Information processing algorithms in the brain

Pre-frontal cortex guides dimension-reducing transformations in the occipito-ventral pathway for categorization behaviors

To interpret our surroundings, the brain uses a visual categorization process. Current theories and models suggest that this process comprises a hierarchy of different computations that transforms complex, high-dimensional inputs into lower-dimensional representations (i.e., manifolds) in support of multiple categorization behaviors. Here, we tested this hypothesis by analyzing these transformations reflected in dynamic MEG source activity while individual participants actively categorized the same stimuli according to different tasks: face expression, face gender, pedestrian gender, and vehicle type. Results reveal three transformation stages guided by the pre-frontal cortex. At stage 1 (high-dimensional, 50-120 ms), occipital sources represent both task-relevant and task-irrelevant stimulus features; task-relevant features advance into higher ventral/dorsal regions, whereas task-irrelevant features halt at the occipital-temporal junction. At stage 2 (121-150 ms), stimulus feature representations reduce to lower-dimensional manifolds, which then transform into the task-relevant features underlying categorization behavior over stage 3 (161-350 ms). Our findings shed light on how the brain's network mechanisms transform high-dimensional inputs into specific feature manifolds that support multiple categorization behaviors.

Computational reconstruction of mental representations using human behavior

Revealing how the mind represents information is a longstanding goal of cognitive science. However, there is currently no framework for reconstructing the broad range of mental representations that humans possess. Here, we ask participants to indicate what they perceive in images made of random visual features in a deep neural network. We then infer associations between the semantic features of their responses and the visual features of the images. This allows us to reconstruct the mental representations of multiple visual concepts, both those supplied by participants and other concepts extrapolated from the same semantic space. We validate these reconstructions in separate participants and further generalize our approach to predict behavior for new stimuli and in a new task. Finally, we reconstruct the mental representations of individual observers and of a neural network. This framework enables a large-scale investigation of conceptual representations.

Multimodal investigations of emotional face processing and social trait judgment of faces

Faces are among the most important visual stimuli that humans perceive in everyday life. While extensive literature has examined emotional processing and social evaluations of faces, most studies have examined either topic using unimodal approaches. In this review, we promote the use of multimodal cognitive neuroscience approaches to study these processes, using two lines of research as examples: ambiguity in facial expressions of emotion and social trait judgment of faces. In the first set of studies, we identified an event-related potential that signals emotion ambiguity using electroencephalography and we found convergent neural responses to emotion ambiguity using functional neuroimaging and single-neuron recordings. In the second set of studies, we discuss how different neuroimaging and personality-dimensional approaches together provide new insights into social trait judgments of faces. In both sets of studies, we provide an in-depth comparison between neurotypicals and people with autism spectrum disorder. We offer a computational account for the behavioral and neural markers of the different facial processing between the two groups. Finally, we suggest new practices for studying the emotional processing and social evaluations of faces. All data discussed in the case studies of this review are publicly available.

Strength of predicted information content in the brain biases decision behavior

Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate their categorization.1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 However, it remains unknown what the information contents of these predictions are, which hinders mechanistic explanations. This is because typical approaches cast predictions as an underconstrained contrast between two categories18,19,20,21,22,23,24-e.g., faces versus cars, which could lead to predictions of features specific to faces or cars, or features from both categories. Here, to pinpoint the information contents of predictions and thus their mechanistic processing in the brain, we identified the features that enable two different categorical perceptions of the same stimuli. We then trained multivariate classifiers to discern, from dynamic MEG brain responses, the features tied to each perception. With an auditory cueing design, we reveal where, when, and how the brain reactivates visual category features (versus the typical category contrast) before the stimulus is shown. We demonstrate that the predictions of category features have a more direct influence (bias) on subsequent decision behavior in participants than the typical category contrast. Specifically, these predictions are more precisely localized in the brain (lateralized), are more specifically driven by the auditory cues, and their reactivation strength before a stimulus presentation exerts a greater bias on how the individual participant later categorizes this stimulus. By characterizing the specific information contents that the brain predicts and then processes, our findings provide new insights into the brain's mechanisms of prediction for perception.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.

Degrees of algorithmic equivalence between the brain and its DNN models

Deep neural networks (DNNs) have become powerful and increasingly ubiquitous tools to model human cognition, and often produce similar behaviors. For example, with their hierarchical, brain-inspired organization of computations, DNNs apparently categorize real-world images in the same way as humans do. Does this imply that their categorization algorithms are also similar? We have framed the question with three embedded degrees that progressively constrain algorithmic similarity evaluations: equivalence of (i) behavioral/brain responses, which is current practice, (ii) the stimulus features that are processed to produce these outcomes, which is more constraining, and (iii) the algorithms that process these shared features, the ultimate goal. To improve DNNs as models of cognition, we develop for each degree an increasingly constrained benchmark that specifies the epistemological conditions for the considered equivalence.

Communicating and reading emotion with masked faces in the Covid era: A short review of the literature

Face masks have proven to be key to slowing down the SARS-Cov2 virus spread in the COVID-19 pandemic context. However, wearing face masks is not devoid of "side-effects", at both the physical and psychosocial levels. In particular, masks hinder emotion reading from facial expressions as they hide a significant part of the face. This disturbs both holistic and featural processing of facial expressions and, therefore, impairs emotion recognition, and influences many aspects of human social behavior. Communication in general is disrupted by face masks, as they modify the wearer's voice and prevent the audience from using lip reading or other non-verbal cues for speech comprehension. Individuals suffering from psychiatric conditions with impairment of communication, are at higher risk of distress because masks increase their difficulties to read emotions from faces. The identification and acknowledgement of these "side-effects" on communication are necessary because they warrant further work on adaptive solutions that will help foster the use of face masks by the greatest number.

A data-driven, hyper-realistic method for visualizing individual mental representations of faces

Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others (e.g., “X type of face looks trustworthy”). However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level variation for many social trait judgments. Despite this insight, little research to date has focused on building and explaining individual models of face perception. Studies and methodologies that have examined individual models are limited in what visualizations they can reliably produce to either noisy and blurry or computer avatar representations. Methods that produce low-fidelity visual representations inhibit generalizability by being clearly computer manipulated and produced. In the present work, we introduce a novel paradigm to visualize individual models of face judgments by leveraging state-of-the-art computer vision methods. Our proposed method can produce a set of photorealistic face images that correspond to an individual's mental representation of a specific attribute across a variety of attribute intensities. We provide a proof-of-concept study which examines perceived trustworthiness/untrustworthiness and masculinity/femininity. We close with a discussion of future work to substantiate our proposed method.

Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity

Deep neural networks (DNNs) can resolve real-world categorization tasks with apparent human-level performance. However, true equivalence of behavioral performance between humans and their DNN models requires that their internal mechanisms process equivalent features of the stimulus. To develop such feature equivalence, our methodology leveraged an interpretable and experimentally controlled generative model of the stimuli (realistic three-dimensional textured faces). Humans rated the similarity of randomly generated faces to four familiar identities. We predicted these similarity ratings from the activations of five DNNs trained with different optimization objectives. Using information theoretic redundancy, reverse correlation, and the testing of generalization gradients, we show that DNN predictions of human behavior improve because their shape and texture features overlap with those that subsume human behavior. Thus, we must equate the functional features that subsume the behavioral performances of the brain and its models before comparing where, when, and how these features are processed.

Visual Imagery and Perception Share Neural Representations in the Alpha Frequency Band

To behave adaptively with sufficient flexibility, biological organisms must cognize beyond immediate reaction to a physically present stimulus. For this, humans use visual mental imagery [1, 2], the ability to conjure up a vivid internal experience from memory that stands in for the percept of the stimulus. Visually imagined contents subjectively mimic perceived contents, suggesting that imagery and perception share common neural mechanisms. Using multivariate pattern analysis on human electroencephalography (EEG) data, we compared the oscillatory time courses of mental imagery and perception of objects. We found that representations shared between imagery and perception emerged specifically in the alpha frequency band. These representations were present in posterior, but not anterior, electrodes, suggesting an origin in parieto-occipital cortex. Comparison of the shared representations to computational models using representational similarity analysis revealed a relationship to later layers of deep neural networks trained on object representations, but not auditory or semantic models, suggesting representations of complex visual features as the basis of commonality. Together, our results identify and characterize alpha oscillations as a cortical signature of representations shared between visual mental imagery and perception.

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Perception and imagery share neural representations in the alpha frequency band

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Shared representations stem from parieto-occipital sources

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Modeling suggests contents of shared representations are complex visual features

Object Recognition: Complexity of Recognition Strategies

Primate brains and state-of-the-art convolutional neural networks can recognize many faces, objects and scenes, though how they do so is often mysterious. New research unveils some of the mystery, revealing unexpected complexity in the recognition strategies of rodents.

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.

Adaptive shape coding for perceptual decisions in the human brain

In its search for neural codes, the field of visual neuroscience has uncovered neural representations that reflect the structure of stimuli of variable complexity from simple features to object categories. However, accumulating evidence suggests an adaptive neural code that is dynamically shaped by experience to support flexible and efficient perceptual decisions. Here, we review work showing that experience plays a critical role in molding midlevel visual representations for perceptual decisions. Combining behavioral and brain imaging measurements, we demonstrate that learning optimizes feature binding for object recognition in cluttered scenes, and tunes the neural representations of informative image parts to support efficient categorical judgements. Our findings indicate that similar learning mechanisms may mediate long-term optimization through development, tune the visual system to fundamental principles of feature binding, and optimize feature templates for perceptual decisions.

Phase noise reveals early category-specific modulation of the event-related potentials

Previous studies have found that the amplitude of the early event-related potential (ERP) components evoked by faces, such as N170 and P2, changes systematically as a function of noise added to the stimuli. This change has been linked to an increased perceptual processing demand and to enhanced difficulty in perceptual decision making about faces. However, to date it has not yet been tested whether noise manipulation affects the neural correlates of decisions about face and non-face stimuli similarly. To this end, we measured the ERPs for faces and cars at three different phase noise levels. Subjects performed the same two-alternative age-discrimination task on stimuli chosen from young–old morphing continua that were created from faces as well as cars and were calibrated to lead to similar performances at each noise-level. Adding phase noise to the stimuli reduced performance and enhanced response latency for the two categories to the same extent. Parallel to that, phase noise reduced the amplitude and prolonged the latency of the face-specific N170 component. The amplitude of the P1 showed category-specific noise dependence: it was enhanced over the right hemisphere for cars and over the left hemisphere for faces as a result of adding phase noise to the stimuli, but remained stable across noise levels for cars over the left and for faces over the right hemisphere. Moreover, noise modulation altered the category-selectivity of the N170, while the P2 ERP component, typically associated with task decision difficulty, was larger for the more noisy stimuli regardless of stimulus category. Our results suggest that the category-specificity of noise-induced modulations of ERP responses starts at around 100 ms post-stimulus.

Seeing Jesus in toast: neural and behavioral correlates of face pareidolia

Face pareidolia is the illusory perception of non-existent faces. The present study, for the first time, contrasted behavioral and neural responses of face pareidolia with those of letter pareidolia to explore face-specific behavioral and neural responses during illusory face processing. Participants were shown pure-noise images but were led to believe that 50% of them contained either faces or letters; they reported seeing faces or letters illusorily 34% and 38% of the time, respectively. The right fusiform face area (rFFA) showed a specific response when participants "saw" faces as opposed to letters in the pure-noise images. Behavioral responses during face pareidolia produced a classification image (CI) that resembled a face, whereas those during letter pareidolia produced a CI that was letter-like. Further, the extent to which such behavioral CIs resembled faces was directly related to the level of face-specific activations in the rFFA. This finding suggests that the rFFA plays a specific role not only in processing of real faces but also in illusory face perception, perhaps serving to facilitate the interaction between bottom-up information from the primary visual cortex and top-down signals from the prefrontal cortex (PFC). Whole brain analyses revealed a network specialized in face pareidolia, including both the frontal and occipitotemporal regions. Our findings suggest that human face processing has a strong top-down component whereby sensory input with even the slightest suggestion of a face can result in the interpretation of a face.

Prestimulus alpha power predicts fidelity of sensory encoding in perceptual decision making

Pre-stimulus α power has been shown to correlate with the behavioral accuracy of perceptual decisions. In most cases, these correlations have been observed by comparing α power for different behavioral outcomes (e.g. correct vs incorrect trials). In this paper we investigate such covariation within the context of behaviorally-latent fluctuations in task-relevant post-stimulus neural activity. Specially we consider variations of pre-stimulus α power with post-stimulus EEG components in a two alternative forced choice visual discrimination task. EEG components, discriminative of stimulus class, are identified using a linear multivariate classifier and only the variability of the components for correct trials (regardless of stimulus class, and for nominally identical stimuli) are correlated with the corresponding pre-stimulus α power. We find a significant relationship between the mean and variance of the pre-stimulus α power and the variation of the trial-to-trial magnitude of an early post-stimulus EEG component. This relationship is not seen for a later EEG component that is also discriminative of stimulus class and which has been previously linked to the quality of evidence driving the decision process. Our results suggest that early perceptual representations, rather than temporally later neural correlates of the perceptual decision, are modulated by pre-stimulus state.

Event-related potentials to unattended changes in facial expressions: detection of regularity violations or encoding of emotions?

Visual mismatch negativity (vMMN), a component in event-related potentials (ERPs), can be elicited when rarely presented “deviant” facial expressions violate regularity formed by repeated “standard” faces. vMMN is observed as differential ERPs elicited between the deviant and standard faces. It is not clear, however, whether differential ERPs to rare emotional faces interspersed with repeated neutral ones reflect true vMMN (i.e., detection of regularity violation) or merely encoding of the emotional content in the faces. Furthermore, a face-sensitive N170 response, which reflects structural encoding of facial features, can be modulated by emotional expressions. Owing to its similar latency and scalp topography with vMMN, these two components are difficult to separate. We recorded ERPs to neutral, fearful, and happy faces in two different stimulus presentation conditions in adult humans. For the oddball condition group, frequently presented neutral expressions (p = 0.8) were rarely replaced by happy or fearful expressions (p = 0.1), whereas for the equiprobable condition group, fearful, happy, and neutral expressions were presented with equal probability (p = 0.33). Independent component analysis (ICA) revealed two prominent components in both stimulus conditions in the relevant latency range and scalp location. A component peaking at 130 ms post stimulus showed a difference in scalp topography between the oddball (bilateral) and the equiprobable (right-dominant) conditions. The other component, peaking at 170 ms post stimulus, showed no difference between the conditions. The bilateral component at the 130-ms latency in the oddball condition conforms to vMMN. Moreover, it was distinct from N170 which was modulated by the emotional expression only. The present results suggest that future studies on vMMN to facial expressions should take into account possible confounding effects caused by the differential processing of the emotional expressions as such.

Learning optimizes decision templates in the human visual cortex

Translating sensory information into perceptual decisions is a core challenge faced by the brain. This ability is understood to rely on weighting sensory evidence in order to form mental templates of the critical differences between objects. Learning is shown to optimize these templates for efficient task performance, but the neural mechanisms underlying this improvement remain unknown. Here, we identify the mechanisms that the brain uses to implement templates for perceptual decisions through experience. We trained observers to discriminate visual forms that were randomly perturbed by noise. To characterize the internal stimulus template that observers learn when performing this task, we adopted a classification image approach (e.g., [5-7]) for the analysis of both behavioral and fMRI data. By reverse correlating behavioral and multivoxel pattern responses with noisy stimulus trials, we identified the critical image parts that determine the observers' choice. Observers learned to integrate information across locations and weight the discriminative image parts. Training enhanced shape processing in the lateral occipital area, which was shown to reflect size-invariant representations of informative image parts. Our findings demonstrate that learning optimizes mental templates for perceptual decisions by tuning the representation of informative image parts in higher ventral cortex.

Reverse-correlating mental representations of sex-typed bodies: the effect of number of trials on image quality

Sex categorization is a critical process in social perception. While psychologists have long theorized that perceivers have distinct mental representations of men and women that help them to achieve efficient sex categorizations, researchers have only recently begun using reverse-correlation to visualize the content of these mental representations. The present research addresses two issues concerning this relatively new methodological tool. First, previous studies of reverse-correlation have focused almost exclusively on perceivers' mental representations of faces. Our study demonstrates that this technique can also be used to visualize mental representations of sex-typed bodies. Second, most studies of reverse-correlation have employed a relatively large number of trials (1000+) to capture perceivers' mental representations of a given category. Our study demonstrated that, at least for sex-typed representations of bodies, high quality reverse-correlation images can be obtained with as few as 100 trials. Overall, our findings enhance knowledge of reverse-correlation methodology in general and sex categorization in particular, providing new information for researchers interested in using this technique to understand the complex processes underlying social perception.

Early ERPs to faces: aging, luminance, and individual differences

Recently, Rousselet et al. reported a 1 ms/year delay in visual processing speed in a sample of healthy aged 62 subjects (Frontiers in Psychology 2010, 1:19). Here, we replicate this finding in an independent sample of 59 subjects and investigate the contribution of optical factors (pupil size and luminance) to the age-related slowdown and to individual differences in visual processing speed. We conducted two experiments. In experiment 1 we recorded EEG from subjects aged 18–79. Subjects viewed images of faces and phase scrambled noise textures under nine luminance conditions, ranging from 0.59 to 60.8 cd/m². We manipulated luminance using neutral density filters. In experiment 2, 10 young subjects (age < 35) viewed similar stimuli through pinholes ranging from 1 to 5 mm. In both experiments, subjects were tested twice. We found a 1 ms/year slowdown in visual processing that was independent of luminance. Aging effects became visible around 125 ms post-stimulus and did not affect the onsets of the face-texture ERP differences. Furthermore, luminance modulated the entire ERP time-course from 60 to 500 ms. Luminance effects peaked in the N170 time window and were independent of age. Importantly, senile miosis and individual differences in pupil size did not account for aging differences and inter-subject variability in processing speed. The pinhole manipulation also failed to match the ERPs of old subjects to those of young subjects. Overall, our results strongly suggest that early ERPs to faces (<200 ms) are delayed by aging and that these delays are of cortical, rather than optical origin. Our results also demonstrate that even late ERPs to faces are modulated by low-level factors.

Measuring internal representations from behavioral and brain data

The study of internal knowledge representations is a cornerstone of the research agenda in the interdisciplinary study of cognition. An influential proposal assumes that the brain uses its internal knowledge of the external world to constrain, in a top-down manner, high-dimensional sensory data into a lower-dimensional representation that enables perceptual decisions and other higher-level cognitive functions [1-9]. This proposal relies on a precise formulation of the observer-specific internal knowledge (i.e., the internal representations, or models) that guides reduction of the high-dimensional retinal input onto a low-dimensional code. Here, we directly revealed the content of subjective internal representations by instructing five observers to detect a face in the presence of only white noise, to force a pure top-down, knowledge-based task. We used reverse correlation methods to visualize each observer's internal representation that supports detection of an illusory face. Using reverse correlation again, this time applied to observers' electroencephalogram activity, we established where and when in the brain specific internal knowledge conceptually interprets the input white noise as a face. We show that internal representations can be reconstructed experimentally from behavioral and brain data, and that their content drives neural activity first over frontal and then over occipitotemporal cortex.

Parsing a perceptual decision into a sequence of moments of thought

Theoretical, computational, and experimental studies have converged to a model of decision-making in which sensory evidence is stochastically integrated to a threshold, implementing a shift from an analog to a discrete form of computation. Understanding how this process can be chained and sequenced – as virtually all real-life tasks involve a sequence of decisions – remains an open question in neuroscience. We reasoned that incorporating a virtual continuum of possible behavioral outcomes in a simple decision task – a fundamental ingredient of real-life decision-making – should result in a progressive sequential approximation to the correct response. We used real-time tracking of motor action in a decision task, as a measure of cognitive states reflecting an internal decision process. We found that response trajectories were spontaneously segmented into a discrete sequence of explorations separated by brief stops (about 200 ms) – which remained unconscious to the participants. The characteristics of these stops were indicative of a decision process – a “moment of thought”: their duration correlated with the difficulty of the decision and with the efficiency of the subsequent exploration. Our findings suggest that simple navigation in an abstract space involves a discrete sequence of explorations and stops and, moreover, that these stops reveal a fingerprint of moments of thought.

Modeling Single-Trial ERP Reveals Modulation of Bottom-Up Face Visual Processing by Top-Down Task Constraints (in Some Subjects)

We studied how task constraints modulate the relationship between single-trial event-related potentials (ERPs) and image noise. Thirteen subjects performed two interleaved tasks: on different blocks, they saw the same stimuli, but they discriminated either between two faces or between two colors. Stimuli were two pictures of red or green faces that contained from 10 to 80% of phase noise, with 10% increments. Behavioral accuracy followed a noise dependent sigmoid in the identity task but was high and independent of noise level in the color task. EEG data recorded concurrently were analyzed using a single-trial ANCOVA: we assessed how changes in task constraints modulated ERP noise sensitivity while regressing out the main ERP differences due to identity, color, and task. Single-trial ERP sensitivity to image phase noise started at about 95-110 ms post-stimulus onset. Group analyses showed a significant reduction in noise sensitivity in the color task compared to the identity task from about 140 ms to 300 ms post-stimulus onset. However, statistical analyses in every subject revealed different results: significant task modulation occurred in 8/13 subjects, one showing an increase and seven showing a decrease in noise sensitivity in the color task. Onsets and durations of effects also differed between group and single-trial analyses: at any time point only a maximum of four subjects (31%) showed results consistent with group analyses. We provide detailed results for all 13 subjects, including a shift function analysis that revealed asymmetric task modulations of single-trial ERP distributions. We conclude that, during face processing, bottom-up sensitivity to phase noise can be modulated by top-down task constraints, in a broad window around the P2, at least in some subjects.

The human Turing machine: a neural framework for mental programs

In recent years much has been learned about how a single computational processing step is implemented in the brain. By contrast, we still have surprisingly little knowledge of the neuronal mechanisms by which multiple such operations are sequentially assembled into mental algorithms. We outline a theory of how individual neural processing steps might be combined into serial programs. We propose a hybrid neuronal device: each step involves massively parallel computation that feeds a slow and serial production system. Production selection is mediated by a system of competing accumulator neurons that extends the role of these neurons beyond the selection of a motor action. Productions change the state of sensory and mnemonic neurons and iteration of such cycles provides a basis for mental programs.

Quantifying the Time Course of Visual Object Processing Using ERPs: It's Time to Up the Game

Hundreds of studies have investigated the early ERPs to faces and objects using scalp and intracranial recordings. The vast majority of these studies have used uncontrolled stimuli, inappropriate designs, peak measurements, poor figures, and poor inferential and descriptive group statistics. These problems, together with a tendency to discuss any effect p < 0.05 rather than to report effect sizes, have led to a research field very much qualitative in nature, despite its quantitative inspirations, and in which predictions do not go beyond condition A > condition B. Here we describe the main limitations of face and object ERP research and suggest alternative strategies to move forward. The problems plague intracranial and surface ERP studies, but also studies using more advanced techniques – e.g., source space analyses and measurements of network dynamics, as well as many behavioral, fMRI, TMS, and LFP studies. In essence, it is time to stop amassing binary results and start using single-trial analyses to build models of visual perception.

Healthy aging delays scalp EEG sensitivity to noise in a face discrimination task

We used a single-trial ERP approach to quantify age-related changes in the time-course of noise sensitivity. A total of 62 healthy adults, aged between 19 and 98, performed a non-speeded discrimination task between two faces. Stimulus information was controlled by parametrically manipulating the phase spectrum of these faces. Behavioral 75% correct thresholds increased with age. This result may be explained by lower signal-to-noise ratios in older brains. ERP 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 fit of the model, indexed by R², was computed at multiple post-stimulus time points. The time-course of the R² function showed significantly delayed noise sensitivity in older observers. This age effect is reliable, as demonstrated by test–retest in 24 subjects, and started about 120 ms after stimulus onset. Our analyses suggest also a qualitative change from a young to an older pattern of brain activity at around 47 ± 4 years old.