in an information system such as the brain, automata theory (Hopcroft and Ullman, 1979) specifies three generic questions that must be addressed to relate brain activity to the states of an information processing mechanism (i.e., a formal automaton). The first question is that of form: what is the nature of the brain activity supporting face processing-i.e., the states of the brain correlated with face processing? The second question is that of content: what is the information content processed in these brain states? The third question is that of transition: how does information flow from one brain state to the next between stimulus onset and behavioural response? So far, cognitive neuroimaging studies have not simultaneously addressed these three questions. Until this is resolved, inferences about cognitive information processing mechanisms will be severely restricted.To illustrate the problem, researchers have proposed that slow brain rhythms could form distinct states of information processing, on the basis of considerable evidence of a consistent relationship between low frequency, theta (4-8 Hz) and alpha (8-12 Hz), oscillatory EEG activity of the neural substrate and behavioural variables (Van Rullen and Koch, 2003;Ward, 2003, for reviews). However, definitive evidence is lacking in part because it has been proven difficult to identify precisely the information content of these states from the parametric modulations (i.e., phase and amplitude) of slow brain rhythms. Consequently, little is known about the detailed f...

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Section: Computation: Behavioural Classification Imagementioning

confidence: 99%

Section: Computation: Time × Frequency Classification Imagesmentioning

confidence: 99%

From a face to its category via a few information processing states in the brain

2007

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“…If you use the statistical functions of the Stat4Ci package called with iMap (i.e., the Pixel or Cluster tests), please cite Chauvin et al (2005), listed below in the References.…”

Section: Methodsmentioning

confidence: 99%

“…iMap relies on spatially normalized smoothed data, which therefore satisfy the formal constraints of the RFT used in fMRI. More precisely, iMap applies the statistical Pixel test from the Stat4Ci toolbox (Chauvin, Worsley, Schyns, Arguin, & Gosselin, 2005), which has been developed and validated for analyzing smooth classification images. The sensitivity of the Pixel test depends on the number of comparisons performed, which is represented here by the size of the search space (i.e., the size of the digital images).…”

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confidence: 99%

“…The differential fixation map highlights significant eye movement biases. The significant areas are determined by using the Pixel test (Chauvin et al, 2005). Finally, statistical fixation maps are produced that merge the fixation patterns, the areas fixated significantly above chance level, and the background weight the density functions by the fixation duration, as implemented in iMap.…”

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iMap: a novel method for statistical fixation mapping of eye movement data

2011

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Eye movement data analyses are commonly based on the probability of occurrence of saccades and fixations (and their characteristics) in given regions of interest (ROIs). In this article, we introduce an alternative method for computing statistical fixation maps of eye movements-iMap-based on an approach inspired by methods used in functional magnetic resonance imaging. Importantly, iMap does not require the a priori segmentation of the experimental images into ROIs. With iMap, fixation data are first smoothed by convolving Gaussian kernels to generate three-dimensional fixation maps. This procedure embodies eyetracker accuracy, but the Gaussian kernel can also be flexibly set to represent acuity or attentional constraints. In addition, the smoothed fixation data generated by iMap conform to the assumptions of the robust statistical random field theory (RFT) approach, which is applied thereafter to assess significant fixation spots and differences across the three-dimensional fixation maps. The RFT corrects for the multiple statistical comparisons generated by the numerous pixels constituting the digital images. To illustrate the processing steps of iMap, we provide sample analyses of real eye movement data from face, visual scene, and memory processing. The iMap MATLAB toolbox is editable and freely available for download online (www.unifr.ch/psycho/ibmlab/).Keywords Eye movements . Statistical fixation maps . Data-driven analyses . Random field theory . Matlab toolboxThe human visual system is equipped with the most sophisticated machinery to effectively adapt to the visual world. Where, when, and how human eyes are moved to gather information to adapt to the visual environment has been a question that has fascinated scientists for more than a century. Javal (1879) coined the term saccade to describe the rapid movement of the eyes produced during reading, an oculomotor phenomenon identified by Hering (1879) and Lamare (1892) during this period. However, a comprehensive sense of the very nature of those ballistic movements, a description of the use of fixations to gather the information relevant to solving the task at hand, and the scientific definition of saccades came with Dodge (1916) and the development of photographic techniques for recording corneal reflections. This novel recording approach paved the way for the scientific study of eye movements (see Wade, Tatler, & Heller, 2003). Buswell (1935) published the first systematic study on How People Look at Pictures: A Study of The Psychology of Perception in Art. Buswell observed that trained and untrained artists deployed similar fixation patterns to analyze paintings. All observers shared similar oculomotor behavior, deploying initial short fixations over the main features of the paintings, which were subsequently followed by a series of longer fixations. Interestingly, when fixations were collapsed across observers, they highlighted areas containing salient or diagnostic parts of the images. Critically, these observations revealed that eye moveme...

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Do Individuals with and without Autism Spectrum Disorder Scan Faces Differently? A New Multi‐Method Look at an Existing Controversy

Feng

Quinn

et al. 2013

Autism Research

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Individuals with autism spectrum disorder (ASD) are known to process faces atypically. However, there has been considerable controversy regarding whether ASD individuals also scan faces differently from typical adults. Here we compared ASD individuals' face-scanning patterns with those of typically developing (TD) controls and intellectually disabled (ID) but non-ASD individuals with the use of an eye tracker and multiple approaches to analyze eye-tracking data. First, we analyzed the eye movement data with a traditional approach, measuring fixation duration on each area of interest within the face. We found that compared with TD and ID individuals, ASD individuals looked significantly shorter at the right eye. Second, we used a data-driven method that analyzes fixations on each pixel of the face stimulus and found that individuals with ASD looked more at the central nasal area than TD and ID individuals. Third, we used a novel saccade path analysis that measures frequencies of saccades between major face areas. We found that ASD individuals scanned less often between core facial features than TD individuals but did not differ from ID individuals. Findings from the multi-method approaches show that individuals with ASD appear not to have a pervasive ASD-specific atypicality in visual attention toward the face. The ASD-specific atypical face-scanning patterns were shown to be limited to fixations on the eyes and nose.

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Accurate statistical tests for smooth classification images

Cited by 184 publications

References 54 publications

From a face to its category via a few information processing states in the brain

From a face to its category via a few information processing states in the brain

iMap: a novel method for statistical fixation mapping of eye movement data

Do Individuals with and without Autism Spectrum Disorder Scan Faces Differently? A New Multi‐Method Look at an Existing Controversy

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