The Relation Between Preference for Predictability and Autistic Traits

Goris, Judith; Braß, Marcel; Cambier, Charlotte; Delplanque, Jeroen; Wiersema, Jan R.; Braem, Senne

doi:10.1002/aur.2244

Cited by 37 publications

(43 citation statements)

References 53 publications

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“…In light of finding that our proposed model had a better fit to the dataset of Niv et al [14] than previous models, we then performed model selection using a dataset from Goris et al [15]. Here we utilized decision-making tasks unrelated with riskiness in order to investigate the generalizability of our proposed model to different tasks and populations.…”

Section: Resultsmentioning

confidence: 99%

“…Here we utilized decision-making tasks unrelated with riskiness in order to investigate the generalizability of our proposed model to different tasks and populations. The dataset of Goris et al [15] consists of data from participants who performed a gamble task, a form of four-armed bandit task. Only the predictability of the gain was manipulated between choices.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed model fits these datasets better than the previous models, with same or fewer free parameters, thus providing a more parsimonious and robust account for risk averse choices. First this model fitted to the dataset of Niv et al [14] and Goris et al [15] than previous winning model, risk sensitive Q-learning model which utilized separate learning rate for positive or negative prediction error, having asymmetric effects on changes in predictions. It has been shown that the learning rate asymmetry is robust across experiments and the negative learning rate is generally higher than the positive learning rate, which may reflect risk aversion by being higher negative learning rate drives choices away from risky options [20].…”

Section: Discussionmentioning

confidence: 99%

“…We validated the simulated results by performing model selection with real experimental data taken from Niv et al [14]. As we found a better fit of our proposed model to the dataset than previous models, we conducted model selection using an additional dataset, from Goris et al [15], which utilized decision-making tasks unrelated with riskiness to investigate the generalizability of our proposed model to different task and population. We further simulated the same task and the simple two-armed bandit task to investigate how parameters within the proposed model modulate the choice preference.…”

Section: Introductionmentioning

confidence: 87%

“…To ensure the fitting of the proposed model in different tasks and population, we also conducted the model selection with the actual experimental datasets from Goris et al [15] (https://osf.io/pkq3u) whose datasets are presented on the open science framework. The dataset included score of autism spectrum condition for each participant.…”

Section: Analysis 2-2: Model Selection With Actual Experimental Datamentioning

confidence: 99%

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Surprise acts as a reducer of outcome value in human reinforcement learning

Sumiya¹,

Katahira²

2019

Preprint

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Surprise occurs because of differences between a decision outcome and its predicted outcome (prediction error), regardless of whether the error is positive or negative. It has recently been postulated that surprise affects the reward value of the action outcome itself; studies have indicated that increasing surprise, as absolute value of prediction error, decreases the value of the outcome. However, how surprise affects the value of the outcome and subsequent decision making is unclear. We suggested that, on the assumption that surprise decreases the outcome value, agents will increase their risk averse choices when an outcome is often surprisal. Here, we propose the surprise-sensitive utility model, a reinforcement learning model that states that surprise decreases the outcome value, to explain how surprise affects subsequent decision-making. To investigate the assumption, we compared this model with previous reinforcement learning models on a risky probabilistic learning task with simulation analysis, and model selection with two experimental datasets with different tasks and population. We further simulated a simple decision-making task to investigate how parameters within the proposed model modulate the choice preference. As a result, we found the proposed model explains the risk averse choices in a manner similar to the previous models, and risk averse choices increased as the surprise-based modulation parameter of outcome value increased. The model fits these datasets better than the other models, with same free parameters, thus providing a more parsimonious and robust account for risk averse choices. These findings indicate that surprise acts as a reducer of outcome value and decreases the action value for risky choices in which prediction error often occurs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 87%

Section: Analysis 2-2: Model Selection With Actual Experimental Datamentioning

confidence: 99%

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Surprise acts as a reducer of outcome value in human reinforcement learning

Sumiya¹,

Katahira²

2019

Preprint

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Priors Bias Perceptual Decisions in Autism, But Are Less Flexibly Adjusted to the Context

2020

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According to the predictive coding framework, percepts emerge from combinations of sensory input and prior knowledge, whose relative contributions depend on their reliability. Recent predictive coding theories suggest that Autism Spectrum Disorder (ASD) could be characterized by an atypical weighting of priors. Here, we assessed whether individuals with ASD can flexibly adjust the weight (precision) of the prior to the context. Thirty‐one neurotypical adults (NT) and 26 adults with ASD participated in a visual discrimination task designed to elicit a time‐order effect (TOE). The TOE reflects the integration of priors with sensory estimates. We used two experimental contexts: a narrow stimulus range (Narrow condition) and a broader range (Broad condition) in order to induce a prior with a higher and lower precision, respectively. Both groups learned a prior that biased their perception, as shown with the TOE. As expected, the NT group had a larger TOE in the Narrow condition than in the Broad condition, revealing a contextual adjustment of the prior precision. In contrast, ASD participants were more inflexible: the extent of the TOE was not modulated by the context. In addition, the accuracy increased when the stimulus range decreased in both group, which may be interpreted as a contextual adjustment of the sensory precision. To conclude, adults with and without ASD implicitly learned a prior mean, but ASD participants failed to flexibly adjust the prior precision to the context. This increased inflexibility in ASD could account for many symptoms, such as their intolerance of uncertainty. Lay Summary Based on our experience, we have expectations about our environment. Theories suggest that the symptoms encountered in autism could be due to atypical expectations, leading to an impression of an unpredictable world. Using a visual discrimination task, we showed that adults with and without autism were biased by their expectations. Yet, the extent to which expectations biased perception did not depend on the context in autism. This higher inflexibility found in autism may explain symptoms such as resistance to change.

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