The neural architecture of language: Integrative modeling converges on predictive processing

Proceedings of the National Academy of Sciences, Journal Year: 2021, Volume and Issue: 118(45)

Published: Nov. 4, 2021

Significance Language is a quintessentially human ability. Research has long probed the functional architecture of language in mind and brain using diverse neuroimaging, behavioral, computational modeling approaches. However, adequate neurally-mechanistic accounts how meaning might be extracted from are sorely lacking. Here, we report first step toward addressing this gap by connecting recent artificial neural networks machine learning to recordings during processing. We find that most powerful models predict behavioral responses across different datasets up noise levels. Models perform better at predicting next word sequence also measurements—providing computationally explicit evidence predictive processing fundamentally shapes comprehension mechanisms brain.

Language: Английский

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If deep learning is the answer, what is the question?

Nature reviews. Neuroscience, Journal Year: 2020, Volume and Issue: 22(1), P. 55 - 67

Published: Nov. 16, 2020

Language: Английский

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A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

eLife, Journal Year: 2021, Volume and Issue: 10

Published: Oct. 26, 2021

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which experimentally challenging study. In insects, circuit dynamics a region called the central complex (CX) enable directed locomotion, sleep, and context- experience-dependent spatial navigation. We describe first complete electron microscopy-based connectome of

Language: Английский

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Shared computational principles for language processing in humans and deep language models

Nature Neuroscience, Journal Year: 2022, Volume and Issue: 25(3), P. 369 - 380

Published: March 1, 2022

Departing from traditional linguistic models, advances in deep learning have resulted a new type of predictive (autoregressive) language models (DLMs). Using self-supervised next-word prediction task, these generate appropriate responses given context. In the current study, nine participants listened to 30-min podcast while their brain were recorded using electrocorticography (ECoG). We provide empirical evidence that human and autoregressive DLMs share three fundamental computational principles as they process same natural narrative: (1) both are engaged continuous before word onset; (2) match pre-onset predictions incoming calculate post-onset surprise; (3) rely on contextual embeddings represent words contexts. Together, our findings suggest biologically feasible framework for studying neural basis language.

Language: Английский

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Keep it real: rethinking the primacy of experimental control in cognitive neuroscience

NeuroImage, Journal Year: 2020, Volume and Issue: 222, P. 117254 - 117254

Published: Aug. 13, 2020

Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive highly-controlled experiments real-world contexts. In many cases, however, such efforts led realization that developed under particular manipulations failed capture much variance outside context manipulation. The critique non-naturalistic is not recent development; it echoes persistent and subversive thread history modern psychology. brain has evolved guide behavior multidimensional world with interacting variables. assumption artificially decoupling manipulating these variables will lead satisfactory understanding may be untenable. We develop an argument for primacy naturalistic paradigms, point developments machine learning as example transformative power relinquishing control. should deployed afterthought if hope build extend beyond laboratory into real world.

Language: Английский

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Deep Reinforcement Learning and Its Neuroscientific Implications

Neuron, Journal Year: 2020, Volume and Issue: 107(4), P. 603 - 616

Published: July 13, 2020

Language: Английский

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Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits

European Journal of Personality, Journal Year: 2020, Volume and Issue: 34(6), P. 1175 - 1201

Published: Dec. 1, 2020

We argue that it is useful to distinguish between three key goals of personality science—description, prediction and explanation—and attaining them often requires different priorities methodological approaches. put forward specific recommendations such as publishing findings with minimum a priori aggregation exploring the limits predictive models without being constrained by parsimony intuitiveness but instead maximizing out–of–sample accuracy. naturally occurring variance in many decontextualized multidetermined constructs interest scientists may not have individual causes, at least this term generally understood ways are human–interpretable, never mind intervenable. If so, explanations narratives summarize pieces descriptive rather than target cause–effect associations. By meticulously studying contextualized behaviours, thoughts, feelings goals, however, causes ultimately be identifiable, although causal will likely far more complex, phenomenon–specific person–specific anticipated thus far. Progress all areas—description, explanation—requires higher dimensional currently dominant ‘Big Few’ supplementing subjective trait–ratings alternative sources information informant–reports behavioural measurements. Developing new generation psychometric tools provides immediate research opportunities. © 2020 European Association Personality Psychology

Language: Английский

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Ecological Psychology

Published: March 27, 2024

Ecological psychology is one of the main alternative theories perception and action available in contemporary literature. This Element explores analyzes its most relevant ideas, concepts, methods, experimental results. It discusses historical roots ecological approach. The then works two founders psychology: James Eleanor Gibson. also development since 1980s until nowadays. Finally, identifies evaluates future approach to action.

Language: Английский

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Spine dynamics in the brain, mental disorders and artificial neural networks

Nature reviews. Neuroscience, Journal Year: 2021, Volume and Issue: 22(7), P. 407 - 422

Published: May 28, 2021

Language: Английский

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Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 54558 - 54578

Published: Jan. 1, 2021

The current electric power system witnesses a significant transition into Smart Grids (SG) as promising landscape for high grid reliability and efficient energy management. This ongoing undergoes rapid changes, requiring plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely Deep Learning (DL) an emerging technology creating more decentralized intelligent paradigm while integrating intelligence in supervisory operational decision-making. Motivated outstanding success DL-based prediction methods, article attempts provide thorough review from broad perspective on state-of-the-art advances DL systems. Firstly, bibliometric analysis has been conducted categorize review's methodology. Further, we taxonomically delve mechanism behind some trending algorithms. We then showcase enabling technologies SG, such federated learning, edge intelligence, distributed computing. Finally, challenges research frontiers are provided serve guidelines future work futuristic domain. study's core objective is foster synergy between these two fields decision-makers researchers accelerate DL's practical deployment

Language: Английский

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