Bibliographic record
Abstract
Research on patients with damage to ventromedial frontal cortices suggests a key role for emotions in practical decision making. This field of investigation is often associated with Antonio Damasio’s Somatic Marker Hypothesis—a putative account of the mechanism through which autonomic tags guide decision making in typical individuals. Here we discuss two questionable assumptions—or ‘myths’—surrounding the direction and interpretation of this research. First, it is often assumed that there is a single somatic marker hypothesis. As others have noted, however, Damasio’s ‘hypothesis’ admits of multiple interpretations (Dunn et al. [2006]; Colombetti [2008]). Our analysis builds upon this point by characterizing decision making as a multi-stage process and identifying the various potential roles for somatic markers. The second myth is that the available evidence suggests a role for somatic markers in the core stages of decision making, that is, during the generation, deliberation, or evaluation of candidate options. On the contrary, we suggest that somatic markers most likely have a peripheral role, in the recognition of decision points, or in the motivation of action. This conclusion is based on an examination of the past twenty-five years of research conducted by Damasio and colleagues, focusing in particular on some early experiments that have been largely neglected by the critical literature. 1 Introduction2 What is the Somatic Marker Model?3 Multiple Somatic Marker Hypotheses 3.1 Are somatic markers necessary for practical decision making? 3.2 Speed, accuracy, or both? 3.3 At which of the five stages of decision making are somatic markers engaged?4 Anecdotal Evidence Suggests a Peripheral Role for Somatic Markers 4.1 Chronic indecisiveness 4.2 Extreme impulsiveness 4.3 Enhanced decision making in the lab 4.4 Lack of motivation.5 Early Experiments Suggest that VMF Damage Leaves Core Processes Intact 5.1 The evocative images study 5.2 Five problem solving tasks6 Recent Experiments Fail to Discriminate among Alternate Versions of SMH7 Conclusion
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 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 | 0.002 | 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 | 0.005 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".