Selective Theta-Synchronization of Choice-Relevant Information Subserves Goal-Directed Behavior
Bibliographic record
Abstract
Theta activity reflects a state of rhythmic modulation of excitability at the level of single neuron membranes, within local neuronal groups and between distant nodes of a neuronal network. A wealth of evidence has shown that during theta states distant neuronal groups synchronize, forming networks of spatially confined neuronal clusters at specific time periods during task performance. Here, we show that a functional commonality of networks engaging in theta rhythmic states is that they emerge around decision points, reflecting rhythmic synchronization of choice-relevant information. Decision points characterize a point in time shortly before a subject chooses to select one action over another, i.e., when automatic behavior is terminated and the organism reactivates multiple sources of information to evaluate the evidence for available choices. As such, decision processes require the coordinated retrieval of choice-relevant information including (i) the retrieval of stimulus evaluations (stimulus-reward associations) and reward expectancies about future outcomes, (ii) the retrieval of past and prospective memories (e.g., stimulus-stimulus associations), (iii) the reactivation of contextual task rule representations (e.g., stimulus-response mappings), along with (iv) an ongoing assessment of sensory evidence. An increasing number of studies reveal that retrieval of these multiple types of information proceeds within few theta cycles through synchronized spiking activity across limbic, striatal, and cortical processing nodes. The outlined evidence suggests that evolving spatially and temporally specific theta synchronization could serve as the critical correlate underlying the selection of a choice during goal-directed behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".