Separating Subjective from Objective Food Value in the Human Insula: An Exploratory Study Using Intracranial EEG
Bibliographic record
Abstract
Background/Objectives: The human insula is a key structure implicated in integrating internal states and external food cues, yet its precise role remains unclear, in part due to the temporal limitations of neuroimaging techniques like fMRI. To address this gap, we conducted an exploratory study using an intracranial EEG (iEEG) to investigate how the insula encodes both the subjective and objective properties of food-related stimuli, and how this encoding is modulated by hunger and satiety. Methods: Eight patients with drug-resistant epilepsy undergoing a pre-surgical evaluation between 2017 and 2023 participated in this study. Depth electrodes implanted in the insular cortex recorded event-related potentials (ERPs) in response to visual food cues. The sessions were conducted in two prandial states (hungry and satiated). The subjective ratings (appetite and palatability) and objective nutritional values (e.g., calories, carbohydrates) were collected and analyzed using paired t-tests, MANOVAs, and partial correlations. Results: Hunger increased the ERP amplitudes within the 350–450 ms interval, consistent with the EPIC model and positive alliesthesia, while satiety unexpectedly enhanced the early responses (150–250 ms). Importantly, the neural activity related to nutritional values was largely uncorrelated with the subjective ratings, suggestive of distinct processing streams. The mid- and posterior insula showed greater sensitivity to both subjective and nutritional information than the anterior insula. Conclusions: These findings offer novel electrophysiological insights into how the insula differentiates between implicit and explicit food-related signals, depending on the homeostatic state. This work supports a dual-route model of food cue processing, and may inform interventions targeting insular activity in disordered eating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".