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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.06344 |
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| _version_ | 1866915538262818816 |
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| author | Cabral-Passos, P. R. Azevedo, P. S. Moraes, V. H. Ramalho, B. L. Duarte, A. Vargas, C. D. |
| author_facet | Cabral-Passos, P. R. Azevedo, P. S. Moraes, V. H. Ramalho, B. L. Duarte, A. Vargas, C. D. |
| contents | This work draws on the conjecture that fingerprints of stochastic event sequences can be retrieved from electroencephalographic data (EEG) recorded during a behavioral task. To test this, we used the Goalkeeper Game (game.numec.prp.usp.br). Acting as a goalkeeper, the participant predicted each kick in a probabilistic sequence while EEG activity was recorded. At each trial, driven by a context tree, the kicker chose one of three options: left, center, or right. The goalkeeper then predicted the next kick by pressing a button. Tree estimation was performed by applying the Context Algorithm to EEG segments locked to the button press (-300 to 0 ms). We calculated the distance between the penalty taker's tree and the trees retrieved per participant and electrode. This metric was then correlated with the goalkeeper's success rates. We observed a clear reduction in the overall distance distribution over time for a subset of electrodes, indicating that EEG dependencies become more congruent with the penalty taker's tree as the goalkeeper learns the sequence. This distance is inversely proportional to the goalkeepers' success rates, indicating a clear relationship between performance and the neural signatures associated with the sequence structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Retrieving the structure of probabilistic sequences from EEG data during the goalkeeper game Cabral-Passos, P. R. Azevedo, P. S. Moraes, V. H. Ramalho, B. L. Duarte, A. Vargas, C. D. Neurons and Cognition Probability This work draws on the conjecture that fingerprints of stochastic event sequences can be retrieved from electroencephalographic data (EEG) recorded during a behavioral task. To test this, we used the Goalkeeper Game (game.numec.prp.usp.br). Acting as a goalkeeper, the participant predicted each kick in a probabilistic sequence while EEG activity was recorded. At each trial, driven by a context tree, the kicker chose one of three options: left, center, or right. The goalkeeper then predicted the next kick by pressing a button. Tree estimation was performed by applying the Context Algorithm to EEG segments locked to the button press (-300 to 0 ms). We calculated the distance between the penalty taker's tree and the trees retrieved per participant and electrode. This metric was then correlated with the goalkeeper's success rates. We observed a clear reduction in the overall distance distribution over time for a subset of electrodes, indicating that EEG dependencies become more congruent with the penalty taker's tree as the goalkeeper learns the sequence. This distance is inversely proportional to the goalkeepers' success rates, indicating a clear relationship between performance and the neural signatures associated with the sequence structure. |
| title | Retrieving the structure of probabilistic sequences from EEG data during the goalkeeper game |
| topic | Neurons and Cognition Probability |
| url | https://arxiv.org/abs/2510.06344 |