Using Single-Neuron Recording in Marketing: Opportunities, Challenges, and an Application to Fear Enhancement in Communications
Bibliographic record
Abstract
This article introduces the method of single-neuron recording in humans to marketing and consumer researchers. First, the authors provide a general description of this methodology, discuss its advantages and disadvantages, and describe findings from previous single-neuron human research. Second, they discuss the relevance of this method for marketing and consumer behavior and, more specifically, how it can be used to gain insights into the areas of categorization, sensory discrimination, reactions to novel versus familiar stimuli, and recall of experiences. Third, they present a study designed to illustrate how single-neuron studies are conducted and how data from them are processed and analyzed. This study examines people's ability to up-regulate (i.e., enhance) the emotion of fear, which has implications for designing effective fear appeals. The study shows that the firing rates of neurons previously shown to respond selectively to fearful content increased with emotion enhancement instructions, but only for a video that did not automatically evoke substantial fear. The authors discuss how the findings help illustrate which conclusions can and cannot be drawn from single-neuron research.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".