Human Learning of New Structured Information Across Time and Sleep

Официальное название

Learning Novel Structure Across Time and Sleep

Обзор

Acting adaptively requires quickly picking up on structure in the environment and storing the acquired knowledge for effective future use. Dominant theories of the hippocampus have focused on its ability to encode individual snapshots of experience, but the investigators and others have found evidence that it is also crucial for finding structure across experiences. The mechanisms of this essential form of learning have not been established. The investigators have developed a neural network model of the hippocampus instantiating the theory that one of its subfields can quickly encode structure using distributed representations, a powerful form of representation in which populations of neurons become responsive to multiple related features of the environment. The first aim of this project is to test predictions of this model using high resolution functional magnetic resonance imaging (fMRI) in paradigms requiring integration of information across experiences. The results will clarify fundamental mechanisms of how humans learn novel structure, adjudicating between existing models of this process, and informing further model development. There are also competing theories as to the eventual fate of new hippocampal representations. One view posits that during sleep, the hippocampus replays recent information to build longer-term distributed representations in neocortex. Another view claims that memories are directly and independently formed and consolidated within the hippocampus and neocortex. The second aim of this project is to test between these theories. The investigators will assess changes in hippocampal and cortical representations over time by re-scanning participants and tracking changes in memory at a one-week delay. Any observed changes in the brain and behavior across time, however, may be due to generic effects of time or to active processing during sleep. The third aim is thus to assess the specific causal contributions of sleep to the consolidation of structured information. The investigators will use real-time sleep electroencephalography to play sound cues to bias memory reactivation. The investigators expect that this work will clarify the anatomical substrates and, critically, the nature of the representations that support encoding and consolidation of novel structure in the environment.

Вмешательства

• Поведенческое Associative inference
  Participants will engage in an associative inference paradigm. Memory will be assessed behaviorally and neural representations will be assessed using functional magnetic resonance imaging.
• Поведенческое Category learning
  Participants will engage in a category learning paradigm. Memory will be assessed behaviorally (Arms 2 and 3), and neural representations will be assessed using functional magnetic resonance imaging (Arm 2).
• Поведенческое Sleep
  Participants will sleep after engaging in a category learning paradigm while electroencephalography data are collected, and memory will be assessed behaviorally after sleep.

Первичные конечные точки

• Changes in multivariate representations [Срок оценки: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)]
• Brain-behavior correlations [Срок оценки: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)]
• Correlations between activity across brain regions [Срок оценки: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)]
• Memory accuracy [Срок оценки: Within single study session (spanning 4-5 hrs.)]

Критерии участия

Критерии включения

• Between 18 and 35 years of age (all aims)
• Not a member of a vulnerable population (all aims)
• Normal or corrected-to-normal vision (all aims)
• Normal hearing (all aims)
• Able to speak English fluently (all aims)
• No prior history of major psychiatric or neurological disorders (Aims 1 and 2; MRI-specific)
• Not currently taking any antidepressants or sedatives (Aims 1 and 2; MRI-specific)
• No known neurological disorders (Aim 3; EEG-specific)

Критерии исключения

• The investigators will exclude individuals with MR contraindications such as non-removable biomedical devices or metal in or on the body (Aims 1 and 2; MRI-specific)
• Claustrophobia (Aims 1 and 2; MRI-specific)
• Pregnant women will also be excluded from neuroimaging, as the effects of MR on pregnancy are not fully understood (Aims 1 and 2; MRI-specific)

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Простое слепое

Основная цель

Фундаментальное исследование

Центры проведения

США · 1 центр

• University of Pennsylvania — Philadelphia

Публикации

• Schapiro AC, Kustner LV, Turk-Browne NB. Shaping of object representations in the human medial temporal lobe based on temporal regularities. Curr Biol. 2012 Sep 11;22(17):1622-7. doi: 10.1016/j.cub.2012.06.056. Epub 2012 Aug 9. PMID 22885059
• Schapiro AC, Turk-Browne NB, Norman KA, Botvinick MM. Statistical learning of temporal community structure in the hippocampus. Hippocampus. 2016 Jan;26(1):3-8. doi: 10.1002/hipo.22523. Epub 2015 Oct 13. PMID 26332666
• Schapiro AC, Gregory E, Landau B, McCloskey M, Turk-Browne NB. The necessity of the medial temporal lobe for statistical learning. J Cogn Neurosci. 2014 Aug;26(8):1736-47. doi: 10.1162/jocn_a_00578. Epub 2014 Jan 23. PMID 24456393
• Covington NV, Brown-Schmidt S, Duff MC. The Necessity of the Hippocampus for Statistical Learning. J Cogn Neurosci. 2018 May;30(5):680-697. doi: 10.1162/jocn_a_01228. Epub 2018 Jan 8. PMID 29308986
• Schlichting ML, Preston AR. Memory integration: neural mechanisms and implications for behavior. Curr Opin Behav Sci. 2015 Feb;1:1-8. doi: 10.1016/j.cobeha.2014.07.005. PMID 25750931
• Hinton, GE. Distributed representations. Technical Report CMU-CS-84-157. 1984.
• McClelland JL, McNaughton BL, O'Reilly RC. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol Rev. 1995 Jul;102(3):419-457. doi: 10.1037/0033-295X.102.3.419. PMID 7624455
• Schapiro AC, Turk-Browne NB, Botvinick MM, Norman KA. Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos Trans R Soc Lond B Biol Sci. 2017 Jan 5;372(1711):20160049. doi: 10.1098/rstb.2016.0049. PMID 27872368

Идентификаторы

NCT: NCT05910762 · 833228B · R01MH129436

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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