Impaired reinforcement learning and Bayesian inference in psychiatric disorders: from maladaptive decision making to psychosis in schizophrenia
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Date
29/06/2015Author
Valton, Vincent
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Abstract
Computational modelling has been gaining an increasing amount of support from the
neuroscience community as a tool to assay cognition and computational processes in
the brain. Lately, scientists have started to apply computational methods from neuroscience
to the study of psychiatry to gain further insight into the mechanisms leading
to mental disorders. In fact, only recently has psychiatry started to move away from
categorising illnesses using behavioural symptoms in an attempt for a more biologically
driven diagnosis. To date, several neurobiological anomalies have been found
in schizophrenia and led to a multitude of conceptual framework attempting to link
the biology to the patients’ symptoms. Computational modelling can be applied to
formalise these conceptual frameworks in an effort to test the validity or likelihood
of each hypothesis. Recently, a novel conceptual model has been proposed to describe
how positive symptoms (delusions, hallucinations and thought disorder) and
cognitive symptoms (poor decision-making, i.e. “executive functioning”) might arise
in schizophrenia. This framework however, has not been tested experimentally or
against computational models. The focus of this thesis was to use a combination of
behavioural experiments and computational models to independently assess the validity
of each component that make up this framework.
The first study of this thesis focused on the computational analysis of a disrupted
prediction-error signalling and its implications for decision-making performances in
complex tasks. Briefly, we used a reinforcement-learning model of a gambling task
in rodents and disrupted the prediction-error signal known to be critical for learning.
We found that this disruption can account for poor performances in decision-making
due to an incorrect acquisition of the model of the world. This study illustrates how
disruptions in prediction-error signalling (known to be present in schizophrenia) can
lead to the acquisition of an incorrect world model which can lead to poor executive
functioning or false beliefs (delusions) as seen in patients.
The second study presented in this thesis addressed spatial working memory performances
in chronic schizophrenia, bipolar disorder, first episode psychosis and family
relatives of DISC1 translocation carriers. We build a probabilistic inference model
to solve the working memory task optimally and then implemented various alterations
of this model to test commonly debated hypotheses of cognitive deficiency
in schizophrenia. Our goal was to find which of these hypotheses accounts best for
the poor performance observed in patients. We found that while the performance at
the task was significantly different for most patients groups in comparison to controls,
this effect disappeared after controlling for IQ in one group. The models were
nonetheless fitted to the experimental data and suggest that working memory maintenance
is most likely to account for the poor performances observed in patients. We
propose that the maintenance of information in working memory might have indirect
implications for measures of general cognitive performance, as these rely on a correct
filtering of information against distractions and cortical noise.
Finally the third study presented in this thesis assessed the performance of medicated
chronic schizophrenia patients in a statistical learning task of visual stimuli
and measured how the acquired statistics influenced their perception. We find that
patient with chronic schizophrenia appear to be unimpaired at statistical learning
of visual stimuli. The acquired statistics however appear to induce less expectation-driven
‘hallucinations’ of the stimuli in the patients group than in controls. We find
that this is in line with previous literature showing that patients are less susceptible
to expectation-driven illusions than controls. This study highlights however the idea
that perceptual processes during sensory integration diverge from this of healthy controls.
In conclusion, this thesis suggests that impairments in reinforcement learning and
Bayesian inference appear to be able to account for the positive and cognitive symptoms
observed in schizophrenia, but that further work is required to merge these
findings. Specifically, while our studies addressed individual components such as
associative learning, working memory, implicit learning & perceptual inference, we
cannot conclude that deficits of reinforcement learning and Bayesian inference can
collectively account for symptoms in schizophrenia. We argue however that the studies
presented in this thesis provided evidence that impairments of reinforcement
learning and Bayesian inference are compatible with the emergence of positive and
cognitive symptoms in schizophrenia.
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