Human Learning of New Structured Information Across Time and Sleep

Not Applicable

Recruiting

• Conditions: Humans; Learning; Consolidation; Sleep
• Interventions: Behavioral: Category learning; Behavioral: Associative inference; Behavioral: Sleep

• Registration Number: NCT05910762
• Lead Sponsor: University of Pennsylvania

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2023-05-10
• Study Start: 2023-06-05
• Study First Submitted QC: 2023-06-12
• Study First Posted: 2023-06-20
• Status Verified: 2024-07
• Last Update Submitted: 2024-07-18
• Last Update Posted: 2024-07-19
• Primary Completion: 2028-03
• Study Completion: 2028-03

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 105

Inclusion Criteria

• 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)

Exclusion Criteria

• 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)

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Learning and consolidation in category learning	Category learning	The proposed functional magnetic resonance imaging study assesses the neural representations contributing to humans' ability to learn new categories of objects. All participants will undergo the same procedure. Participants will learn about novel objects, each with several colored parts. Some parts are unique to individual objects and others are shared among the members of the category. The investigators will assess how different regions of the brain contribute to learning and remembering these different kinds of parts, and how the resulting representations support category understanding. Participants will be brought back one week later for a second scan, to evaluate how the neural substrates of these processes change with consolidation.
Manipulating replay during sleep using real-time EEG	Category learning	In the proposed electroencephalography (EEG) study, all participants will undergo the same procedure. Participants will learn the visual features and spoken names associated with three categories of novel objects. Participants' memory for these objects and the objects' parts will be tested before and after a nap. The investigators will monitor brain activity during the nap in real time and, at optimal moments, quietly play the spoken names of the objects to encourage reactivation of particular objects in particular orders. The investigators will assess how this manipulation impacts memory for these objects.
Manipulating replay during sleep using real-time EEG	Sleep	In the proposed electroencephalography (EEG) study, all participants will undergo the same procedure. Participants will learn the visual features and spoken names associated with three categories of novel objects. Participants' memory for these objects and the objects' parts will be tested before and after a nap. The investigators will monitor brain activity during the nap in real time and, at optimal moments, quietly play the spoken names of the objects to encourage reactivation of particular objects in particular orders. The investigators will assess how this manipulation impacts memory for these objects.
Learning and consolidation in Associative Inference	Associative inference	The proposed functional magnetic resonance imaging study assesses the neural representations contributing to humans' ability to associate objects in the support of simple inferences and generalization. All participants will undergo the same procedure. Participants will learn about pairs of objects and then be asked to make judgments and inferences about the relationships between the objects. The order of presentation of the objects will be manipulated within subjects, as different learning theories make different predictions about how learning will unfold under different orderings. Participants will be brought back one week later for a second scan, to evaluate how the neural substrates of these processes change with consolidation.

Primary Outcome Measures

Name	Time	Method
Correlations between activity across brain regions	Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)	Relationships between BOLD activity across different regions of the brain as a function of trial type and delay.
Brain-behavior correlations	Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)	Correlations between BOLD signal in the brain and participant behavior during judgments about objects.
Memory accuracy	Within single study session (spanning 4-5 hrs.)	Change in generalization ability from before to after the nap as a function of the different conditions of object cueing during sleep.
Changes in multivariate representations	Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)	Changes in spatial correlations between the MRI BOLD pattern associated with related objects over the course of learning and across the one-week delay.

Trial Locations

Locations (1)

University of Pennsylvania; 🇺🇸 Philadelphia, Pennsylvania, United States