Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-639Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Harkness, Dominic L. | - |
| dc.contributor.author | Keshava, Ashima | - |
| dc.date.accessioned | 2017-06-01T10:37:03Z | - |
| dc.date.available | 2017-06-01T12:37:03Z | - |
| dc.date.issued | 2017 | - |
| dc.identifier.uri | https://openscience.ub.uni-mainz.de/handle/20.500.12030/641 | - |
| dc.description.abstract | The general question of our paper concerns the relationship between Bayesian models of cognition and predictive processing, and whether predictive processing can provide explanatory insight over and above Bayesian models. Bayesian models have been gaining influence in neuroscience and the cognitive sciences since they are able to predict human behavior with high accuracy. Models based on a Bayesian optimal observer are fitted on behavioral data. A good fit is hence interpreted as human subjects “behaving” in a Bayes’ optimal fashion. However, these models are performance-oriented and do not specify which processes could give rise to the observed behavior. Here, David Marr’s (Marr 1982) levels of analysis can help understand the relationship between performance- and process-oriented models or explanations. Bayesian models are situated at the computational level since they specify what the system (in this case the brain) does and why it does it in this manner. Although Bayesian models can constrain the search space for hypotheses at the algorithmic level, they do not provide a precise solution about how a system realizes the observed behavior. Here predictive processing can shed more light on the underlying principles. Predictive processing provides a unifying functional theory of cognition and can thus i) provide an answer at the algorithmic level by answering how the brain realizes cognition, ii) can aid in the interpretation of neurophysiological findings at the implementational level. | en_GB |
| dc.language.iso | eng | - |
| dc.rights | CC BY-ND | de_DE |
| dc.rights.uri | https://creativecommons.org/licenses/by-nd/4.0/ | - |
| dc.subject.ddc | 100 Philosophie | de_DE |
| dc.subject.ddc | 100 Philosophy | en_GB |
| dc.title | Moving from the what to the how and where : Bayesian models and predictive processing | en_GB |
| dc.type | Buchbeitrag | de_DE |
| dc.identifier.urn | urn:nbn:de:hebis:77-publ-566583 | - |
| dc.identifier.doi | http://doi.org/10.25358/openscience-639 | - |
| jgu.type.dinitype | bookPart | - |
| jgu.type.version | Published version | en_GB |
| jgu.type.resource | Text | - |
| jgu.organisation.department | FB 05 Philosophie und Philologie | - |
| jgu.organisation.number | 7920 | - |
| jgu.organisation.name | Johannes Gutenberg-Universität Mainz | - |
| jgu.rights.accessrights | openAccess | - |
| jgu.book.title | Philosophy and predictive processing | - |
| jgu.book.editor | Metzinger, Thomas | - |
| jgu.pages.start | 254 | - |
| jgu.pages.end | 263 | - |
| jgu.publisher.year | 2017 | - |
| jgu.publisher.name | MIND Group | - |
| jgu.publisher.place | Frankfurt am Main | - |
| jgu.publisher.uri | http://dx.doi.org/10.15502/9783958573178 | - |
| jgu.organisation.place | Mainz | - |
| jgu.subject.ddccode | 100 | - |
| opus.date.accessioned | 2017-06-01T10:37:03Z | - |
| opus.date.modified | 2017-06-02T08:27:01Z | - |
| opus.date.available | 2017-06-01T12:37:03 | - |
| opus.subject.dfgcode | 00-000 | - |
| opus.organisation.string | FB 05: Philosophie und Philologie: Philosophisches Seminar | de_DE |
| opus.identifier.opusid | 56658 | - |
| opus.relation.ispartofcollection | Philosophy and predictive processing | de_DE |
| opus.institute.number | 0508 | - |
| opus.metadataonly | false | - |
| opus.type.contenttype | Keine | de_DE |
| opus.type.contenttype | None | en_GB |
| jgu.publisher.doi | 10.15502/9783958573178 | |
| jgu.organisation.ror | https://ror.org/023b0x485 | |
| Appears in collections: | JGU-Publikationen | |
