2021
DOI: 10.1016/j.jmp.2021.102503
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Pupil dilation indexes automatic and dynamic inference about the precision of stimulus distributions

Abstract: Learning about the statistics of one's environment is a fundamental requirement of adaptive behaviour. In this experiment we probe whether pupil dilation in response to brief auditory stimuli reflects automatic statistical learning about the underlying stimulus distributions. Specifically, we consider whether pupil dilation reflects automatic (task-irrelevant) learning about the precision of Gaussian distributions of pitch in a sequence of tones. We provide clear evidence, both by comparing responses to percep… Show more

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Cited by 4 publications
(5 citation statements)
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“…A dissimilarity between our study and the majority of previous auditory studies is the specific choice of stimuli, as previous studies predominantly used tones 1 , 19 , 20 , 32 , 33 , whereas we used vowels spoken by different speakers. However, some studies have successfully used verbal auditory stimuli to investigate statistical learning based on transition probabilities in sequences 34 , especially in the field of language acquisition 35 .…”
Section: Discussionmentioning
confidence: 94%
“…A dissimilarity between our study and the majority of previous auditory studies is the specific choice of stimuli, as previous studies predominantly used tones 1 , 19 , 20 , 32 , 33 , whereas we used vowels spoken by different speakers. However, some studies have successfully used verbal auditory stimuli to investigate statistical learning based on transition probabilities in sequences 34 , especially in the field of language acquisition 35 .…”
Section: Discussionmentioning
confidence: 94%
“…With regard to the underlying neurotransmitters, pupil dilation has often been interpreted as reflecting global arousal, as linked to the locus coeruleus‐norepinephrine (LC‐NE) system (Krishnamurthy et al, 2017; Murphy et al, 2011; Nassar et al, 2012; Urai et al, 2017; Vincent et al, 2019), and surprise‐driven stimulus responses (Raisig et al, 2010; Preuschoff et al, 2011; Nassar et al, 2012; Lavin et al, 2014; Kloosterman et al, 2015; de Berker et al, 2016; Damsma & van Rijn, 2017; Quirins et al, 2018; Alamia et al, 2019; Richter & de Lange, 2019; Silvestrin et al, 2021). But in addition to the LC‐NE system, there are more brain circuits related to the mediation of pupil size which involve, for example, the superior colliculus, and the basal forebrain (Joshi & Gold, 2020; Wang & Munoz, 2015), so for this study, we cannot determine which neural pathways or neurotransmitters drive the observed stimulus‐evoked pupil dilation response.…”
Section: Discussionmentioning
confidence: 99%
“…Previous pupillometry studies induced expectations by explicitly informing their participants about stimulus distributions (de Berker et al, 2016; Friedman et al, 1973; Qiyuan et al, 1985), by sampling from a Gaussian distribution (Krishnamurthy et al, 2017; Silvestrin et al, 2021), or by creating transition regularities in a stimulus sequence (Alamia et al, 2019). While we informed participants that there are different conditional probabilities of each vowel related to each face, we did not inform them about the specific pairings which the participants had to learn.…”
Section: Discussionmentioning
confidence: 99%
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“…There is a growing body of work investigating inference and learning as Bayesian model inversion in humans (FitzGerald, Hämmerer, Friston, Li, & Dolan, 2017;Mathys, Daunizeau, Friston, & Stephan, 2011;Silvestrin, Penny, & FitzGerald, 2021;De Berker et al, 2016;Diaconescu et al, 2017). However, in all this work strong assumptions are made about the generative model.…”
Section: Structure Learningmentioning
confidence: 99%