{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T10:28:53Z","timestamp":1773829733641,"version":"3.50.1"},"reference-count":98,"publisher":"MIT Press - Journals","issue":"5","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.<\/jats:p>","DOI":"10.1162\/neco_a_01383","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:36:03Z","timestamp":1618446963000},"page":"1402-1432","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":41,"title":["Predictive Processing in Cognitive Robotics: A Review"],"prefix":"10.1162","volume":"33","author":[{"given":"Alejandra","family":"Ciria","sequence":"first","affiliation":[{"name":"Facultad de Psicolog\u00eda, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Mexico City, CP 04510, Mexico ciriacontacto@gmail.com"}]},{"given":"Guido","family":"Schillaci","sequence":"additional","affiliation":[{"name":"BioRobotics Institute, Scuola Superiore Sant'Anna, 34 56025 Pontedera, Italy guido.schillaci@santannapisa.it"}]},{"given":"Giovanni","family":"Pezzulo","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council, 44 00185 Rome, Italy giovanni.pezzulo@istc.cnr.it"}]},{"given":"Verena V.","family":"Hafner","sequence":"additional","affiliation":[{"name":"Adaptive Systems Group, Department of Computer Science, Humboldt-Universit\u00e4t zu Berlin, D-12489, Germany hafner@informatik.hu-berlin.de"}]},{"given":"Bruno","family":"Lara","sequence":"additional","affiliation":[{"name":"Laboratorio de Rob\u00f3tica Cognitiva, Centro de Investigaci\u00f3n en Ciencias, Universidad Aut\u00f3noma del Estado de Morelos, Cuernavaca CP 62209, Mexico bruno.lara@uaem.mx"}]}],"member":"281","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"issue":"3","key":"2021041321534993500_B1","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s00429-012-0475-5","article-title":"Predictions not commands: Active inference in the motor system","volume":"218","author":"Adams","year":"2013","journal-title":"Brain Structure and Function"},{"key":"2021041321534993500_B2","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyt.2013.00047","article-title":"The computational anatomy of psychosis","volume":"4","author":"Adams","year":"2013","journal-title":"Frontiers in Psychiatry"},{"key":"2021041321534993500_B3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neunet.2017.02.015","article-title":"How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? 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