Some readers who advocate for neurodiversity in understanding disability got new evidence to help them with their arguments, but the evidence comes from research that depends on the not-so-comfortable areas of mathematical models and operant actions. Many people believe that individuals with autism see things and process information differently than those who are “neurotypical.”
In a Science Advances paper entitled “Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks,” Sucheta Chakravarty, Benjamin B. Scott, and colleagues of Boston University reported research supported by the Simon Foundation Autism Research Initiative that showed that individuals with autism showed slower learning and less accuracy than peers while playing a video game when more “noise “ was present. They contend that. “These findings implicate noisy evidence integration as a computational mechanism for altered visual perception across the autism spectrum and establish a scalable, accessible platform for probing altered perception and learning in neurodevelopmental and neuropsychiatric disorders.”
To study variation in visual perception of individuals with autism, Professor Chakravarty and colleagues measured people’s behavior when they played a computer game called GEODE that they say isolates “distinct cognitive processes.” Based on research with rats, GEODE showed flashes of light to the left and right parts of the brain and provided rewards when players chose the side with more flashes.
GEODE used brief light flashes presented to the left and right hemifields,1 at random time points. Participants were rewarded for choosing the side (left/right) with more flashes. When there was more “noise” during game play, the game play of individuals with autism was slower and less accurate.
Here’s the abstract:
Altered perception is a hallmark of autism spectrum disorder (ASD), yet its underlying mechanisms remain unclear, in part because individuals with greater impairments are often excluded from psychophysics research. Here, we introduce GEODE (gathering evidence to optimize decisions), an online video game designed to measure visual perception across the full autism spectrum. GEODE incorporates feedback-based shaping from animal training to teach task mechanics, customized graphics and storyline elements to sustain engagement, and touchscreen compatibility for broader accessibility, enabling participation from adolescents including those with profound autism. Across a large, heterogeneous cohort, autistic participants successfully played GEODE but showed slower learning and reduced accuracy compared with typically developing siblings. These deficits were best explained by increased noise in the integration of sensory evidence, which scaled nonlinearly with stimulus complexity and tracked standardized survey measures of adaptive functioning more closely than diagnostic category alone. Injecting equivalent noise into artificial neural networks produced agents that recapitulated ASD-like patterns of learning and decision-making. These findings implicate noisy evidence integration as a computational mechanism for altered visual perception across the autism spectrum and establish a scalable, accessible platform for probing altered perception and learning in neurodevelopmental and neuropsychiatric disorders.
The researchers did lots of clever work in the design of their research. The study is in a usually trustworthy journal. The researchers used typically developing siblings of the individuals with autism as a comparison group. They recruited participants carefully. They controlled aspects of the study environment precisely.
But, let me ask a fundamental question. What is “noise?” Here’s what the researchers tell us:
Noise injection
To examine the effects of stochasticity in the sensory representation on learning performance, Gaussian noise was introduced at multiple stages of the neural network. Specifically, noise was applied to both the input embedding layer and the LSTM layer to investigate how variability at different processing stages affects learning dynamics.
Noise was generated by sampling random values from a standard normal distribution N(0,1) where the mean is 0 and the SD is 1. The noise was then scaled by a parameter σ, transforming the noise distribution to N(0,σ2). This transformation ensures that the injected noise retains a mean of 0 while allowing precise control over its SD through the parameter σ. The scaled noise was added directly to the input tensor x before being processed by the network.
To prevent the noise scaling process from interfering with gradient computations during backpropagation, the scaling factor σ was applied using a detached version of the input tensor. This ensures that the noise injection does not influence the optimization process, isolating its effects from the network’s learning dynamics.
Both input noise and LSTM noise were systematically varied across different conditions. Noise levels (σ) ranged from 0 to 5 in increments of 0.1, generating a wide spectrum of variability conditions for analysis.
The findings are fascinating. The Laboratory of Comparative Cognition is doing damn great work. But I am hard pressed to draw implications for those of us who work with individuals with autism. Maybe we should learn that the neurocognitive science implies that educators should keep instructional situations free from “noise?” Uhm, thanks very much. Got it. Big whup…. We’ll quiet our instruction.
Reference
Chakravarty, S., Do, Q., Li, Yutong, Torres-Lacarra, V., Tager-Flusberg, McGuire, J. T/, & Scott, B. B., (2026). Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks. Science Advanceses, 12,
Footnote
Think about how the flashes of light are done, please. One could, for example, attach tiny wires to one side of two sides of the brain and send “flashes” to only one at a time. I’m not recommending research using that method with little humans (whether they have autism or don’t)…rats, maybe. See the article for real explanations.