DOI: 10.5061/dryad.4xgxd25k6
title: Autistic traits relate to reduced reward sensitivity in learning from social point-light displays (PLDs)

description: 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.
description: 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. “<em>I prefer to do things on my
 own rather than with others.</em>”) 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.
 <strong>Social Reinforcement Learning
 Task</strong> 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/
description: # Autistic traits and reinforcement learning ## General This repository
 contains data and scripts for the RSOS submission "Autistic Traits
 relate to reduced Reward Sensitivity in Learning from Social Point-Light
 Displays (PLD’s)". The data was collected to investigate the
 relationship between autistic traits and learning performance using
 point-light displays (PLDs) in a reinforcement learning task. Sixty-three
 participants categorized two-digit numbers while receiving probabilistic
 feedback. Social feedback was provided via PLDs depicting facial emotions
 (happiness or anger), while non-social feedback used PLDs forming
 checkmarks or crosses. Computational modeling (Rescorla-Wagner framework)
 was used to fit parameters such as learning rates and reward sensitivity
 to explore differences in social versus non-social learning and their
 associations with autistic traits, measured via the autism spectrum
 quotient (AQ). Results showed that higher autistic traits were associated
 with reduced reward sensitivity. Figure 1 in the supplementary material
 illustrates a single trial. ## Description The analysis is split in two
 parts: raw choice data (**raw_choices.csv**) and parameters of the
 reinforcement learning model (**parameters.csv**). Data files and scripts
 are named accordingly. Scripts have been uploaded to Zenodo for the
 reported analyses (**raw_choices_GLM.R** and **parameters_glmmTMB.R**) and
 for plotting (**raw_choices_plots.R** and **parameters_plots.R**). Figures
 included in the publication have been uploaded to Zenodo as well.
 **Codebook for raw_choices.csv** This file contains raw trial-by-trial
 choice data from participants in the reinforcement learning task. *
 **ParticipantID**: Unique identifier for each participant. * **stim**: The
 presented number stimulus for the trial. * **ParticipantAnswer**: The
 choice made by the participant (`0` for "A", `1` for
 "B"). * **high_prob_choice**: The option (`0` for "A",
 `1` for "B") associated with a higher reward probability for the
 given trial. * **Reward**: Indicates whether a reward was delivered for
 the trial (`1` for reward, `0` for no reward). *
 **CorrectAns_participant**: Whether the participant's answer was
 correct (`1`) or incorrect (`0`) based on task contingencies, i.e. whether
 the participant's choice aligned with the option in high_prob_choice.
 * **BlockType**: Type of block in the trial, either "social"
 (PLDs of human faces) or "nonsocial" (checkmarks and crosses). *
 **TrialNumber**: The sequential number of a trial within its respective
 block type (social or nonsocial). Each block type was presented twice in
 an interleaved manner during the experiment. * **RunNumber**: The sequence
 number of the trial within a block. * **BlockNumber**: The sequence number
 of the block within the experiment. * **PredictedChoice**: The predicted
 choice made by the reinforcement learning model for the participant on
 this trial. * **CorrectPrediction**: Whether the model’s predicted choice
 for the trial matched the correct choice (`1` for correct, `0`for
 incorrect). * **AQ_score**: Autism Spectrum Quotient score for each
 participant. Measures autistic traits on a scale (range 0-33) with higher
 values indicating more autistic traits. * **AQ_group**: A categorical
 variable splitting participants into three groups based on their AQ
 scores: "Low", "Medium", and "High". *
 **Med_Split**: A binary variable splitting participants into two groups
 based on the median AQ score ("Low" and "High").
 **Codebook for parameters.csv** This file contains subject-level
 parameters derived from the Rescorla-Wagner reinforcement learning model
 applied to trial-by-trial choice data. * **participant**: Unique
 identifier for each participant. - **AQ_score**: Autism Spectrum Quotient
 score for each participant. Measures autistic traits on a scale (range
 0-33), with higher values indicating more autistic traits. - **Gender**:
 Self-reported gender of the participant. - **BlockType**: Type of block in
 the trial, either "social" (PLDs of human faces) or
 "nonsocial" (checkmarks and crosses). - **Alpha_Win**: Learning
 rate parameter for reward feedback in win trials (0-1). Reflects how
 quickly participants update expectations based on positive feedback. -
 **Theta_Win**: Exploration parameter for win trials. Higher values
 indicate more randomness in choice behavior under uncertainty. -
 **Rho_Win**: Reward sensitivity parameter for win trials (0-1). Represents
 the subjective valuation of rewards received. - **Alpha_Loss**: Learning
 rate parameter for punishment feedback in loss trials (0-1). Reflects how
 quickly participants update expectations based on negative feedback. -
 **Theta_Loss**: Exploration parameter for loss trials. Higher values
 indicate more randomness in choice behavior under uncertainty. -
 **Rho_Loss**: Sensitivity parameter for loss trials (0-1). Represents the
 subjective valuation of punishments received. - **Neg_LL**: Negative
 log-likelihood of the reinforcement learning model during the optimization
 process. Lower values indicate a better fit of the model to the
 participant's data. - **BIC**: Bayesian Information Criterion for the
 reinforcement learning model. Lower values indicate a better balance
 between model fit and complexity. ## Code/Software R is required to run
 the scripts. The following description gives an overview of the analyses
 performed in each file: **raw_choices_GLM.R** This script implements a
 single GLMM to investigate the influence of AQ scores, block type, and
 trial number on choice correctness. It calculates fixed effect sizes (odds
 ratios with confidence intervals) and conducts post-hoc comparisons using
 the emmeans package to analyze interactions and trends. Interaction
 effects are visualized for further interpretation. **raw_choices_plot.R**
 This script visualizes trial-by-trial choice data to highlight behavioral
 patterns. It includes histograms of AQ scores split into groups (low,
 medium, high), line plots showing learning curves across trials for
 different AQ groups and block types, violin and box plots comparing
 correctness across blocks, and scatter plots exploring relationships
 between AQ scores and choice behavior. **parameters_glmmTMB.R** This
 script fits Beta regression models using the glmmTMB package to analyze
 subject-level parameters derived from the reinforcement learning model.
 The models predict parameters such as Alpha_Win, Rho_Win, Alpha_Loss, and
 Rho_Loss based on AQ scores and block type. The script also evaluates
 model assumptions using the DHARMa package, generating diagnostic plots.
 **parameters_plots.R** This script creates visualizations of the
 reinforcement learning model parameters. It includes histograms to show
 parameter distributions by domain (win/loss), violin and box plots
 comparing parameters across social and nonsocial blocks, and scatter plots
 examining relationships between AQ scores and parameters.