Cooperation and heterogeneity of the autistic mind

Abstract

Individuals with autism spectrum conditions (ASCs) have a core difficulty in recursively inferring the intentions of others. The precise cognitive dysfunctions that determine the heterogeneity at the heart of this spectrum, however, remains unclear. Furthermore, it remains possible that impairment in social interaction is not a fundamental deficit but a reflection of deficits in distinct cognitive processes. To better understand heterogeneity within ASCs, we employed a game-theoretic approach to characterize unobservable computational processes implicit in social interactions. Using a social hunting game with autistic adults, we found that a selective difficulty representing the level of strategic sophistication of others, namely inferring others' mindreading strategy, specifically predicts symptom severity. In contrast, a reduced ability in iterative planning was predicted by overall intellectual level. Our findings provide the first quantitative approach that can reveal the underlying computational dysfunctions that generate the autistic "spectrum."

Figure 1.

Stag-hunt game. Two players, a subject (green circle) and a computer agent (blue circle), try to catch prey: a mobile stag (big square, big payoff) by cooperation or two stationary rabbits (small squares, small payoff), by moving in a sequential manner. At the end of each game, both players receive points equal to the sum of prey and points relating to the remaining time.

Figure 2.

Subjects and behavioral results. A, Mean scores of diagnostic (left panel) and intellectual measurements (right panel) of the control group (black dotted line) and the ASC group (gray solid line). The error bars show the SDs. ASQ score was significantly higher for the ASC group than for the control group (p < 0.1 × 10⁻¹²), while there was no significant difference between the groups for both verbal and strategic intelligence scores. B, Both the control and the ASC group attempted to catch a stag, in effect, when they behaved more cooperatively, when the computer agent was more sophisticated. C, The rate of stag hunt and rabbit hunt in the games with sophisticated computer agent with the fifth-order strategy. The participants in the ASC group (gray crosses) showed a larger variety of behavior than the control participants (black circles).

Figure 3.

Model-based behavioral results. A, The probability for the ToM model (98.2%) was higher than that for the fixed strategy model for controls, while the fixed strategy model was dominant (78.6%) in individuals with ASCs. B, Diagnosis measurement scores, ADI-R and ASDI, were significantly higher for the ASC participants whose behavior fit better with the fixed strategy model (n = 12) than those showing a better fit with the ToM model (n = 5). C, In the ASC group, the greater the expectation of recursive belief inference, the more severe was the autism symptomatology (n = 14, r = −0.52, p = 0.055), as measured by the sum of scores on the ADI-R and the ASDI. D, The estimated forgetting parameter for the ASC group (mean ± SD = 0.57 ± 0.19) was significantly higher than that for the control group (0.93 ± 0.13) (p < 0.1 × 10⁻⁶). E, The estimated sophistication for the individuals with autism showed significant positive correlation with individual IQ scores (left panel: n = 17, r = 0.54, p = 0.026), while there was no correlation for the control participants (right panel: n = 17, r = 0.02).