Analysis Script accompaning Affective Cognition paper
Desmond Ong
Updated: Mar 27, 2016
This markdown document contains code and analyses to accompany the following paper.
Ong, D. C., Zaki, J., & Goodman, N. D. (2015). Affective Cognition: Exploring lay theories of emotion. Cognition, 143, 141-162.
I have appended only the results sections of the paper, side-by-side with the corresponding analyses. The paper itself is much easier to read in the journal-formatted form, here: http://www.sciencedirect.com/science/article/pii/S0010027715300196
Experiment 1 Results
Model selection (in Appendix A) revealed that participants’ emotion ratings were significantly predicted only by three of the seven regressors we initially proposed: amount won, the prediction error (PE), and the absolute value of the prediction error (|PE|) (see also Section 3.3 for a re-analysis with more data). Crucially, PE and |PE| account for significant variance in emotion ratings after accounting for amount won. This suggests that affective cognition is remarkably consistent with economic and psychological models of subjective utility. In particular, emotion inferences exhibited reference-dependence—tracking prediction error in addition to amount won—and loss aversion—in that emotion inferences were more strongly predicted by negative, as opposed to positive prediction error. These features suggest that lay observers spontaneously use key features of prospect theory ( Kahneman and Tversky, 1979 and Kahneman and Tversky, 1984) in reasoning about others’ emotions: a remarkable connection between formal and everyday theorizing. It is worth noting as well that the significant regressors for surprise followed a slightly different pattern from the rest of the other emotions, where the win probability, as well as regret and relief, seem just as important as the amount won, PE, and |PE|.
The aforementioned analysis suggests that amount won, PE, and |PE| are necessary to model emotion inferences in a gambling context and suggest a low dimensional structure for the situation features. Next, we explored the underlying dimensionality of participants’ inferences about agents’ emotions via an a priori planned Principal Component Analysis (PCA). Previous work on judgments of facial and verbal emotions (e.g., Russell, 1980 and Schlosberg, 1954) and self-reported emotions (e.g., Kuppens et al., 2012 and Watson and Tellegen, 1985) have suggested a low-dimensional structure, and we planned this analysis to see if a similar low-dimensional structure might emerge in attributed emotions in our paradigm.
The first principal component (PC) accounted for 59% of the variance in participants’ ratings along all 8 emotions, while the second PC accounted for 16%; subsequent PCs individually explained less than 10% of the variance. The first PC accounted for most of the variance in the emotion ratings, although the second PC accounted for a far lower, but still noteworthy, amount of variance. Full details of the PCA procedure and loading results are given in Appendix A.
Post-hoc exploratory analysis of the first two PCs revealed that the first PC positively tracked happiness and contentment, while negatively tracking all negative emotions; by contrast, the second PC positively tracked the intensity of both positive and negative emotions (Fig. 4A). Interestingly, this connects with classic concepts of valence and arousal, respectively, which feature centrally in emotion science6 (e.g., Kuppens et al., 2012, Russell, 1980 and Schlosberg, 1954). In particular, some theorists view emotional valence as a crucial form of feedback to the agent: positively valenced emotions like happiness signal a positive prediction error—that the agent is doing better than expected—hence, positively reinforcing successful behavior. Conversely, negatively valenced emotions could signal to the agent to change some behavior to achieve a better outcome (e.g., Carver and Scheier, 2004 and Ortony et al., 1988). In line with this, we find that the first PC (“valence”) of emotions attributed by the observer correlated strongly with the PE of the situation (r = 0.737, 95% C.I. = [0.707; 0.764]). Additionally, we find that the second PC (“arousal”) correlated with |PE| (r = 0.280 [0.222, 0.364]; Fig. 4B).
## Warning: Removed 2 rows containing missing values (geom_point).
Figure 4: (A) Participants’ emotion ratings projected onto the dimensions of the first two principal components (PCs), along with the loadings of the PCs on each of the eight emotions. The loading of the PCs onto the eight emotions suggests a natural interpretation of the first two PCs as “valence” and “arousal” respectively. The labels for disgust and anger are overlapping. (B) Participant’s emotion ratings projected onto the dimensions of the first two PCs, this time colored by the prediction error (PE = amount won − expected value of wheel). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
We started with a priori predictions for a low-dimensional summary of outcome features, and followed up with a post hoc dimensionality reduction analysis of the emotion ratings. It is intriguing that the low-dimensional value computations (e.g. PE, |PE|) are intimately tied with the principal components of the emotion ratings (“valence” and “arousal”). However, note that the second PC (“arousal”) accounts for much less variance than the first PC (“valence”). One possibility is that the paradigm we used is limited in the range of emotions that it elicits in an agent, which restricts the complexity of emotion inferences in this paradigm. A second possibility is that emotional valence is the central feature of affective cognition, and valence would carry most of the variance in emotion inferences across more complex scenarios. Although we are not able to address the second possibility in this paper, there is much theoretical evidence from affective science (e.g., Barrett and Russell, 1999, Russell, 1980 and Schlosberg, 1954) in favor of the second possibility; future work is needed to explore this further.
Together, these findings provide two key insights into the structure of affective cognition: (i) lay theories of emotion are low dimensional, consistent with affective science concepts of valence and arousal, and (ii) these core dimensions of emotion inference also track aspects of the situation and outcome that reflect value computation parameters described by economic models such as prospect theory. Although the specific structure of affective cognition likely varies depending on the complexity and details of a given context, we believe that observers’ use of low-dimensional “active psychological ingredients” in drawing inferences constitutes a core feature of affective cognition.
## Iteration number 1 out of 1Experiment 2 Results
Model-based posterior probabilities tightly tracked the observer judgments in Experiment 2 (Fig. 6B). The optimal noise parameter was 3.2 [2.9, 3.6], which resulted in a model RMSE of 0.116 [0.112, 0.120]. The model’s predictions explained much of the variance in participants’ judgments, achieving a high correlation of 0.806 [0.792, 0.819]. For comparison, the bootstrapped split-half correlation of the posterior probability estimates in Experiment 2 is 0.895 [0.866, 0.920]. The split-half correlation for the emotion attributions in Experiment 1, the data that this model is fit to, is 0.938 [0.925, 0.950]. Together these two split-half reliabilities give an upper-bound for model performance, and our model performs very comparably to these upper limits.
This results suggest that a Bayesian framework can accurately describe how observers make reverse inferences, P(o|e), given how they make forward inferences, P(e|o). At a broader level, the results imply that the causal knowledge in an observer’s lay theory of emotion is abstract enough to use for multiple patterns of reasoning. In the next section, we extend this work further by considering inferences about emotion from multiple sources of information.
Cue Integration
## [1] 0.8619## [1] 0.781## [1] 0.7347##
## Paired t-test
##
## data: posFaceAgg1$faceEntropy and negFaceAgg1$faceEntropy
## t = -2.7, df = 7, p-value = 0.03065
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.9891 -0.1316
## sample estimates:
## mean of the differences
## -1.06##
## Paired t-test
##
## data: posWheelAgg1$wheelEntropy and negWheelAgg1$wheelEntropy
## t = -4.1, df = 7, p-value = 0.004575
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.6193 -0.1662
## sample estimates:
## mean of the differences
## -0.3927## bayesCor faceCor wheelCor
## 0.8125 0.7333 0.6916
## negFacePosWheelBayes negFacePosWheelFace negFacePosWheelWheel
## 0.5705 0.3428 0.4647
## posFaceNegWheelBayes posFaceNegWheelFace posFaceNegWheelWheel
## 0.6665 0.6784 0.2972
## rmseBayes rmseFace rmseWheel
## 1.2176 1.3857 1.5244
## rmseNegFacePosWheelBayes rmseNegFacePosWheelFace rmseNegFacePosWheelWheel
## 1.6900 1.8346 2.2972
## rmsePosFaceNegWheelBayes rmsePosFaceNegWheelFace rmsePosFaceNegWheelWheel
## 1.4955 1.8735 2.0579## bayesCor faceCor wheelCor
## 0.7902 0.7055 0.6636
## negFacePosWheelBayes negFacePosWheelFace negFacePosWheelWheel
## 0.4538 0.2134 0.3320
## posFaceNegWheelBayes posFaceNegWheelFace posFaceNegWheelWheel
## 0.5683 0.5509 0.1649
## rmseBayes rmseFace rmseWheel
## 1.1490 1.3144 1.4622
## rmseNegFacePosWheelBayes rmseNegFacePosWheelFace rmseNegFacePosWheelWheel
## 1.4906 1.6058 2.1023
## rmsePosFaceNegWheelBayes rmsePosFaceNegWheelFace rmsePosFaceNegWheelWheel
## 1.2888 1.5842 1.8283## bayesCor faceCor wheelCor
## 0.8333 0.7585 0.7171
## negFacePosWheelBayes negFacePosWheelFace negFacePosWheelWheel
## 0.6681 0.4682 0.5757
## posFaceNegWheelBayes posFaceNegWheelFace posFaceNegWheelWheel
## 0.7477 0.7779 0.4223
## rmseBayes rmseFace rmseWheel
## 1.2871 1.4642 1.5893
## rmseNegFacePosWheelBayes rmseNegFacePosWheelFace rmseNegFacePosWheelWheel
## 1.9153 2.0874 2.5122
## rmsePosFaceNegWheelBayes rmsePosFaceNegWheelFace rmsePosFaceNegWheelWheel
## 1.7326 2.2060 2.3008
## [1] 0.8097## [1] 0.6385## [1] 0.7241##
## Paired t-test
##
## data: posWheelAgg1$wheelEntropy and negWheelAgg1$wheelEntropy
## t = -5.009, df = 7, p-value = 0.001549
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5868 -0.2105
## sample estimates:
## mean of the differences
## -0.3986##
## Paired t-test
##
## data: posFaceAgg1$faceEntropy and negFaceAgg1$faceEntropy
## t = -2.832, df = 7, p-value = 0.02532
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.6875 -0.1519
## sample estimates:
## mean of the differences
## -0.9197## [1] 2.8## [1] 1.804## [1] 6.975## [1] 2.626## [1] 6.5## [1] 3.786## bayesCor utteranceCor wheelCor
## 0.7326 0.5702 0.6685
## negFacePosWheelBayes negFacePosWheelFace negFacePosWheelWheel
## 0.7842 -0.1470 0.7752
## posFaceNegWheelBayes posFaceNegWheelFace posFaceNegWheelWheel
## 0.5166 0.3985 0.4856
## rmseBayes rmseFace rmseWheel
## 1.4940 1.8475 1.6193
## rmseNegFacePosWheelBayes rmseNegFacePosWheelFace rmseNegFacePosWheelWheel
## 1.4402 2.9079 1.6548
## rmsePosFaceNegWheelBayes rmsePosFaceNegWheelFace rmsePosFaceNegWheelWheel
## 1.7947 2.4849 1.7458## bayesCor utteranceCor wheelCor
## 0.6870 0.5177 0.6231
## negFacePosWheelBayes negFacePosWheelFace negFacePosWheelWheel
## 0.7022 -0.2762 0.6838
## posFaceNegWheelBayes posFaceNegWheelFace posFaceNegWheelWheel
## 0.3665 0.2450 0.3214
## rmseBayes rmseFace rmseWheel
## 1.3912 1.7308 1.5213
## rmseNegFacePosWheelBayes rmseNegFacePosWheelFace rmseNegFacePosWheelWheel
## 1.2477 2.5693 1.4174
## rmsePosFaceNegWheelBayes rmsePosFaceNegWheelFace rmsePosFaceNegWheelWheel
## 1.5719 2.1880 1.4522## bayesCor utteranceCor wheelCor
## 0.76965 0.61801 0.70987
## negFacePosWheelBayes negFacePosWheelFace negFacePosWheelWheel
## 0.84494 -0.01233 0.84398
## posFaceNegWheelBayes posFaceNegWheelFace posFaceNegWheelWheel
## 0.61959 0.53193 0.62844
## rmseBayes rmseFace rmseWheel
## 1.61206 1.96858 1.72354
## rmseNegFacePosWheelBayes rmseNegFacePosWheelFace rmseNegFacePosWheelWheel
## 1.65683 3.24052 1.92477
## rmsePosFaceNegWheelBayes rmsePosFaceNegWheelFace rmsePosFaceNegWheelWheel
## 2.05869 2.78860 2.06391
Figure 11
Figure 12
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: happy ~ win + PE + absPE + affectiveCloseness + (1 | workerid)
## Data: expt1_data
##
## REML criterion at convergence: 3503
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.556 0.745
## Residual 1.594 1.262
## Number of obs: 1000, groups: workerid, 100
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.50e+00 2.09e-01 9.31e+02 21.55 < 2e-16 ***
## win 3.88e-02 3.32e-03 9.22e+02 11.68 < 2e-16 ***
## PE 3.52e-02 3.45e-03 9.24e+02 10.20 < 2e-16 ***
## absPE -1.75e-02 2.94e-03 9.32e+02 -5.95 3.9e-09 ***
## affectiveCloseness -1.61e-05 1.33e-05 9.34e+02 -1.22 0.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE
## win -0.859
## PE 0.768 -0.887
## absPE -0.390 0.090 -0.107
## affctvClsns 0.041 -0.040 -0.043 -0.059## Linear mixed model fit by REML ['merModLmerTest']
## Formula: happy ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | spinnerID) + (1 | workerid) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 10703
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.42076 0.6487
## spinnerID (Intercept) 0.19580 0.4425
## expt (Intercept) 0.00575 0.0758
## Residual 1.58904 1.2606
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.39e+00 6.90e-01 4.70e+01 6.36 7.3e-08 ***
## win 3.56e-02 7.02e-03 4.70e+01 5.08 6.5e-06 ***
## PE 2.12e-02 1.24e-02 4.80e+01 1.71 0.0937 .
## absPE -1.69e-02 5.83e-03 4.80e+01 -2.90 0.0057 **
## Regret 1.26e-02 1.04e-02 4.30e+01 1.21 0.2333
## Relief 6.73e-03 8.42e-03 4.40e+01 0.80 0.4282
## lwinProb -3.24e-01 2.55e-01 5.00e+01 -1.27 0.2107
## affectiveCloseness -3.31e-05 1.12e-05 2.74e+03 -2.96 0.0031 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.782
## PE -0.125 0.077
## absPE 0.051 0.056 0.068
## Regret 0.734 -0.574 -0.661 0.112
## Relief -0.055 -0.222 -0.710 -0.325 0.241
## lwinProb 0.468 -0.080 0.035 0.236 0.089 -0.026
## affctvClsns 0.009 -0.015 -0.004 -0.024 -0.001 -0.010 -0.010## Linear mixed model fit by REML ['merModLmerTest']
## Formula: sad ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | spinnerID) + (1 | workerid) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11417
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.721643 0.8495
## spinnerID (Intercept) 0.252618 0.5026
## expt (Intercept) 0.000585 0.0242
## Residual 1.926566 1.3880
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.72e+00 7.78e-01 4.80e+01 4.79 1.6e-05 ***
## win -2.72e-02 7.92e-03 4.80e+01 -3.44 0.0012 **
## PE -1.41e-02 1.40e-02 5.00e+01 -1.01 0.3175
## absPE 2.83e-02 6.57e-03 4.90e+01 4.30 8.1e-05 ***
## Regret -3.80e-03 1.18e-02 4.50e+01 -0.32 0.7490
## Relief -3.87e-03 9.51e-03 4.50e+01 -0.41 0.6860
## lwinProb 1.86e-01 2.88e-01 5.10e+01 0.65 0.5214
## affectiveCloseness 4.37e-06 1.24e-05 2.67e+03 0.35 0.7251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.783
## PE -0.127 0.078
## absPE 0.053 0.055 0.068
## Regret 0.736 -0.575 -0.661 0.114
## Relief -0.055 -0.223 -0.710 -0.325 0.239
## lwinProb 0.468 -0.080 0.034 0.236 0.090 -0.025
## affctvClsns 0.009 -0.015 -0.004 -0.024 -0.001 -0.010 -0.009## Linear mixed model fit by REML ['merModLmerTest']
## Formula: happy ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 10703
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.42076 0.6487
## spinnerID (Intercept) 0.19580 0.4425
## expt (Intercept) 0.00575 0.0758
## Residual 1.58904 1.2606
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.39e+00 6.90e-01 4.70e+01 6.36 7.3e-08 ***
## win 3.56e-02 7.02e-03 4.70e+01 5.08 6.5e-06 ***
## PE 2.12e-02 1.24e-02 4.80e+01 1.71 0.0937 .
## absPE -1.69e-02 5.83e-03 4.80e+01 -2.90 0.0057 **
## Regret 1.26e-02 1.04e-02 4.30e+01 1.21 0.2333
## Relief 6.73e-03 8.42e-03 4.40e+01 0.80 0.4282
## lwinProb -3.24e-01 2.55e-01 5.00e+01 -1.27 0.2107
## affectiveCloseness -3.31e-05 1.12e-05 2.74e+03 -2.96 0.0031 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.782
## PE -0.125 0.077
## absPE 0.051 0.056 0.068
## Regret 0.734 -0.574 -0.661 0.112
## Relief -0.055 -0.222 -0.710 -0.325 0.241
## lwinProb 0.468 -0.080 0.035 0.236 0.089 -0.026
## affctvClsns 0.009 -0.015 -0.004 -0.024 -0.001 -0.010 -0.010## Linear mixed model fit by REML ['merModLmerTest']
## Formula: sad ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11417
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.721643 0.8495
## spinnerID (Intercept) 0.252618 0.5026
## expt (Intercept) 0.000585 0.0242
## Residual 1.926566 1.3880
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.72e+00 7.78e-01 4.80e+01 4.79 1.6e-05 ***
## win -2.72e-02 7.92e-03 4.80e+01 -3.44 0.0012 **
## PE -1.41e-02 1.40e-02 5.00e+01 -1.01 0.3175
## absPE 2.83e-02 6.57e-03 4.90e+01 4.30 8.1e-05 ***
## Regret -3.80e-03 1.18e-02 4.50e+01 -0.32 0.7490
## Relief -3.87e-03 9.51e-03 4.50e+01 -0.41 0.6860
## lwinProb 1.86e-01 2.88e-01 5.10e+01 0.65 0.5214
## affectiveCloseness 4.37e-06 1.24e-05 2.67e+03 0.35 0.7251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.783
## PE -0.127 0.078
## absPE 0.053 0.055 0.068
## Regret 0.736 -0.575 -0.661 0.114
## Relief -0.055 -0.223 -0.710 -0.325 0.239
## lwinProb 0.468 -0.080 0.034 0.236 0.090 -0.025
## affctvClsns 0.009 -0.015 -0.004 -0.024 -0.001 -0.010 -0.009## Linear mixed model fit by REML ['merModLmerTest']
## Formula: anger ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11143
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.88754 0.9421
## spinnerID (Intercept) 0.18984 0.4357
## expt (Intercept) 0.00126 0.0355
## Residual 1.68145 1.2967
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.58e+00 6.88e-01 5.20e+01 3.75 0.00045 ***
## win -1.49e-02 7.00e-03 5.10e+01 -2.13 0.03779 *
## PE -1.72e-02 1.24e-02 5.30e+01 -1.39 0.17137
## absPE 2.81e-02 5.81e-03 5.20e+01 4.84 1.2e-05 ***
## Regret 1.44e-03 1.04e-02 4.70e+01 0.14 0.89009
## Relief -3.03e-03 8.39e-03 4.80e+01 -0.36 0.71953
## lwinProb 2.21e-02 2.55e-01 5.40e+01 0.09 0.93114
## affectiveCloseness 4.73e-06 1.17e-05 2.61e+03 0.41 0.68545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.783
## PE -0.124 0.076
## absPE 0.049 0.058 0.069
## Regret 0.733 -0.574 -0.661 0.110
## Relief -0.055 -0.222 -0.711 -0.325 0.242
## lwinProb 0.468 -0.079 0.036 0.236 0.089 -0.028
## affctvClsns 0.010 -0.015 -0.005 -0.025 -0.001 -0.011 -0.009## Linear mixed model fit by REML ['merModLmerTest']
## Formula: surprise ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 12696
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 1.32059 1.1492
## spinnerID (Intercept) 0.12555 0.3543
## expt (Intercept) 0.00479 0.0692
## Residual 2.88412 1.6983
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.10e+00 6.55e-01 4.40e+01 4.74 2.3e-05 ***
## win 4.20e-03 6.66e-03 4.30e+01 0.63 0.5317
## PE -1.96e-02 1.19e-02 5.10e+01 -1.64 0.1066
## absPE 3.10e-02 5.58e-03 4.90e+01 5.57 1.1e-06 ***
## Regret 3.29e-02 9.72e-03 3.80e+01 3.38 0.0017 **
## Relief 1.58e-02 7.88e-03 4.00e+01 2.01 0.0516 .
## lwinProb -1.66e+00 2.47e-01 5.10e+01 -6.75 1.3e-08 ***
## affectiveCloseness 3.94e-06 1.52e-05 2.63e+03 0.26 0.7954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.783
## PE -0.103 0.059
## absPE 0.013 0.091 0.082
## Regret 0.717 -0.570 -0.665 0.070
## Relief -0.055 -0.213 -0.722 -0.324 0.270
## lwinProb 0.471 -0.072 0.053 0.236 0.081 -0.054
## affctvClsns 0.013 -0.019 -0.007 -0.031 -0.001 -0.014 -0.011## Linear mixed model fit by REML ['merModLmerTest']
## Formula: disgust ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11145
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.9190 0.959
## spinnerID (Intercept) 0.1510 0.389
## expt (Intercept) 0.0228 0.151
## Residual 1.6759 1.295
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.67e+00 6.38e-01 5.80e+01 4.18 1.0e-04 ***
## win -1.48e-02 6.44e-03 5.60e+01 -2.30 0.025 *
## PE -1.95e-02 1.14e-02 5.90e+01 -1.71 0.093 .
## absPE 2.26e-02 5.35e-03 5.80e+01 4.22 8.7e-05 ***
## Regret 5.15e-03 9.53e-03 5.10e+01 0.54 0.592
## Relief -1.42e-03 7.69e-03 5.20e+01 -0.18 0.855
## lwinProb 1.57e-02 2.35e-01 6.00e+01 0.07 0.947
## affectiveCloseness -5.87e-07 1.16e-05 2.59e+03 -0.05 0.960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.776
## PE -0.119 0.072
## absPE 0.042 0.064 0.072
## Regret 0.724 -0.574 -0.662 0.102
## Relief -0.055 -0.220 -0.713 -0.325 0.247
## lwinProb 0.464 -0.077 0.039 0.235 0.087 -0.033
## affctvClsns 0.010 -0.016 -0.005 -0.027 -0.001 -0.011 -0.010## Linear mixed model fit by REML ['merModLmerTest']
## Formula: fear ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 8506
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.657657 0.8110
## spinnerID (Intercept) 0.002395 0.0489
## expt (Intercept) 0.000181 0.0135
## Residual 0.647069 0.8044
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.99e+00 1.84e-01 1.30e+01 10.79 9e-08 ***
## win -8.28e-03 1.83e-03 1.10e+01 -4.52 0.00097 ***
## PE -2.05e-03 3.68e-03 2.30e+01 -0.56 0.58287
## absPE 2.66e-03 1.66e-03 1.70e+01 1.60 0.12729
## Regret 3.12e-03 2.62e-03 1.10e+01 1.19 0.26006
## Relief -1.27e-04 2.18e-03 1.30e+01 -0.06 0.95433
## lwinProb 5.64e-02 7.39e-02 1.70e+01 0.76 0.45632
## affectiveCloseness 5.20e-06 7.23e-06 2.49e+03 0.72 0.47183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.778
## PE -0.047 0.016
## absPE -0.127 0.224 0.087
## Regret 0.649 -0.538 -0.696 -0.053
## Relief -0.040 -0.194 -0.770 -0.310 0.390
## lwinProb 0.495 -0.068 0.109 0.209 0.054 -0.121
## affctvClsns 0.017 -0.020 -0.013 -0.030 -0.003 -0.021 -0.007## Linear mixed model fit by REML ['merModLmerTest']
## Formula: content ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 12940
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 1.4340 1.198
## spinnerID (Intercept) 0.1085 0.329
## expt (Intercept) 0.0304 0.174
## Residual 3.1288 1.769
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.79e+00 6.45e-01 4.40e+01 5.88 5.1e-07 ***
## win 2.86e-02 6.49e-03 4.30e+01 4.40 7.0e-05 ***
## PE 1.13e-02 1.17e-02 5.40e+01 0.96 0.340
## absPE -1.45e-02 5.46e-03 5.00e+01 -2.65 0.011 *
## Regret 1.03e-02 9.44e-03 3.80e+01 1.09 0.281
## Relief 8.87e-03 7.66e-03 4.00e+01 1.16 0.254
## lwinProb -2.48e-01 2.42e-01 5.30e+01 -1.02 0.311
## affectiveCloseness -1.96e-05 1.58e-05 2.64e+03 -1.24 0.215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.775
## PE -0.097 0.054
## absPE 0.001 0.101 0.085
## Regret 0.704 -0.568 -0.666 0.058
## Relief -0.054 -0.211 -0.726 -0.323 0.280
## lwinProb 0.468 -0.070 0.058 0.234 0.078 -0.062
## affctvClsns 0.013 -0.020 -0.007 -0.032 -0.001 -0.015 -0.011## Linear mixed model fit by REML ['merModLmerTest']
## Formula: disapp ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11952
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.692 0.832
## spinnerID (Intercept) 0.164 0.405
## expt (Intercept) 0.000 0.000
## Residual 2.387 1.545
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.49e+00 6.85e-01 5.10e+01 6.55 2.9e-08 ***
## win -2.67e-02 6.99e-03 5.10e+01 -3.81 0.00037 ***
## PE -2.98e-02 1.24e-02 5.60e+01 -2.40 0.01966 *
## absPE 2.33e-02 5.82e-03 5.40e+01 4.01 0.00019 ***
## Regret -1.30e-02 1.03e-02 4.60e+01 -1.26 0.21335
## Relief -6.50e-03 8.32e-03 4.70e+01 -0.78 0.43846
## lwinProb 1.42e-01 2.56e-01 5.70e+01 0.55 0.58123
## affectiveCloseness -2.13e-06 1.37e-05 2.73e+03 -0.16 0.87653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.784
## PE -0.114 0.067
## absPE 0.033 0.073 0.076
## Regret 0.727 -0.572 -0.662 0.091
## Relief -0.055 -0.217 -0.716 -0.325 0.255
## lwinProb 0.471 -0.076 0.044 0.238 0.085 -0.040
## affctvClsns 0.011 -0.017 -0.005 -0.028 -0.001 -0.013 -0.011##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## expt 0.63 1 1 0.4257
## workerid 232.47 1 kept <1e-07
## spinnerID 191.63 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## Relief 0.8269 0.8269 1 54.72 0.7046 1
## Regret 2.2129 2.2129 1 45.07 1.0855 2
## lwinProb 3.1916 3.1916 1 52.37 1.9250 3
## win 1075.6677 1075.6677 1 52.07 48.3592 kept
## PE 46.4506 46.4506 1 53.41 34.6099 kept
## absPE 12.7759 12.7759 1 53.23 7.5699 kept
## affectiveCloseness 13.9366 13.9366 1 2743.97 8.7687 kept
## Pr(>F)
## Relief 0.4049
## Regret 0.3030
## lwinProb 0.1712
## win <1e-07
## PE 0
## absPE 0.0081
## affectiveCloseness 0.0031
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = happy ~ win + PE + absPE + affectiveCloseness +
## (1 | workerid) + (1 | spinnerID), data = meta_full, REML = reml,
## contrasts = l.lmerTest.private.contrast)##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## expt 0.0 1 1 1
## workerid 348.2 1 kept <1e-07
## spinnerID 188.7 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num Pr(>F)
## Regret 0.1692 0.1692 1 54.14 0.1186 1 0.7319
## Relief 0.1884 0.1884 1 45.22 0.1221 2 0.7284
## affectiveCloseness 0.2317 0.2317 1 2668.72 0.1203 3 0.7288
## lwinProb 0.8937 0.8937 1 53.62 0.4640 4 0.4987
## win 465.9189 465.9189 1 53.77 21.0126 kept 0
## PE 9.9094 9.9094 1 55.23 9.3450 kept 0.0034
## absPE 41.5074 41.5074 1 55.11 21.5529 kept 0
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = sad ~ win + PE + absPE + (1 | workerid) +
## (1 | spinnerID), data = meta_full, REML = reml, contrasts = l.lmerTest.private.contrast)##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## expt 0.0 1 1 1
## workerid 507.0 1 kept <1e-07
## spinnerID 176.6 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num Pr(>F)
## lwinProb 0.0157 0.0157 1 67.85 0.0097 1 0.9220
## Regret 0.0904 0.0904 1 49.17 0.0185 2 0.8924
## affectiveCloseness 0.2792 0.2792 1 2612.17 0.1661 3 0.6837
## Relief 0.2886 0.2886 1 49.61 0.1717 4 0.6804
## win 225.2690 225.2690 1 58.50 6.9547 kept 0.0107
## PE 8.7843 8.7843 1 60.43 10.2213 kept 0.0022
## absPE 47.2049 47.2049 1 60.52 28.0916 kept 0
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = anger ~ win + PE + absPE + (1 | workerid) +
## (1 | spinnerID), data = meta_full, REML = reml, contrasts = l.lmerTest.private.contrast)##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## expt 0.00 1 1 0.9509
## workerid 409.28 1 kept <1e-07
## spinnerID 37.58 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## affectiveCloseness 0.1951 0.1951 1 2636.28 0.0678 1
## win 516.1163 516.1163 1 43.22 0.4063 2
## PE 607.6774 607.6774 1 52.65 2.8713 3
## Relief 3.8385 3.8385 1 31.54 2.1165 4
## absPE 249.1086 249.1086 1 49.49 45.4633 kept
## Regret 564.8841 564.8841 1 43.27 151.5106 kept
## lwinProb 125.3493 125.3493 1 50.61 43.4531 kept
## Pr(>F)
## affectiveCloseness 0.7946
## win 0.5272
## PE 0.0961
## Relief 0.1556
## absPE <1e-07
## Regret <1e-07
## lwinProb <1e-07
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = surprise ~ absPE + Regret + lwinProb + (1 |
## workerid) + (1 | spinnerID), data = meta_full, REML = reml,
## contrasts = l.lmerTest.private.contrast)##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## expt 0.8 1 1 0.3723
## workerid 509.1 1 kept <1e-07
## spinnerID 147.3 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num Pr(>F)
## affectiveCloseness 0.0046 0.0046 1 2598.90 0.0028 1 0.9581
## lwinProb 0.0256 0.0256 1 59.84 0.0153 2 0.9020
## Relief 0.0547 0.0547 1 52.93 0.0327 3 0.8572
## Regret 0.5905 0.5905 1 51.76 0.3529 4 0.5551
## win 213.8615 213.8615 1 62.87 6.1233 kept 0.0161
## PE 10.0026 10.0026 1 65.46 10.2850 kept 0.0021
## absPE 32.9739 32.9739 1 65.84 19.7080 kept 0
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = disgust ~ win + PE + absPE + (1 | workerid) +
## (1 | spinnerID), data = meta_full, REML = reml, contrasts = l.lmerTest.private.contrast)##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## expt 0.0 1 1 1
## spinnerID 0.0 1 2 1
## workerid 892.7 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num Pr(>F)
## Relief 0.0071 0.0071 1 2507 0.0046 1 0.9457
## affectiveCloseness 0.3717 0.3717 1 2494 0.5732 2 0.4491
## PE 0.5575 0.5575 1 2490 0.8723 3 0.3504
## lwinProb 0.8643 0.8643 1 2521 1.3327 4 0.2484
## absPE 2.0066 2.0066 1 2502 1.8747 5 0.1711
## Regret 2.3408 2.3408 1 2559 3.6078 6 0.0576
## win 106.2710 106.2710 1 2531 163.7070 kept <1e-07
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = fear ~ win + (1 | workerid), data = meta_full,
## REML = reml, contrasts = l.lmerTest.private.contrast)##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## expt 2.02 1 1 0.1551
## workerid 439.36 1 kept <1e-07
## spinnerID 28.56 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num Pr(>F)
## PE 55.151 55.151 1 65.91 1.055 1 0.3081
## lwinProb 3.790 3.790 1 53.42 1.180 2 0.2822
## affectiveCloseness 4.816 4.816 1 2641.86 1.537 3 0.2151
## win 1503.843 1503.843 1 46.09 18.049 kept 0.0001
## absPE 7.617 7.617 1 53.50 6.725 kept 0.0122
## Regret 47.227 47.227 1 37.51 6.305 kept 0.0165
## Relief 21.595 21.595 1 35.55 6.891 kept 0.0127
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = content ~ win + absPE + Regret + Relief +
## (1 | workerid) + (1 | spinnerID), data = meta_full, REML = reml,
## contrasts = l.lmerTest.private.contrast)##
## Random effects:
## Chi.sq Chi.DF elim.num p.value
## expt 0.0 1 1 1
## workerid 262.5 1 kept <1e-07
## spinnerID 103.7 1 kept <1e-07
##
## Fixed effects:
## Sum Sq Mean Sq NumDF DenDF F.value elim.num
## affectiveCloseness 0.0576 0.0576 1 2738.54 0.0242 1
## lwinProb 0.7300 0.7300 1 56.71 0.3060 2
## Relief 1.4023 1.4023 1 48.48 0.5878 3
## Regret 3.1904 3.1904 1 47.10 1.3375 4
## win 1657.0215 1657.0215 1 56.31 32.1906 kept
## PE 107.7720 107.7720 1 59.56 54.7067 kept
## absPE 43.6627 43.6627 1 60.32 18.2955 kept
## Pr(>F)
## affectiveCloseness 0.8764
## lwinProb 0.5823
## Relief 0.4470
## Regret 0.2533
## win 0e+00
## PE <1e-07
## absPE 1e-04
##
## Least squares means:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Differences of LSMEANS:
## Estimate Standard Error DF t-value Lower CI Upper CI p-value
##
## Final model:
## lme4::lmer(formula = disapp ~ win + PE + absPE + (1 | workerid) +
## (1 | spinnerID), data = meta_full, REML = reml, contrasts = l.lmerTest.private.contrast)## Linear mixed model fit by REML ['merModLmerTest']
## Formula: sad ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11417
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.721643 0.8495
## spinnerID (Intercept) 0.252618 0.5026
## expt (Intercept) 0.000585 0.0242
## Residual 1.926566 1.3880
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.72e+00 7.78e-01 4.80e+01 4.79 1.6e-05 ***
## win -2.72e-02 7.92e-03 4.80e+01 -3.44 0.0012 **
## PE -1.41e-02 1.40e-02 5.00e+01 -1.01 0.3175
## absPE 2.83e-02 6.57e-03 4.90e+01 4.30 8.1e-05 ***
## Regret -3.80e-03 1.18e-02 4.50e+01 -0.32 0.7490
## Relief -3.87e-03 9.51e-03 4.50e+01 -0.41 0.6860
## lwinProb 1.86e-01 2.88e-01 5.10e+01 0.65 0.5214
## affectiveCloseness 4.37e-06 1.24e-05 2.67e+03 0.35 0.7251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.783
## PE -0.127 0.078
## absPE 0.053 0.055 0.068
## Regret 0.736 -0.575 -0.661 0.114
## Relief -0.055 -0.223 -0.710 -0.325 0.239
## lwinProb 0.468 -0.080 0.034 0.236 0.090 -0.025
## affctvClsns 0.009 -0.015 -0.004 -0.024 -0.001 -0.010 -0.009## Linear mixed model fit by REML ['merModLmerTest']
## Formula: anger ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11143
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.88754 0.9421
## spinnerID (Intercept) 0.18984 0.4357
## expt (Intercept) 0.00126 0.0355
## Residual 1.68145 1.2967
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.58e+00 6.88e-01 5.20e+01 3.75 0.00045 ***
## win -1.49e-02 7.00e-03 5.10e+01 -2.13 0.03779 *
## PE -1.72e-02 1.24e-02 5.30e+01 -1.39 0.17137
## absPE 2.81e-02 5.81e-03 5.20e+01 4.84 1.2e-05 ***
## Regret 1.44e-03 1.04e-02 4.70e+01 0.14 0.89009
## Relief -3.03e-03 8.39e-03 4.80e+01 -0.36 0.71953
## lwinProb 2.21e-02 2.55e-01 5.40e+01 0.09 0.93114
## affectiveCloseness 4.73e-06 1.17e-05 2.61e+03 0.41 0.68545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.783
## PE -0.124 0.076
## absPE 0.049 0.058 0.069
## Regret 0.733 -0.574 -0.661 0.110
## Relief -0.055 -0.222 -0.711 -0.325 0.242
## lwinProb 0.468 -0.079 0.036 0.236 0.089 -0.028
## affctvClsns 0.010 -0.015 -0.005 -0.025 -0.001 -0.011 -0.009## Linear mixed model fit by REML ['merModLmerTest']
## Formula: surprise ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 12696
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 1.32059 1.1492
## spinnerID (Intercept) 0.12555 0.3543
## expt (Intercept) 0.00479 0.0692
## Residual 2.88412 1.6983
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.10e+00 6.55e-01 4.40e+01 4.74 2.3e-05 ***
## win 4.20e-03 6.66e-03 4.30e+01 0.63 0.5317
## PE -1.96e-02 1.19e-02 5.10e+01 -1.64 0.1066
## absPE 3.10e-02 5.58e-03 4.90e+01 5.57 1.1e-06 ***
## Regret 3.29e-02 9.72e-03 3.80e+01 3.38 0.0017 **
## Relief 1.58e-02 7.88e-03 4.00e+01 2.01 0.0516 .
## lwinProb -1.66e+00 2.47e-01 5.10e+01 -6.75 1.3e-08 ***
## affectiveCloseness 3.94e-06 1.52e-05 2.63e+03 0.26 0.7954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.783
## PE -0.103 0.059
## absPE 0.013 0.091 0.082
## Regret 0.717 -0.570 -0.665 0.070
## Relief -0.055 -0.213 -0.722 -0.324 0.270
## lwinProb 0.471 -0.072 0.053 0.236 0.081 -0.054
## affctvClsns 0.013 -0.019 -0.007 -0.031 -0.001 -0.014 -0.011## Linear mixed model fit by REML ['merModLmerTest']
## Formula: disgust ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11145
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.9190 0.959
## spinnerID (Intercept) 0.1510 0.389
## expt (Intercept) 0.0228 0.151
## Residual 1.6759 1.295
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.67e+00 6.38e-01 5.80e+01 4.18 1.0e-04 ***
## win -1.48e-02 6.44e-03 5.60e+01 -2.30 0.025 *
## PE -1.95e-02 1.14e-02 5.90e+01 -1.71 0.093 .
## absPE 2.26e-02 5.35e-03 5.80e+01 4.22 8.7e-05 ***
## Regret 5.15e-03 9.53e-03 5.10e+01 0.54 0.592
## Relief -1.42e-03 7.69e-03 5.20e+01 -0.18 0.855
## lwinProb 1.57e-02 2.35e-01 6.00e+01 0.07 0.947
## affectiveCloseness -5.87e-07 1.16e-05 2.59e+03 -0.05 0.960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.776
## PE -0.119 0.072
## absPE 0.042 0.064 0.072
## Regret 0.724 -0.574 -0.662 0.102
## Relief -0.055 -0.220 -0.713 -0.325 0.247
## lwinProb 0.464 -0.077 0.039 0.235 0.087 -0.033
## affctvClsns 0.010 -0.016 -0.005 -0.027 -0.001 -0.011 -0.010## Linear mixed model fit by REML ['merModLmerTest']
## Formula: fear ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 8506
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.657657 0.8110
## spinnerID (Intercept) 0.002395 0.0489
## expt (Intercept) 0.000181 0.0135
## Residual 0.647069 0.8044
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.99e+00 1.84e-01 1.30e+01 10.79 9e-08 ***
## win -8.28e-03 1.83e-03 1.10e+01 -4.52 0.00097 ***
## PE -2.05e-03 3.68e-03 2.30e+01 -0.56 0.58287
## absPE 2.66e-03 1.66e-03 1.70e+01 1.60 0.12729
## Regret 3.12e-03 2.62e-03 1.10e+01 1.19 0.26006
## Relief -1.27e-04 2.18e-03 1.30e+01 -0.06 0.95433
## lwinProb 5.64e-02 7.39e-02 1.70e+01 0.76 0.45632
## affectiveCloseness 5.20e-06 7.23e-06 2.49e+03 0.72 0.47183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.778
## PE -0.047 0.016
## absPE -0.127 0.224 0.087
## Regret 0.649 -0.538 -0.696 -0.053
## Relief -0.040 -0.194 -0.770 -0.310 0.390
## lwinProb 0.495 -0.068 0.109 0.209 0.054 -0.121
## affctvClsns 0.017 -0.020 -0.013 -0.030 -0.003 -0.021 -0.007## Linear mixed model fit by REML ['merModLmerTest']
## Formula: content ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 12940
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 1.4340 1.198
## spinnerID (Intercept) 0.1085 0.329
## expt (Intercept) 0.0304 0.174
## Residual 3.1288 1.769
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.79e+00 6.45e-01 4.40e+01 5.88 5.1e-07 ***
## win 2.86e-02 6.49e-03 4.30e+01 4.40 7.0e-05 ***
## PE 1.13e-02 1.17e-02 5.40e+01 0.96 0.340
## absPE -1.45e-02 5.46e-03 5.00e+01 -2.65 0.011 *
## Regret 1.03e-02 9.44e-03 3.80e+01 1.09 0.281
## Relief 8.87e-03 7.66e-03 4.00e+01 1.16 0.254
## lwinProb -2.48e-01 2.42e-01 5.30e+01 -1.02 0.311
## affectiveCloseness -1.96e-05 1.58e-05 2.64e+03 -1.24 0.215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.775
## PE -0.097 0.054
## absPE 0.001 0.101 0.085
## Regret 0.704 -0.568 -0.666 0.058
## Relief -0.054 -0.211 -0.726 -0.323 0.280
## lwinProb 0.468 -0.070 0.058 0.234 0.078 -0.062
## affctvClsns 0.013 -0.020 -0.007 -0.032 -0.001 -0.015 -0.011## Linear mixed model fit by REML ['merModLmerTest']
## Formula: disapp ~ win + PE + absPE + Regret + Relief + lwinProb + affectiveCloseness + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 11952
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.692 0.832
## spinnerID (Intercept) 0.164 0.405
## expt (Intercept) 0.000 0.000
## Residual 2.387 1.545
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.49e+00 6.85e-01 5.10e+01 6.55 2.9e-08 ***
## win -2.67e-02 6.99e-03 5.10e+01 -3.81 0.00037 ***
## PE -2.98e-02 1.24e-02 5.60e+01 -2.40 0.01966 *
## absPE 2.33e-02 5.82e-03 5.40e+01 4.01 0.00019 ***
## Regret -1.30e-02 1.03e-02 4.60e+01 -1.26 0.21335
## Relief -6.50e-03 8.32e-03 4.70e+01 -0.78 0.43846
## lwinProb 1.42e-01 2.56e-01 5.70e+01 0.55 0.58123
## affectiveCloseness -2.13e-06 1.37e-05 2.73e+03 -0.16 0.87653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE Regret Relief lwnPrb
## win -0.784
## PE -0.114 0.067
## absPE 0.033 0.073 0.076
## Regret 0.727 -0.572 -0.662 0.091
## Relief -0.055 -0.217 -0.716 -0.325 0.255
## lwinProb 0.471 -0.076 0.044 0.238 0.085 -0.040
## affctvClsns 0.011 -0.017 -0.005 -0.028 -0.001 -0.013 -0.011## Linear mixed model fit by REML ['merModLmerTest']
## Formula: content ~ win + absPE + Regret + Relief + (1 | workerid) + (1 | spinnerID) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 12916
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 1.4348 1.198
## spinnerID (Intercept) 0.1101 0.332
## expt (Intercept) 0.0299 0.173
## Residual 3.1289 1.769
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.20232 0.56678 45.70000 7.41 2.3e-09 ***
## win 0.02752 0.00649 45.90000 4.24 0.00011 ***
## absPE -0.01375 0.00531 53.40000 -2.59 0.01246 *
## Regret 0.01754 0.00698 37.40000 2.51 0.01646 *
## Relief 0.01384 0.00529 35.40000 2.62 0.01291 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win absPE Regret
## win -0.843
## absPE -0.116 0.116
## Regret 0.907 -0.714 0.123
## Relief -0.191 -0.254 -0.387 -0.399## Data: meta_full
## Models:
## object: content ~ win + PE + absPE + (1 | workerid) + (1 | spinnerID) +
## object: (1 | expt)
## ..1: content ~ win + absPE + Regret + Relief + (1 | workerid) + (1 |
## ..1: spinnerID) + (1 | expt)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## object 8 12894 12943 -6439 12878
## ..1 9 12895 12950 -6439 12877 1 1 0.32## Linear mixed model fit by REML ['merModLmerTest']
## Formula: happy ~ win + PE + absPE + affectiveCloseness + (1 | spinnerID) + (1 | workerid) + (1 | expt)
## Data: meta_full
##
## REML criterion at convergence: 10690
##
## Random effects:
## Groups Name Variance Std.Dev.
## workerid (Intercept) 0.42062 0.649
## spinnerID (Intercept) 0.19680 0.444
## expt (Intercept) 0.00639 0.080
## Residual 1.58928 1.261
## Number of obs: 3048, groups: workerid, 690; spinnerID, 50; expt, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.23e+00 3.47e-01 5.60e+01 12.21 < 2e-16 ***
## win 4.05e-02 5.72e-03 5.10e+01 7.08 4.2e-09 ***
## PE 3.59e-02 6.04e-03 5.20e+01 5.95 2.3e-07 ***
## absPE -1.49e-02 5.26e-03 5.20e+01 -2.84 0.0065 **
## affectiveCloseness -3.32e-05 1.12e-05 2.74e+03 -2.96 0.0031 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) win PE absPE
## win -0.902
## PE 0.815 -0.886
## absPE -0.442 0.131 -0.176
## affctvClsns 0.022 -0.020 -0.021 -0.028## Data: meta_full
## Models:
## object: happy ~ win + PE + absPE + affectiveCloseness + (1 | spinnerID) +
## object: (1 | workerid) + (1 | expt)
## ..1: happy ~ win + PE + absPE + affectiveCloseness + abs(affectiveCloseness) +
## ..1: (1 | spinnerID) + (1 | workerid) + (1 | expt)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## object 9 10657 10712 -5320 10639
## ..1 10 10659 10719 -5320 10639 0.09 1 0.77## Computing profile confidence intervals ...## [1] -3.302## [1] 0.001797## [1] -6.465## [1] 4.467e-08## [1] 4.546## [1] 3.594e-05## [1] 7.063## [1] 5.304e-09## [1] -1.31## [1] 0.1963## [1] -2.61## [1] 0.01197## [1] -4.191## [1] 0.0001158## [1] -4.548## [1] 3.573e-05## [1] -4.181## [1] 0.0001193## [1] -1.538## [1] 0.1305## [1] 4.805## [1] 1.503e-05## [1] 2.045## [1] 0.04622## [1] 0.1646## [1] 0.8699## [1] 0.2942## [1] 0.7699