Evolutionary neuromarketing: darwinizing the neuroimaging paradigm for consumer behavior
Bibliographic record
Abstract
Abstract The current paper serves two purposes. First, it reviews the neuroimaging literature most relevant to the field of marketing (e.g., neuroeconomics, decision neuroscience, and neuromarketing). Second, it posits that evolutionary theory is a consilient and organizing meta‐theoretical framework for neuromarketing research. The great majority of neuroimaging studies suffer from the illusion of explanatory depth namely the sophistication of the neuroimaging technologies provides a semblance of profundity to the reaped knowledge, which is otherwise largely disjointed and atheoretical. Evolutionary theory resolves this conundrum by recognizing that the human mind has evolved via the processes of natural and sexual selection. Hence, in order to provide a complete understanding of any given neuromarketing phenomenon requires that it be tackled at both the proximate level (as is currently the case) and the ultimate level (i.e., understanding the adaptive reason that would generate a particular neural activation pattern). Evolutionary psychology posits that the human mind consists of a set of domain‐specific computational systems that have evolved to solve recurring adaptive problems. Accordingly, rather than viewing the human mind as a general‐purpose domain‐independent organ, evolutionary cognitive neuroscientists recognize that many neural activation patterns are instantiations of evolved computational systems in evolutionarily relevant domains such as survival, mating, kin selection, and reciprocity. As such, an evolutionary neuromarketing approach recognizes that the neural activation patterns associated with numerous marketing‐related phenomena can be mapped onto the latter Darwinian modules thus providing a unifying meta‐theory for this budding discipline. Copyright © 2008 John Wiley & Sons, Ltd.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".