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Autism Spectrum Disorder (ASD) is characterized by restricted and repetitive behaviors and social differences, both of which may manifest, in part, from underlying differences in corticostriatal circuits and reinforcement learning. Here, we investigated reinforcement learning in mice with mutations in either Tsc2 or Shank3 , both high-confidence ASD risk genes associated with major syndromic forms of ASD. Using an odor-based two-alternative forced choice (2AFC) task, we tested adolescent mice of both sexes and found male Tsc2 and Shank3B heterozygote (Het) mice showed enhanced learning performance compared to their wild type (WT) siblings. No gain of function was observed in females. Using a novel reinforcement learning (RL) based computational model to infer learning rate as well as policy-level task engagement and disengagement, we found that the gain of function in males was driven by an enhanced positive learning rate in both Tsc2 and Shank3B Het mice. The gain of function in Het males was absent when mice were trained with a probabilistic reward schedule. These findings in two ASD mouse models reveal a convergent learning phenotype that shows similar sensitivity to sex and environmental uncertainty. These data can inform our understanding of both strengths and challenges associated with autism, while providing further evidence that sex and experience of uncertainty modulate autism-related phenotypes., Significance Statement: Reinforcement learning is a foundational form of learning that is widely used in behavioral interventions for autism. Here, we measured reinforcement learning in adolescent mice carrying genetic mutations linked to two different syndromic forms of autism. We found that males showed strengths in reinforcement learning compared to their wild type siblings, while females showed no differences. This gain of function in males was no longer observed when uncertainty was introduced into the reward schedule for correct choices. These findings support a model in which diverse genetic changes interact with sex to generate common phenotypes underlying autism. Our data further support the idea that autism risk genes may produce strengths as well as challenges in behavioral function., Competing Interests: Competing Interest Statement: The authors have no competing interests.

Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data., Competing Interests: RW, AC No competing interests declared, (© 2019, Wilson and Collins.)