Multifaceted empathy differences in children and adults with autism

Although empathy impairments have been reported in autistic individuals, there is no clear consensus on how emotional valence influences this multidimensional process. In this study, we use the Multifaceted Empathy Test for juveniles (MET-J) to interrogate emotional and cognitive empathy in 184 participants (ages 8–59 years, 83 autistic) under the robust Bayesian inference framework. Group comparisons demonstrate previously unreported interaction effects between: (1) valence and autism diagnosis in predictions of emotional resonance, and (2) valence and age group in predictions of arousal to images portraying positive and negative facial expressions. These results extend previous studies using the MET by examining differential effects of emotional valence in a large sample of autistic children and adults with average or above-average intelligence. We report impaired cognitive empathy in autism, and subtle differences in emotional empathy characterized by less distinction between emotional resonance to positive vs. negative facial expressions in autism compared to neurotypicals. Reduced emotional differentiation between positive and negative affect in others could be a mechanism for diminished social reciprocity that poses a universal challenge for people with autism. These component- and valence- specific findings are of clinical relevance for the development and implementation of target-specific social interventions in autism.

Multifaceted Empathy Differences in Children and Adults with Autism

Although empathy impairments have been reported in autistic individuals, there is no clear consensus on how emotional valence influences this multidimensional process. In this study, we use the Multifaceted Empathy Test for juveniles (MET-J) to interrogate emotional and cognitive empathy in 184 participants (ages 8–59 years, 83 autistic) under the robust Bayesian inference framework. Group comparisons demonstrate previously unreported interaction effects between: (1) valence and autism diagnosis in predictions of emotional resonance, and (2) valence and age group in predictions of arousal to images portraying positive and negative facial expressions. These results extend previous studies using the MET by examining differential effects of emotional valence in a large sample of autistic children and adults with average or above-average intelligence. We report impaired cognitive empathy in autism, and subtle differences in emotional empathy characterized by less distinction between emotional resonance to positive vs. negative facial expressions in autism compared to neurotypicals. Reduced emotional differentiation between positive and negative affect in others could be a mechanism for diminished social reciprocity that poses a universal challenge for people with autism. These component- and valence- specific findings are of clinical relevance for the development and implementation of target-specific social interventions in autism.

Robust Bayesian Inference for Set‐Identified Models

This paper reconciles the asymptotic disagreement between Bayesian and frequentist inference in set‐identified models by adopting a multiple‐prior (robust) Bayesian approach. We propose new tools for Bayesian inference in set‐identified models and show that they have a well‐defined posterior interpretation in finite samples and are asymptotically valid from the frequentist perspective. The main idea is to construct a prior class that removes the source of the disagreement: the need to specify an unrevisable prior for the structural parameter given the reduced‐form parameter. The corresponding class of posteriors can be summarized by reporting the ‘posterior lower and upper probabilities’ of a given event and/or the ‘set of posterior means’ and the associated ‘robust credible region’. We show that the set of posterior means is a consistent estimator of the true identified set and the robust credible region has the correct frequentist asymptotic coverage for the true identified set if it is convex. Otherwise, the method provides posterior inference about the convex hull of the identified set. For impulse‐response analysis in set‐identified Structural Vector Autoregressions, the new tools can be used to overcome or quantify the sensitivity of standard Bayesian inference to the choice of an unrevisable prior.

Robust Bayesian Inference in Stochastic Frontier Models

We use the concept of coarsened posteriors to provide robust Bayesian inference via coarsening in order to robustify posteriors arising from stochastic frontier models. These posteriors arise from tempered versions of the likelihood when at most a pre-specified amount of data is used, and are robust to changes in the model. Specifically, we examine robustness to changes in the distribution of the composed error in the stochastic frontier model (SFM). Moreover, coarsening is a form of regularization, reduces overfitting and makes inferences less sensitive to model choice. The new techniques are illustrated using artificial data as well as in a substantive application to large U.S. banks.