Over-reliance on English hinders cognitive science

Trends in Cognitive Sciences, Год журнала: 2022, Номер 26(12), С. 1153 - 1170

Опубликована: Окт. 14, 2022

English is the dominant language in study of human cognition and behavior: individuals studied by cognitive scientists, as well most scientists themselves, are frequently speakers. However, differs from other languages ways that have consequences for whole sciences, reaching far beyond itself. Here, we review an emerging body evidence highlights how particular characteristics linguistic habits speakers bias field both warping research programs (e.g., overemphasizing features mechanisms present over others) overgeneralizing observations speakers' behaviors, brains, to our entire species. We propose mitigating strategies could help avoid some these pitfalls.

Язык: Английский

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A hierarchy of linguistic predictions during natural language comprehension

Proceedings of the National Academy of Sciences, Год журнала: 2022, Номер 119(32)

Опубликована: Авг. 3, 2022

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction guide interpretation incoming input. However, role in processing remains disputed, with disagreement about both ubiquity and representational nature predictions. Here, we address issues by analyzing recordings participants listening audiobooks, using deep neural network (GPT-2) precisely quantify contextual First, establish responses words are modulated ubiquitous Next, disentangle model-based predictions distinct dimensions, revealing dissociable signatures syntactic category (parts speech), phonemes, semantics. Finally, show high-level (word) inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore processing, showing spontaneously predicts upcoming at multiple levels abstraction.

Язык: Английский

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Brains and algorithms partially converge in natural language processing

Communications Biology, Год журнала: 2022, Номер 5(1)

Опубликована: Фев. 16, 2022

Deep learning algorithms trained to predict masked words from large amount of text have recently been shown generate activations similar those the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety deep language models identify computational principles that lead them brain-like representations sentences. Specifically, analyze brain responses 400 isolated sentences in cohort 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where when these maps onto responses. Finally, estimate how architecture, training, performance independently account generation representations. Our analyses reveal main findings. First, between primarily depends on their ability context. Second, reveals rise maintenance perceptual, lexical, compositional within cortical region. Overall, study shows modern partially converge towards solutions, thus delineates promising path unravel foundations natural processing.

Язык: Английский

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Semantic reconstruction of continuous language from non-invasive brain recordings

Nature Neuroscience, Год журнала: 2023, Номер 26(5), С. 858 - 866

Опубликована: Май 1, 2023

Язык: Английский

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An investigation across 45 languages and 12 language families reveals a universal language network

Nature Neuroscience, Год журнала: 2022, Номер 25(8), С. 1014 - 1019

Опубликована: Июль 18, 2022

Язык: Английский

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Evidence of a predictive coding hierarchy in the human brain listening to speech

Nature Human Behaviour, Год журнала: 2023, Номер 7(3), С. 430 - 441

Опубликована: Март 2, 2023

Abstract Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these models still fail match the abilities of humans. Predictive coding theory offers a tentative explanation this discrepancy: while optimized predict nearby words, human brain would continuously hierarchy representations that spans multiple timescales. To test hypothesis, we analysed functional magnetic resonance imaging signals 304 participants listening short stories. First, confirmed activations modern linearly map onto responses speech. Second, showed enhancing with predictions span timescales improves mapping. Finally, organized hierarchically: frontoparietal cortices higher-level, longer-range more contextual than temporal cortices. Overall, results strengthen role hierarchical predictive processing illustrate how synergy between neuroscience artificial intelligence can unravel computational bases cognition.

Язык: Английский

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Symbols and mental programs: a hypothesis about human singularity

Trends in Cognitive Sciences, Год журнала: 2022, Номер 26(9), С. 751 - 766

Опубликована: Авг. 3, 2022

Язык: Английский

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Dissociating language and thought in large language models

Trends in Cognitive Sciences, Год журнала: 2024, Номер 28(6), С. 517 - 540

Опубликована: Март 19, 2024

Язык: Английский

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Using artificial neural networks to ask ‘why’ questions of minds and brains

Trends in Neurosciences, Год журнала: 2023, Номер 46(3), С. 240 - 254

Опубликована: Янв. 17, 2023

Neuroscientists have long characterized the properties and functions of nervous system, are increasingly succeeding in answering how brains perform tasks they do. But question 'why' work way do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like now enables us approach these questions by asking when optimized a given task mirror behavioral characteristics humans performing same task. Here we highlight recent success this strategy explaining why visual auditory systems do, at both levels.

Язык: Английский

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Deep problems with neural network models of human vision

Behavioral and Brain Sciences, Год журнала: 2022, Номер 46

Опубликована: Дек. 1, 2022

Abstract Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models biological vision. This conclusion is largely based on three sets findings: (1) DNNs more accurate than any other model taken from various datasets, (2) do job predicting pattern human errors behavioral (3) brain signals response to datasets (e.g., single cell responses or fMRI data). However, these not test hypotheses regarding what features contributing good predictions we show that may be mediated by share little overlap with More problematically, account for almost no results psychological research. contradicts common claim good, let alone best, object recognition. We argue theorists interested developing biologically plausible vision need direct their attention explaining findings. generally, build explain experiments manipulate independent variables designed rather compete making predictions. conclude briefly summarizing promising modeling approaches focus data.

Язык: Английский

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