Planning is the ability to think ahead by considering possible future actions and their consequences. This research study aims to understand how the brain supports multi-step planning by testing whether people simulate promising future move sequences while deciding what to do next. Healthy adult volunteers will learn and play a strategy game called "Four-in-a-Row" (similar to Connect Four). Participants will complete two sessions on successive days: an online behavioral training/playing session and an in-person brain-recording session at New York University. During the brain-recording session, participants will view mid-game board positions and choose the best move while the study team records brain activity (using magnetoencephalography [MEG] or functional MRI [fMRI]) and eye movements. Data from the game and eye tracking will also be used to fit computational models of planning that help interpret the neural measurements.

This is a human neuroimaging study consisting of two related experiments designed to characterize the neural correlates of mental simulation during multi-step planning in the "Four-in-a-Row" game. Planning is modeled as a feature-based heuristic evaluation combined with look-ahead (tree search) that evaluates candidate actions by simulating future states and outcomes.

Participants complete two sessions on successive days. Session 1 is a ~60-minute online behavioral session in which participants learn the rules of Four-in-a-Row (including a comprehension/quiz check) and play multiple games against computer opponents spanning difficulty levels. Behavioral data from Session 1 are used to fit a computational model of planning for each participant.

Session 2 is an in-person neuroimaging session with simultaneous eye tracking. In the MEG experiment, participants complete a feature localizer followed by a primary planning task in which they evaluate mid-game board positions with a fixed decision window (e.g., 15 seconds) to encourage planning. B2 In the fMRI experiment, participants complete a planning task while BOLD activity and eye movements are recorded, using a trial structure designed to dissociate model-derived quantities such as myopic value and tree-search value.

The main analyses will test where (fMRI) and when (MEG) the brain represents simulated future states, their values, and the evolving decision process, guided by participant-specific computational-model predictions.

Inclusion Criteria:

• N/A

Exclusion Criteria:

• History of neurological or psychiatric illness
• Vulnerable populations

De-identified participant-level behavioral task data (choices/RT), eye-tracking measures, and analysis code. Neuroimaging data will be shared as de-identified derivatives (and/or raw data if allowed by consent and de-identification procedures). Supporting metadata/documentation (e.g., scanning protocols) and analytic code used for published analyses will also be shared.

IPD and supporting information will be made available starting 12 months after the NIH award begins, with updates deposited every 6 months thereafter, and will remain available indefinitely.

De-identified IPD will be available through the NIMH Data Archive (NDA) and via OSF/OpenNeuro for relevant datasets, with analytic code shared via GitHub. We will not impose any investigator-specific limitations on access, distribution, or reuse beyond standard repository requirements (e.g., account registration and agreement to repository terms). All shared data will be de-identified.