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Application of t-RNN to behavioral data of psychiatric and non-psychiatric individuals [9].
| العنوان: | Application of t-RNN to behavioral data of psychiatric and non-psychiatric individuals [9]. |
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| المؤلفون: | Yoav Ger, Eliya Nachmani, Lior Wolf, Nitzan Shahar |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Science Policy, Infectious Diseases, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, found better performance, descriptive modeling paradigm, recurrent neural network, likelihood rl method, known rl parameters, humans varied dynamically, rl agents performing, two independent datasets, study human behavior, rnn ), whereby, armed bandit task, neural network models, theoretical rl models, humans performing, neural networks, term theoretical, normative models, used extensively, trial behavior, three studies, task phase, stress interpretability, stationary maximum, second dataset |
| الوصف: | (A) Action prediction (measured with BCE; black lines indicate s.e.m) divided by diagnostic labels. Bipolar and depressed subjects at the group level are explained significantly better by t-RNN (blue) compared to the QP-stationary model (green) that assumes that the RL parameters are fixed, as well as to the Bayesian model (yellow). (B) Boxplot of the time-varying κ preservation parameter estimates by t-RNN across the three diagnostic groups (middle black solid lines denote the median; light dots indicate a single trial estimate of the κ preservation parameter). Result suggests higher volatility in the clinical groups mainly the bipolar group, compared with the healthy group. (C) Distribution of the Pearson correlation between stay probability (calculated using moving average of 10 trials) and time-varying κ preservation parameter produced by t-RNN for each subject individually. Result shows a strong relation between t-RNN time-varying RL κ parameters estimation and moving average of the stay probability. (D) Sequence of selected actions of two example subjects, with bipolar on the left and depressed on the right. The top panel shows the action prediction, and the bottom panel shows the RL κ parameter estimation produced by t-RNN (blue) and QP-stationary model (green) for a 50-trial segment (see S4 Fig for all trials). Both subjects seemed to switch between selecting the same action for several trials to repeatedly alternating between both actions every trial (red dots). We found that the t-RNN model was able to detect this visually apparent shift in behavior, estimating a change in the κ action perseveration parameter. In contrast, the QP-stationary model was not able to capture this transition. (E) Trial-by-trial κ preservation parameter estimates by t-RNN averaged over the first 100 trials of each block and for each diagnostic group separately (shaded area signifies the s.e.m). In all three groups, subjects show a steady increase in their tendency to perseverate their actions as the block progresses. ... |
| نوع الوثيقة: | still image |
| اللغة: | unknown |
| Relation: | https://figshare.com/articles/figure/Application_of_t-RNN_to_behavioral_data_of_psychiatric_and_non-psychiatric_individuals_9_/24944466 |
| DOI: | 10.1371/journal.pcbi.1011678.g003 |
| الاتاحة: | https://doi.org/10.1371/journal.pcbi.1011678.g003 https://figshare.com/articles/figure/Application_of_t-RNN_to_behavioral_data_of_psychiatric_and_non-psychiatric_individuals_9_/24944466 |
| Rights: | CC BY 4.0 |
| رقم الانضمام: | edsbas.99574FC4 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1371/journal.pcbi.1011678.g003 |
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