Prey positions as consumers' behavioural patterns: Exploratory evidence from an<i>f</i>MRI study
Bibliographic record
Abstract
Abstract The present article reviews some of the tenets of the Consolidated Model of Financial Predation (CMFP). The CMFP is used to explain how investors behave as either predators or prey in the financial markets, for example, during the 2008 predatory‐mortgages crisis. The article tests one of its key assumptions: that is, that people adopt different levels of prey positions. In the last four years, a number of articles have been published on the CMFP, which states that people adopt either a predator or a prey position (PPP), or else a mixture of both. The model has emerged as a result of a five‐year study and has found various applications, in particular, in the field of behavioural finance. According to this model, consumers of financial products tend to position themselves as either predators or prey. In the latter case, this causes them to judge the relationship in negative terms and to experience it as less rewarding, if not punishing altogether. This has two effects: first, perceived predation tends to gain in power and second, purchasing decisions may not be optimal. Results from an exploratory functional magnetic resonance imaging ( f MRI) study aimed at generating prey positions in minimal stress conditions are presented; they show that there is a significant difference between at least two prey positions, labelled “known predator–prey position” (KPPP) and “unknown predator–prey position” (UPPP). This means that consumers of financial products could potentially face two levels of apprehension (perceived predation): a high one under uncertainty and a lower one when conditions are volatile. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".