As Soon as You Taste It: Evidence for Sequential and Parallel Processing of Gustatory Information
Why this work is in the frame
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Bibliographic record
Abstract
The quick and reliable detection and identification of a tastant in the mouth regulate nutrient uptake and toxin expulsion. Consistent with the pivotal role of the gustatory system, taste category information (e.g., sweet, salty) is represented during the earliest phase of the taste-evoked cortical response (Crouzet et al., 2015), and different tastes are perceived and responded to within only a few hundred milliseconds, in rodents (Perez et al., 2013) and humans (Bujas, 1935). Currently, it is unknown whether taste detection and discrimination are sequential or parallel processes, i.e., whether you know what it is as soon as you taste it. To investigate the sequence of processing steps involved in taste perceptual decisions, participants tasted sour, salty, bitter, and sweet solutions and performed a taste-detection and a taste-discrimination task. We measured response times (RTs) and 64-channel scalp electrophysiological recordings and tested the link between the timing of behavioral decisions and the timing of neural taste representations determined with multivariate pattern analyses. Irrespective of taste and task, neural decoding onset and behavioral RTs were strongly related, demonstrating that differences between taste judgments are reflected early during chemosensory encoding. Neural and behavioral detection times were faster for the iso-hedonic salty and sour tastes than their discrimination time. No such latency difference was observed for sweet and bitter, which differ hedonically. Together, these results indicate that the human gustatory system detects a taste faster than it discriminates between tastes, yet hedonic computations may run in parallel (Perez et al., 2013) and facilitate taste identification.
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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