Assessment & Research

Autistic traits are related to worse performance in a volatile reward learning task despite adaptive learning rates.

Goris et al. (2021) · Autism : the international journal of research and practice 2021
★ The Verdict

Among neurotypical adults, higher autistic traits predict poorer performance when reward rules keep changing, even though their learning-speed adjustments look typical.

✓ Read this if BCBAs who teach flexible problem-solving to teens or adults with sub-clinical or clinical autism features.
✗ Skip if Clinicians working only with very young children or non-verbal learners.

01Research in Context

01

What this study did

The team asked neurotypical adults to play a computer game where the reward rules kept changing.

People earned points by picking the better picture, but the better picture switched without warning.

Everyone also filled out an autism-trait questionnaire so the researchers could compare high- and low-trait groups.

02

What they found

Adults with more autistic traits scored fewer points even though they adjusted their learning speed like everyone else.

In plain words, they had trouble figuring out the new rule after each switch, not trouble changing how fast they learned.

03

How this fits with other research

Panasiti et al. (2016) saw the same pattern: higher traits weakened the link between learning a social reward and later friendly behavior.

Laurie-Masi et al. (2022) moved from traits to diagnosed autism and found autistic adults also struggled when rules reversed, but their learning-rate updates were slower, not just the final choice.

Muller Spaniol et al. (2018) looks like a contradiction: high-trait adults were actually better at ignoring distractions.

The studies differ because Judith tested quick rule switches while Mayra tested steady attention; traits can help with focus yet hurt with flexible learning.

04

Why it matters

If your client has high autistic traits, do not assume slow learning equals slow brain.

Instead, give extra practice when rules flip—use clear signals, written prompts, and more reversal trials.

Keep the learning rate the same; just offer more chances to catch the new rule.

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Add three extra reversal trials after you change a token-earning rule and mark the switch with a colored card.

02At a glance

Intervention
not applicable
Design
other
Population
neurotypical
Finding
negative

03Original abstract

Recent theories propose that autism is characterized by an impairment in determining when to learn and when not. Here, we investigated this hypothesis by estimating learning rates (i.e. the speed with which one learns) in three different environments that differed in rule stability and uncertainty. We found that neurotypical participants with more autistic traits performed worse in a volatile environment (with unstable rules), as they chose less often for the most rewarding option. Exploratory analyses indicated that performance was specifically worse when reward rules were opposite to those initially learned for participants with more autistic traits. However, there were no differences in the adjustment of learning rates between participants with more versus less autistic traits. Together, these results suggest that performance in volatile environments is lower in participants with more autistic traits, but that this performance difference cannot be unambiguously explained by an impairment in adjusting learning rates.

Autism : the international journal of research and practice, 2021 · doi:10.1177/1362361320962237