Entropy Reduction correlates with temporal lobe activity

Nelson, Matthew J.; Dehaene, Stanislas; Pallier, Christophe; Hale, John

doi:10.18653/v1/w17-0701

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…In a recent ECoG study, Nelson, Karoui, et al (2017) showed that bigram word entropy during sentence reading is negatively related with fluctuations in the high gamma (70-150 Hz) power on electrodes overlying left 60 posterior temporal regions. In a separate analysis of the same dataset, entropy reduction (changes in word-by-word syntactic uncertainty) was found to have a positive linear relationship with high gamma power in the left anterior and posterior inferior temporal electrode sites (Nelson, Dehaene, Pallier, & Hale, 2017). 65 Although fMRI work provided insights into spatial fingerprints of probabilistic computations and work in electrophysiology showed their temporal and high-frequency neural characteristics during sentence reading, it is as of yet unclear whether these statistical quantities are also reflected in ongoing electrophysiological activity during natural language listening.…”

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confidence: 86%

Frequency-specific brain dynamics related to prediction during language comprehension

Armeni

Willems

Bosch

et al. 2018

Preprint

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The brain's remarkable capacity to process spoken language virtually in real time requires fast and efficient information processing machinery.In this study, we investigated how frequency-specific brain dynamics relate to models of probabilistic language prediction during auditory narrative comprehension. We recorded MEG activity while participants were listening to auditory stories in Dutch. Using trigram statistical language models, we estimated for every word in a story its conditional probability of occurrence. On the basis of word probabilities, we computed how unexpected the current word is given its context (word perplexity) and how (un)predictable the current linguistic context is (word entropy). We then evaluated whether source-reconstructed MEG oscillations at different frequency bands are modulated as a function of these language processing metrics. We show that theta-band source dynamics are increased in high relative to low entropy states, likely reflecting lexical computations. * k.armeni@donders.ru.nl Beta-band dynamics are increased in situations of low word entropy and perplexity possibly reflecting maintenance of ongoing cognitive context. These findings lend support to the idea that the brain engages in the active generation and evaluation of predicted language based on the statistical properties of the input signal.

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confidence: 86%

Frequency-specific brain dynamics related to prediction during language comprehension

Armeni

Willems

Bosch

et al. 2018

Preprint

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“…The entropy reduction hypothesis has been applied to both naturalistic text stimuli (Frank, 2013; Wu, Bachrach, Cardenas, & Schuler, 2010) and controlled experimental materials (Linzen & Jaeger, 2015). A number of studies have also shown that it correlates with measured reading times and neural signals related to sentence‐medial ambiguities, including those observed in processing RCs (Chen, Jäger, & Hale, 2012; Frank, 2013; Lowder, Choi, Ferreira, & Henderson, 2018; Nelson, Dehaene, Pallier, & Hale, 2017; Yun, Chen, Hunter, Whitman, & Hale, 2015). In the next section, we review some of the most crucial experimental findings in RC processing and discuss in detail the predictions made by different processing principles.…”

Section: Principles Proposed To Account For Rc Processingmentioning

confidence: 97%

Quantifying Structural and Non‐structural Expectations in Relative Clause Processing

Chen

Hale

2021

Cognitive Science

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Information‐theoretic complexity metrics, such as Surprisal (Hale, 2001; Levy, 2008) and Entropy Reduction (Hale, 2003), are linking hypotheses that bridge theorized expectations about sentences and observed processing difficulty in comprehension. These expectations can be viewed as syntactic derivations constrained by a grammar. However, this expectation‐based view is not limited to syntactic information alone. The present study combines structural and non‐structural information in unified models of word‐by‐word sentence processing difficulty. Using probabilistic minimalist grammars (Stabler, 1997), we extend expectation‐based models to include frequency information about noun phrase animacy. Entropy reductions derived from these grammars faithfully reflect the asymmetry between subject and object relatives (Staub, 2010; Staub, Dillon, & Clifton, 2017), as well as the effect of animacy on the measured difficulty profile (Lowder & Gordon, 2012; Traxler, Morris, & Seely, 2002). Visualizing probability distributions on the remaining alternatives at particular parser states allows us to explore new, linguistically plausible interpretations for the observed processing asymmetries, including the way that expectations about the relativized argument influence the processing of particular types of relative clauses (Wagers & Pendleton, 2016).

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“…Complexity is non-linear and complex to define but is often computationally quantified with entropy measurements. Sample entropy, proposed by Richman and Moorman (Richman and Moorman, 2000 ), is a well-defined index of complexity and has been applied to brain activity (Yao et al, 2013 ; Wang et al, 2014 , 2017 ; Lebedev et al, 2016 ; Li et al, 2016 ; Zhou et al, 2016 ; Jia et al, 2017 ; Nelson et al, 2017 ; Chang et al, 2018 ; Song et al, 2018 ). It is noteworthy that multiscale entropy (MSE) analysis (Costa et al, 2002 , 2005 ; Yang and Tsai, 2013 ; Courtiol et al, 2016 ) calculates a series of sample entropy over multiple time scales, which captures the temporal complexity characteristcs of time-series neural signals from microscopic to macroscopic aspects.…”

Section: Introductionmentioning

confidence: 99%

Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity

Low

Kuo

Tsai³

et al. 2018

Front. Neurosci.

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The irregularity and uncertainty of neurophysiologic signals across different time scales can be regarded as neural complexity, which is related to the adaptability of the nervous system and the information processing between neurons. We recently reported general loss of brain complexity, as measured by multiscale sample entropy (MSE), at pain-related regions in females with primary dysmenorrhea (PDM). However, it is unclear whether this loss of brain complexity is associated with inter-subject genetic variations. Brain-derived neurotrophic factor (BDNF) is a widely expressed neurotrophin in the brain and is crucial to neural plasticity. The BDNF Val66Met single-nucleotide polymorphism (SNP) is associated with mood, stress, and pain conditions. Therefore, we aimed to examine the interactions of BDNF Val66Met polymorphism and long-term menstrual pain experience on brain complexity. We genotyped BDNF Val66Met SNP in 80 PDM females (20 Val/Val, 31 Val/Met, 29 Met/Met) and 76 healthy female controls (25 Val/Val, 36 Val/Met, 15 Met/Met). MSE analysis was applied to neural source activity estimated from resting-state magnetoencephalography (MEG) signals during pain-free state. We found that brain complexity alterations were associated with the interactions of BDNF Val66Met polymorphism and menstrual pain experience. In healthy female controls, Met carriers (Val/Met and Met/Met) demonstrated lower brain complexity than Val/Val homozygotes in extensive brain regions, suggesting a possible protective role of Val/Val homozygosity in brain complexity. However, after experiencing long-term menstrual pain, the complexity differences between different genotypes in healthy controls were greatly diminished in PDM females, especially in the limbic system, including the hippocampus and amygdala. Our results suggest that pain experience preponderantly affects the effect of BDNF Val66Met polymorphism on brain complexity. The results of the present study also highlight the potential utilization of resting-state brain complexity for the development of new therapeutic strategies in patients with chronic pain.

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Entropy Reduction correlates with temporal lobe activity

Cited by 8 publications

References 35 publications

Frequency-specific brain dynamics related to prediction during language comprehension

Frequency-specific brain dynamics related to prediction during language comprehension

Quantifying Structural and Non‐structural Expectations in Relative Clause Processing

Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity

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