Children with autism spectrum disorder (ASD) have deficits in motor control, imitation and social function. Does a dysfunction in the neural basis of representing internal models of action contribute to these problems? We measured patterns of generalization as children learned to control a novel tool and found that the autistic brain built a stronger than normal association between self-generated motor commands and proprioceptive feedback; furthermore, the greater the reliance on proprioception, the greater the child's impairments in social function and imitation. Theory suggests that when the brain learns to perform a movement, it builds an association between motor commands and sensory feedback. These internal models allow the brain to predict the sensory consequences of self-generated motor commands and to produce motor commands that maximize expected rewards at a minimum effort 1 . Children with autism have impairments in motor control 2 and imitation 3 . Is there a fundamental difference in how these children build associations between their motor commands and sensory feedback? Generalization is a signature of the activation fields of neurons with which the brain forms an internal model 4 . To quantify the representation of internal models in the autistic brain, we measured patterns of generalization as autistic children learned to control a novel tool. We asked 14 children with ASD (age, 10.5 ± 1.7 years) and 13 typically developing children (age, 10.4 ± 1.8 years) to play a game in which they held a robotic arm in their hand and reached with it to capture animals that had escaped from a zoo (see Supplementary Methods). The robot perturbed the children's arm movements by producing a force field and the children learned to control the tool so as to capture the animals. In this task, the typically developing brain builds an association between self-generated motor commands and the sensory consequences (visual and proprioceptive). The strength of each association can be inferred by how the brain generalizes the learning from the trained movements to novel movements. The training took place in the left workspace (target 1; Fig. 1a ) while a velocity-dependent field pushed their hand perpendicular to the direction of motion. We quantified generalization in the right workspace in the intrinsic coordinates of the arm (target 3, identical joint rotations as compared to target 1), and in the extrinsic coordinates of the task (target 2, identical hand motion as compared to target 1). Movements to targets 2 and 3 were always made in 'error-clamp' trials, in which the robot produced a channel that artificially eliminated movement errors, but allowed us to measure force output at the hand. In the baseline period in which no perturbations were present, both ASD and typically developing groups produced straight reaching movements (Fig. 1b) . On presentation of the field, hand trajectory was perturbed (Fig. 1b) and the lateral deviations declined with training (Fig. 1c) , indicating comparable learning rates (F 1,979 ¼ 1.8, P ¼ 0.20). In randomly selected trials, an error clamp was presented. We quantified the amount of adaptation/generalization on each errorclamp trial by computing the ratio of the peak lateral force produced by the child and the ideal force required for compensation on that trial (Fig. 1d) . The three targets were presented randomly. For target 1, 6 out of 96 trials were error clamp, whereas all trials were error clamp for the other targets. Therefore, for targets 2 and 3, the children were never trained in a force field and never experienced error. This design allowed us to simultaneously assay learning and generalization. We plotted the adaptation index for each target direction during the error-clamp trials (Fig. 1d) . The average of the first five trials in the test block was used as a measure of generalization (Fig. 1e) . Superficially, learning appeared to be normal in children with ASD; the performance for target 1 was indistinguishable from that of typically developing children on both the last trial of learning (P ¼ 0.18) and the test trials (P ¼ 0.94). However, the generalization patterns were markedly different (F 1,25 ¼ 15, P o 0.001, interaction between group and target direction). Typically developing children generalized to the right workspace both in intrinsic (P o 0.001) and extrinsic (P ¼ 0.003) coordinates, whereas children with ASD generalized in intrinsic coordinates (P o 0.0001), but not in extrinsic coordinates (P ¼ 0.30). Furthermore, children with ASD generalized about twice as strong as typically developing children in intrinsic coordinates (Bonferroni post hoc t test, P ¼ 0.0017), reflecting a much stronger than normal association between motor commands and proprioceptive feedback 5 . In this task, the neurons that participate in representing the internal model include cells in the primary motor cortex (M1) 6 and the premotor cortex 7 . These cells have distinct activation fields and axonal connectivity. The activation fields of M1 cells tend to be in the intrinsic coordinates of joints and muscles 8 and these cells are strongly connected to the adjacent somatosensory cortex. In contrast, the activation fields of premotor cells tend to be in the extrinsic coordinates of the task 9 and the cells have dense, long-range connections to the posterior parietal cortex. In the brains of typically developing children, reach adaptation produced generalization in both coordinate systems, which is consistent with a representation that engaged both the shortrange connections of the primary motor/somatosensory regions and the long-range connections of the premotor/posterior parietal regions. In the brains of children with ASD, however, there is an overgrowth of localized cortical connections 10 with increased white matter volume in
Haswell et al. REF , found that children with ASD rely more on proprioceptive information to learn motor tasks.
Representation of internal models of action in the autistic brain
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