%0 Conference Paper %1 10178837 %A Jamaludeen, Noor %A Kuhn, Felix %A Brechmann, André %A Fuhrmann, Falko %A Remy, Stefan %A Spiliopoulou, Myra %B 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) %D 2023 %K Granger_Causality Learning_Motif Predictive_Pattern Reward-Based_Learning_Experiment %P 245-251 %R 10.1109/CBMS58004.2023.00225 %T Inferring Salient Motifs during Learning Experiments %U https://ieeexplore.ieee.org/document/10178837/ %X Learning is an essential skill that humans and animals need to interact successfully with stimuli in their surrounding environment. Discovering the learning motifs applied by humans and animals is a highly complex task. Indeed, identifying significant motifs in behavioural actions by analyzing how actions are affected by stimuli and prior actions enables us to understand how a learning motif is formed by a subject. Granger causality (GC) is a statistical tool used to check whether the past of a variable X is predictive of the future of another variable Y, in this case, we say that X Granger causes Y. Furthermore, subjects may also change the learning motif followed over time due to the learning process. In this study, we propose a method that (1) models a ‘learning motif’ as a set of Granger causality relationships involving past stimuli and past actions, (2) employs a hidden Markov model (HMM) to capture the change in the followed learning motifs and (3) identifies the salient learning motifs that are common to many subjects. The evaluation of our proposed method is not a trivial task due to the absence of the ground truth of the learning motifs. In general, it is difficult to acquire the ground truth of the learning motifs because for example, animals cannot articulate these learning motifs and this also applies to humans in complex learning tasks. Therefore, we also propose a solution that does not require ground truth to validate the derived learning motifs. We evaluate our proposed method on behavioural data collected from two groups of mice, a group of healthy mice and a group of mice with induced cognitive impairment. We show that our model is appropriate for identifying the learning motifs followed by these two groups of animals during a learning experiment in which animals are expected to maximize the reward gain.

@inproceedings{10178837, abstract = {Learning is an essential skill that humans and animals need to interact successfully with stimuli in their surrounding environment. Discovering the learning motifs applied by humans and animals is a highly complex task. Indeed, identifying significant motifs in behavioural actions by analyzing how actions are affected by stimuli and prior actions enables us to understand how a learning motif is formed by a subject. Granger causality (GC) is a statistical tool used to check whether the past of a variable X is predictive of the future of another variable Y, in this case, we say that X Granger causes Y. Furthermore, subjects may also change the learning motif followed over time due to the learning process. In this study, we propose a method that (1) models a ‘learning motif’ as a set of Granger causality relationships involving past stimuli and past actions, (2) employs a hidden Markov model (HMM) to capture the change in the followed learning motifs and (3) identifies the salient learning motifs that are common to many subjects. The evaluation of our proposed method is not a trivial task due to the absence of the ground truth of the learning motifs. In general, it is difficult to acquire the ground truth of the learning motifs because for example, animals cannot articulate these learning motifs and this also applies to humans in complex learning tasks. Therefore, we also propose a solution that does not require ground truth to validate the derived learning motifs. We evaluate our proposed method on behavioural data collected from two groups of mice, a group of healthy mice and a group of mice with induced cognitive impairment. We show that our model is appropriate for identifying the learning motifs followed by these two groups of animals during a learning experiment in which animals are expected to maximize the reward gain.}, added-at = {2023-08-24T09:56:34.000+0200}, author = {Jamaludeen, Noor and Kuhn, Felix and Brechmann, André and Fuhrmann, Falko and Remy, Stefan and Spiliopoulou, Myra}, biburl = {https://www.bibsonomy.org/bibtex/294bae8c36182a575acf652295c48f069/kmd-ovgu}, booktitle = {2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)}, doi = {10.1109/CBMS58004.2023.00225}, interhash = {d3e0e1fe0ccc0c4bfc474ddd2ca13eec}, intrahash = {94bae8c36182a575acf652295c48f069}, issn = {2372-9198}, keywords = {Granger_Causality Learning_Motif Predictive_Pattern Reward-Based_Learning_Experiment}, month = {June}, pages = {245-251}, timestamp = {2023-08-24T09:56:34.000+0200}, title = {Inferring Salient Motifs during Learning Experiments}, url = {https://ieeexplore.ieee.org/document/10178837/}, year = 2023 }