The Influence of Individual Differences on the Role of Information Quantity in Statistical Inferences
Notice bibliographique
Résumé
The Influence of Individual Differences on the Role of Information Quantity in Statistical Inferences Justin M. Gilkey (jgilkey@bgsu.edu) Department of Psychology, Bowling Green State University Bowling Green, Ohio 43402 USA Richard B. Anderson (randers@bgsu.edu) Department of Psychology, Bowling Green State University Bowling Green, Ohio 43402 USA Michael E. Doherty (mdoher2@bgsu.edu) Department of Psychology, Bowling Green State University Bowling Green, Ohio 43402 USA Keywords: Perception of Correlation; Contingency Judgment; Working Memory Capacity; Inference Results and Discussion It has been argued on statistical grounds that population correlations (ρ) are more readily detected given a small number of paired stimuli (N s ) than given a large N s (e.g., Kareev, Lieberman, & Lev, 1997). Kareev, et al. (1997, Experiment 1) tested this claim with a prediction task in which participants used a binary cue to predict a binary outcome. The researchers derived subjective correlations (ρ′) by computing the correlation between the cues and participants' predicted outcomes. Using working memory capacity (WMC) as an indirect measure of N s, ρ′ was found to be more extreme for participants with low WMC than for those with high WMC, and found to decrease with N s . Anderson, Doherty, and Gilkey (2006) manipulated N s directly. The stimuli varied on two binary dimensions, and were drawn randomly from a population in which the correlation between the two stimulus dimensions was fixed. Participants used the samples to estimate population frequencies for various combinations of the dimension levels; the researchers computed ρ′ from participants’ estimates. Contrary to Kareev et al. (1997, Experiment 1), ρ′ decreased with N s , and WMC had no effect. WMC was dichotomized via a median split. In the prediction task there was an N s × WMC interaction, F(1, 36) = 8.81, p = .005, and there was a positive effect of N s on ρ′ for participants with high WMC, F(1, 17) = 14.54, p = .01l, but not for those with low WMC. Also in the prediction task, the mean ρ′ tended to be greater for those with high WMC than for those with low WMC, but only when ρ was .4, t(36) = 2.78, p = .009. Both results are inconsistent with the theory of small sample advantages. Similarly, in the rating task, there was an N s × WMC interaction, F(1, 32) = 19.37, p < .001; the effect of N s on ρ′ was positive when WMC was high, F(1, 14) = 4.87, p = .044, and negative when WMC was low, F(1, 18) = 8.38, p = .010. The findings contrast with those of Kareev et al. (1997, Experiment 1). Overall, the study provided a direct comparison of the effects of N s in three different correlation judgment tasks, demonstrated a small sample advantage for the rating task only, and demonstrated the importance of individual differences in WMC. Acknowledgments This work was supported by a grant from the National Science Foundation. Rationale and Method Previously, the task used to assess ρ′ has tended to vary across experiments, with the potential for extraneous, between-study differences to impact the results. Therefore, the present study was designed to assess effects of task, N s , and WMC on ρ′ within a single experiment that included a prediction task, a frequency estimation task, and a rating task (see Clement, Mercier, & Pasto, 2002) in which ρ′ was assessed via a -100 to 100 scale. Participants (N = 107) saw sequences of 3, 6, 12, or 24 stimulus pairs consisting of pictures of brown or white envelopes containing a cash or credit card payment. Each stimulus sample was drawn randomly from a population in which ρ between envelope color and payment was 0, .4, or .8. Each participant’s WMC was assessed at end of the experimental session, using a digit span task. References Anderson, R. B., Doherty, M. E., & Gilkey, J. M. (2006). Effects of sampling ecology on correlational judgment. Poster presented at the Annual Meeting of the Cognitive Science Society. Clement, M., Mercier, P., & Pasto, L. (2002). Sample size, confidence, and contingency judgment. Canadian Journal of Experimental Psychology, 56, 128-137. Kareev, Y., Lieberman, I., & Lev, M. (1997). Through a narrow window: Sample size and the perception of correlation. Journal of Experimental Psychology: General 126, 278
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».