<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-24T23:03:25Z</responseDate><request verb="GetRecord" identifier="oai:www.repository.cam.ac.uk:1810/395999" metadataPrefix="uketd_dc">https://api.repository.cam.ac.uk/server/oai/request</request><GetRecord><record><header><identifier>oai:www.repository.cam.ac.uk:1810/395999</identifier><datestamp>2026-01-28T01:42:30Z</datestamp><setSpec>com_1810_245310</setSpec><setSpec>com_1810_256062</setSpec><setSpec>col_1810_245312</setSpec></header><metadata><uketd_dc:uketddc xmlns:uketd_dc="http://naca.central.cranfield.ac.uk/ethos-oai/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:uketdterms="http://naca.central.cranfield.ac.uk/ethos-oai/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://naca.central.cranfield.ac.uk/ethos-oai/2.0/ http://naca.central.cranfield.ac.uk/ethos-oai/2.0/uketd_dc.xsd">
   <dc:title>The Interaction Between Learning and Effort-Based Decision-Making: Mechanisms, Structural Brain Correlates, and Links to Neuropsychiatric Symptoms</dc:title>
   <dc:identifier xsi:type="dcterms:DOI">https://doi.org/10.17863/CAM.125317</dc:identifier>
   <dc:creator>Guinea, Calum</dc:creator>
   <uketdterms:authoridentifier xsi:type="uketdterms:ORCID">0009000694449838</uketdterms:authoridentifier>
   <uketdterms:advisor>Lawson, Rebecca P</uketdterms:advisor>
   <dcterms:abstract>We must learn about the inherent uncertainty in our environment to behave
adaptively, and a key aspect of adaptive behaviour includes how we decide to allocate
effort when making decisions. How we learn about our environment is therefore
necessarily connected to how we make choices about effort. However, the literature
on effort choice has largely neglected this connection. Studies have shown that across
neuropsychiatric disorders, there are impairments in both learning and effort-based
decision-making, though no studies to date have systematically examined whether
the translation of learned expectations into effort might account for mood and anxiety
disorder symptoms such as apathy and anhedonia. One brain region strongly
implicated in both learning and motivated action is the habenula, which sits at the
crossroads of the serotonergic learning circuits and dopaminergic control of
behaviour. Habenula structural integrity has been implicated in both mood disorders
and neurological conditions with co-occurring mood symptoms such as Parkinson’s
disease (PD), but the relationship between habenula volume, learning and effortchoice
is largely unknown.
This thesis will investigate the association between expressions of learning and
habenula volume, then present a novel experiment optimised to quantify the
interaction between learning and effort decision-making. To investigate the
relevance of neuropsychiatric symptoms to these behaviours, we collect data from the
general population and a clinical sample before returning to the habenula volume and
its relationship to learning and motivated behaviour.
v
Chapter 1 introduces the ideas that motivate the thesis, including transdiagnostic,
computational/precision psychiatry approaches. Followed by a description of the
relevant literature on learning, effort-based decision making and their neural basis.
Then, the chapter will outline the transdiagnostic, neuropsychiatric relevance of
these behaviours.
In Chapter 2, the first empirical chapter, I re-analyse data from a reinforcement
learning task and structural brain images from Parkinson’s Disease (PD) and matched
controls to investigate the relationship between habenula volume and avoidance
behaviour. The results demonstrate that PD patients have larger habenulas than
controls. Those larger habenula volumes are related to greater avoidance behaviour,
control analyses show this effect is specific to avoidance behaviour, not only present
in the control group and unaffected by PD medication status.
Chapter 3 describes the task development of the paradigm central to this thesis, which
aims to characterise the interaction between learning and effort decision-making.
Participants first learn the probabilistic associations between eight stimuli, with four
associated with reward and four with loss and report their estimate of the reward
probability on every trial. Then they complete a tournament task where all eight
stimuli are paired together repeatedly to enable assessment of their learning. Finally,
participants make accept/reject choices for a given learned stimulus and varying
levels of effort. Participants can learn accurately about the outcome probability and
magnitude of the stimuli then guide their effort decision-making using these
variables in conjunction with effort level.
Chapter 4 presents the data from the final version of the learning and effort choice
task, collected in a large online sample (n=252). The larger dataset is leveraged to
address questions about whether individual measures of learning better predict
participants’ effort choice than the objective stimulus features and questions about
the effect of transdiagnostic dimensions, anhedonia and fatigue on these behaviours.
Individualised measures of learning outperform the objective outcome probabilities
in the prediction of effort choice and anhedonia is associated with reduced effort
vi
exertion and an alteration in the integration of subjective beliefs about probability
and effort choice.
Chapter 5 aims to further understand the effects of neuropsychiatric symptoms on
learning, effort decision-making and their interaction by recruiting a population of
anxious/depressed participants and healthy controls to complete the task.
Participants’ mental health symptoms are richly characterised then factor analysed.
Individualised learning measures better predict participants’ effort choices than the
objective outcome probabilities. Despite clear differences in symptoms between the
clinical group and controls, they exhibit no clear differences in their learning and
effort choice behaviour.
In Chapter 6, we conducted an in-person replication of the learning and effort
decision-making task to validate the findings from the earlier online studies
(Chapters 3, 4 and 5). The behavioural effects observed online were replicated,
including the main result of Chapter 4, as individual learning measures outperformed
the objective outcome probabilities when predicting participants’ effort choices. In
addition to the behavioural replication, we acquired 7T structural scans for each
participant, to precisely delineate the habenula, offering a substantial improvement
in spatial resolution compared to the 3T scans used in Chapter 2. These preliminary
results allowed us to begin characterising how inter-individual variation in habenula
structure relates to learning, effort decisions and their interaction.
Finally, Chapter 7 situates these results in the wider literature and evaluates their
contribution to the understanding of learning, effort decision-making, their
interaction and neuropsychiatric relevance. Then, by reflecting critically on the
empirical chapters, I posit some questions that future research might take up to
advance the field.</dcterms:abstract>
   <uketdterms:institution>University of Cambridge</uketdterms:institution>
   <dcterms:issued>2025-09-29</dcterms:issued>
   <dc:type>Thesis</dc:type>
   <uketdterms:qualificationlevel>Doctoral</uketdterms:qualificationlevel>
   <uketdterms:qualificationname>Doctor of Philosophy (PhD)</uketdterms:qualificationname>
   <dc:language>eng</dc:language>
   <uketdterms:sponsor>Harding Distinguished Postgraduate Scholarship</uketdterms:sponsor>
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   <uketdterms:embargotype>embargo</uketdterms:embargotype>
   <uketdterms:embargodate>2027-01-27</uketdterms:embargodate>
   <dc:identifier xsi:type="dcterms:URI">https://www.repository.cam.ac.uk/bitstreams/25989a7c-a69f-48ef-b757-6535ed9ca4e5/download</dc:identifier>
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   <dcterms:license>https://www.repository.cam.ac.uk/bitstreams/1ea256c1-6282-4087-bbaf-c50af4a3a9da/download</dcterms:license>
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   <dc:rights>http://purl.org/NET/rdflicense/allrightsreserved</dc:rights>
   <dc:subject>Anhedonia</dc:subject>
   <dc:subject>Anxiety</dc:subject>
   <dc:subject>Apathy</dc:subject>
   <dc:subject>Cognitive Neuroscience</dc:subject>
   <dc:subject>Computational Psychiatry</dc:subject>
   <dc:subject>Depression</dc:subject>
   <dc:subject>Effort-based decision-making</dc:subject>
   <dc:subject>Habenula</dc:subject>
   <dc:subject>Learning</dc:subject>
   <dc:subject>Mental Health</dc:subject>
   <dc:subject>Motivation</dc:subject>
   <dc:subject>Neuroscience</dc:subject>
   <dc:subject>Parkinson's Disease</dc:subject>
   <dc:subject>Psychology</dc:subject>
   <dc:subject>Structural MRI</dc:subject>
   <dc:subject>Uncertainty</dc:subject>
</uketd_dc:uketddc>
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