Rating norms should be calculated from cumulative link mixed effects models.

Studies which provide norms of Likert ratings typically report per-item summary statistics. Traditionally, these summary statistics comprise the mean and the standard deviation (SD) of the ratings, and the number of observations. Such summary statistics can preserve the rank order of items, but provide distorted estimates of the relative distances between items because of the ordinal nature of Likert ratings. Inter-item relations in such ordinal scales can be more appropriately modelled by cumulative link mixed effects models (CLMMs). In a series of simulations, and with a reanalysis of an existing rating norms dataset, we show that CLMMs can be used to more accurately norm items, and can provide summary statistics analogous to the traditionally reported means and SDs, but which are disentangled from participants' response biases. CLMMs can be applied to solve important statistical issues that exist for more traditional analyses of rating norms.

Healthy aging delays the neural processing of face features relevant for behavior by 40 ms.

Fast and accurate face processing is critical for everyday social interactions, but it declines and becomes delayed with age, as measured by both neural and behavioral responses. Here, we addressed the critical challenge of understanding how aging changes neural information processing mechanisms to delay behavior. Young (20-36 years) and older (60-86 years) adults performed the basic social interaction task of detecting a face versus noise while we recorded their electroencephalogram (EEG). In each participant, using a new information theoretic framework we reconstructed the features supporting face detection behavior, and also where, when and how EEG activity represents them. We found that occipital-temporal pathway activity dynamically represents the eyes of the face images for behavior ~170 ms poststimulus, with a 40 ms delay in older adults that underlies their 200 ms behavioral deficit of slower reaction times. Our results therefore demonstrate how aging can change neural information processing mechanisms that underlie behavioral slow down.

Beyond differences in means: robust graphical methods to compare two groups in neuroscience.

If many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs: to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here, we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientist's toolbox, we present two powerful tools that can help us understand how groups of observations differ: the shift function and the difference asymmetry function. These tools can be combined with detailed visualisations to provide complementary perspectives about the data. We provide implementations in R and MATLAB of the graphical tools, and all the examples in the article can be reproduced using R scripts.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc.

The Deceptively Simple N170 Reflects Network Information Processing Mechanisms Involving Visual Feature Coding and Transfer Across Hemispheres.

A key to understanding visual cognition is to determine "where", "when", and "how" brain responses reflect the processing of the specific visual features that modulate categorization behavior-the "what". The N170 is the earliest Event-Related Potential (ERP) that preferentially responds to faces. Here, we demonstrate that a paradigmatic shift is necessary to interpret the N170 as the product of an information processing network that dynamically codes and transfers face features across hemispheres, rather than as a local stimulus-driven event. Reverse-correlation methods coupled with information-theoretic analyses revealed that visibility of the eyes influences face detection behavior. The N170 initially reflects coding of the behaviorally relevant eye contralateral to the sensor, followed by a causal communication of the other eye from the other hemisphere. These findings demonstrate that the deceptively simple N170 ERP hides a complex network information processing mechanism involving initial coding and subsequent cross-hemispheric transfer of visual features.

A robust and representative lower bound on object processing speed in humans.

How early does the brain decode object categories? Addressing this question is critical to constrain the type of neuronal architecture supporting object categorization. In this context, much effort has been devoted to estimating face processing speed. With onsets estimated from 50 to 150 ms, the timing of the first face-sensitive responses in humans remains controversial. This controversy is due partially to the susceptibility of dynamic brain measurements to filtering distortions and analysis issues. Here, using distributions of single-trial event-related potentials (ERPs), causal filtering, statistical analyses at all electrodes and time points, and effective correction for multiple comparisons, we present evidence that the earliest categorical differences start around 90 ms following stimulus presentation. These results were obtained from a representative group of 120 participants, aged 18-81, who categorized images of faces and noise textures. The results were reliable across testing days, as determined by test-retest assessment in 74 of the participants. Furthermore, a control experiment showed similar ERP onsets for contrasts involving images of houses or white noise. Face onsets did not change with age, suggesting that face sensitivity occurs within 100 ms across the adult lifespan. Finally, the simplicity of the face-texture contrast, and the dominant midline distribution of the effects, suggest the face responses were evoked by relatively simple image properties and are not face specific. Our results provide a new lower benchmark for the earliest neuronal responses to complex objects in the human visual system.

The time course of visual influences in letter recognition.

This study builds on a specific characteristic of letters of the Roman alphabet-namely, that each letter name is associated with two visual formats, corresponding to their uppercase and lowercase versions. Participants had to read aloud the names of single letters, and event-related potentials (ERPs) for six pairs of visually dissimilar upper- and lowercase letters were recorded. Assuming that the end product of processing is the same for upper- and lowercase letters sharing the same vocal response, ERPs were compared backward, starting from the onset of articulatory responses, and the first significant divergence was observed 120 ms before response onset. Given that naming responses were produced at around 414 ms, on average, these results suggest that letter processing is influenced by visual information until 294 ms after stimulus onset. This therefore provides new empirical evidence regarding the time course and interactive nature of visual letter perception processes.

Eye coding mechanisms in early human face event-related potentials.

In humans, the N170 event-related potential (ERP) is an integrated measure of cortical activity that varies in amplitude and latency across trials. Researchers often conjecture that N170 variations reflect cortical mechanisms of stimulus coding for recognition. Here, to settle the conjecture and understand cortical information processing mechanisms, we unraveled the coding function of N170 latency and amplitude variations in possibly the simplest socially important natural visual task: face detection. On each experimental trial, 16 observers saw face and noise pictures sparsely sampled with small Gaussian apertures. Reverse-correlation methods coupled with information theory revealed that the presence of the eye specifically covaries with behavioral and neural measurements: the left eye strongly modulates reaction times and lateral electrodes represent mainly the presence of the contralateral eye during the rising part of the N170, with maximum sensitivity before the N170 peak. Furthermore, single-trial N170 latencies code more about the presence of the contralateral eye than N170 amplitudes and early latencies are associated with faster reaction times. The absence of these effects in control images that did not contain a face refutes alternative accounts based on retinal biases or allocation of attention to the eye location on the face. We conclude that the rising part of the N170, roughly 120-170 ms post-stimulus, is a critical time-window in human face processing mechanisms, reflecting predominantly, in a face detection task, the encoding of a single feature: the contralateral eye.

Neural repetition suppression to identity is abolished by other-race faces.

Human beings are remarkably skilled at recognizing faces, with the marked exception of other-race faces: the so-called "other-race effect." As reported nearly a century ago [Feingold CA (1914) Journal of Criminal Law and Police Science 5:39-51], this face-recognition impairment is accompanied by the popular belief that other-race faces all look alike. However, the neural mechanisms underlying this high-level "perceptual illusion" are still unknown. To address this question, we recorded high-resolution electrophysiological scalp signals from East Asian (EA) and Western Caucasian (WC) observers as they viewed two EA or WC faces. The first adaptor face was followed by a target face of either the same or different identity. We quantified repetition suppression (RS), a reduction in neural activity in stimulus-sensitive regions following stimulus repetition. Conventional electrophysiological analyses on target faces failed to reveal any RS effect. However, to fully account for the paired nature of RS events, we subtracted the signal elicited by target to adaptor faces for each single trial and performed unbiased spatiotemporal data-driven analyses. This unique approach revealed stronger RS to same-race faces of same identity in both groups of observers on the face-sensitive N170 component. Such neurophysiological modulation in RS suggests efficient identity coding for same-race faces. Strikingly, OR faces elicited identical RS regardless of identity, all looking alike to the neural population underlying the N170. Our data show that sensitivity to race begins early at the perceptual level, providing, after nearly 100 y of investigations, a neurophysiological correlate of the "all look alike" perceptual experience.

An Updated Guide to Robust Statistical Methods in Neuroscience.

There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of false positives, and yielding a deeper and more nuanced understanding of data. These new techniques effectively deal with four insights into when and why conventional methods can be unsatisfactory. But for the non-statistician, this vast array of techniques for comparing groups and studying associations can seem daunting. This article briefly reviews when and why conventional methods can have relatively low power and yield misleading results. The main goal is to suggest guidelines regarding the use of modern techniques that improve upon classic approaches such as Pearson's correlation, ordinary linear regression, ANOVA, and ANCOVA. This updated version includes recent advances dealing with effect sizes, including situations where there is a covariate. The R code, figures, and accompanying notebooks have been updated as well. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Early ERPs to faces and objects are driven by phase, not amplitude spectrum information: evidence from parametric, test-retest, single-subject analyses.

One major challenge in determining how the brain categorizes objects is to tease apart the contribution of low-level and high-level visual properties to behavioral and brain imaging data. So far, studies using stimuli with equated amplitude spectra have shown that the visual system relies mostly on localized information, such as edges and contours, carried by phase information. However, some researchers have argued that some event-related potentials (ERP) and blood-oxygen-level-dependent (BOLD) categorical differences could be driven by nonlocalized information contained in the amplitude spectrum. The goal of this study was to provide the first systematic quantification of the contribution of phase and amplitude spectra to early ERPs to faces and objects. We conducted two experiments in which we recorded electroencephalograms (EEG) from eight subjects, in two sessions each. In the first experiment, participants viewed images of faces and houses containing original or scrambled phase spectra combined with original, averaged, or swapped amplitude spectra. In the second experiment, we parametrically manipulated image phase and amplitude in 10% intervals. We performed a range of analyses including detailed single-subject general linear modeling of ERP data, test-retest reliability, and unique variance analyses. Our results suggest that early ERPs to faces and objects are due to phase information, with almost no contribution from the amplitude spectrum. Importantly, our results should not be used to justify uncontrolled stimuli; to the contrary, our results emphasize the need for stimulus control (including the amplitude spectrum), parametric designs, and systematic data analyses, of which we have seen far too little in ERP vision research.

Reliability of ERP and single-trial analyses.

A reliable measure is one we can trust in the long run. Thus, the reliability of measurements is as important as their validity. Here we investigated the reliability of brain electrical visual evoked responses to faces and noise textures. For the first time, we provide reliability measures for the full time course of event-related potentials (ERPs). Our analyses were also performed on a R(2)(t) metric that reflects results from single-trial analyses, therefore providing the first reliability analysis of ERP single-trial analyses. Results show that ERPs and R(2)(t) are highly reliable (cross-correlation ~0.9, lag ~4/6ms, intra-class correlation ~0.9) but also idiosyncratic: ERPs and R(2)(t) are highly reproducible within subjects, who differ reliably from each other and the grand average across subjects. Consequently, grand averages, although highly reliable, can be misleading because they might not reflect the actual brain dynamic of any subjects.

Inverting faces elicits sensitivity to race on the N170 component: a cross-cultural study.

Human beings are natural experts at processing faces, with some notable exceptions. Same-race faces are better recognized than other-race faces: the so-called other-race effect (ORE). Inverting faces impairs recognition more than for any other inverted visual object: the so-called face inversion effect (FIE). Interestingly, the FIE is stronger for same- compared to other-race faces. At the electrophysiological level, inverted faces elicit consistently delayed and often larger N170 compared to upright faces. However, whether the N170 component is sensitive to race is still a matter of ongoing debate. Here we investigated the N170 sensitivity to race in the framework of the FIE. We recorded EEG from Western Caucasian and East Asian observers while presented with Western Caucasian, East Asian and African American faces in upright and inverted orientations. To control for potential confounds in the EEG signal that might be evoked by the intrinsic and salient differences in the low-level properties of faces from different races, we normalized their amplitude-spectra, luminance and contrast. No differences on the N170 were observed for upright faces. Critically, inverted same-race faces lead to greater recognition impairment and elicited larger N170 amplitudes compared to inverted other-race faces. Our results indicate a finer-grained neural tuning for same-race faces at early stages of processing in both groups of observers.

Dissociable effects of prediction and integration during language comprehension: evidence from a large-scale study using brain potentials.

Composing sentence meaning is easier for predictable words than for unpredictable words. Are predictable words genuinely predicted, or simply more plausible and therefore easier to integrate with sentence context? We addressed this persistent and fundamental question using data from a recent, large-scale (n = 334) replication study, by investigating the effects of word predictability and sentence plausibility on the N400, the brain's electrophysiological index of semantic processing. A spatio-temporally fine-grained mixed-effect multiple regression analysis revealed overlapping effects of predictability and plausibility on the N400, albeit with distinct spatio-temporal profiles. Our results challenge the view that the predictability-dependent N400 reflects the effects of either prediction or integration, and suggest that semantic facilitation of predictable words arises from a cascade of processes that activate and integrate word meaning with context into a sentence-level meaning. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.

Brain classification reveals the right cerebellum as the best biomarker of dyslexia.

BACKGROUND: Developmental dyslexia is a specific cognitive disorder in reading acquisition that has genetic and neurological origins. Despite histological evidence for brain differences in dyslexia, we recently demonstrated that in large cohort of subjects, no differences between control and dyslexic readers can be found at the macroscopic level (MRI voxel), because of large variances in brain local volumes. In the present study, we aimed at finding brain areas that most discriminate dyslexic from control normal readers despite the large variance across subjects. After segmenting brain grey matter, normalizing brain size and shape and modulating the voxels' content, normal readers' brains were used to build a 'typical' brain via bootstrapped confidence intervals. Each dyslexic reader's brain was then classified independently at each voxel as being within or outside the normal range. We used this simple strategy to build a brain map showing regional percentages of differences between groups. The significance of this map was then assessed using a randomization technique. RESULTS: The right cerebellar declive and the right lentiform nucleus were the two areas that significantly differed the most between groups with 100% of the dyslexic subjects (N = 38) falling outside of the control group (N = 39) 95% confidence interval boundaries. The clinical relevance of this result was assessed by inquiring cognitive brain-based differences among dyslexic brain subgroups in comparison to normal readers' performances. The strongest difference between dyslexic subgroups was observed between subjects with lower cerebellar declive (LCD) grey matter volumes than controls and subjects with higher cerebellar declive (HCD) grey matter volumes than controls. Dyslexic subjects with LCD volumes performed worse than subjects with HCD volumes in phonologically and lexicon related tasks. Furthermore, cerebellar and lentiform grey matter volumes interacted in dyslexic subjects, so that lower and higher lentiform grey matter volumes compared to controls differently modulated the phonological and lexical performances. Best performances (observed in controls) corresponded to an optimal value of grey matter and they dropped for higher or lower volumes. CONCLUSION: These results provide evidence for the existence of various subtypes of dyslexia characterized by different brain phenotypes. In addition, behavioural analyses suggest that these brain phenotypes relate to different deficits of automatization of language-based processes such as grapheme/phoneme correspondence and/or rapid access to lexicon entries.

Age-related delay in information accrual for faces: evidence from a parametric, single-trial EEG approach.

BACKGROUND: In this study, we quantified age-related changes in the time-course of face processing by means of an innovative single-trial ERP approach. Unlike analyses used in previous studies, our approach does not rely on peak measurements and can provide a more sensitive measure of processing delays. Young and old adults (mean ages 22 and 70 years) performed a non-speeded discrimination task between two faces. The phase spectrum of these faces was manipulated parametrically to create pictures that ranged between pure noise (0% phase information) and the undistorted signal (100% phase information), with five intermediate steps. RESULTS: Behavioural 75% correct thresholds were on average lower, and maximum accuracy was higher, in younger than older observers. ERPs from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The earliest age-related ERP differences occurred in the time window of the N170. Older observers had a significantly stronger N170 in response to noise, but this age difference decreased with increasing phase information. Overall, manipulating image phase information had a greater effect on ERPs from younger observers, which was quantified using a hierarchical modelling approach. Importantly, visual activity was modulated by the same stimulus parameters in younger and older subjects. The fit of the model, indexed by R2, was computed at multiple post-stimulus time points. The time-course of the R2 function showed a significantly slower processing in older observers starting around 120 ms after stimulus onset. This age-related delay increased over time to reach a maximum around 190 ms, at which latency younger observers had around 50 ms time lead over older observers. CONCLUSION: Using a component-free ERP analysis that provides a precise timing of the visual system sensitivity to image structure, the current study demonstrates that older observers accumulate face information more slowly than younger subjects. Additionally, the N170 appears to be less face-sensitive in older observers.

Electrophysiological evidence for an early processing of human voices.

BACKGROUND: Previous electrophysiological studies have identified a "voice specific response" (VSR) peaking around 320 ms after stimulus onset, a latency markedly longer than the 70 ms needed to discriminate living from non-living sound sources and the 150 ms to 200 ms needed for the processing of voice paralinguistic qualities. In the present study, we investigated whether an early electrophysiological difference between voice and non-voice stimuli could be observed. RESULTS: ERPs were recorded from 32 healthy volunteers who listened to 200 ms long stimuli from three sound categories - voices, bird songs and environmental sounds - whilst performing a pure-tone detection task. ERP analyses revealed voice/non-voice amplitude differences emerging as early as 164 ms post stimulus onset and peaking around 200 ms on fronto-temporal (positivity) and occipital (negativity) electrodes. CONCLUSION: Our electrophysiological results suggest a rapid brain discrimination of sounds of voice, termed the "fronto-temporal positivity to voices" (FTPV), at latencies comparable to the well-known face-preferential N170.

Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise.

This study aimed to determine the extent to which rapid visual context categorization relies on global scene statistics, such as diagnostic amplitude spectrum information. We measured performance in a Natural vs. Man-made context categorization task using a set of achromatic photographs of natural scenes equalized in average luminance, global contrast, and spectral energy. Results suggest that the visual system might use amplitude spectrum characteristics of the scenes to speed up context categorization processes. In a second experiment, we measured performance impairments with a parametric degradation of phase information applied to power spectrum averaged scenes. Results showed that performance accuracy was virtually unaffected up to 50% of phase blurring, but then rapidly fell to chance level following a sharp sigmoid curve. Response time analysis showed that subjects tended to make their fastest responses based on the presence of diagnostic man-made information; if no man-made characteristics enable to reach rapidly a decision threshold, because of a natural scene display or a high level of noise, the alternative decision for a natural response became increasingly favored. This two-phase strategy could maximize categorization performance if the diagnostic features of man-made environments tolerate higher levels of noise than natural features, as proposed recently.