Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T02:36:39.867282Z
Paper Citation Record · LEDGER
As of 19 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 4 inbound Pith citation observations for arXiv:2603.03414.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T02:36:39.867282Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-12T01:53:15.463453Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
85 of 85 outbound references displayed
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Observation 303a8f0a-52ca-4b95-bdb1-805ed44bdfdf · outbound
Cognitive Dark Matter: Measuring What AI Misses Im- ageNet classification with deep convolutional neural networks,
Reference 1
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Observation e513caa1-b04a-4647-87f3-0f104ccf1649 · outbound
Cognitive Dark Matter: Measuring What AI Misses A general reinforcement learning algorithm that masters chess, shogi, and go through self-play,
Reference 2
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Observation bd1814ae-ca51-4812-b295-d4dc6026ce9c · outbound
Cognitive Dark Matter: Measuring What AI Misses Accurate structure prediction of biomolecular interactions with AlphaFold 3,
Reference 3
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Observation a7bf664d-fb4e-4d74-ac5e-62d74a64ce86 · outbound
Cognitive Dark Matter: Measuring What AI Misses Language models are Few-Shot learners,
Reference 4
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Observation af3705e1-c102-4b33-8418-b491d93b73eb · outbound
Cognitive Dark Matter: Measuring What AI Misses Dynabench: Rethinking benchmarking in NLP,
Reference 5
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Observation eb68f2ca-62ea-461e-b8aa-bb75e2537e0d · outbound
Cognitive Dark Matter: Measuring What AI Misses Navi- gating the jagged technological frontier: Field exper- imental evidence of the effects of AI on knowledge worker productivity and quality,
Reference 6
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Observation b45c4fac-ad51-462a-9899-e0ad83358147 · outbound
Cognitive Dark Matter: Measuring What AI Misses Measuring AI abil- ity to complete long tasks,
Reference 7
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Observation 7fafffcb-65d0-4682-a365-eef941917ded · outbound
Cognitive Dark Matter: Measuring What AI Misses Measuring AI ability to complete long tasks,
Reference 8
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Observation f2731160-16b3-4811-86e7-5c7625f67c18 · outbound
Cognitive Dark Matter: Measuring What AI Misses Is there a half-life for the success rates of AI agents?
Reference 9
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Observation 0dd040ad-1319-4515-bba7-11ff9df9e83e · outbound
Cognitive Dark Matter: Measuring What AI Misses Toolformer: Language Models Can Teach Themselves to Use Tools
Reference 10
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Observation 241dbe7b-d2ff-40ab-bf9a-5f5a87e9f20a · outbound
Cognitive Dark Matter: Measuring What AI Misses Faith and fate: Limits of trans- formers on compositionality,
Reference 11
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Observation d90e5489-4646-4d69-919f-e8d473736788 · outbound
Cognitive Dark Matter: Measuring What AI Misses SSL, JEPA, world models and the future of AI,
Reference 12
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Observation 565e8423-0980-4fef-ae1d-9723d6fb6d54 · outbound
Cognitive Dark Matter: Measuring What AI Misses The breakdown of vigilance during prolonged visual search,
Reference 13
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Observation 10964de6-3759-4f5b-a109-5d72b798f59e · outbound
Cognitive Dark Matter: Measuring What AI Misses Why are some problems hard? evidence from tower of hanoi,
Reference 14
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Observation 91d4d69d-d55e-47fc-a542-4502a69c263f · outbound
Cognitive Dark Matter: Measuring What AI Misses Intuition in insight and noninsight problem solving,
Reference 15
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Observation 7d282aeb-8add-48d8-9a65-4da721554988 · outbound
Cognitive Dark Matter: Measuring What AI Misses Does incubation enhance problem solving? a meta-analytic review,
Reference 16
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Observation d6e83d37-e7e6-4b85-bb5f-5b52a9acfdaa · outbound
Cognitive Dark Matter: Measuring What AI Misses Metacognition and confidence: A review and synthesis,
Reference 17
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Observation 9fb82b85-c729-43dd-b33b-302f7f234b15 · outbound
Cognitive Dark Matter: Measuring What AI Misses Improve mathematical reasoning in language models by automated process supervision,
Reference 18
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Observation 4a3eb211-e05e-4f79-a94e-4dbcef7b2cb1 · outbound
Cognitive Dark Matter: Measuring What AI Misses Newell and H
Reference 19
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Observation ebe10712-8a82-4bac-9338-be3d9047812c · outbound
Cognitive Dark Matter: Measuring What AI Misses Unresolved cited work
Reference 20
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Observation 881f4a75-b1fa-430f-a7ca-0d057f0016c8 · outbound
Cognitive Dark Matter: Measuring What AI Misses Using large-scale exper- iments and machine learning to discover theories of human decision-making,
Reference 21
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Observation a2c12027-b989-4fc5-be29-4434a4bfc893 · outbound
Cognitive Dark Matter: Measuring What AI Misses Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences,
Reference 22
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Observation d61bf944-4039-49b6-a65a-1fb612278a84 · outbound
Cognitive Dark Matter: Measuring What AI Misses Advances and challenges in foun- dation agents: From brain-inspired intelligence to evolutionary, collaborative, and safe systems,
Reference 23
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Observation baa8a671-e9f2-4e8b-9dd1-6ff6c11c7296 · outbound
Cognitive Dark Matter: Measuring What AI Misses Eye movement monitoring as a process tracing methodology in deci- sion making research,
Reference 24
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Observation 65888307-e0f8-4416-b321-bd5b2db2583c · outbound
Cognitive Dark Matter: Measuring What AI Misses Mouse tracking as a window into decision making,
Reference 25
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Observation 3c0cc761-d7d8-4db6-b5cb-578be65bdad7 · outbound
Cognitive Dark Matter: Measuring What AI Misses Aligning machine and human visual representations across abstraction levels,
Reference 26
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Observation c887b917-5584-4395-9a76-aa3c5fd433a0 · outbound
Cognitive Dark Matter: Measuring What AI Misses Schulte-Mecklenbeck, A
Reference 27
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Observation 3803dc0f-23e1-4e94-93c3-f281be4e2427 · outbound
Cognitive Dark Matter: Measuring What AI Misses Improving semantic understanding in speech language models via brain-tuning,
Reference 28
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Observation 175b69f5-723b-4e17-93ba-eca021c35530 · outbound
Cognitive Dark Matter: Measuring What AI Misses BrainWavLM: Fine-tuning speech rep- resentations with brain responses to language,
Reference 29
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Observation ecd53bbb-1093-4229-a76a-ef7677618b50 · outbound
Cognitive Dark Matter: Measuring What AI Misses The one where they brain-tune for social cognition: Multi- modal brain-tuning on friends,
Reference 30
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Observation 4f2535c5-f5ce-45ea-8916-37c6a98991c4 · outbound
Cognitive Dark Matter: Measuring What AI Misses Learning from brains how to regularize machines,
Reference 31
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Observation 3f54e466-7794-4ff3-914a-839c04fc84f8 · outbound
Cognitive Dark Matter: Measuring What AI Misses Simulating a primary vi- sual cortex at the front of CNNs improves robustness to image perturbations,
Reference 32
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Observation da3e1b2c-a9f2-4105-9a04-6f7b3ea6d3a0 · outbound
Cognitive Dark Matter: Measuring What AI Misses Towards robust vision by multi-task learning on mon- key visual cortex,
Reference 33
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Observation 344fc2a8-1365-49fc-8f72-891ed4077272 · outbound
Cognitive Dark Matter: Measuring What AI Misses Aligning model and macaque inferior temporal cortex representations improves model-to-human behavioral alignment and adversarial robustness,
Reference 34
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Observation 3635e0c1-4b03-41ed-93a6-079254d1c23d · outbound
Cognitive Dark Matter: Measuring What AI Misses Using human brain activity to guide machine learning,
Reference 35
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Observation 1880eca2-048d-432d-9e26-135ee9809379 · outbound
Cognitive Dark Matter: Measuring What AI Misses NeuroQuery, comprehensive meta-analysis of human brain mapping,
Reference 36
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Observation f88a003c-7a7d-4c57-8831-e01817bac2b6 · outbound
Cognitive Dark Matter: Measuring What AI Misses Gemini 3 pro: Model evalua- tion – approach, methodology and results,
Reference 37
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Observation 24943af7-0685-4b7a-a05a-d9f59e6bb8e5 · outbound
Cognitive Dark Matter: Measuring What AI Misses System card: Claude opus 4.5,
Reference 38
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Observation 727435cb-3a7f-4e20-95ff-fc7e318f6b02 · outbound
Cognitive Dark Matter: Measuring What AI Misses Introducing GPT-5.2,
Reference 39
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Observation 91722e67-dc39-4c76-a21e-719d949c9672 · outbound
Cognitive Dark Matter: Measuring What AI Misses GPT-4 technical report,
Reference 40
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Observation 951363cb-7db3-43e2-9bbd-394f031c72a3 · outbound
Cognitive Dark Matter: Measuring What AI Misses ARC-AGI-2: A new challenge for fron- tier AI reasoning systems,
Reference 41
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Observation 9294b555-86b0-4fc0-9f6c-448807c6b6f8 · outbound
Cognitive Dark Matter: Measuring What AI Misses τ 2-bench: Evaluating conversational agents in a dual- control environment,
Reference 42
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Observation a9ce7e96-c157-48d2-b335-3edb56888282 · outbound
Cognitive Dark Matter: Measuring What AI Misses Using games to understand the mind,
Reference 43
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Observation 0cf2dd75-7fac-4caf-89b7-3855a845022c · outbound
Cognitive Dark Matter: Measuring What AI Misses Principles of intensive human neuroimaging,
Reference 44
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Observation f4c77f45-96c3-421e-940a-54b9b56e0f6d · outbound
Cognitive Dark Matter: Measuring What AI Misses Large-scale neural recordings with single neuron resolution using neuropixels probes in human cortex,
Reference 45
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Observation aeb7ab3c-b1ed-47a4-b6bf-079b90290932 · outbound
Cognitive Dark Matter: Measuring What AI Misses AJILE12: Long- term naturalistic human intracranial neural record- ings and pose,
Reference 46
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Observation 01166f24-bc2c-40ac-b072-252a9930bcd8 · outbound
Cognitive Dark Matter: Measuring What AI Misses Resource-rational anal- ysis: Understanding human cognition as the optimal use of limited computational resources,
Reference 47
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Observation 56ed877a-62c7-4fdb-ae40-9ef4fcb519a2 · outbound
Cognitive Dark Matter: Measuring What AI Misses Self-evaluation of decision-making: A general bayesian framework for metacognitive computation,
Reference 48
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Observation 7f452b48-eee8-473a-bdc6-a9ddbd955066 · outbound
Cognitive Dark Matter: Measuring What AI Misses Meta-reasoning: Monitoring and control of thinking and reasoning,
Reference 49
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Observation c58670ab-61e6-4eed-a75a-08498f81fb27 · outbound
Cognitive Dark Matter: Measuring What AI Misses Strategy selection as rational metareasoning,
Reference 50
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Observation 7d19cf7a-22a7-4d80-8dc8-0114c330819e · outbound
Cognitive Dark Matter: Measuring What AI Misses The trouble with overconfidence,
Reference 51
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Observation 48d6c4bd-0279-4ce6-af08-5b77af3c78dd · outbound
Cognitive Dark Matter: Measuring What AI Misses Can LLMs express their uncertainty? an empirical evaluation of confidence elicitation in LLMs,
Reference 52
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Observation 36d50f59-42e0-4a2f-a399-43ce14430565 · outbound
Cognitive Dark Matter: Measuring What AI Misses Why language models hallucinate,
Reference 53
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Observation 3ace6008-7687-4df9-9a4b-79b9987fafc9 · outbound
Cognitive Dark Matter: Measuring What AI Misses Stroop color and word test,
Reference 54
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Observation f2801378-7cd4-451b-a312-7d9759e34355 · outbound
Cognitive Dark Matter: Measuring What AI Misses Prefrontal cortex lesions disrupt the contextual control of response conflict,
Reference 55
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Observation f3ac39d7-be88-4061-a372-6509e392d4bf · outbound
Cognitive Dark Matter: Measuring What AI Misses Stroop-like effects for monkeys and humans: processing speed or strength of association?
Reference 56
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Observation 5c09a0dd-ec96-4d53-b48d-aebcaa975911 · outbound
Cognitive Dark Matter: Measuring What AI Misses A definition of AGI,
Reference 57
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Observation 67a7cb39-4d21-44aa-a579-59a75621acbf · outbound
Cognitive Dark Matter: Measuring What AI Misses Human-level concept learning through probabilistic program induction,
Reference 58
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Observation 49f739b0-084f-4cf4-8e24-035d7f20186e · outbound
Cognitive Dark Matter: Measuring What AI Misses The reversal curse: LLMs trained on “a is b
Reference 59
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Observation 7f98625e-a813-48da-8cb8-e0636324108b · outbound
Cognitive Dark Matter: Measuring What AI Misses Chain- of-thought prompting elicits reasoning in large lan- guage models,
Reference 60
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Observation deb59fd3-4712-41f8-b3ae-6cd2e665d34c · outbound
Cognitive Dark Matter: Measuring What AI Misses Bayes in the age of intelligent machines,
Reference 61
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Observation 8e2e06a9-9f7a-4f90-9423-4e3f0fb0e3ef · outbound
Cognitive Dark Matter: Measuring What AI Misses An explanation of in-context learning as implicit bayesian inference,
Reference 62
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Observation d0582a35-5f45-45e4-9c8b-775e5c531a92 · outbound
Cognitive Dark Matter: Measuring What AI Misses Walton,Abductive Reasoning
Reference 63
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Observation 27b58417-1de0-4fc6-8e98-1c224dea2e4b · outbound
Cognitive Dark Matter: Measuring What AI Misses Intuitive physics,
Reference 64
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Observation 81c9f394-9ec3-4c35-a619-6a1dd4eabd7e · outbound
Cognitive Dark Matter: Measuring What AI Misses Theory- of-mind deficits and causal attributions,
Reference 65
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Observation 1ceb148d-06dd-42cf-a174-a82c47b3d0e0 · outbound
Cognitive Dark Matter: Measuring What AI Misses Evaluating the world model implicit in a generative model,
Reference 66
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Observation 1596140a-2c2c-478f-ad88-cd4887edaa58 · outbound
Cognitive Dark Matter: Measuring What AI Misses Measuring emo- tional intelligence with the Mayer-Salovery-Caruso emotional intelligence test (MSCEIT),
Reference 67
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Observation 4a082786-aac6-442a-9f4a-97602caab44c · outbound
Cognitive Dark Matter: Measuring What AI Misses Emotion and decision making,
Reference 68
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Observation 8515d81d-be81-4b22-b647-1378f1c317e8 · outbound
Cognitive Dark Matter: Measuring What AI Misses The role of affect in decision making,
Reference 69
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Observation 2a110c89-b03e-4ca8-887f-52aa628647b1 · outbound
Cognitive Dark Matter: Measuring What AI Misses The theory of constructed emotion: an active inference account of interoception and cate- gorization,
Reference 70
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Observation fa653232-65ac-4a79-9f8e-2ee96c1e2640 · outbound
Cognitive Dark Matter: Measuring What AI Misses Unresolved cited work
Reference 71
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Observation a73dd7dd-7b2b-4c14-a1a9-08afd4e5ef57 · outbound
Cognitive Dark Matter: Measuring What AI Misses The social neuroscience of empathy,
Reference 72
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Observation 85eb2983-3d38-40d8-853e-99b1e11b9d69 · outbound
Cognitive Dark Matter: Measuring What AI Misses Crawford,Atlas of AI: Power, politics, and the planetary costs of artificial intelligence
Reference 73
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Observation 51f44c5f-5c8d-4d5f-a762-402149b8913f · outbound
Cognitive Dark Matter: Measuring What AI Misses On the morality of artificial agents,
Reference 74
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Observation d1e13f6d-265e-47b3-9de3-4cebffb28cdc · outbound
Cognitive Dark Matter: Measuring What AI Misses A teen was suicidal. ChatGPT was the friend he confided in,
Reference 75
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Observation ac46966c-c410-4cc3-a677-fd61f2e1a012 · outbound
Cognitive Dark Matter: Measuring What AI Misses How a chatbot encouraged a man who wanted to kill the queen,
Reference 76
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Observation 5e614d35-530f-482d-b70a-5274d475f762 · outbound
Cognitive Dark Matter: Measuring What AI Misses A massive 7T fMRI dataset to bridge cognitive neu- roscience and artificial intelligence,
Reference 77
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Observation a5e8f509-b0e9-4b71-869f-639822349984 · outbound
Cognitive Dark Matter: Measuring What AI Misses A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex,
Reference 78
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Observation b0652cce-62a4-450e-958a-7a6e69060f1d · outbound
Cognitive Dark Matter: Measuring What AI Misses Brain-wide representations of prior infor- mation in mouse decision-making,
Reference 79
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Observation 7ae8fc8f-1f3c-4b78-aee7-51b72dd5297c · outbound
Cognitive Dark Matter: Measuring What AI Misses Time spent think- ing in online chess reflects the value of computation,
Reference 80
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Observation 2d7dd202-6b3c-4ffb-97f4-cb2e9116377d · outbound
Cognitive Dark Matter: Measuring What AI Misses Ex- plaining world-wide variation in navigation ability from millions of people: Citizen science project sea hero quest,
Reference 81
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Observation b3e684f5-8a45-4222-83b0-3a78c8be14a2 · outbound
Cognitive Dark Matter: Measuring What AI Misses No Press Diplomacy: Modeling Multi-Agent Gameplay
Reference 82
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Observation d257d619-70c4-4eb4-b6b4-c8357be07331 · outbound
Cognitive Dark Matter: Measuring What AI Misses ImageNet large scale visual recognition challenge,
Reference 83
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Observation 63e5e672-0447-4011-8af8-0a44025027e5 · outbound
Cognitive Dark Matter: Measuring What AI Misses Quantitative models reveal the organization of diverse cognitive functions in the brain,
Reference 84
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Observation 09634470-1e12-4579-83f6-fd3a124ac3e2 · outbound
Cognitive Dark Matter: Measuring What AI Misses Spacetop: A multi- modal fMRI dataset unifying naturalistic processes with a rich array of experimental tasks,
Reference 85
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Observation b9ed458f-64a0-418d-9f1f-8e7d0ffd7a44 · inbound
Hypothesis generation and updating in large language models Cognitive Dark Matter: Measuring What AI Misses
Reference 70
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Observation eec1a8ca-471e-4c44-8b93-4286306ba6f5 · inbound
Process Matters more than Output for Distinguishing Humans from Machines Cognitive Dark Matter: Measuring What AI Misses
Reference 33
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Observation 51e0a1c0-612e-4478-8050-401b2caf9086 · inbound
Process Matters more than Output for Distinguishing Humans from Machines Cognitive Dark Matter: Measuring What AI Misses
Reference 33
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Observation 149e3452-37ca-4888-9360-6aeb5287da29 · inbound
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners Cognitive Dark Matter: Measuring What AI Misses
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