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MetaAwareness analyses
Prelim semantic similarity analysis
Combined Meta-Awareness
N = 188; box_percent, change_percent, and eccentricity added to model.
Combined Meta-Awareness
New analyses
Combined Meta-Awareness
N = 173; Raw and log RTs; Fixed effects: likelihood rating and change type; Random effects: workerId, image, and type of change
Wolfe2 Meta-Awareness
N = 85; Raw and log RTs; Fixed effects: likelihood rating; Random effects: workerId and image; Only one type of change (disappear).
Wolfe1 Meta-Awareness
N = 50; Raw and log RTs; Fixed effects: likelihood rating and change type; Random effects: workerId and image
Ma Meta-Awareness
N = 22; Raw and log RTs; Fixed effects: likelihood rating and change type; Random effects: workerId and image
Combined Meta-Awareness
N = 154; Raw and log RTs; Fixed effects: likelihood rating and change type; Random effects: workerId, image, and type of change
Wolfe2 Meta-Awareness
N = 71; Raw and log RTs; Fixed effects: likelihood rating; Random effects: workerId and image; Only one type of change (disappear).
Wolfe1 Meta-Awareness
N = 49; Raw and log RTs; Fixed effects: likelihood rating and change type; Random effects: workerId and image
Ma Meta-Awareness
N = 21; Raw and log RTs; Fixed effects: likelihood rating and change type; Random effects: workerId and image
Rensink Meta-Awareness
N = 16; Raw and log RTs; Fixed effects: likelihood rating and change type; Random effects: workerId and image
Combined Meta-Awareness
N = 83; detection_rt ~ likelihood_rating; random effects: worker ID, image, stimulus set, and type of change
Wolfe1 Meta-Awareness
N = 47; detection_rt ~ likelihood_rating; random effects: worker ID, image, and type of change
Ma Meta-Awareness
N = 20; detection_rt ~ likelihood_rating; random effects: worker ID, image, and type of change
Rensink Meta-Awareness
N = 16; detection_rt ~ likelihood_rating; random effects: worker ID, image, and type of change
Combined Meta-Awareness
Combining data from Rensink, Ma, and Wolfe1 stimulus sets; N = 75 subjs, 227 images
Wolfe1 Meta-Awareness
N = 37
Ma Meta-Awareness
N = 22
Combined Meta-Awareness
Combining data from Rensink, Ma, and Wolfe1 stimulus sets
Wolfe1 Meta-Awareness
N = 28
Ma Meta-Awareness
N = 10
Rensink Meta-Awareness
N = 16
Predicting IB v4
Original sample consisted of 127 subjects. Subjects were excluded if (1) performance on matching task was at or below chance, (2) if median RT on matching task was at or below 200 ms, and (3) if IB counting error rate was greater than 40%. Final sample contained 52 subjects. There are 3 logistic regressions with median RT, accuracy, and both as predictors. Three additional regressions control for IB error with same predictors.
Predicting IB v2
Accuracy as covariate
Predicting IB v3
Original sample consisted of 127 subjects. Subjects were excluded if they performed at or below chance on matching task, if median RT is at or below 200 ms on matching task, and if they missed one or both catch trials in the line judgment task. Final sample contained 65 subjects. There are 12 logistic regressions. Median RT, accuracy, and both are used as predictors; Outcome is whether subject noticed a shape (in general), noticed a shape in the correct location, noticed the square, and noticed the square in the correct location.
Predicting IB v2
Subjects excluded with 50% or less accuracy on embedded and matching tasks and with greater than 20% error on counting task. Subjects are categorized as noticers or non-noticers by AJB.
Predicting IB v2
Subjects excluded based on accuracy on matching/embedded tasks and percent error on counting task
Wolfe2 Mudsplash
lmer modeling
Rensink Mudsplash
lmer modeling
Predicting IB
63 MTurk subjects
Predicting IB
Excluded subjects with poor performance on matching and/or embedded figures task. Sona (N = 18) and MTurk (N = 36/31).
Predicting IB
Sona (N = 18), MTurk (N = 36), and combined (N=54); Continuous (ratio) and dichotomous (cognitive style) predicting IB. With and without MTurk outlier
Predicting IB
N = 18
Rensink Mudsplash
Raw RTs, log RTs, and cycles
Wolfe2 Mudsplash
Raw RTs, log RTs, and cycles
Predicting IB
N = 14
Predicting IB
N = 10
ObjectBasedCB_v5
N = 36; change/no-change catch trials; 500 ms fixation before response window
ObjectBasedCB_v4
N = 36; change/no-change catch trials
ObjectBasedCB_v2_logRTs
Two sets of analyses with 58 and 57 subjects
ObjectBasedCB_v2_rawRTs
Two sets of analyses with 58 and 57 subjects
ObjectBasedCB_v3
N = 36; correlations between catch, main trial, and no-change accuracies
ObjectBasedCB_v2
N = 58; log-transformed RTs and linear modeling
ObjectBasedCB_v2
N = 58
ObjectBasedCB_v2
N = 50, w/ bootstrapping individual subjects
Wolfe2 Mudsplash
Only inaccurate trials removed
Wolfe2 Mudsplash
Subjects with less than 75% main trial accuracy and all inaccurate trials removed
Wolfe1 Mudsplash
Only inaccurate trials removed
Ma Mudsplash
Only inaccurate trials removed
Rensink Mudsplash
Only inaccurate trials removed
ObjectBasedCB_v2
N = 34
ObjectBasedCB_v2
N = 16
Wolfe1 Mudsplash
Subjects with less than 75% main trial accuracy and all inaccurate trials removed
Ma Mudsplash
Subjects with less than 75% main trial accuracy and all inaccurate trials removed
Rensink Mudsplash
Subjects with less than 75% main trial accuracy and all inaccurate trials removed