Modelling sensory attenuation as Bayesian causal inference across two datasets.
<h4>Introduction</h4>To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317924 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832540204982861824 |
|---|---|
| author | Anna-Lena Eckert Elena Fuehrer Christina Schmitter Benjamin Straube Katja Fiehler Dominik Endres |
| author_facet | Anna-Lena Eckert Elena Fuehrer Christina Schmitter Benjamin Straube Katja Fiehler Dominik Endres |
| author_sort | Anna-Lena Eckert |
| collection | DOAJ |
| description | <h4>Introduction</h4>To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.<h4>Methods</h4>Experiment 1investigates sensory attenuation during a stroking movement. Tactile stimuli on the stroking finger were suppressed, especially when they were predictable. Experiment 2 showed impaired delay detection between an arm movement and a video of the movement when participants were moving vs. when their arm was moved passively. We reconsider these results from the perspective of Bayesian Causal Inference (BCI). Using a hierarchical Markov Model (HMM) and variational message passing, we first qualitatively capture patterns of task behavior and sensory attenuation in simulations. Next, we identify participant-specific model parameters for both experiments using optimization.<h4>Results</h4>A sequential BCI model is well equipped to capture empirical patterns of SA across both datasets. Using participant-specific optimized model parameters, we find a good agreement between data and model predictions, with the model capturing both tactile detections in Experiment 1 and delay detections in Experiment 2.<h4>Discussion</h4>BCI is an appropriate framework to model sensory attenuation in humans. Computational models of sensory attenuation may help to bridge the gap across different sensory modalities and experimental paradigms and may contribute towards an improved description and understanding of deficits in specific patient groups (e.g. schizophrenia). |
| format | Article |
| id | doaj-art-b8f5e4a159e2413eb4d66547ab3a4fcb |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b8f5e4a159e2413eb4d66547ab3a4fcb2025-02-05T05:32:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031792410.1371/journal.pone.0317924Modelling sensory attenuation as Bayesian causal inference across two datasets.Anna-Lena EckertElena FuehrerChristina SchmitterBenjamin StraubeKatja FiehlerDominik Endres<h4>Introduction</h4>To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.<h4>Methods</h4>Experiment 1investigates sensory attenuation during a stroking movement. Tactile stimuli on the stroking finger were suppressed, especially when they were predictable. Experiment 2 showed impaired delay detection between an arm movement and a video of the movement when participants were moving vs. when their arm was moved passively. We reconsider these results from the perspective of Bayesian Causal Inference (BCI). Using a hierarchical Markov Model (HMM) and variational message passing, we first qualitatively capture patterns of task behavior and sensory attenuation in simulations. Next, we identify participant-specific model parameters for both experiments using optimization.<h4>Results</h4>A sequential BCI model is well equipped to capture empirical patterns of SA across both datasets. Using participant-specific optimized model parameters, we find a good agreement between data and model predictions, with the model capturing both tactile detections in Experiment 1 and delay detections in Experiment 2.<h4>Discussion</h4>BCI is an appropriate framework to model sensory attenuation in humans. Computational models of sensory attenuation may help to bridge the gap across different sensory modalities and experimental paradigms and may contribute towards an improved description and understanding of deficits in specific patient groups (e.g. schizophrenia).https://doi.org/10.1371/journal.pone.0317924 |
| spellingShingle | Anna-Lena Eckert Elena Fuehrer Christina Schmitter Benjamin Straube Katja Fiehler Dominik Endres Modelling sensory attenuation as Bayesian causal inference across two datasets. PLoS ONE |
| title | Modelling sensory attenuation as Bayesian causal inference across two datasets. |
| title_full | Modelling sensory attenuation as Bayesian causal inference across two datasets. |
| title_fullStr | Modelling sensory attenuation as Bayesian causal inference across two datasets. |
| title_full_unstemmed | Modelling sensory attenuation as Bayesian causal inference across two datasets. |
| title_short | Modelling sensory attenuation as Bayesian causal inference across two datasets. |
| title_sort | modelling sensory attenuation as bayesian causal inference across two datasets |
| url | https://doi.org/10.1371/journal.pone.0317924 |
| work_keys_str_mv | AT annalenaeckert modellingsensoryattenuationasbayesiancausalinferenceacrosstwodatasets AT elenafuehrer modellingsensoryattenuationasbayesiancausalinferenceacrosstwodatasets AT christinaschmitter modellingsensoryattenuationasbayesiancausalinferenceacrosstwodatasets AT benjaminstraube modellingsensoryattenuationasbayesiancausalinferenceacrosstwodatasets AT katjafiehler modellingsensoryattenuationasbayesiancausalinferenceacrosstwodatasets AT dominikendres modellingsensoryattenuationasbayesiancausalinferenceacrosstwodatasets |