Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space

Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human fronto-parietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively maintained on the map while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior beta1/alpha oscillations desynchronize within 0.4-0.7 s, consistent..., fMRI acquisition
Neuroimaging data during the clock task were acquired in a Siemens Tim Trio 3T scanner for the original study and Siemens Tim Prisma 3T scanner for the replication study at the Magnetic Resonance Research Center, University of Pittsburgh. Due participant-dependent variation in response times on the task, each fMRI run varied in length from 3.15 to 5.87 minutes (M = 4.57 minutes, SD = 0.52). Functional imaging data for the original/replication study were acquired using a simultaneous multislice sequence sensitive to BOLD contrast, TR = 1.0/0.6s, TE = 30/27ms, flip angle = 55/45°, multiband acceleration factor = 5/5, voxel size = 2.3/3.1mm3. We also obtained a sagittal MPRAGE T1-weighted scan, voxel size = 1/1mm3, TR = 2.2/2.3s, TE = 3.58/3.35ms, GRAPPA 2/2x acceleration. The anatomical scan was used for coregistration and nonlinear transformation to functional and stereotaxic templates. We also acquired gradient echo fieldmap images (TEs = 4.93/4.47ms and 7.39/6.93ms) fo..., , # Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space

Behavioral, fMRI and MEG data.

## Description of the data and file structure

* Directories and file within hallquist_etal_supplemental_data.zip:

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fig_1: behavioral data from the fMRI study

trial_data_compact.RData - RData file with the following variables:

$ dataset: study name

$ id: participants's numeric id

$ run: sequential number of the current 50-trial block, 1-8

$ trial: trial

$ rewFunc: Contingency, \"DEV\",\"CEV\",\"CEVR\", \"IEV\"

$ rt_csv: response time in seconds

$ magnitude: expected reward magnitude

$ probability: expected reward probability

$ ev: expected reward value

$ rt_vmax: response time with the highest learned value, as predicted by the SCEPTIC model

$ score_csv: reward received

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fig_2: DAN parcellation, w...