Behavior is governed by rules that associate stimuli with responses and outcomes. Human and monkey studies have shown that rulespecific information is widely represented in the frontoparietal cortex. However, it is not known how establishing a rule under different contexts affects its neural representation. Here, we use event-related functional MRI (fMRI) and multivoxel pattern classification methods to investigate the human brain's mechanisms of establishing and maintaining rules for multiple perceptual decision tasks. Rules were either chosen by participants or specifically instructed to them, and the fMRI activation patterns representing rule-specific information were compared between these contexts. We show that frontoparietal regions differ in the properties of their rule representations during active maintenance before execution. First, rule-specific information maintained in the dorsolateral and medial frontal cortex depends on the context in which it was established (chosen vs specified). Second, rule representations maintained in the ventrolateral frontal and parietal cortex are independent of the context in which they were established. Furthermore, we found that the rule-specific coding maintained in anticipation of stimuli may change with execution of the rule: representations in context-independent regions remain invariant from maintenance to execution stages, whereas rule representations in context-dependent regions do not generalize to execution stage. The identification of distinct frontoparietal systems with context-independent and context-dependent task rule representations, and the distinction between anticipatory and executive rule representations, provide new insights into the functional architecture of goal-directed behavior.

SummaryHumans show a remarkable ability to discriminate others' gaze direction, even though a given direction can be conveyed by many physically dissimilar configurations of different eye positions and head views. For example, eye contact can be signaled by a rightward glance in a left-turned head or by direct gaze in a front-facing head. Such acute gaze discrimination implies considerable perceptual invariance. Previous human research found that superior temporal sulcus (STS) responds preferentially to gaze shifts [1], but the underlying representation that supports such general responsiveness remains poorly understood. Using multivariate pattern analysis (MVPA) of human functional magnetic resonance imaging (fMRI) data, we tested whether STS contains a higher-order, head view-invariant code for gaze direction. The results revealed a finely graded gaze direction code in right anterior STS that was invariant to head view and physical image features. Further analyses revealed similar gaze effects in left anterior STS and precuneus. Our results suggest that anterior STS codes the direction of another's attention regardless of how this information is conveyed and demonstrate how high-level face areas carry out fine-grained, perceptually relevant discrimination through invariance to other face features.

Humans are highly sensitive to another's gaze direction, and use this information to support a range of social cognitive functions. Here we review recent studies that have begun to delineate a neural system for gaze perception. We focus in particular on a set of core gaze processes: perceptual coding of another's eye gaze direction, which may involve anterior superior temporal sulcus (STS); gaze-cued attentional orienting, which may be mediated by lateral parietal regions; and the experience of joint attention with another individual, which recruits medial prefrontal cortex. We conclude that understanding this gaze processing system will require a combination of multivariate pattern analysis approaches to characterise the role of individual nodes as well as connectivity-based methods to study interactions at the systems level.

The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging.

Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets. Historically, researchers have dealt with this complexity idiosyncratically, with every lab or individual implementing their own preprocessing and analysis procedures. The resulting lack of field-wide standards has severely limited reproducibility and data sharing and reuse.To address this problem, we and others recently introduced the Brain Imaging Data Standard (BIDS; (Gorgolewski et al., 2016)), a specification meant to standardize the process of representing brain imaging data. BIDS is deliberately designed with adoption in mind; it adheres to a user-focused philosophy that prioritizes common use cases and discourages complexity. By successfully encouraging a large and ever-growing subset of the community to adopt a common standard for naming and organizing files, BIDS has made it much easier for researchers to share, re-use, and process their data .The ability to efficiently develop high-quality spec-compliant applications itself depends to a large extent on the availability of good tooling. Because many operations recur widely across diverse contexts-for example, almost every tool designed to work with BIDS datasets involves regular file-filtering operations-there is a strong incentive to develop utility libraries that provide common functionality via a standardized, simple API.PyBIDS is a Python package that makes it easier to work with BIDS datasets. In principle, its scope includes virtually any functionality that is likely to be of general use when working with BIDS datasets (i.e., that is not specific to one narrow context). At present, its core and most widely used module supports simple and flexible querying and manipulation of BIDS datasets. PyBIDS makes it easy for researchers and developers working in Python to search for BIDS files by keywords and/or metadata; to consolidate and retrieve file-associated metadata spread out across multiple levels of a BIDS hierarchy; to construct BIDS-valid path names for new files; and to validate projects against the BIDS specification, among other applications.

Humans and other primates are adept at using the direction of another's gaze or head turn to infer where that individual is attending. Research in macaque neurophysiology suggests that anterior superior temporal sulcus (STS) contains a direction-sensitive code for such social attention cues. By contrast, most human functional Magnetic resonance imaging (fMRI) studies report that posterior STS is responsive to social attention cues. It is unclear whether this functional discrepancy is caused by a species difference or by experimental design differences. Furthermore, social attention cues are dynamic in naturalistic social interaction, but most studies to date have been restricted to static displays. In order to address these issues, we used multivariate pattern analysis of fMRI data to test whether response patterns in human right STS distinguish between leftward and rightward dynamic head turns. Such head turn discrimination was observed in right anterior STS/superior temporal gyrus (STG). Response patterns in this region were also significantly more discriminable for head turn direction than for rotation direction in physically matched ellipsoid control stimuli. Our findings suggest a role for right anterior STS/STG in coding the direction of motion in dynamic social attention cues.

We evaluated the effectiveness of prospective motion correction (PMC) on a simple visual task when no deliberate subject motion was present. The PMC system utilizes an in‐bore optical camera to track an external marker attached to the participant via a custom‐molded mouthpiece. The study was conducted at two resolutions (1.5 mm vs 3 mm) and under three conditions (PMC On and Mouthpiece On vs PMC Off and Mouthpiece On vs PMC Off and Mouthpiece Off). Multiple data analysis methods were conducted, including univariate and multivariate approaches, and we demonstrated that the benefit of PMC is most apparent for multi‐voxel pattern decoding at higher resolutions. Additional testing on two participants showed that our inexpensive, commercially available mouthpiece solution produced comparable results to a dentist‐molded mouthpiece. Our results showed that PMC is increasingly important at higher resolutions for analyses that require accurate voxel registration across time.

The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging.Author SummaryHumans recognize conspecifics by their faces. Understanding how faces are recognized is an open computational problem with relevance to theories of perception, social cognition, and the engineering of computer vision systems. Here we measured brain activity with functional MRI while human participants viewed individual faces. We developed multiple computational models inspired by known response preferences of single neurons in the primate visual cortex. We then compared these neuronal models to patterns of brain activity corresponding to individual faces. The data were consistent with a model where neurons respond to directions in a high-dimensional space of faces. It also proved essential to model how functional MRI voxels locally average the responses of tens of thousands of neurons. The study highlights the challenges in adjudicating between alternative computational theories of visual information processing.