Molecular autism · 2026
Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Plank Irene Sophia, Pior Alexandra, Yurova Anna, Nowak Julia, Shi Zhuanghua, Falter-Wagner Christine M
Аннотация
BACKGROUND: Autism spectrum disorder (ASD) affects social interaction, communication and behavioural flexibility. Recent theories grounded in the Bayesian Brain Framework propose that autistic individuals process environmental uncertainty differently, overweighting volatility when updating predictions about the world. However, the robustness of these predictive processing differences and their relevance to core symptoms remains unclear.
METHODS: Lawson and colleagues [1] reported a tendency to overestimate environmental volatility in autistic adults based on belief states extracted using a Hierarchical Gaussian Filter. We extended their paradigm to a domain central to the autistic experience: rather than distinguishing houses from faces, participants detected valence in emotional facial expressions. Preregistered hypotheses were evaluated using Bayesian linear mixed models based on data of 22 autistic and 22 non-autistic participants.
RESULTS: Despite using an identical computational model to extract belief states, we did not find robust differences between autistic and non-autistic adults, though autistic participants showed a non-credible trend towards increased processing of phasic volatility. Largely comparable probabilistic associative learning was also reflected in response times and pupil sizes.
LIMITATIONS: While participants reacted faster to expected than unexpected trials in the beginning, this effect decreased over the course of the experiment; thus, possibly indicating that the individual blocks of stable cue-outcome associations were too short.
CONCLUSIONS: We were unable to find credible differences in environmental volatility and learning rate updates using a task designed to probe a key autistic difficulty, facial emotion recognition. The current results call into question the generalisability and clinical relevance of prior findings.
METHODS: Lawson and colleagues [1] reported a tendency to overestimate environmental volatility in autistic adults based on belief states extracted using a Hierarchical Gaussian Filter. We extended their paradigm to a domain central to the autistic experience: rather than distinguishing houses from faces, participants detected valence in emotional facial expressions. Preregistered hypotheses were evaluated using Bayesian linear mixed models based on data of 22 autistic and 22 non-autistic participants.
RESULTS: Despite using an identical computational model to extract belief states, we did not find robust differences between autistic and non-autistic adults, though autistic participants showed a non-credible trend towards increased processing of phasic volatility. Largely comparable probabilistic associative learning was also reflected in response times and pupil sizes.
LIMITATIONS: While participants reacted faster to expected than unexpected trials in the beginning, this effect decreased over the course of the experiment; thus, possibly indicating that the individual blocks of stable cue-outcome associations were too short.
CONCLUSIONS: We were unable to find credible differences in environmental volatility and learning rate updates using a task designed to probe a key autistic difficulty, facial emotion recognition. The current results call into question the generalisability and clinical relevance of prior findings.