Graded Representations of Emotional Expressions in the Left Superior Temporal Sulcus

Perceptual categorization is a fundamental cognitive process that gives meaning to an often graded sensory environment. Previous research has subdivided the visual pathway into posterior regions that processes the physical properties of a stimulus, and frontal regions that process more abstract properties such as category information. The superior temporal sulcus (STS) is known to be involved in face and emotion perception, but the nature of its processing remains unknown. Here, we used targeted fMRI measurements of the STS to investigate whether its representations of facial expressions are categorical or noncategorical. Multivoxel pattern analysis showed that even though subjects were performing a categorization task, the left STS contained graded, noncategorical representations. In the right STS, representations showed evidence for both stimulus-related gradations and a categorical boundary

Data-driven approaches in the investigation of social perception

The complexity of social perception poses a challenge to traditional approaches to understand its psychological and neurobiological underpinnings. Data-driven methods are particularly well suited to tackling the often high-dimensional nature of stimulus spaces and of neural representations that characterize social perception. Such methods are more exploratory, capitalize on rich and large datasets, and attempt to discover patterns often without strict hypothesis testing. We present four case studies here: behavioural studies on face judgements, two neuroimaging studies of movies, and eyetracking studies in autism. We conclude with suggestions for particular topics that seem ripe for data-driven approaches, as well as caveats and limitations

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities

Naturalistic stimuli reveal a dominant role for agentic action in visual representation

Abstract Naturalistic, dynamic movies evoke strong, consistent, and information-rich patterns of activity over a broad expanse of cortex and engage multiple perceptual and cognitive systems in parallel. The use of naturalistic stimuli enables functional brain imaging research to explore cognitive domains that are poorly sampled in highly-controlled experiments. These domains include perception and understanding of agentic action, which plays a larger role in visual representation than was appreciated from experiments using static, controlled stimuli

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA

Selective Effects of Cholinergic Modulation on Task Performance during Selective Attention

The cholinergic neurotransmitter system is critically linked to cognitive functions including attention. The current studies were designed to evaluate the effect of a cholinergic agonist and an antagonist on performance during a selective visual attention task where the inherent salience of attended/unattended stimuli was modulated. Two randomized, placebo-controlled, crossover studies were performed, one (n=9) with the anticholinesterase physostigmine (1.0 mg/h), and the other (n=30) with the anticholinergic scopolamine (0.4 mc/kg). During the task, two double-exposure pictures of faces and houses were presented side by side. Subjects were cued to attend to either the face or the house component of the stimuli, and were instructed to perform a matching task with the two exemplars from the attended category. The cue changed every 4–7 trials to instruct subjects to shift attention from one stimulus component to the other. During placebo in both studies, reaction time (RT) associated with the first trial following a cued shift in attention was longer than RT associated with later trials (p<0.05); RT also was significantly longer when attending to houses than to faces (p<0.05). Physostigmine decreased RT relative to placebo preferentially during trials greater than one (p<0.05), with no change during trial one; and decreased RT preferentially during the attention to houses condition (p<0.05) vs attention to faces. Scopolamine increased RT relative to placebo selectively during trials greater than one (p<0.05), and preferentially increased RT during the attention to faces condition (p<0.05). The results suggest that enhancement or impairment of cholinergic activity preferentially influences the maintenance of selective attention (ie trials greater than 1). Moreover, effects of cholinergic manipulation depend on the selective attention condition (ie faces vs houses), which may suggest that cholinergic activity interacts with stimulus salience. The findings are discussed within the context of the role of acetylcholine both in stimulus processing and stimulus salience, and in establishing attention biases through top-down and bottom-up mechanisms of attention

Function-based Intersubject Alignment of Human Cortical Anatomy

Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis

Neural Responses to Naturalistic Clips of Behaving Animals Under Two Different Task Contexts

The human brain rapidly deploys semantic information during perception to facilitate our interaction with the world. These semantic representations are encoded in the activity of distributed populations of neurons (Haxby et al., 2001; McClelland and Rogers, 2003; Kriegeskorte et al., 2008b) and command widespread cortical real estate (Binder et al., 2009; Huth et al., 2012). The neural representation of a stimulus can be described as a location (i.e., response vector) in a high-dimensional neural representational space (Kriegeskorte and Kievit, 2013; Haxby et al., 2014). This resonates with behavioral and theoretical work describing mental representations of objects and actions as being organized in a multidimensional psychological space (Attneave, 1950; Shepard, 1958, 1987; Edelman, 1998; Gärdenfors and Warglien, 2012). Current applications of this framework to neural representation (e.g., Kriegeskorte et al., 2008b) often implicitly assume that these neural representational spaces are relatively fixed and context-invariant. In contrast, earlier work emphasized the importance of attention and task demands in actively reshaping representational space (Shepard, 1964; Tversky, 1977; Nosofsky, 1986; Kruschke, 1992). A growing body of work in both electrophysiology (e.g., Sigala and Logothetis, 2002; Sigala, 2004; Cohen and Maunsell, 2009; Reynolds and Heeger, 2009) and human neuroimaging (e.g., Hon et al., 2009; Jehee et al., 2011; Brouwer and Heeger, 2013; Çukur et al., 2013; Sprague and Serences, 2013; Harel et al., 2014; Erez and Duncan, 2015; Nastase et al., 2017) has suggested mechanisms by which behavioral goals dynamically alter neural representation