What are naturalistic comprehension paradigms teaching us about language?

Hasson, Uri; Egidi, Giovanna

doi:10.1017/cbo9781107323667.011

“…This tradeoff has had profound implications for research into auditory story comprehension in terms of neuroimaging used, leading to a preference for fMRI and a curious omission of M/EEG from most discussions of naturalistic language processing (cf. Andric & Small, 2015;Hasson & Egidi, 2015) In particular, fMRI's poor temporal resolution is actually advantageous for temporally variable stimuli. In some sense, an impulse stimulus is essentially identical with a stimulus lasting up to about a second.…”

mentioning

confidence: 99%

M/EEG analysis of naturalistic stories: a review from speech to language processing

Alday

¹

2018

Language, Cognition and Neuroscience

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M/EEG research using naturally spoken stories as stimuli has focused largely on speech and not language processing. The temporal resolution of M/EEG is a two-edged sword, allowing for the study of the fine acoustic structure of speech, yet easily overwhelmed by the temporal noise of variation in constituent length. Recent theories on the neural encoding of linguistic structure require the temporal resolution of M/EEG, yet suffer from confounds when studied on traditional, heavily controlled stimuli. Recent methodological advances allow for synthesising naturalistic designs and traditional, controlled designs into effective M/EEG research on naturalistic language. In this review, we highlight common threads throughout the at-times distinct research traditions of speech and language processing. We conclude by examining the tradeoffs and successes of three M/EEG studies on fully naturalistic language paradigms and the future directions they suggest.

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“…Additionally, the naturalistic setup of the experiments used in this work are crucial for this benchmark task and for neuroscience in general (Nastase et al, 2020). Not only does it increase the ecological validity of the recordings by allowing natural reading without controlling the individual reading speed, but it also supports the extraction of signals on various linguistic levels (Hasson and Egidi, 2015; Brennan, 2016; Hamilton and Huth, 2020; Kandylaki and Bornkessel-Schlesewsky, 2019; Alday, 2019). Frey et al (2018) investigated how two different reading tasks modulate both eye movements and brain activity.…”

Section: Discussionmentioning

confidence: 73%

The ZuCo Benchmark on Cross-Subject Reading Task Classification with EEG and Eye-Tracking Data

Hollenstein¹,

Tröndle

²

,

Płomecka

³

et al. 2022

Preprint

2

0

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We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com.HighlightsWe present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research.We provide an interface to evaluate new approaches with an accompanying public leaderboard.The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading.The data is based on the Zurich Cognitive Language Processing Corpus of simultaneous eye-tracking and EEG signals from natural reading.

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“…Additionally, the naturalistic setup of the experiments used in this work are crucial for this benchmark task and for neuroscience in general (Nastase et al, 2020). Not only does it increase the ecological validity of the recordings by allowing natural reading without controlling the individual reading speed, but it also supports the extraction of signals on various linguistic levels (Hasson and Egidi, 2015;Brennan, 2016;Alday, 2019;Kandylaki and Bornkessel-Schlesewsky, 2019;Hamilton and Huth, 2020). Frey et al (2018) investigated how two different reading tasks modulate both eye movements and brain activity.…”

Section: Discussionmentioning

confidence: 99%

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

Hollenstein

¹

,

Tröndle²,

Płomecka³

et al. 2023

Front. Psychol.

1

0

View full text Add to dashboard Cite

We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com.

show abstract

What are naturalistic comprehension paradigms teaching us about language?

Cited by 7 publications

References 73 publications

M/EEG analysis of naturalistic stories: a review from speech to language processing

M/EEG analysis of naturalistic stories: a review from speech to language processing

The ZuCo Benchmark on Cross-Subject Reading Task Classification with EEG and Eye-Tracking Data

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

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