Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Individualism and collectivism

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Statistical Parametric Mapping The Analysis Of Functional Brain Images

Faces are among the most important visual stimuli we perceive, informing us not only about a person’s identity, but also about their mood, sex, age and direction of gaze The ability to extract this information within a fraction of a second of viewing a face is important for normal social interactions and has probably played a critical role in the survival of our primate ancestors Considerable evidence from behavioural, neuropsychological and neurophysiological investigations supports the hypothesis that humans have specialized cognitive and neural mechanisms dedicated to the perception of faces (the face-specificity hypothesis) Here, we review the literature on a region of the human brain that appears to play a key role in face perception, known as the fusiform face area (FFA) Section 1 outlines the theoretical background for much of this work The face-specificity hypothesis falls squarely on one side of a longstanding debate in the fields of cognitive science and cognitive neuroscience concerning the extent to which the mind/brain is composed of: (i) special-purpose (‘domain-specific’) mechanisms, each dedicated to processing a specific kind of information (eg faces, according to the face-specificity hypothesis), versus (ii) general-purpose (‘domain-general’) mechanisms, each capable of operating on any kind of information Face perception has long served both as one of the prime candidates of a domain-specific process and as a key target for attack by proponents of domain-general theories of brain and mind Section 2 briefly reviews the prior literature on face perception from behaviour and neurophysiology This work supports the face-specificity hypothesis and argues against its domain-general alternatives (the individuation hypothesis, the expertise hypothesis and others) Section 3 outlines the more recent evidence on this debate from brain imaging, focusing particularly on the FFA We review the evidence that the FFA is selectively engaged in face perception, by addressing (and rebutting) five of the most widely discussed alternatives to this hypothesis In §4, we consider recent findings that are beginning to provide clues into the computations conducted in the FFA and the nature of the representations the FFA extracts from faces We argue that the FFA is engaged both in detecting faces and in extracting the necessary perceptual information to recognize them, and that the properties of the FFA mirror previously identified behavioural signatures of face-specific processing (eg the face-inversion effect) Section 5 asks how the computations and representations in the FFA differ from those occurring in other nearby regions of cortex that respond strongly to faces and objects The evidence indicates clear functional dissociations between these regions, demonstrating that the FFA shows not only functional specificity but also area specificity We end by speculating in § 6o n some of the broader questions raised by current research on the FFA, including the developmental origins of this region and the question of whether faces are unique versus whether similarly specialized mechanisms also exist for other domains of high-level perception and cognition

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The fusiform face area: a cortical region specialized for the perception of faces.

Objective Recent evidence showed that individuals with congenital face processing impairment (congenital prosopagnosia [CP]) are highly accurate when they have to recognize their own face (self-face advantage) in an implicit matching task, with a preference for the right-half of the self-face (right perceptual bias). Yet the perceptual strategies underlying this advantage are unclear. Here, we aimed to verify whether both the self-face advantage and the right perceptual bias emerge in an explicit task, and whether those effects are linked to a different scanning strategy between the self-face and unfamiliar faces. Method Eye movements were recorded from 7 CPs and 13 controls, during a self/other discrimination task of stimuli depicting the self-face and another unfamiliar face, presented upright and inverted. Results Individuals with CP and controls differed significantly in how they explored faces. In particular, compared with controls, CPs used a distinct eye movement sampling strategy for processing inverted faces, by deploying significantly more fixations toward the nose and mouth areas, which resulted in more efficient recognition. Moreover, the results confirmed the presence of a self-face advantage in both groups, but the eye movement analyses failed to reveal any differences in the exploration of the self-face compared with the unfamiliar face. Finally, no bias toward the right-half of the self-face was found. Conclusions Our data suggest that the self-face advantage emerges both in implicit and explicit recognition tasks in CPs as much as in good recognizers, and it is not linked to any specific visual exploration strategies. (PsycINFO Database Record

Mapping self-face recognition strategies in congenital prosopagnosia.

Determining the familiarity and identity of a face have been considered as independent processes. Covert face recognition in cases of acquired prosopagnosia, as well as rapid detection of familiarity have been taken to support this view. We tested P.S. a well-described case of acquired prosopagnosia, and two healthy controls (her sister and daughter) in two saccadic reaction time (SRT) experiments. Stimuli depicted their family members and well-matched unfamiliar distractors in the context of binary gender, or familiarity decisions. Observers’ minimum SRTs were estimated with Bayesian approaches. For gender decisions, P.S. and her daughter achieved sufficient performance, but displayed different SRT distributions. For familiarity decisions, her daughter exhibited above chance level performance and minimum SRTs corresponding to those reported previously in healthy observers, while P.S. performed at chance. These findings extend previous observations, indicating that decisional space determines perf...

Decisional space determines saccadic reaction times in healthy observers and acquired prosopagnosia.

Eye movements provide a functional signature of how human vision is achieved. Many recent studies have reported idiosyncratic visual sampling strategies during face recognition. Whether these inter-individual differences are mirrored by idiosyncratic neural responses has not been investigated yet. Here, we tracked observers’ eye movements during face recognition; additionally, we obtained an objective index of neural face discrimination through EEG that was recorded while subjects fixated different facial information. Across all observers, we found that those facial features that were fixated longer during face recognition elicited stronger neural face discrimination responses. This relationship occurred independently of inter-individual differences in fixation biases. Our data show that eye movements play a functional role during face processing by providing the neural system with information that is diagnostic to a specific observer. The effective processing of face identity involves idiosyncratic, rather than universal representations.

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Neural representations of faces are tuned to eye movements

First fixation toward the geometric center of human faces is common across tasks and culture

A major challenge in modern eye movement research is to statistically map where observers are looking, by isolating the significant differences between groups and conditions. As compared to the signals from contemporary neuroscience measures, such as magneto/electroencephalography and functional magnetic resonance imaging, eye movement data are sparser, with much larger variations in space across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved (Liversedge, Gilchrist, & Everling, 2011). Here, we present a new version of the iMap toolbox (Caldara & Miellet, 2011) that tackles this issue by implementing a statistical framework comparable to those developed in state-of-the-art neuroimaging data-processing toolboxes. iMap4 uses univariate, pixel-wise linear mixed models on smoothed fixation data, with the flexibility of coding for multiple between- and within-subjects comparisons and performing all possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced novel nonparametric tests based on resampling, to assess statistical significance. Finally, we validated this approach by using both experimental and Monte Carlo simulation data. iMap4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy-to-interpret statistical graphical outputs. iMap4 matches the standards of robust statistical neuroimaging methods and represents an important step in the data-driven processing of eye movement fixation data, an important field of vision sciences.

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Roberto Caldara

Papers

Mapping self-face recognition strategies in congenital prosopagnosia.

Decisional space determines saccadic reaction times in healthy observers and acquired prosopagnosia.

Neural representations of faces are tuned to eye movements

First fixation toward the geometric center of human faces is common across tasks and culture

iMap4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modeling