{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:49:28Z","timestamp":1760237368704,"version":"build-2065373602"},"reference-count":131,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T00:00:00Z","timestamp":1589155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In recent years, the \u201cplanning as inference\u201d paradigm has become central to the study of behaviour. The advance offered by this is the formalisation of motivation as a prior belief about \u201chow I am going to act\u201d. This paper provides an overview of the factors that contribute to this prior. These are rooted in optimal experimental design, information theory, and statistical decision making. We unpack how these factors imply a functional architecture for motivated behaviour. This raises an important question: how can we put this architecture to work in the service of understanding observed neurobiological structure? To answer this question, we draw from established techniques in experimental studies of behaviour. Typically, these examine the influence of perturbations of the nervous system\u2014which include pathological insults or optogenetic manipulations\u2014to see their influence on behaviour. Here, we argue that the message passing that emerges from inferring what to do can be similarly perturbed. If a given perturbation elicits the same behaviours as a focal brain lesion, this provides a functional interpretation of empirical findings and an anatomical grounding for theoretical results. We highlight examples of this approach that influence different sorts of goal-directed behaviour, active learning, and decision making. Finally, we summarise their implications for the neuroanatomy of inferring what to do (and what not to).<\/jats:p>","DOI":"10.3390\/e22050536","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T10:01:18Z","timestamp":1589191278000},"page":"536","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Inferring What to Do (And What Not to)"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5108-5743","authenticated-orcid":false,"given":"Thomas","family":"Parr","sequence":"first","affiliation":[{"name":"Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,11]]},"reference":[{"key":"ref_1","unstructured":"Attias, H. (2003, January 3\u20136). Planning by Probabilistic Inference. 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