The temporal representation of experience in subjective mood
- Section of Clinical and Computational Psychiatry, National Institute of Mental Health, National Institutes of Health, United States
- Machine Learning Team, National Institute of Mental Health, National Institutes of Health, United States
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, United States
- Department of Psychology, Yale University, United States
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
Figures
Figure 1 with 1 supplement
The Primacy versus Recency mood models.
(A) Participants played a probabilistic task where they experienced different reward prediction error values, while reporting subjective mood every 2-3 gambling trials. In each trial, participants …
Figure 1—figure supplement 1
The Primacy effect of outcomes on mood.
In the Primacy model, the expectation term is the unweighted average of previous outcomes. At each trial, all previous expectation terms are combined in an exponentially weighted sum: . Here, …
Figure 2
Different experimental reward environments.
(A) Reward prediction error (RPE) values received during each task version, averaged across all participants (shaded areas represent SEM). (B) The influence of RPE values on mood reports along the …
Figure 3 with 3 supplements
The better performance of the Primacy model.
(A) Model comparison between the Primacy and the Recency models, using the streaming prediction criterion, where the model is predicting each mood rating using the preceding ratings. On the left, …
Figure 3—figure supplement 1
Distributions of the estimated coefficients for the parameters of the Primacy and the different Recency models.
Figure 3—figure supplement 2
Expanding the Primacy model.
(A) Equations S1 and S2 show the two additional parameters that were included in the Primacy model to test other scaling curves for most influential events on mood. (B) Examples of theoretical …
Figure 3—figure supplement 3
Mood ratings and the respective trial-wise model parameters.
The left two columns show the task mood ratings and outcomes (A) above the expectation and reward prediction error (RPE) parameters of the Primacy (B) and the Recency (C) models (of a single …
Figure 4 with 2 supplements
Neural correlates of the Primacy model.
(A) Extracting individual whole-brain BOLD signal activation maps () during the time interval preceding each mood rating, and individual model parameters by fitting mood ratings with the Primacy …
Figure 4—figure supplement 1
Uncorrected raw data neural correlates of the Primacy model and two Recency models, the original one and the one with the most similar characteristics to the Primacy model (with both dynamic win probability and elimination of the Certain term).
None of the Recency models’ clusters survived correction. Images show correlation across participants between the individual weights of the model expectation term, βE, and the individual whole-brain …
Figure 4—figure supplement 2
Mood encoding at the whole-brain level in the structured-adaptive task: mood encoding values are derived using the mood ratings as the parametric linear modulator of the BOLD signals during the pre-rating interval (at this interval, which lasts between 2.5 and 4 s, participants are presented with the mood question, but cannot rate their mood yet).
Cluster peaks in the nucleus accumbens (NACC) and covers the ACC and Caudate (337 voxels, t = 4.96).
Tables
Table 1
Performance of the Primacy model versus alternative Recency models in three different reward environments (in adults) and in a lab-based sample comprising adolescent participants, of which 40% were diagnosed as clinically depressed (using the structured-adaptive task).
Statistical comparison is of the streaming prediction errors. (MSE: mean squared error; IQR: interquartile range).
| Model | MSE median | IQR | z-Value | p-Value | ||
|---|---|---|---|---|---|---|
| Reward environment | Random task | Primacy | 0.0165 | 0.0099 | - | - |
| Recency | 0.0171 | 0.0091 | 1.8480 | 0.0323 | ||
| Recency with dynamic win probability | 0.0170 | 0.0078 | 2.7440 | 0.0030 | ||
| Recency without a Certain term | 0.0176 | 0.0099 | 1.9973 | 0.0229 | ||
| Recency with outcome as expectation | 0.0187 | 0.0114 | 3.0053 | 0.0013 | ||
| Recency with both dynamic win and no Certain | 0.0171 | 0.0079 | 2.7440 | 0.0030 | ||
| Structured task | Primacy | 0.0088 | 0.0036 | - | - | |
| Recency | 0.0097 | 0.0069 | 1.6613 | 0.0483 | ||
| Recency with dynamic win probability | 0.0090 | 0.0043 | 1.8853 | 0.0297 | ||
| Recency without a Certain term | 0.0109 | 0.0091 | 3.0053 | 0.0013 | ||
| Recency with outcome as expectation | 0.0141 | 0.0044 | 3.8266 | 0.0001 | ||
| Recency with both dynamic win and no Certain | 0.0090 | 0.0044 | 1.8853 | 0.0290 | ||
| Structured adaptive | Primacy | 0.0137 | 0.0041 | - | - | |
| Recency | 0.0160 | 0.0040 | 3.4533 | 0.0003 | ||
| Recency with dynamic win probability | 0.0171 | 0.0040 | 3.7146 | 0.0001 | ||
| Recency without a Certain term | 0.0189 | 0.0060 | 3.5279 | 0.0002 | ||
| Recency with outcome as expectation | 0.0179 | 0.0063 | 3.6773 | 0.0001 | ||
| Recency with both dynamic win and no Certain | 0.0172 | 0.0040 | 3.6770 | 0.0001 | ||
| Age | Adolescents lab-based | Primacy | 0.0066 | 0.0021 | - | - |
| Recency | 0.0077 | 0.0028 | 3.4533 | 0.0003 | ||
| Recency with dynamic win probability | 0.0079 | 0.0026 | 2.8559 | 0.0021 | ||
| Recency without a Certain term | 0.0094 | 0.0029 | 3.9013 | 0.0000 | ||
| Recency with outcome as expectation | 0.0093 | 0.0038 | 3.6773 | 0.0001 | ||
| Recency with both dynamic win and no Certain | 0.0079 | 0.0027 | 2.9306 | 0.0017 | ||
| Diagnosis | Depressed adolescents | Primacy | 0.0043 | 0.0069 | - | - |
| Recency | 0.0072 | 0.0053 | 3.2666 | 0.0005 | ||
| Recency with dynamic win probability | 0.0075 | 0.0042 | 3.3039 | 0.0004 | ||
| Recency without a Certain term | 0.0074 | 0.0042 | 3.3786 | 0.0003 | ||
| Recency with outcome as expectation | 0.0089 | 0.0043 | 3.9013 | 0.0000 | ||
| Recency with both dynamic win and no Certain | 0.0086 | 0.0069 | 3.4159 | 0.0003 |
Table 2
Participants’ demographics for all datasets.
| Random online MTurk sample | Age | ||
|---|---|---|---|
| (n = 67) | Average | 39.81 | |
| SD | 13 | ||
| Sex | |||
| Male | 37 | ||
| Female | 32 | ||
| Structured online MTurk sample | Age | ||
| (n = 89) | Average | 37.55 | |
| SD | 10.46 | ||
| Sex | |||
| Male | 48 | ||
| Female | 41 | ||
| Structured-adaptive online MTurk sample | Age | ||
| (n = 80) | Average | 37.76 | |
| SD | 11.23 | ||
| Sex | |||
| Male | 46 | ||
| Female | 34 | ||
| Structured-adaptive lab-based sample | Age | ||
| (n = 72) | Average | 15.49 | |
| SD | 1.48 | ||
| Sex | |||
| Male | 17 | ||
| Female | 55 | ||
| MFQ score | |||
| Average | 5.81 | ||
| SD | 5.98 | ||
| Diagnosis | |||
| Healthy volunteer | 29 | ||
| MDD | 43 |
Additional files
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Supplementary file 1
Model parameters recovery analysis: results of fitting the Primacy and Recency models on simulated datasets as well as a statistical comparison between the two models in both the training errors and the streaming prediction errors.
Note that only streaming prediction errors were used for model selection, and we show the training errors for illustrative purposes.
- https://cdn.elifesciences.org/articles/62051/elife-62051-supp1-v3.docx
-
Supplementary file 2
The formulation of alternative variants of the Recency model.
- https://cdn.elifesciences.org/articles/62051/elife-62051-supp2-v3.docx
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Transparent reporting form
- https://cdn.elifesciences.org/articles/62051/elife-62051-transrepform-v3.pdf
