README.md

Similar history biases for distinct prospective decisions of self-performance

Scientific Reports volume 10, Article number: 5854 (2020)

Exp1: {Probability of Success}(POS, low vs. high) --> {gabor patch} --> response (correct vs. incorrect) --> {awareness}(unseen vs. seen) --> {confidence}(low vs. high)

Exp2: {Attention of the coming trial}(ATT, low vs. high) --> {gabor patch} --> response (correct vs. incorrect) --> {awareness}(unseen vs. seen) --> {confidence}(low vs. high)

Goals:

• • predict POS/ATT with correct, awareness, and confidence ratings
• • cross POS-ATT experiment generalization
• • cross POS-ATT AUC ANOVA, with between subject factor (Exp) and within subject factor (trial window)
• • use features from the previous trials to predict POS/ATT in the next N trials, where 0 < N <= 4
• • interpret the results and infer information processing

Decodings

1. RandomForest (n_estimators = 500) - to increase biases and avoid overfitting
2. Logistic Regression (C = 1e9) - to reduce regularization so that we can interpret the results

Windows

1. -- use the features from the previous trial to the target
2. -- features from 2 trials prior to the target
3. -- features from 3 trials prior to the target
4. -- features from 4 trials prior to the target

Result - 1.1 - Exp 1.logistic regression

POS, correct, awareness and confidence as features, decoding scores of logistic regression

Decoding Probability of Success with awareness, correctness, and confidence as features as a function of N-back trials, and factored by the classifiers. The logistic regression decode the POS above chance at the group level (see p values below). Black dotted line is the theoretical chance level, 0.5. Error bars represent bootstrapped 95% confidence intervals*, resampled from the distribution of decoding scores of individual participants by each classifier with 10000 iterations.

Awareness, correctness, and confidence carried lots of information of how participants' POS for the next trial. And these features in the 2-back, 3-back, and 4-back trials carried enough information of how participants' POS for the successive trials for the classifiers to learn and make predictions.

*Reference:

DiCiccio and Efron, 1996. Bootstrap confidence intervals, Statistical Science, 11(3), 189 - 228

P values

1-back: 0.0004, 2-back: 0.0004, 3-back: 0.0057, 4-back: 0.0085

odd ratios estimated by scitkit-learn logistic regression

There is a significant main effect of window, F(3.0,42.0) = 17.4610,p = 0.00000016

There is a significant main effect of attributes, F(2.0,28.0) = 7.5553,p = 0.00237703

A post hoc comparison reveal that:

confidence is significantly different from correct, p = 0.00030297

awareness is significantly different from correct, p = 0.00114289

awareness is not different from confidence, p = 1.00000000

There is a significant interaction between Window and Attributes,F(6.0,84.0) = 7.2377, p = 0.00000301

A post hoc multiple comparision reveal that:

confidence at 1-back is significantly different from correct at 1-back, p = 0.00119988

confidence at 2-back is significantly different from correct at 2-back, p = 0.00179982

awareness at 1-back is significantly different from correct at 1-back, p = 0.02753725

awareness at 2-back is significantly different from correct at 2-back, p = 0.03803620

The reset are not statitically significant, p > 0.0826

Result - 1.2 - Exp 1.Random Forest

POS, correct, awareness and confidence as features, decoding scores of random forest

Decoding Probability of Success with awareness, correctness, and confidence as features as a function of N-back trials, and factored by the classifiers. The RF decode the POS above chance at the group level (see p values below). Black dotted line is the theoretical chance level, 0.5. Error bars represent bootstrapped 95% confidence intervals, resampled from the distribution of decoding scores of individual participants by each classifier with 10000 iterations.

P values

1-back: 0.0004, 2-back: 0.0004, 3-back: 0.0036, 4-back: 0.0076

feature importance estimated by scitkit-learn random forest

There is a significant main effect of window, F(3.0,42.0) = 28.0000,p = 0.00000000

There is a significant main effect of attributes, F(2.0,28.0) = 8.2440,p = 0.00153045

A post hoc comparison reveal that:

awareness is significantly different from correct, p = 0.00029997

confidence is significantly different from correct, p = 0.00029997

awareness is not different from confidence, p = 1.00000000

There is a significant interaction between Window and Attributes,F(6.0,84.0) = 6.0626, p = 0.00002631

A post hoc multiple comparision reveal that:

confidence at 1-back is significantly different from correct at 1-back, p = 0.00119988

awareness at 2-back is significantly different from correct at 2-back, p = 0.00191981

awareness at 1-back is significantly different from correct at 1-back, p = 0.00309569

confidence at 2-back is significantly different from correct at 2-back, p = 0.00623938

The reset are not statitically significant, p > 0.2759

Result - 2.1 - Exp 2.logistic regression

ATT, correct, awareness and confidence as features, decoding scores of logistic regression

Decoding decision of engagement with awareness, correctness, and confidence as features as a function of N-back trials, and factored by the classifiers. The logistic regression decode the POS above chance at the group level (see p values below). Black dotted line is the theoretical chance level, 0.5. Error bars represent bootstrapped 95% confidence intervals, resampled from the distribution of decoding scores of individual participants by each classifier with 10000 iterations.

P values

1-back: 0.0004, 2-back: 0.0004, 3-back: 0.0057, 4-back: 0.0085

Odd ratio estimated by scitkit-learn logistic regression

There is a significant main effect of window, F(3.0,45.0) = 5.3268,p = 0.00315804

There is no main effect of attributes, F(2.0,30.0) = 1.9860,p = 0.15488542

A post hoc comparison reveal that:

confidence is not different from correct, p = 0.07139886

confidence is not different from awareness, p = 0.15112189

awareness is not different from correct, p = 1.00000000

There is a no interaction between window and attributes, F(6.0,90.0) = 1.5951, p = 0.15768893

A post hoc multiple comparision reveal that:

confidence at 1-back is significantly different from correct at 1-back, p = 0.03454455

The reset are not statitically significant, p > 1.0000

Result - 2.2 - Exp 1.Random Forest

ATT, correct, awareness and confidence as features, decoding scores of random forest

Decoding Decision of Engagement with awareness, correctness, and confidence as features as a function of N-back trials, and factored by the classifiers. The RF decode the POS above chance at the group level (see p values below). Black dotted line is the theoretical chance level, 0.5. Error bars represent bootstrapped 95% confidence intervals, resampled from the distribution of decoding scores of individual participants by each classifier with 10000 iterations.

P values

1-back: 0.0004, 2-back: 0.0005, 3-back: 0.3959, 4-back: 0.0139

feature importance estimated by scitkit-learn random forest

There is no main effect of window, F(3.0,45.0) = -240.0000,p = 1.00000000

There is a significant main effect of attributes, F(2.0,30.0) = 5.3776,p = 0.01009451

A post hoc comparison reveal that:

awareness is significantly different from correct, p = 0.00185681

confidence is significantly different from correct, p = 0.00329067

awareness is not different from confidence, p = 0.72927507

There is a a significant interaction between window and attributes, F(6.0,90.0) = 3.6673, p = 0.00264570

A post hoc multiple comparision reveal that:

awareness at 1-back is significantly different from correct at 1-back, p = 0.00321568

confidence at 1-back is significantly different from correct at 1-back, p = 0.00487151

The reset are not statitically significant, p > 0.0716

Cross Experiment Validation

Train Classifier in Exp.1 and test the trained classifier in Exp.2

p values of POS --> ATT by LogisticRegression

1-back = 0.0106, 2-back = 1.0000, 3-back = 1.0000, 4-back = 1.0000

p values of ATT --> POS by LogisticRegression

1-back = 0.0004, 2-back = 0.0630, 3-back = 0.0722, 4-back = 1.0000

p values of POS --> ATT by RandomForestClassifier

1-back = 0.0105, 2-back = 1.0000, 3-back = 1.0000, 4-back = 1.0000

p values of ATT --> POS by RandomForestClassifier

1-back = 0.0005, 2-back = 0.0574, 3-back = 0.0368, 4-back = 1.0000

Linear Mixed Model

As requested

for POS:

from the output of the R lmer package:

coefficient of confidence at time 1 = 0.23927, t(8266.31) = 22.80,p = 1.352e-110

coefficient of awareness at time 1 = 0.15090, t(8264.78) = 13.94,p = 1.429e-42

coefficient of confidence at time 2 = 0.10159, t(8265.97) = 9.66,p = 6.918e-21

coefficient of awareness at time 2 = 0.05940, t(8264.44) = 5.46,p = 5.784e-07

coefficient of confidence at time 3 = 0.04486, t(8265.98) = 4.27,p = 2.412e-04

coefficient of confidence at time 4 = 0.03412, t(8266.48) = 3.25,p = 1.409e-02

for ATT:

from the output of the R lmer package:

coefficient of confidence at time 1 = 0.06531, t(8192.99) = 5.67,p = 1.781e-07