Examining the Neural Correlates of Updating Mental Representations
Why this work is in the frame
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Bibliographic record
Abstract
The ability to build mental models is critical for goal setting, decision making, and predicting environmental contingencies. Much is known about how unsupervised, passive integration of regularities in visual, auditory, semantic, and/or kinesthetic information in the environment influences goal-directed behavior. However, less is known about how such mental models change in response to expanded information sets or novel observations. In the current study we examined this process, termed representational updating, using functional magnetic resonance imaging to elucidate the brain networks that support updating. Participants played a visual analogue of the popular children’s game of rock, paper, scissors against a computer that utilized multiple strategies that participants' had to exploit in order to maximize success. Behavioral results indicated that participants quickly and reliably adjusted their play choice to exploit the biases in the computer's play strategy in a way that mimicked probability matching behavior. Imaging results revealed a network of areas activated during these changes in play including cingulate cortex (both posterior and anterior cingulate), bilateral superior temporal gyrus, middle frontal gyrus, and the precuneus. Taken together, these results suggest the presence of a cortical network that supports the updating of mental models consisting of areas involved in error monitoring, statistical learning, and cognitive control. Furthermore, areas in this network (namely the STG) are commonly lesioned in neglect patients, who show deficits in updating on similar behavioral tasks. Meeting abstract presented at VSS 2013
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it