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Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question 'why' brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these 'why' questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.

Face perception has long served as a classic example of domain specificity of mind and brain. But an alternative "expertise" hypothesis holds that putatively face-specific mechanisms are actually domain-general, and can be recruited for the perception of other objects of expertise (e.g., cars for car experts). Here, we demonstrate the computational implausibility of this hypothesis: Neural network models optimized for generic object categorization provide a better foundation for expert fine-grained discrimination than do models optimized for face recognition.

© 2019 Many studies have investigated the development of face-, scene-, and body-selective regions in the ventral visual pathway. This work has primarily focused on comparing the size and univariate selectivity of these neural regions in children versus adults. In contrast, very few studies have investigated the developmental trajectory of more distributed activation patterns within and across neural regions. Here, we scanned both children (ages 5–7) and adults to test the hypothesis that distributed representational patterns arise before category selectivity (for faces, bodies, or scenes) in the ventral pathway. Consistent with this hypothesis, we found mature representational patterns in several ventral pathway regions (e.g., FFA, PPA, etc.), even in children who showed no hint of univariate selectivity. These results suggest that representational patterns emerge first in each region, perhaps forming a scaffold upon which univariate category selectivity can subsequently develop. More generally, our findings demonstrate an important dissociation between category selectivity and distributed response patterns, and raise questions about the relative roles of each in development and adult cognition.

© 2019 Elsevier Ltd Adults and children recruit a specific network of brain regions when engaged in “Theory of Mind” (ToM) reasoning. Recently, fMRI studies of adults have used multivariate analyses to provide a deeper characterization of responses in these regions. These analyses characterize representational distinctions within the social domain, rather than comparing responses across preferred (social) and non-preferred stimuli. Here, we conducted opportunistic multivariate analyses in two previously collected datasets (Experiment 1: n = 20 5–11 year old children and n = 37 adults; Experiment 2: n = 76 neurotypical and n = 29 5–12 year old children diagnosed with Autism Spectrum Disorder (ASD)) in order to characterize the structure of representations in the developing social brain, and in order to discover if this structure is disrupted in ASD. Children listened to stories that described characters' mental states (Mental), non-mentalistic social information (Social), and causal events in the environment (Physical), while undergoing fMRI. We measured the extent to which neural responses in ToM brain regions were organized according to two ToM-relevant models: 1) a condition model, which reflected the experimenter-generated condition labels, and 2) a data-driven emotion model, which organized stimuli according to their emotion content. We additionally constructed two control models based on linguistic and narrative features of the stories. In both experiments, the two ToM-relevant models outperformed the control models. The fit of the condition model increased with age in neurotypical children. Moreover, the fit of the condition model to neural response patterns was reduced in the RTPJ in children diagnosed with ASD. These results provide a first glimpse into the conceptual structure of information in ToM brain regions in childhood, and suggest that there are real, stable features that predict responses in these regions in children. Multivariate analyses are a pro, NSF (Award 1122374)

Three of the most robust functional landmarks in the human brain are the selective responses to faces in the fusiform face area (FFA), scenes in the parahippocampal place area (PPA), and bodies in the extrastriate body area (EBA). Are the selective responses of these regions present early in development or do they require many years to develop? Prior evidence leaves this question unresolved. We designed a new 32-channel infant magnetic resonance imaging (MRI) coil and collected high-quality functional MRI (fMRI) data from infants (2-9 months of age) while they viewed stimuli from four conditions-faces, bodies, objects, and scenes. We find that infants have face-, scene-, and body-selective responses in the location of the adult FFA, PPA, and EBA, respectively, powerfully constraining accounts of cortical development.

© 2019 Many studies have investigated the development of face-, scene-, and body-selective regions in the ventral visual pathway. This work has primarily focused on comparing the size and univariate selectivity of these neural regions in children versus adults. In contrast, very few studies have investigated the developmental trajectory of more distributed activation patterns within and across neural regions. Here, we scanned both children (ages 5–7) and adults to test the hypothesis that distributed representational patterns arise before category selectivity (for faces, bodies, or scenes) in the ventral pathway. Consistent with this hypothesis, we found mature representational patterns in several ventral pathway regions (e.g., FFA, PPA, etc.), even in children who showed no hint of univariate selectivity. These results suggest that representational patterns emerge first in each region, perhaps forming a scaffold upon which univariate category selectivity can subsequently develop. More generally, our findings demonstrate an important dissociation between category selectivity and distributed response patterns, and raise questions about the relative roles of each in development and adult cognition.

© 2017 Elsevier B.V. Prior work suggests that our understanding of how things work (“intuitive physics”) and how people work (“intuitive psychology”) are distinct domains of human cognition. Here we directly test the dissociability of these two domains by investigating knowledge of intuitive physics and intuitive psychology in adults with Williams syndrome (WS) – a genetic developmental disorder characterized by severely impaired spatial cognition, but relatively spared social cognition. WS adults and mental-age matched (MA) controls completed an intuitive physics task and an intuitive psychology task. If intuitive physics is a distinct domain (from intuitive psychology), then we should observe differential impairment on the physics task for individuals with WS compared to MA controls. Indeed, adults with WS performed significantly worse on the intuitive physics than the intuitive psychology task, relative to controls. These results support the hypothesis that knowledge of the physical world can be disrupted independently from knowledge of the social world.

© 2020 Facial and vocal cues provide critical social information about other humans, including their emotional and attentional states and the content of their speech. Recent work has shown that the face-responsive region of posterior superior temporal sulcus (“fSTS”) also responds strongly to vocal sounds. Here, we investigate the functional role of this region and the broader STS by measuring responses to a range of face movements, vocal sounds, and hand movements using fMRI. We find that the fSTS responds broadly to different types of audio and visual face action, including both richly social communicative actions, as well as minimally social noncommunicative actions, ruling out hypotheses of specialization for processing speech signals, or communicative signals more generally. Strikingly, however, responses to hand movements were very low, whether communicative or not, indicating a specific role in the analysis of face actions (facial and vocal), not a general role in the perception of any human action. Furthermore, spatial patterns of response in this region were able to decode communicative from noncommunicative face actions, both within and across modality (facial/vocal cues), indicating sensitivity to an abstract social dimension. These functional properties of the fSTS contrast with a region of middle STS that has a selective, largely unimodal auditory response to speech sounds over both communicative and noncommunicative vocal nonspeech sounds, and nonvocal sounds. Region of interest analyses were corroborated by a data-driven independent component analysis, identifying face-voice and auditory speech responses as dominant sources of voxelwise variance across the STS. These results suggest that the STS contains separate processing streams for the audiovisual analysis of face actions and auditory speech processing.

The ability to perceive others’ social interactions, here defined as the directed contingent actions between two or more people, is a fundamental part of human experience that develops early in infancy and is shared with other primates. However, the neural computations underlying this ability remain largely unknown. Is social interaction recognition a rapid feedforward process or a slower post-perceptual inference? Here we used magnetoencephalography (MEG) decoding to address this question. Subjects in the MEG viewed snapshots of visually matched real-world scenes containing a pair of people who were either engaged in a social interaction or acting independently. The presence versus absence of a social interaction could be read out from subjects’ MEG data spontaneously, even while subjects performed an orthogonal task. This readout generalized across different people and scenes, revealing abstract representations of social interactions in the human brain. These representations, however, did not come online until quite late, at 300 ​ms after image onset, well after feedforward visual processes. In a second experiment, we found that social interaction readout still occurred at this same late latency even when subjects performed an explicit task detecting social interactions. We further showed that MEG responses distinguished between different types of social interactions (mutual gaze vs joint attention) even later, around 500 ​ms after image onset. Taken together, these results suggest that the human brain spontaneously extracts information about others’ social interactions, but does so slowly, likely relying on iterative top-down computations.

Objects in motion activate multiple cortical regions in every lobe of the human brain. Do these regions represent a collection of independent systems, or is there an overarching functional architecture spanning all of object-driven cortex? Inspired by recent work in artificial intelligence (AI), machine learning, and cognitive science, we consider the hypothesis that these regions can be understood as a coherent network implementing an integrative computational system that unifies the functions needed to perceive, predict, reason about, and plan with physical objects—as in the paradigmatic case of using or making tools. Our proposal draws on a modeling framework that combines multiple AI methods, including causal generative models, hybrid symbolic-continuous planning algorithms, and neural recognition networks, with object-centric, physics-based representations. We review evidence relating specific components of our proposal to the specific regions that comprise object-driven cortex, and lay out future research directions with the goal of building a complete functional and mechanistic account of this system. ©2019, NSF STC (award no.: CCF-1231216), ONR MURI (no. N00014-13-1-0333), NIH (no. DP1HD091947)