Biology subjects

Huys, Q.

Publications and source records attributed to Huys, Q..

6 recordsLinked to original sources

bioRxiv2024

Hidden state inference requires abstract contextual representations in ventral hippocampus

The ability to form and utilize subjective, latent contextual representations to influence decision making is a crucial determinant of everyday life. The hippocampus is widely hypothesized to bind together otherwise abstract combinations of stimuli to represent such latent contexts, and to allow their use to support the process of hidden state inference. Yet, direct evidence for this remains limited. Here we show that the CA1 area of the ventral hippocampus is necessary for mice to perform hidden state inference during a 2-armed bandit task. vCA1 neurons robustly differentiate between the two abstract contexts required for this strategy in a manner similar to the differentiation of spatial locations, despite the contexts being formed only from past probabilistic outcomes. These findings offer insight into how latent contextual information is used to optimize decision-making processes, and emphasize a key role of the hippocampus in hidden state inference.

neuroscience↗

bioRxiv2021

A Hierarchical Reinforcement Learning Model Explains Individual Differences in Attentional Set Shifting

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks such as CANTAB IED reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability, and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED and psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data, and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. This data showcase a new methodology to analyse data from the CANTAB IED task, and suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.

neuroscience↗

bioRxiv2020

Explaining distortions in metacognition with an attractor network model of decision uncertainty

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the models uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible. Author SummaryIn this work, we use a biologically-plausible model of decision uncertainty to show that shifts in metacognition are associated with disturbances in the interaction between decision-making and higher-order uncertainty-monitoring networks. Specifically, we show that stronger uncertainty modulation is associated with decreased metacognitive bias, sensitivity, and efficiency, with no effect on perceptual sensitivity. Our approach not only enables inferences about uncertainty modulation (and, in turn, these facets of metacognition) from fits to first-order performance data alone - but also provides a first step towards relating dynamical models of decision-making to metacognition. We also relate our models uncertainty modulation to psychopathology, and show that it can offer an implicit, low-dimensional marker of metacognitive (dys)function - opening the door to richer analysis of the interaction between metacognitive performance and psychopathology from first-order performance data.

neuroscience↗

bioRxiv2020

A Comparison of Pruning During Multi-Step Planning in Depressed and Healthy Individuals

BackgroundReal-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people "prune" (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals. MethodsThirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms. ResultsConsistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety. LimitationsThe relatively small sample size limited the examination of individual differences. The use of other heuristics that are used to render complex decisions feasible, such as memoization and fragmentation, were not examined. ConclusionsWe found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression.

systems biology↗

bioRxiv2019

Individual differences in dopamine function underlying the balance between model-based and model-free control

Reinforcement learning involves a balance between model-free (MF) and model-based (MB) systems. Recent studies suggest that individuals with either pharmacologically enhanced levels of dopamine (DA) or higher baseline levels of DA exhibit more MB control. However, it remains unknown whether such pharmacological effects depend on baseline DA. Here, we investigated whether effects of L-DOPA on the balance of MB/MF control depend on ventral striatal baseline DA. Sixty participants had two functional magnetic resonance imaging (fMRI) scans while performing a two-stage sequential decision-making task under 150 mg L-DOPA or placebo (counterbalanced), followed by a 4-hour 18F-DOPA positron emission tomography (PET) scan (on a separate occasion). We found an interaction between baseline DA levels and L-DOPA induced changes in MB control. Individuals with higher baseline DA levels showed a greater L-DOPA induced enhancement in MB control. Surprisingly, we found a corresponding drug-by-baseline DA interaction on MF, but not MB learning signals in the ventromedial prefrontal cortex. We did not find a significant interaction between baseline DA levels and L-DOPA effects on MF control or MB/MF balance. In sum, our findings point to a baseline dependency of L-DOPA effects on differential aspects of MB and MF control. Individual differences in DA washout may be an important moderator of L-DOPA effects. Overall, our findings complement the general notion where higher DA levels is related to a greater reliance on MB control. Although the relationship between phasic DA firing and MF learning is conventionally assumed in the animal literature, the relationship between DA and MF control is not as straightforward and requires further clarification.

neuroscience↗

bioRxiv2018

Neuroticism Impairs the Use of Reward Values for Decision-Making in Major Depression

Depression is a debilitating condition with a high prevalence, but aetiology and pathophysiology are still unclear. Various reward-learning paradigms have been used to show impairments in depression. Both trait pessimism and neuroticism are associated with depression, but their link with the impairments in reward learning and decision-making have not been investigated. A Pavlovian conditioning task was performed by 32 subjects, 15 with depression. Participants had to estimate the probability of some fractal stimuli to be associated with a binary reward, based on a few observations. They then had to make a choice between one of the observed fractals and another target for which the reward probability was explicitly given. Computational modelling was used to succinctly describe participants behaviour. Patients performed worse than controls at the task. Computational modelling revealed that this was caused by behavioural impairments during both learning and decision phases. Neuroticism scores across participants were significantly correlated with participants inability to follow their internal value estimations. Our results demonstrate behavioural differences in probabilistic reward learning between depressed patients and healthy controls. Neuroticism was associated with the impaired ability to follow internal reward values and consequently with worse decision-making.

neuroscience↗