Are You As Good As You Think You Are? Malingering, Narcissism, and Feedback
Bibliographic record
Abstract
Past studies have revealed that narcissism may be related to enhanced malingering abilities (i.e., production of “realistic” profiles) in relation to cognitive functioning. However, narcissism also has been linked to aggressive (and impaired) responding following negative evaluation. The present study aimed to simultaneously evaluate narcissism in relation to malingering across different types of disorders and under different conditions of performance feedback. Undergraduate participants will be assessed for their levels of narcissism using the Narcissism Personality Inventory (NPI), and then randomly assigned to one of five malingering conditions: psychosis, neurological impairment, amnesia, low intelligence, or affective disorder. All participants will be given a short description of the disorder, and instructed to do their best to appear as if they have the disorder (i.e., produce a feigned but realistic profile) by completing a “psychological symptom test” (i.e., Structured Inventory of Malingered Symptomatology; SIMS). Following initial completion of the SIMS, participants will be randomly assigned to a positive (ego-boost), neutral (none), or negative (ego-threat) feedback condition regarding their malingering performance. Based on this feedback, they will be asked to complete the SIMS again in relation to the disorder, and reminded to try to successfully feign the disorder. Change scores in symptom endorsement as a function of narcissism and feedback will be evaluated, as well as variations in profiles across different categories of malingering. This study could provide insight into how feedback influences the strategy that narcissistic individuals use to malinger specific disorders. Discipline: Psychology Honours Faculty Mentor: Dr. Kristine Peace
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".