Show simple item record

dc.contributor.supervisorBaldassarre, Gianluca
dc.contributor.authorGranato, Giovanni
dc.contributor.otherSchool of Engineering, Computing, and Mathematicsen_US
dc.date.accessioned2022-10-05T14:42:04Z
dc.date.issued2022
dc.identifier10664997en_US
dc.identifier.urihttp://hdl.handle.net/10026.1/19674
dc.descriptionThe theoretical and computational works presented in this thesis have been published at international peer-reviewed conferences and journals. The candidate is the first author of all publications listed below. Indeed, he has personally carried out all the key steps that have led to these publications such as literature reviews, computational models development, data analysis and interpretations, drawing of figures/plots/schemes, manuscript writing and revision until final publication. Supervisors and other co-authors have supported these projects, providing suggestions at every stage. Chapter 2 and 3 are adapted from m Granato & Baldassarre (2021), Granato et al. (2020), Granato et al. (2022b), and Baldassarre & Granato (2020). In particular, chapter 2 is adapted from the theoretical sections of the published papers (e.g., literature reviews and theoretical proposals) while chapter 3 is adapted from the computational sections of the published papers (e.g. experimental task/conditions, models descriptions, results, discussions). Chapter 4 is adapted from m Granato et al. (2022a) and Baldassarre & Granato (2022). In particular, section 4.1 is adapted from the first and section 4.2 is adapted from the second paper. Finally, section 4.2.3 (`Implications of the RIM framework') focuses on similar topics that I have approached in Baldassarre & Granato (2020), i.e. `representations manipulation in AI'.en_US
dc.description.abstract

The brain is able to manipulate itself, adapting its internal world in a changing environment. In particular it continuously manipulates its representations to achieve goals. This competence is supported by many neurocognitive high-order processes that interact during the performance of a goal-directed behaviour (e.g. attention processes, executive functions, motivational systems). Overall, adult humans often face a new problem trough a change of the `perceptual point of view' (i.e. a representational change), rather then an extended research of the correct action to perform. On the other hand, infants and children contemporary develop motor competence and task-directed perceptual representations (e.g. categorical perception). Here I approach the main research question `how the brain manipulates its representations to solve a task that requires cognitive flexibility?'. Moreover, I started to approach the second related research question `how the brain acquires suitable representations to solve a categorisation task?'. First, adopting a synergistic theoretical and computational approach, I identified the systems and basic computational principles that allow the brain to learn, to generate and to manipulate its internal representations in order to achieve a goal. Second, I built a set of computational models and I tested them against experimental human data extracted from already published experimental works. This translational approach corroborates my theoretical proposals and, vice versa, provides scientific and clinical knowledge on the investigated processes. In particular, my models represent a novel computational tool for the investigation of flexible cognition and categorical perception in case of clinical populations (e.g. autistic people). Moreover, they represent a starting point to propose a new theory of conscious cognition showing both scientific implications (e.g new models of consciousness) and technological implications (e.g consciousness-inspired robots).

en_US
dc.language.isoen
dc.publisherUniversity of Plymouth
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectCognitive flexibilityen_US
dc.subjectExecutive functionsen_US
dc.subjectConsciousnessen_US
dc.subjectComputational neuropsychology and psychiatryen_US
dc.subjectMachine Learningen_US
dc.subject.classificationPhDen_US
dc.titleFlexible goal-directed manipulation of representations: computational models of healthy and pathological human cognitionen_US
dc.typeThesis
plymouth.versionpublishableen_US
dc.identifier.doihttp://dx.doi.org/10.24382/661
dc.identifier.doihttp://dx.doi.org/10.24382/661
dc.rights.embargodate2023-10-05T14:42:04Z
dc.rights.embargoperiod12 monthsen_US
dc.type.qualificationDoctorateen_US
rioxxterms.versionNA


Files in this item

Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
Atmire NV