Reinforcement Learning Describes the Computational and Neural Processes Underlying Flexible Learning of Values and Attentional Selection

Attention and learning are cognitive control processes that are closely related. This thesis investigates this inter-relatedness by using computational models to describe the mechanisms that are shared between these processes. Computational models describe the transformation of stimuli to observable variables (behaviour) and contain the latent mechanisms that affect this transformation. Here, I captured these mechanisms with the reinforcement learning (RL) framework applied in two different task contexts and three different projects to show 1) how attentional selection of stimuli involves the learning of values for stimuli, 2) how the learning of stimulus values is influenced by previously learned rules, and 3) how explorations of value-related mechanisms in the brain benefit from using intracranial EEG to investigate the strength of oscillatory activity in ventromedial prefrontal cortex. In the first project, the RL framework is applied to a feature-based attention task that required macaques to learn the value of stimulus features while ignoring non-relevant information. By comparing different RL schemes I found that trial-by-trial covert attentional selections were best predicted by a model that only represents expected values for the task relevant feature dimension. In the second project, I explore mechanisms of stimulus-feature value learning in humans in order to understand the influence of learned rules for the flexible, on-going learning of expected values. I test the hypothesis that naive subjects will show enhanced learning of feature specific reward associations by switching to the use of an abstract rule that associates stimuli by feature type. I found that two-thirds of subjects (n=22/32) exhibited behaviour that was best fit by a ‘flexible-rule-selection’ model. Low-frequency oscillatory activity in frontal cortex has been associated with cognitive control and integrative brain functions, however, the relationship between expected values for stimuli and band-limited, rhythmic neural activity in the human brain is largely unknown. In the third project, I used intracranial electrocorticography (ECoG) in a proof-of-principle study to reveal spectral power signatures in vmPFC related to the expected values of stimuli predicted by a RL model for a single human subject

Feature-specific prediction errors and surprise across macaque fronto-striatal circuits

[EN] To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention.This work was supported by grant MOP 102482 from the Canadian Institutes of Health Research (T.W.) and the Natural Sciences and Engineering Research Council of Canada (T.W.), as well as by the Brain in Action CREATE-IRTG program (M.O. and T.W.), and by grant LPDS 2012-08 from the Deutsche Akademie der Naturforscher Leopoldina (S.W.). Imaging data provided by the Duke Center for In Vivo Microscopy, an NIH Biomedical Technology Resource (NIHP41EB015897, 1S10OD010683-01). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of this manuscript. The authors would like to thank Hongying Wang for technical supportOemisch, M.; Westendorff, S.; Azimi, M.; Hassani, SA.; Ardid-Ramírez, JS.; Tiesinga, P.; Womelsdorf, T. (2019). 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A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque

[EN] Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.This research was supported by grants from the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Ontario Ministry of Economic Development and Innovation (MEDI). We thank Dr. Hongying Wang for invaluable help with drug administration and animal careHassani, SA.; Oemisch, M.; Balcarras, M.; Westendorff, S.; Ardid-Ramírez, JS.; Van Der Meer, MA.; Tiesinga, P.... (2017). 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Neurocognitive Mechanisms of Learning and Decision-Making in Adolescent-OCD: A Computational Approach

Early-onset obsessive-compulsive disorder (OCD) is substantially less researched than adult-OCD, resulting in prevalent equivocation surrounding the neurocognitive profile of child-OCD. Research into this area is pivotal as population studies report that youths with OCD struggle significantly in academic settings. In the General Introduction of this thesis, I reviewed existing literature and found that strikingly, young patients do not show impairment on features that are considered both hallmarks of adult OCD and tightly linked to disorder symptomatology, such as response inhibition and cognitive flexibility. Among the characteristics that are thought to be present in children and adolescents with OCD are abnormal decision-making under uncertainty and impaired learning, and I decided to focus on these features as they may be driving poor academic attainment in young people with the disorder. In addition, I sought to investigate other cognitive processes that have not been well-researched in adolescent-OCD but are found to be robustly altered in adult OCD such as goal directed/model-based reasoning, meta-cognition, and feedback sensitivity. I aimed to delineate these various processes using a battery of suitably complex cognitive tasks. Moreover, I highlighted that majority of past studies fail to find differences between young patients and controls due to behavioural signatures being too subtle to be uncovered by standard statistical analyses. Hence, I employed computational modelling of cognitive task data to disentangle latent decision-making processes displayed by adolescents with OCD. In Chapter 2, I modelled data from the Wisconsin Card Sorting task, a frequently used paradigm of cognitive flexibility, and confirmed that youths with OCD show equivalent performance on the task to controls. Only patients on serotonergic medication showed increased response latencies and a tendency to make unique errors (choosing a deck associated with no rule present on the test card). Next, in Chapter 3, I sought to understand instrumental and Pavlovian learning, and whether adolescents with OCD show increased punishment sensitivity on a novel aversive Pavlovian-to Instrumental Transfer paradigm. Once again, patient performance was equivalent to that of controls. Hence, the remaining chapters were dedicated to probing behaviour on probabilistic paradigms. In Chapter 4, I formally investigated model-based and model-free learning using a well-validated two step decision-making task, and fit a reinforcement learning drift diffusion model to both choice and reaction time data. Patients showed increased exploration on the task as well as faster and more erratic decisions compared to controls. Nonetheless, model-based learning was equivalent between groups. In the penultimate chapter, I demonstrate on a predictive-inference task that patients with OCD update their choices more frequently compared to controls independent of prediction error magnitude. Finally, in Chapter 6, I administered a probabilistic reversal learning paradigm to a large sample of 50 adolescent patients and 53 matched controls. Standard analyses revealed a significant reversal learning deficit in patients with OCD, wherein they displayed more errors and a lower propensity to repeat choices following positive feedback during the post-reversal phase. Crucially, computational modelling revealed striking group differences where adolescents with OCD displayed elevated reward learning and lower punishment learning, increased exploration, and decreased perseveration compared to controls. In the General Discussion, I emphasise that atypical learning and decision-making in adolescent-OCD are more pronounced on probabilistic tasks, where task environments are more volatile. Results are partly discussed in the context of the uncertainty model of OCD, where subjective feelings of doubt experienced by patients drive compulsive behaviours such as checking and certainty-seeking in daily life, alongside excessive exploration on probabilistic tasks. I also consider various explanations for cognitive distinctions between adult- and adolescent OCD. More general implications of the findings are discussed for understanding OCD in the context of adolescent development and for treatment/support strategies.WELLCOME TRUST (104631/Z/14/Z

Neurochemical modulation of affective and behavioural control: Models and applications for psychiatry

Impairments in emotional reactivity and behavioural flexibility are pervasive across disparate psychiatric conditions as traditionally defined. Here, I provide new evidence on how these processes are altered by neuromodulators in humans, with a primary focus on serotonin (5- HT; 5-hydroxytryptamine). Emotional reactions prepare the body for action. Some emotion is primitive, implicit, and critical for surviving threats, yet can inappropriately persist in times of safety. Other emotions are more complex, self-conscious and important in maintaining harmonious interpersonal relationships. At the same time, learned behaviours that are adaptive in the first instance, may become irrelevant or even disadvantageous as circumstances change. In Chapters 3 through 6, I report on experiments in healthy human volunteers that employed the dietary technique acute tryptophan depletion (ATD). ATD temporarily lowers serotonin synthesis and release by depleting its biosynthetic precursor tryptophan. Chapter 3 is a study of self-reported social emotion. ATD enhanced emotion in response to social injustice non-specifically; however, consideration of personality traits revealed that highly empathic participants reported more guilt under ATD, whereas individuals high in trait psychopathy demonstrated more annoyance. Chapter 4, in contrast, considers evolutionarily ancient automatic emotional reactions to threats. This was assayed instead by an objective measure, the skin conductance response (SCR). Here, ATD conversely attenuated the retention of Pavlovian conditioned emotional memory to threat. Traits again influenced this response: individuals more intolerant of uncertainty displayed the greatest attenuation of emotional reactions. Chapter 5 both extends the studies on emotion and bridges to the remaining empirical work by investigating reversal learning, an index of cognitive flexibility, in two experiments. Individuals again underwent Pavlovian (stimulus-outcome) threat conditioning, whereby one stimulus predicted threat, and another was safe. These contingencies then swapped (reversed). In a separate experiment, participants underwent instrumental (stimulus-response-outcome) conditioning on a deterministic schedule (the correct option was always correct), followed by reversal of the contingencies. ATD impaired both Pavlovian and instrumental reversal learning. Chapters 6 through 8 instead examine instrumental reversal learning that was probabilistic (the correct option was correct most but not all of the time), rather than deterministic. Chapter 6 expands on previous ATD studies of probabilistic reversal learning (PRL) in the literature, which had not found effects on choice behaviour. Despite nearly tripling the sample size, behaviour here assessed by conventional methods was unaffected, replicating previously published null results. Applying reinforcement learning (RL) models, however, revealed ATD elevated a basic perseverative tendency, referred to as “stimulus stickiness”; behaviour was more stimulus-bound and insensitive to the outcome of actions, consistent with the deterministic instrumental reversal impairment following ATD. Chapters 7 and 8 apply RL models as well, to existing datasets on PRL for comparison. Chapter 7 shows that healthy volunteers under lysergic acid diethylamide (LSD), which acts both at serotonin but also dopamine receptors, showed enhanced learning from positive feedback in particular, which was related to perseveration. Chapter 8 applies computational methods to PRL in clinical populations. RL modelling revealed a computational signature that dissociated PRL in stimulant use disorder (SUD) and obsessive-compulsive disorder (OCD): Individuals with SUD showed heightened stimulus stickiness, as occurred following ATD in healthy volunteers, whereas the OCD group (under serotonergic medication) demonstrated lower stimulus stickiness than healthy controls. Dopaminergic agents remediated a reward learning deficit in SUD, among other measures. The general discussion considers these various findings in terms of theories of central serotonin function, in relation to the animal literature, and its relevance to mental disorder. These results, collectively, advance knowledge of neurochemical and computational mechanisms underlying psychiatric conditions trans-diagnostically, with implications for revised psychiatric classifications in line with the Research Domain Criteria (RDoC).Gates Cambridge Trust Wellcome Trus

Habits without values

Habits form a crucial component of behavior. In recent years, key computational models have conceptualized habits as arising from model-free reinforcement learning (RL) mechanisms, which typically select between available actions based on the future value expected to result from each. Traditionally, however, habits have been understood as behaviors that can be triggered directly by a stimulus, without requiring the animal to evaluate expected outcomes. Here, we develop a computational model instantiating this traditional view, in which habits develop through the direct strengthening of recently taken actions rather than through the encoding of outcomes. We demonstrate that this model accounts for key behavioral manifestations of habits, including insensitivity to outcome devaluation and contingency degradation, as well as the effects of reinforcement schedule on the rate of habit formation. The model also explains the prevalent observation of perseveration in repeated-choice tasks as an additional behavioral manifestation of the habit system. We suggest that mapping habitual behaviors onto value-free mechanisms provides a parsimonious account of existing behavioral and neural data. This mapping may provide a new foundation for building robust and comprehensive models of the interaction of habits with other, more goal-directed types of behaviors and help to better guide research into the neural mechanisms underlying control of instrumental behavior more generally

The neuro-computational role of uncertainty in anxiety

Anxiety disorders are the most common mental health disorders and comprise a large number of years lost to disability. The work in this thesis is oriented towards understanding anxiety using a computational approach, focusing on uncertainty estimation as a key process. Chapter 1 introduces the role of uncertainty within anxiety and motivates the subsequent experimental chapters. Chapter 2 is a review of the computational role of the amygdala in humans, a key area for uncertainty computation. Chapter 3 is an experimental chapter which aimed to address gaps in the literature highlighted in the preceding chapters, namely the link between sensory uncertainty processing and anxiety and the role of the amygdala in this process. This chapter focuses on the development of a novel computational hierarchical Bayesian model to quantify sensory uncertainty and its application to neuroimaging data, with intolerance of uncertainty relating to greater neural activation in the insula but not amygdala. Chapter 4 targets the computational mechanisms underlying the negative self-bias observed in subclinical social anxiety. Again, this chapter focuses on the development of novel computational belief-update models which explicitly model uncertainty. Here, we see that a reduced trait self-positivity underpins this negative social evaluation process. The final experimental chapter presented in Chapter 5 investigates the link between different computational mechanisms, such as uncertainty, and a range of mood and anxiety symptomatology. This study revealed cognitive, social and somatic computational profiles that share a threat bias mechanism but have distinct negative-self bias and aversive learning signatures. Contrary to expectations, none of the uncertainty measures showed any associations with anxiety symptom subtypes. Finally, chapter 6 brings together the work in this thesis and alongside limitations of the work, discusses how these experiments contribute to our understanding of anxiety and the role of uncertainty across the anxiety spectrum

Functional organisation of behavioural inhibitory control mechanisms in cortico-basal ganglia circuitry: implications for stimulant use disorder.

The neural and psychological mechanisms of inhibitory control processes were investigated, focusing on the cortico-basal ganglia circuits in rats and humans. These included behavioural flexibility, ‘waiting’ and ‘stopping’ impulsivity and involved serial spatial reversal learning task in rodents, and in humans, premature responses in the Monetary Incentive Delay (MID) task and the stop-signal reaction time task. Chapter 2 and Chapter 3 focus on individual differences in behavioural flexibility in rats while Chapter 4, Chapter 5 and Chapter 6 consider how inhibitory control mechanisms are affected by the psychostimulant drug cocaine in both rats and humans. As reported in Chapter 2, systemic modulation of monoaminergic transmission by monoamine oxidase A (MAO-A) inhibitors enhanced reversal learning performance, selectively by decreasing the lose-shift probability, thereby implicating a role for dopamine, serotonin and noradrenaline in facilitating learning from negative feedback. Resting state functional magnetic resonance imaging (fMRI) revealed enhanced functional connectivity of the orbitofrontal and motor cortices as a correlate of flexible reversal learning performance, consistent with elevated levels of monoamines in these region (Chapter 3). Having clarified the mechanisms underlying behavioural flexibility in rats, Chapter 4 reports that escalation of intravenous cocaine self-administration induces behavioural inflexibility in rats even after a relatively short period of cocaine intake. Computational models, including a reinforced and Bayesian learner, revealed a lack of exploitation of the learned response-outcome relationships in cocaine-exposed rats. Chapter 5 focused on impulse control in human volunteers, identifying the striatal and cingulo-opercular networks as substrates of impulsive, premature responding in healthy 4 volunteers, stimulant-dependent individuals and their unaffected siblings. Loss of impulse control was elicited by different incentives for drug-free participants as opposed to drug users. Drug cues elicited striatal activation and increased premature responses in the stimulant-dependent group compared with the control group. In contrast, the ventral striatum was linked to incentive specific activation to reward anticipation. Task-based fMRI demonstrated that interactions between dorsal striatum and cingulo-opercular “cold cognition” networks underlie failures of impulse control in the control, at-risk and stimulant-dependent groups. However, whereas the cingulo-opercular networks were associated with premature responding in all groups, the reward system was activated specifically by the drug incentive cues in the stimulant group, and by monetary incentive cues in the drug-free groups. Chapter 6 presents evidence that corticostriatal functional and effective connectivity in an overlapping network that includes the anterior cingulate and inferior frontal cortices as well as motor cortex, the subthalamic nucleus and dorsal striatum, is critical to stopping impulse control in both control and cocaine individuals. No stopping efficiency impairments were observed in the cocaine-dependent group. Nevertheless, lower structural corticostriatal connectivity measured using diffusion MRI was associated with response execution impairments in cocaine participants performing a stop-signal reaction time task. Further, response execution was rescued by the selective noradrenaline reuptake inhibitor atomoxetine, which also increased corticostriatal effective connectivity. Finally, increased impulsivity and behavioural inflexibility seen in stimulant use disorder in Chapter 5 and Chapter 4, respectively, were not observed in the endophenotype at risk for developing stimulant abuse but were rather a consequence of stimulant abuse. These results further clarify the monoaminergic substrates of behavioural flexibility and specify the neural and computational impairments in inhibitory control induced by stimulant dependence.Pinsent Darwin Studentship from the Dept of Physiology, Development and Neuroscienc

Decisions, decisions, decisions: the development and plasticity of reinforcement learning, social and temporal decision making in children

Human decision-making is the flexible way people respond to their environment, take actions, and plan toward long-term goals. It is commonly thought that humans rely on distinct decision-making systems, which are either more habitual and reflexive or deliberate and calculated. How we make decisions can provide insight into our social functioning, mental health and underlying psychopathology, and ability to consider the consequences of our actions. Notably, the ability to make appropriate, habitual or deliberate decisions depending on the context, here referred to as metacontrol, remains underexplored in developmental samples. This thesis aims to investigate the development of different decision-making mechanisms in middle childhood (ages 5-13) and to illuminate the potential neurocognitive mechanisms underlying value-based decision-making. Using a novel sequential decision-making task, the first experimental chapter presents robust markers of model-based decision-making in childhood (N = 85), which reflects the ability to plan through a sequential task structure, contrary to previous developmental studies. Using the same paradigm, in a new sample via both behavioral (N = 69) and MRI-based measures (N = 44), the second experimental chapter explores the neurocognitive mechanisms that may underlie model-based decision-making and its metacontrol in childhood and links individual differences in inhibition and cortical thickness to metacontrol. The third experimental chapter explores the potential plasticity of social and intertemporal decision-making in a longitudinal executive function training paradigm (N = 205) and initial relationships with executive functions. Finally, I critically discuss the results presented in this thesis and their implications and outline directions for future research in the neurocognitive underpinnings of decision-making during development

The brain as a generative model: information-theoretic surprise in learning and action

Our environment is rich with statistical regularities, such as a sudden cold gust of wind indicating a potential change in weather. A combination of theoretical work and empirical evidence suggests that humans embed this information in an internal representation of the world. This generative model is used to perform probabilistic inference, which may be approximated through surprise minimization. This process rests on current beliefs enabling predictions, with expectation violation amounting to surprise. Through repeated interaction with the world, beliefs become more accurate and grow more certain over time. Perception and learning may be accounted for by minimizing surprise of current observations, while action is proposed to minimize expected surprise of future events. This framework thus shows promise as a common formulation for different brain functions. The work presented here adopts information-theoretic quantities of surprise to investigate both perceptual learning and action. We recorded electroencephalography (EEG) of participants in a somatosensory roving-stimulus paradigm and performed trial-by-trial modeling of cortical dynamics. Bayesian model selection suggests early processing in somatosensory cortices to encode confidence-corrected surprise and subsequently Bayesian surprise. This suggests the somatosensory system to signal surprise of observations and update a probabilistic model learning transition probabilities. We also extended this framework to include audition and vision in a multi-modal roving-stimulus study. Next, we studied action by investigating a sensitivity to expected Bayesian surprise. Interestingly, this quantity is also known as information gain and arises as an incentive to reduce uncertainty in the active inference framework, which can correspond to surprise minimization. In comparing active inference to a classical reinforcement learning model on the two-step decision-making task, we provided initial evidence for active inference to better account for human model-based behaviour. This appeared to relate to participants’ sensitivity to expected Bayesian surprise and contributed to explaining exploration behaviour not accounted for by the reinforcement learning model. Overall, our findings provide evidence for information-theoretic surprise as a model for perceptual learning signals while also guiding human action.Unsere Umwelt ist reich an statistischen Regelmäßigkeiten, wie z. B. ein plötzlicher kalter Windstoß, der einen möglichen Wetterumschwung ankündigt. Eine Kombination aus theoretischen Arbeiten und empirischen Erkenntnissen legt nahe, dass der Mensch diese Informationen in eine interne Darstellung der Welt einbettet. Dieses generative Modell wird verwendet, um probabilistische Inferenz durchzuführen, die durch Minimierung von Überraschungen angenähert werden kann. Der Prozess beruht auf aktuellen Annahmen, die Vorhersagen ermöglichen, wobei eine Verletzung der Erwartungen einer Überraschung gleichkommt. Durch wiederholte Interaktion mit der Welt nehmen die Annahmen mit der Zeit an Genauigkeit und Gewissheit zu. Es wird angenommen, dass Wahrnehmung und Lernen durch die Minimierung von Überraschungen bei aktuellen Beobachtungen erklärt werden können, während Handlung erwartete Überraschungen für zukünftige Beobachtungen minimiert. Dieser Rahmen ist daher als gemeinsame Bezeichnung für verschiedene Gehirnfunktionen vielversprechend. In der hier vorgestellten Arbeit werden informationstheoretische Größen der Überraschung verwendet, um sowohl Wahrnehmungslernen als auch Handeln zu untersuchen. Wir haben die Elektroenzephalographie (EEG) von Teilnehmern in einem somatosensorischen Paradigma aufgezeichnet und eine trial-by-trial Modellierung der kortikalen Dynamik durchgeführt. Die Bayes'sche Modellauswahl deutet darauf hin, dass frühe Verarbeitung in den somatosensorischen Kortizes confidence corrected surprise und Bayesian surprise kodiert. Dies legt nahe, dass das somatosensorische System die Überraschung über Beobachtungen signalisiert und ein probabilistisches Modell aktualisiert, welches wiederum Wahrscheinlichkeiten in Bezug auf Übergänge zwischen Reizen lernt. In einer weiteren multimodalen Roving-Stimulus-Studie haben wir diesen Rahmen auch auf die auditorische und visuelle Modalität ausgeweitet. Als Nächstes untersuchten wir Handlungen, indem wir die Empfindlichkeit gegenüber der erwarteten Bayesian surprise betrachteten. Interessanterweise ist diese informationstheoretische Größe auch als Informationsgewinn bekannt und stellt, im Rahmen von active inference, einen Anreiz dar, Unsicherheit zu reduzieren. Dies wiederum kann einer Minimierung der Überraschung entsprechen. Durch den Vergleich von active inference mit einem klassischen Modell des Verstärkungslernens (reinforcement learning) bei der zweistufigen Entscheidungsaufgabe konnten wir erste Belege dafür liefern, dass active inference menschliches modellbasiertes Verhalten besser abbildet. Dies scheint mit der Sensibilität der Teilnehmer gegenüber der erwarteten Bayesian surprise zusammenzuhängen und trägt zur Erklärung des Explorationsverhaltens bei, das jedoch nicht vom reinforcement learning-Modell erklärt werden kann. Insgesamt liefern unsere Ergebnisse Hinweise für Formulierungen der informationstheoretischen Überraschung als Modell für Signale wahrnehmungsbasierten Lernens, die auch menschliches Handeln steuern