Information‐Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory

Agres, Kat; Abdallah, Samer A.; Pearce, Marcus T.

doi:10.1111/cogs.12477

Cited by 51 publications

(49 citation statements)

References 92 publications

(222 reference statements)

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“…More generally, surprisal theory links language comprehension to theories of perception and brain function that are organized around the idea of prediction and predictive coding (Clark, 2013;Friston, 2010;Friston & Kiebel, 2009), in which an internal model of the world is used to generate top-down predictions about the future stimuli, then these predictions are compared with the actual stimuli, and action is taken as a result of the difference between predictions and perception. Such predictive mechanisms are well-documented in other cognitive domains, such as visual perception (Egner, Monti, & Summerfield, 2010), auditory and music perception (Agres, Abdallah, & Pearce, 2018), and motor planning (Wolpert & Flanagan, 2001).…”

Section: Expectation-based Theories: Surprisal Theorymentioning

confidence: 86%

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Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing

2020

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A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context. Models that explain and predict this difficulty can be broadly divided into two kinds, expectation‐based and memory‐based. In this work, we present a new model of incremental sentence processing difficulty that unifies and extends key features of both kinds of models. Our model, lossy‐context surprisal, holds that the processing difficulty at a word in context is proportional to the surprisal of the word given a lossy memory representation of the context—that is, a memory representation that does not contain complete information about previous words. We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language‐dependent structural forgetting, where the effects of memory on sentence processing appear to be moderated by language statistics. Furthermore, we demonstrate that dependency locality effects, a signature prediction of memory‐based theories, can be derived from lossy‐context surprisal as a special case of a novel, more general principle called information locality.

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Section: Expectation-based Theories: Surprisal Theorymentioning

confidence: 86%

“…It is a correction term that changes the unconditional surprisal h(X) into the conditional surprisal hðXÞ. It is also called coding gain in related literature (Agres et al, 2018). Viewing surprisal as information content, pointwise mutual information can be thought of as measuring the number of shared bits between two representations.…”

Section: Of 54mentioning

confidence: 99%

Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing

2020

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“…The IDyOM results reviewed above, therefore, are consistent with statistical learning as a mechanism for musical enculturation but the relationship is correlational rather than causal (with the exception of Ref. , which examined statistical learning directly but using an artificial musical system). In the following, I will outline a new modeling approach for a causal empirical investigation of the SLH of enculturation in musical styles.…”

Section: Statistical Learning In Musical Enculturationmentioning

confidence: 66%

“…While these results are consistent with the SLH, since an IDyOM model trained on Western music accurately simulates Western listeners across a range of tasks, they do not provide causal evidence for the SLH. However, the results of a recognition memory study 108 show that memory performance is causally related to dynamic statistical learning of an artificial musical system. Finally, I presented data from computational simulations suggesting that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles on perception, providing a formal, quantitative model of cultural distance.…”

Section: Discussionmentioning

confidence: 99%

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation

Pearce

2018

Annals of the New York Academy of Sciences

148

197

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Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here.

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“…IDyOM can use representations of different features of the auditory input, known as viewpoints, to make its probabilistic predictions. Following Omigie et al, (2013) and other behavioral work (Agres, Abdallah, & Pearce, 2018;Hansen & Pearce, 2014;Hansen et al, 2016), we used a mixture of scale degree ("cpint") and pitch interval ("cpintfref") viewpoints to predict pitch ("cpitch") continuations in the melodies. This is our reference model.…”

Section: A Computational Model Of Auditory Expectationmentioning

confidence: 99%

Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system

Quiroga-Martinez¹,

Hansen

Hoejlund

et al. 2019

Preprint

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AbstractNeural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than probabilistic prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper probabilistic prediction were reflected in later components: the mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning.Highlights- In melodies, sound expectedness (modeled with IDyOM) is associated with the amplitude of the N1m.- This effect is not different between musicians and non-musicians.- Sensory adaptation related to melodic pitch intervals explains better the N1m effect.- Auditory regularities and the expectations captured by IDyOM are reflected in the MMNm and P3am.- Evidence for a hierarchy of auditory predictions during melodic listening.

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Information‐Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory

Cited by 51 publications

References 92 publications

Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing

Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation

Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system

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