Theory Is All You Need: AI, Human Cognition, and Causal Reasoning

Strategy Science, Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

Scholars argue that artificial intelligence (AI) can generate genuine novelty and new knowledge and, in turn, AI computational models of cognition will replace human decision making under uncertainty. We disagree. AI’s data-based prediction is different from theory-based causal logic reasoning. highlight problems with the decades-old analogy between computers minds as input–output devices, using large language an example. Human better conceptualized a form reasoning rather than emphasis on information processing prediction. uses probability-based approach to largely backward looking imitative, whereas forward-looking capable generating novelty. introduce idea data–belief asymmetries difference cognition, example heavier-than-air flight illustrate our arguments. Theory-based provides cognitive mechanism for humans intervene world engage directed experimentation data. Throughout article, we discuss implications argument understanding origins novelty, knowledge,

Язык: Английский

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Circular and unified analysis in network neuroscience

eLife, Год журнала: 2023, Номер 12

Опубликована: Ноя. 28, 2023

Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test speculative hypotheses against benchmark empirical facts. Some of these inadvertently use circular reasoning present knowledge as discovery. Here, I discuss that this problem can confound key results and estimate it has affected more than three thousand studies network over the last decade. suggest future reduce by limiting evidence, integrating into models, rigorously testing proposed discoveries models. conclude with a summary practical challenges recommendations.

Язык: Английский

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The representational similarity between visual perception and recent perceptual history

Journal of Neuroscience, Год журнала: 2023, Номер unknown, С. JN - 22

Опубликована: Март 21, 2023

From moment to moment, the visual properties of objects in world fluctuate due external factors like ambient lighting, occlusion and eye movements, internal (proximal) noise. Despite this variability incoming information, our perception is stable. Serial dependence, behavioral attraction current perceptual responses towards previously seen stimuli, may reveal a mechanism underlying stability: spatio-temporally tuned operator that smoothes over spurious fluctuations. The study examined neural underpinnings serial dependence by recording electroencephalographic (EEG) brain response female male human observers prototypical (faces, cars houses) morphs mixed two prototypes. Behavior was biased objects. Representational similarity analysis revealed evoked contained information about previous stimulus. trace representations object occurred immediately upon appearance, suggesting arises from state or set precedes processing new input. However, not representationally similar they leave on subsequent representations. These results while past stimulus history influences representations, influence does imply shared code between trial (memory) (perception). Significance statement pulled instances recent past. remain be fully investigated. present EEG faces, houses, ambiguous between-category morphs. With representational analysis, we showed (1) object-specific patterns differentiate three categories; (2) contains object, mirroring dependence; (3) pattern different response, revealing

Язык: Английский

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The 21st century engram

Wiley Interdisciplinary Reviews Cognitive Science, Год журнала: 2023, Номер 14(5)

Опубликована: Май 12, 2023

Abstract The search for the engram —the neural mechanism of memory—has been a guiding research project neuroscience since its emergence as distinct scientific field. Recent developments in tools and techniques available investigating mechanisms memory have allowed researchers to proclaimed is over. While there ongoing debate about justification that claim, renewed interest clear. This attention highlights impoverished status concept. As accelerates, simple characterization an enduring physical change stretched thin. Now commitment has made more explicit, it must also be precise. If 20th century neurobiology was finding engram, 21st supplying richer account what's found. paper sketches history way forward. article categorized under: Philosophy > Foundations Cognitive Science

Язык: Английский

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Mental state decoders: game-changers or wishful thinking?

Trends in Cognitive Sciences, Год журнала: 2024, Номер 28(10), С. 884 - 895

Опубликована: Июль 10, 2024

Язык: Английский

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Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition

PLoS Computational Biology, Год журнала: 2025, Номер 21(1), С. e1012751 - e1012751

Опубликована: Янв. 27, 2025

The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia—–seeing in inanimate objects. Despite extensive research, it remains unclear why employs such broadly tuned detection capabilities. We hypothesized that pareidolia results from system’s optimization for recognizing both To test this hypothesis, we used task-optimized deep convolutional neural networks (CNNs) evaluated their alignment with behavioral signatures responses, measured via magnetoencephalography (MEG), related processing. Specifically, trained CNNs on tasks involving combinations identification, detection, object categorization, detection. Using representational similarity analysis, found included categorization training represented faces, real matched objects more similarly responses than those did not. Although these showed similar overall data, closer examination internal representations revealed specific had distinct effects how were layers. Finally, interpretability methods only CNN identification relied face-like features—such as ‘eyes’—to classify stimuli mirroring findings perception. Our suggest human-like may emerge within context generalized categorization.

Язык: Английский

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Explainable and Responsible AI in Neuroscience

Опубликована: Март 3, 2025

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Relationships and representations of brain structures, connectivity, dynamics and functions

Progress in Neuro-Psychopharmacology and Biological Psychiatry, Год журнала: 2025, Номер unknown, С. 111332 - 111332

Опубликована: Март 1, 2025

Язык: Английский

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Trial-Level Representational Similarity Analysis

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Neural representation refers to the brain activity that stands in for one's cognitive experience, and neuroscience, principal method studying neural representations is representational similarity analysis (RSA). The classic RSA (cRSA) approach examines overall quality of across numerous items by assessing correspondence between two matrices (RSMs): one based on a theoretical model stimulus other measured data. However, because cRSA cannot at level individual trials, it fundamentally limited its ability assess subject-, stimulus-, trial-level variances all influence representation. Here, we formally introduce (tRSA), an analytical framework estimates strength singular experimental trials evaluates hypotheses using multi-level models. First, verified tRSA quantifying trials. Second, compared statistical inferences drawn from both approaches simulated data reflected wide range scenarios. Compared cRSA, was more theoretically appropriate significantly sensitive true effects. Third, real fMRI datasets, further demonstrated several issues with which robust. Finally, presented some novel findings could only be assessed not cRSA. In summary, proves robust versatile neuroscience beyond.

Язык: Английский

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Comparing representations and computations in single neurons versus neural networks

Trends in Cognitive Sciences, Год журнала: 2023, Номер 27(6), С. 517 - 527

Опубликована: Апрель 1, 2023

Язык: Английский

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