Karteikarten
Recognition Part 2 (Exam 2)
Karten in diesem Stapel (24)
- 1Predictions: UVSD (Unequal Variance Signal Detection)zROC
Linear
Slope < 1.0
ROC
Curvilinear
Asymmetric - 2Predictions: Yonelinas’s DPSD theory (Dual Process Signal Detection)zROC
Concave Up
Slope < 1.0
ROC
Curvilinear
Asymmetric - 3Predictions: Mixture modelszROC
Concave Up
Slope < 1.0
ROC
Curvilinear
Asymmetric - 4Predictions: Mixture modelsStrength of the items
Amount of study time per item
Number of study trials for each item
Number of items
Short lists
Long lists
Test instructions
Speed / accuracy - 5Common manipulations in recognition memory experimentsType of stimuli
Words
Pictures
Sounds
Pre-experimental experience w/ stimuli
Common words
Rare words
Encoding instructions
e.g., Level of processing - 6Pictures are recognized better than their conceptual labels (Paivio, 1969, 1971)Picture superiority effect
- 7Ss studied words and pictures in random orderStenberg, Radeborg, and Hedman (1995)
- 8Items were tested using both pictures and wordsStenberg, Radeborg, and Hedman (1995)
- 9Stenberg, Radeborg, and Hedman (1995)Experiment 1 results
- 10—Experiment 1 results
- 11Low frequency (rare) words are easier to recognize than high frequency (common) words Example experiment Arndt and Reder (2002)Word frequency effect
- 12—Arndt, J., & Reder, L. M. (2002). Word frequency and receiver operating characteristic curves in recognition memory: Evidence for a dual-process interpretation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(5), 830-842. doi:10.1037/0278-7393.28.5.830
- 13Receiver operating characteristics (ROCs) from Experiment 1 as a function of word frequency. Triangles represent performance for high-frequency items, open circles represent performance for low-frequency items. Functions for low-frequency items are dotted and functions for high-frequency items are dashed. The top panel depicts the ROCs for old-new discrimination, with the best-fitting ROC function generated by the Rockit maximum- likelihood estimation algorithm. The bottom two panels depict the ROCs for old-similar discrimination. The left panel depicts the best-fitting ROCs generated by the Rockit maximum-likelihood estimation algorithm and the right panel depicts linear regression fits
- 14—Arndt, J., & Reder, L. M. (2002). Word frequency and receiver operating characteristic curves in recognition memory: Evidence for a dual-process interpretation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(5), 830-842. doi:10.1037/0278-7393.28.5.830
- 15z-transformed receiver operating characteristics (z-ROCs) from Experiment 1 as a function of word frequency. Triangles represent performance for high-frequency items, open circles represent performance for low-frequency items. Functions for low-frequency items are dotted and functions for high-frequency items are dashed. The top panel depicts the z-ROCs for old-new discrimination, with the best-fitting linear trend. The bottom two panels depict the z -ROCs for old-similar discrimination. The left panel depicts the best-fitting linear trend and the right panel depicts the best-fitting regression model with quadratic components
- 16—Arndt, J., & Reder, L. M. (2002). Word frequency and receiver operating characteristic curves in recognition memory: Evidence for a dual-process interpretation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(5), 830-842. doi:10.1037/0278-7393.28.5.830
- 17—Levels of processing
- 18—List strength effect
- 19—Null list strength effect
- 20—list length effect
- 21List length / null list length effect Does the length of the list (# of studied items) affect recognition?Yes:
Strong (1912)
Gronlund & Elam (1994)
No
Dennis & Humphreys (2001)
Dennis et al. (2008) - 22—Experiment 1: In the left-hand panel, proportion of hits are plotted against the proportion of false alarms for short (S) and long (L) lists. In the right-hand panel, the z-transformed proportions of hits are plotted against the z -transformed proportions of false alarms for the short (S) and long (L) conditions
- 23Dennis and Humphreys conducted the first study to systematically control for all four of these potential confounds… And in the GRAPH you can see what they found... LONG lists are the PURPLE bars SHORT lists are the PINK bars Shows HIT and FALSE ALARM rate data And as you can see, HITS and FALSE ALARM rates are very similar across list length conditions (which had a 1:3 RATIO) So this is a NULL list length effect finiding Strong evidence that the 4 variables do CONFOUND the length effect But……………. (Cary & Reder) Dennis & Humphreys (2001) Long List = 72 words Short List = 24 wordsNull List-Length Effect
- 24—Fig. 3 Means and 95% confidence intervals for discriminability in the (A) filler, (B) no filler, and (C) word frequency comparison
Informationen
- Beschreibung
- Karteikarten am Recognition Part 2 (Exam 2), erstellt von sarrowsmith.5 am 29/10/2013.
- Erstellt von
- sarrowsmith.5 · Okt 2013
- Aktualisiert
- 19 Feb 2016
- Aufrufe
- 18
- Bewertung