Autistic traits relate to reduced reward sensitivity in learning from social point-light displays (PLDs)

A number of studies have linked autistic traits to difficulties in learning from social (vs. non-social) stimuli. However, these stimuli are often difficult to match on low-level visual properties, which is especially important given the impact of autistic traits on sensory processing. Additionally, studies often fail to account for dissociable aspects of the learning process in the specification of model parameters (learning rates and reward sensitivity). Here, we investigate whether learning deficits in individuals with high autistic traits exhibit deficits when learning from facial point-light displays (PLDs) depicting emotional expressions. Social and non-social stimuli were created from random arrangements of the same number of point-lights and carefully matched on low-level visual properties. Neurotypical participants (N = 63) were assessed using the Autism Spectrum Quotient (AQ) and completed a total of 96 trials in a reinforcement learning task. Although linear multilevel modeling did not indicate learning deficits, preregistered computational modeling using a Rescorla-Wagner framework revealed that higher autistic traits were associated with reduced reward sensitivity in the win domain, demonstrating attenuated response to received social (compared to non-social) feedback during learning. These findings suggest that autistic traits can significantly impact learning from social feedback beyond a general deficit in learning rates.

Methods
Sample
The sample consisted of typically developing (TD) individuals, which were contacted via a participation recruitment platform of the University of Vienna. Participants were required to fulfill the following inclusion criteria: a) age between 18 and 65 years, b) heterosexual orientation, c) proficiency in English, d) no drug or alcohol addiction or regular drug use, e) no psychiatric or neurological condition. As the experiment was part of a larger project, some of the exclusion criteria are related to another task and not directly relevant for this study.

From the total of 74 recruited individuals, three participants were excluded due to missing data for AQ scores and five participants were dropped based on exclusion criteria. Three individuals were excluded from analyses because of missing data for the task, leading to a final sample of N = 63 participants for analysis. Participants received either a financial compensation of 10€ or 4 study credits.

Measures - AQ
To measure autistic traits, a German shortened version of the Autism Spectrum Quotient (AQ-k; Freitag et al., 2007), widely used in research and clinical practice, was used. AQ-k contains 33 items (e.g. “I prefer to do things on my own rather than with others.”) and is suitable for adults and adolescents aged 16 years and above with normal intellectual functioning. For screening purposes of ASD in clinical practice, a cut-off of 17 was proposed (Freitag et al., 2007). In the present study, item responses were scored using a binary system, where endorsement of an autistic trait is scored with one point, while the opposite response is scored with zero, resulting in  a maximum score of 33 (Ruzich et al., 2015). In the present sample, we report reliability scores as Cronbach's alpha (α) = .83 and McDonald’s omega (ω) = .85, indicating a good scale reliability.

Social Reinforcement Learning Task
To assess learning, we used a social reinforcement learning task with PLDs as feedback (see Figure 1). Participants were required to categorize randomly generated two-digit numbers (e.g. 99) into arbitrary groups (“A” or “B”) via button press. Importantly, feedback was delivered probabilistically: in 85% of trials, correct responses were followed by rewarding feedback, while in 15% of trials correct responses were followed by non-rewarding feedback. This contingency was chosen to provide an appropriate level of difficulty for learning the underlying associations. Participants were informed that categories were arbitrary with no underlying rule, requiring them to learn via trial-and-error from feedback. The exact contingencies were not disclosed.

After each response, participants received PLD feedback. First, a random pattern of point lights was displayed, which transformed into either social or non-social feedback. In social blocks, point lights formed happy or angry human faces to indicate correct or incorrect responses. In non-social blocks, they formed check marks or crosses. Participants completed two blocks for each condition, each block consisting of six trials. Within each trial, 4 unique two-digit numbers were presented and repeated across subsequent trials, with the presentation order randomized. To avoid carry-over effects, even numbers were assigned to the social blocks, uneven numbers to the non-social blocks. Response options (“A” and “B”) were displayed to the left and right of the screen, with their positions randomly switched between trials to avoid simple motor learning.

To control for order effects, the first block type (social / non-social) was randomly selected, and subsequent blocks alternated between the two types. The dependent variable for the GLMM analysis was the proportion of correct answers, defined as the proportion of trials, in which participants chose the high probability option. We expected participants to perform around chance level (50%) in the first trial and improve in subsequent trials, as they learned the underlying reward contingencies. A German version of the task is available online (Chrome recommended): https://raimund-buehler.github.io/SOCIALRL_PLD/