Emotional Conflict Adaptation in Autism

Background: Poor performance and error monitoring in individuals with autism spectrum disorders (ASD) may be associated with the repetitive/restricted behaviors characteristic of the disorder (Thakkar et al., 2008; Larson et al., 2011). Difficulties in emotional processing, compounded with poor behavior monitoring, could negatively affect social interactions. Conflict adaptation refers to the adjustment of cognitive resources based on previous-trial conflict. We adapted an emotional conflict adaptation paradigm used in healthy and generalized anxiety adults (Etkin et al., 2010), to study older children and adolescents diagnosed with ASD, with the goal of better understanding the neural processes of conflict adaptation in an emotional context. To our knowledge, this is the first electrophysiological emotional conflict adaptation study in ASD.

Objectives: We sought to elucidate the behavioral and neural mechanisms involved in emotional conflict adaptation within a cohort of ASD and matched control adolescents.

Methods: Participants (ASD n=22; TYP n=19) matched on age (M=14.6) and IQ (M=107.9) viewed a series of pictures expressing either happy or fearful emotions. Each picture had either the word “happy” or “fear” overlaid across the face. Expressions and words were combined to create either congruent (e.g., happy expression with “happy”) or incongruent (e.g., happy expression with “fear”) trials. Three blocks of 149 trials each were viewed, with pictures appearing for 1250 ms and a fixation screen appearing after each picture (mean duration =1200 ms). Participants indicated via button press whether the facial expression was fearful or happy. We mapped out conflict adaptation based on response to previous and current trials (cC=congruent trial preceded by congruent trial, iC=congruent trial preceded by incongruent trial, iI=incongruent trial preceded by incongruent trial, cI=incongruent trial preceded by congruent trial). EEG data was collected using a 128-electrode geodesic sensor net and EGI amplifier system.

Results: For reaction time (RT), there was an expected main effect of current condition type (p<.001), with incongruent trials having significantly slower RTs than congruent trials. There was also a significant previous x current trial congruency interaction (p<.01), with cC trials having the fastest RT, followed by iC, iI, and cI trials. There was no significant previous trial x current trial x diagnosis interaction. This same pattern was seen for accuracy rates, with significantly lower accuracy rates for incongruent trials (p<.001); previous x current trial congruency interactions (p<.001) also showed the greatest accuracy for cC, followed by iC, iI, and cI trials. Surprisingly, the N200 ERP waveform showed no significant main effects or interactions. In contrast, the conflict-SP waveform showed a significant main effect for current condition (p=.006), with incongruent trials having a greater mean amplitude than congruent trials; and no significant interactions of diagnostic group.

Conclusions: Our behavioral results are similar to our previous study of non-emotion conflict adaptation (Larson et al., in press); however our previous findings of reduced ERP amplitude was not replicated, as both groups showed intact conflict-SP response. We are further investigating the use of the N2 wave for this task in older samples, and recommend further research on conflict adaptation in ASD.