Determining the integrity of emotion recognition in autistic spectrum disorder is important to our theoretical understanding of autism and to teaching social skills. Previous studies have reported both positive and negative results. Here, we take a formal meta-analytic approach, bringing together data from 48 papers testing over 980 participants with autism. Results show there is an emotion recognition difficulty in autism, with a mean effect size of 0.80 which reduces to 0.41 when a correction for publication bias is applied. Recognition of happiness was only marginally impaired in autism, but recognition of fear was marginally worse than recognition of happiness. This meta-analysis provides an opportunity to survey the state of emotion recognition research in autism and to outline potential future directions.

The past few decades have seen a rapid increase in the use of functional near-infrared spectroscopy (fNIRS) in cognitive neuroscience. This fast growth is due to the several advances that fNIRS offers over the other neuroimaging modalities such as functional magnetic resonance imaging and electroencephalography/magnetoencephalography. In particular, fNIRS is harmless, tolerant to bodily movements, and highly portable, being suitable for all possible participant populations, from newborns to the elderly and experimental settings, both inside and outside the laboratory. In this review we aim to provide a comprehensive and state-of-the-art review of fNIRS basics, technical developments, and applications. In particular, we discuss some of the open challenges and the potential of fNIRS for cognitive neuroscience research, with a particular focus on neuroimaging in naturalistic environments and social cognitive neuroscience.

Research on action simulation identifies brain areas that are active while imagining or performing simple overlearned actions. Are areas engaged during imagined movement sensitive to the amount of actual physical practice? In the present study, participants were expert dancers who learned and rehearsed novel, complex whole-body dance sequences 5 h a week across 5 weeks. Brain activity was recorded weekly by fMRI as dancers observed and imagined performing different movement sequences. Half these sequences were rehearsed and half were unpracticed control movements. After each trial, participants rated how well they could perform the movement. We hypothesized that activity in premotor areas would increase as participants observed and simulated movements that they had learnt outside the scanner. Dancers' ratings of their ability to perform rehearsed sequences, but not the control sequences, increased with training. When dancers observed and simulated another dancer's movements, brain regions classically associated with both action simulation and action observation were active, including inferior parietal lobule, cingulate and supplementary motor areas, ventral premotor cortex, superior temporal sulcus and primary motor cortex. Critically, inferior parietal lobule and ventral premotor activity was modulated as a function of dancers' ratings of their own ability to perform the observed movements and their motor experience. These data demonstrate that a complex motor resonance can be built de novo over 5 weeks of rehearsal. Furthermore, activity in premotor and parietal areas during action simulation is enhanced by the ability to execute a learned action irrespective of stimulus familiarity or semantic label.

It has been proposed that the invariant kinematics observed during goal-directed movements result from reducing the consequences of signal-dependent noise (SDN) on motor output. The purpose of this study was to investigate the presence of SDN during isometric force production and determine how central and peripheral components contribute to this feature of motor control. Peripheral and central components were distinguished experimentally by comparing voluntary contractions to those elicited by electrical stimulation of the extensor pollicis longus muscle. To determine other factors of motor-unit physiology that may contribute to SDN, a model was constructed and its output compared with the empirical data. SDN was evident in voluntary isometric contractions as a linear scaling of force variability (SD) with respect to the mean force level. However, during electrically stimulated contractions to the same force levels, the variability remained constant over the same range of mean forces. When the subjects were asked to combine voluntary with stimulation-induced contractions, the linear scaling relationship between the SD and mean force returned. The modeling results highlight that much of the basic physiological organization of the motor-unit pool, such as range of twitch amplitudes and range of recruitment thresholds, biases force output to exhibit linearly scaled SDN. This is in contrast to the square root scaling of variability with mean force present in any individual motor-unit of the pool. Orderly recruitment by twitch amplitude was a necessary condition for producing linearly scaled SDN. Surprisingly, the scaling of SDN was independent of the variability of motoneuron firing and therefore by inference, independent of presynaptic noise in the motor command. We conclude that the linear scaling of SDN during voluntary isometric contractions is a natural by-product of the organization of the motor-unit pool that does not depend on signal-dependent noise in the motor command. Synaptic noise in the motor command and common drive, which give rise to the variability and synchronization of motoneuron spiking, determine the magnitude of the force variability at a given level of mean force output.

Complex human behavior is organized around temporally distal outcomes. Behavioral studies based on tasks such as normal prehension, multi-step object use and imitation establish the existence of relative hierarchies of motor control. The retrieval errors in apraxia also support the notion of a hierarchical model for representing action in the brain. In this review, three functional brain imaging studies of action observation using the method of repetition suppression are used to identify a putative neural architecture that supports action understanding at the level of kinematics, object centered goals and ultimately, motor outcomes. These results, based on observation, may match a similar functional anatomic hierarchy for action planning and execution. If this is true, then the findings support a functional anatomic model that is distributed across a set of interconnected brain areas that are differentially recruited for different aspects of goal oriented behavior, rather than a homogeneous mirror neuron system for organizing and understanding all behavior.