Biology subjects

Lawson, R. P.

Publications and source records attributed to Lawson, R. P..

10 recordsLinked to original sources

bioRxiv2026

Maternal separation recalibrates prefrontal mitochondrial function and protects against stress-induced negative cognitive bias

Ambiguity represents a form of uncertainty in which outcome probabilities cannot be explicitly learned, making decisions dependent on emotional states and cognitive biases. Early-life stress (ELS) increases the risk of adverse mental and physical health outcomes and alters affective processing and learning. ELS may thus affect how ambiguous information is processed, which may depend on interactions with adulthood stress (AS) and mechanistically on bioenergetic mechanisms mediated by top-down cognitive control systems within the prefrontal cortex (PFC). The present study investigated the effects of AS in rats exposed to early maternal separation (MS), a rodent model of ELS, on a task assessing cognitive bias, together with putatively accompanying alterations in PFC mitochondrial function. Cognitive bias was assessed using an ambiguous cue task (ACT) in MS and non-separated control rats tested at baseline and following repeated unpredictable mild stress during adulthood. MS did not affect baseline cognitive bias but increased response latencies. Following AS, control animals showed a significant negative shift in cognitive bias, whereas MS animals were resistant to this shift. MS was also associated with greater PFC mitochondrial respiratory capacity and uncoupling of oxidative phosphorylation following AS. These findings suggest that ELS is associated with a recalibrated phenotype that buffers against the affective consequences of later stress. Enhanced PFC mitochondrial bioenergetics may underlie this resilience, highlighting the importance of developmental context in shaping affective-cognitive responses to stress.

neuroscience↗

bioRxiv2026

Impacts and interactions of stress, noradrenaline and serotonin signalling on probabilistic reversal learning

RationaleEarly life stress (ELS) is acknowledged to underlie cognitive and emotional abnormalities linked to stress-related mood disorders. ELS can lead to persistent biases in how uncertain feedback is processed to affect the flexibility of decision-making. Objectives(1) To investigate the effects of ELS on the flexibility of rats trained on a serial probabilistic reversal learning (PRL) task involving spurious positive and negative feedback. (2) To elucidate the involvement of the stress hormone corticosterone and the noradrenergic and serotonergic systems in modulating how ELS affects PRL. MethodsMale and female rats were intermittently separated from maternal care on postnatal days five to nineteen, inclusively. As adults, the same rats were trained on a deterministic reversal learning task involving certain rewarded or non-rewarded outcomes followed by a PRL task where correct and incorrect responses were rewarded on 80% and 20% of trials, respectively. Dose-dependent effects of the beta-blocker, propranolol, selective serotonin reuptake inhibitor, citalopram and corticosterone were subsequently determined. ResultsELS resulted in an increased responsivity to feedback, specifically in males making more win-stay responses following a reward, that was associated with an increased punishment learning rate. In both control and MS rats, propranolol increased feedback sensitivity, but delayed updating following a rule switch. In contrast, neither citalopram nor corticosterone significantly affected reversal learning. ConclusionsELS is sufficient to cause persistent changes in how feedback is processed by male rats on a reversal learning task. Activation of beta-adrenergic receptors may be necessary for updating learned associations during decision-making involving uncertain feedback.

neuroscience↗

bioRxiv2026

Ketamine attenuates habenula activity in response to aversive outcomes during Pavlovian learning

Ketamine is an NMDA receptor antagonist with rapid-antidepressant properties when administered at a sub-anesthetic dose. Preclinical models indicate that a direct injection of ketamine into lateral habenula (Hb), a small midbrain structure with an evolutionarily preserved role in aversive learning across mammals, can rapidly relieve depression-like behavior. However, there is limited evidence to explain how ketamine acts on the function of the human habenula. In a translational computational neuroscience study, 70 healthy adult volunteers were randomised in a 1:1 ratio to receive ketamine or placebo (NaCl 0.9%). We used an aversive Pavlovian conditioning paradigm combined with 7-Tesla functional neuroimaging to show that ketamine attenuates habenula response during aversive stimuli expectations and outcomes 24 hours post-infusion. We further present preliminary evidence suggesting that when aversive learning occurs after ketamine infusion, reduced habenula activity during the learning process may lead to downstream effects that diminish the aversive impact of negative affective memories. These findings provide translational support for preclinical models of ketamines mechanisms in humans.

neuroscience↗

bioRxiv2025

Transdiagnostic symptomatology amidst real-world environmental uncertainty: a cross-sectional and cross-lagged panel network analysis

BackgroundUncertainty is a potential driver of poor mental health outcomes, and uncertainty is mounting globally across many domains of daily life. However, it remains unclear how anxiety and depression symptoms emerge in response to uncertainty in these real-life contexts. Capitalising on the COVID-19 pandemic as a naturalistic experiment, we investigate how heightened real-world environmental uncertainty interacts with intolerance of uncertainty (IU) to drive mental health symptoms. MethodsWe collected self-reported data on symptoms of anxiety, depression and IU from 301 participants for two time points. We performed cross-sectional network analyses to identify influential contributors and established a cross-lagged panel network (CLPN) to investigate longitudinal effects. ResultsAnxiety, depression and IU symptoms were higher when real-world uncertainty was higher (t2) compared to when it was lower (t1). Network analyses revealed the same three symptom clusters at both timepoints, however worry became more central to the network at t2. In the CLPN, unlike anxiety and depression symptoms, IU symptoms had the highest autoregressive values, meaning IU at t2 was well predicted by IU at t1. The item "inhibition of behaviour due to doubt" positively predicted both anxiety and anhedonia. ConclusionsThis study highlights the relevance of worry during higher real-world uncertainty and the predictive value of predispositional IU. Our findings further suggest behavioural inhibition to be a potential target to alleviate internalising psychopathology. During ever-increasing fluctuations in global uncertainty, we provide novel insights into the temporal relationships of highly prevalent psychopathology, which might inform support strategies for people with existing mental health vulnerabilities.

neuroscience↗

bioRxiv2025

Effort choices are sensitive to prior learning

Goal directed behaviour requires the integration of learned beliefs about the world with decisions about whether an outcome is worth the effort required to obtain it. Although value learning and effort-based choice are central to motivated behaviour, they are typically examined in isolation. Here we introduce a novel behavioural paradigm that directly links probabilistic associative learning with effortful decision making, for the first time allowing trial by trial assessment of how learned beliefs guide willingness to exert effort. In a large sample of participants (n=252), individuals reliably acquired stimulus-outcome associations but showed substantial variability in learning fidelity. Effort allocation was not determined by objective reward probabilities, but was better predicted by participants subjective beliefs, demonstrating that effortful action depends on internal value representations rather than external contingencies alone. Reward and loss contexts showed asymmetric effects on effort motivation, revealing a valence specific dissociation in belief driven action. Critically, higher levels of anhedonia were associated with reduced willingness to engage in effortful behaviour and a weaker coupling between learned value and action, indicating a disruption in the integration of belief and motivation. Together, these findings identify a mechanistic link between learning and effort expenditure and suggest that motivational impairments in psychiatric symptoms may arise from failures to translate learned value into action.

neuroscience↗

bioRxiv2025

Neurochemical markers of uncertainty processing in humans

How individuals process and respond to uncertainty has important implications for cognition and mental health. Here we use computational phenotyping to examine individualised "uncertainty fingerprints" in relation to neurometabolites and trait anxiety in humans. We introduce a novel categorical state-transition extension of the Hierarchical Gaussian Filter (HGF) to capture implicit learning in a four-choice probabilistic sensorimotor reversal learning task by tracking beliefs about stimulus transitions. Using 7-Tesla Magnetic Resonance Spectroscopy, we measured baseline neurotransmitter levels in the primary motor cortex (M1). Model-based results revealed dynamic belief updating in response to environmental changes. We further found region-specific relationships between M1 glutamate+ glutamine levels and prediction errors and volatility beliefs, revealing an important neural marker of probabilistic reversal learning in humans. High trait anxiety was associated with faster post-reversal responses. By integrating computational modelling with neurochemical assessments, this study provides novel insights into the neurocomputations that drive individual differences in processing uncertainty.

neuroscience↗

bioRxiv2024

Computational neurodevelopment: infant decision-making in changing environments

In recognition of the fact that most psychiatric conditions have neurodevelopmental origins, there is an increasing interest in applying the methodological and conceptual approaches from computational psychiatry to developmental cohorts. However, the challenge of acquiring and modelling behavioural responses in very young infants has thus far proven difficult to overcome. To address this we developed a novel gaze-contingent, cued-reversal paradigm that allowed 6-10 month old infants to make overt behavioural responses to assess learning of expectations and updating of behaviour in response to change. We then fit computational models to infant behaviour and, for the first time, were able to validate the winning model to the same standards as would be expected of adults (e.g. good parameter recoverability, model identifiability and simulated behavioural responses). Similar to prior findings in adults, model-based prediction error measures correlated with post-switch increases in pupil size; consistent with noradrenalines hypothesised role in learning about change. Data-driven clustering based on model parameters revealed two infant behavioural subtypes hidden within the data; one with a perseverating profile and the other with a more exploratory decision-making pattern. This approach sheds new light on the classic finding that all infants under 12 months tend to perseverate. Crucially, there were no significant differences in age between the clusters, but differences in terms of adaptive skills and temperament measured via gold-standard developmental assessments. These results prime the field for infant computational psychiatry, demonstrating that we can reliably fit models to infant data and that the parameters from such models can identify subgroups with distinct cognitive profiles that are superior to those derived from the behavioural data alone.

neuroscience↗

bioRxiv2024

Anxious individuals are more sensitive to changes in outcome variability and value differences in dynamic environments

Anxiety is known to alter learning in uncertain environments. Standard experimental paradigms and computational models addressing these differences have mainly assessed the impact of volatility, and anxious individuals have been shown to have a reduced learning rate when moving from a stable to volatile environment. Previous research has not, however, independently assessed the impact of both changes in volatility, i.e., reversals in reward contingency, and changes in outcome variability (noise) in the same individuals. Here we use a simple probabilistic reversal learning paradigm to independently manipulate the level of volatility and noise at the experimental level in a fully orthogonal design. We replicate general increases, irrespective of anxiety levels, in both positive and negative learning rates when moving from low to high volatility, but only in the context of low noise. When low volatility is combined with high noise, more anxious individuals display negative learning rates similar to high volatility with high noise, whereas those lower in anxiety show the usual negative learning rate increase from low to high volatility. Within-individual increases in lose-shift responses from low to high noise conditions scale with levels of anxious traits, but this occurs under low volatility only. We furthermore find that people with higher anxious traits are more accurate overall and utilize a more exploitative decision-making strategy in this dynamic environment. Our findings suggest that changes in both sources of uncertainty, volatility and noise, should be carefully considered when assessing learning, particularly in relation to anxiety and other neuropsychiatric conditions, and implicate anxiety-related differences in dopaminergic and noradrenergic neurotransmitter signalling when learning in highly changeable environments.

animal behavior and cognition↗

bioRxiv2020

Attention and reinforcement learning in Parkinson's disease

Dopamine is known to be involved in several important cognitive processes, most notably in learning from rewards and in the ability to attend to task-relevant aspects of the environment. Both of these features of dopaminergic signalling have been studied separately in research involving Parkinsons disease (PD) patients, who exhibit diminished levels of dopamine. Here, we tie together some of the commonalities in the effects of dopamine on these aspects of cognition by having PD patients (ON and OFF dopaminergic medication) and healthy controls (HCs) perform two tasks that probe these processes. Within-patient behavioural measures of distractibility, from an attentional capture task, and learning performance, from a probabilistic classification reinforcement learning task, were included in one model to assess the role of distractibility during learning. Dopamine medication state and distractibility level were found to have an interactive effect on learning performance; less distractibility in PD ON was associated with higher accuracy during learning, and this was altered in PD OFF. Functional magnetic resonance imaging (fMRI) data acquired during the learning task furthermore allowed us to assess multivariate patterns of positive and negative outcomes in fronto-striatal and visual brain regions involved in both learning processes and the executive control of attention. Here, we demonstrate that while PD ON show a clearer distinction between outcomes than OFF in dorsolateral prefrontal cortex (DLPFC) and putamen, PD OFF show better distinction of activation patterns in visual regions that respond to the stimuli presented during the task. These results demonstrate that dopamine plays a key role in modulating the interaction between attention and learning at the level of both behaviour and activation patterns in the brain.

neuroscience↗

bioRxiv2020

The Effect of Uncertainty on Prediction Error in the Action-Perception Loop

Among all their sensations, agents need to distinguish between those caused by themselves and those caused by external causes. The ability to infer agency is particularly challenging under conditions of uncertainty. Within the predictive processing framework, this should happen through active control of prediction error that closes the action-perception loop. Here we use a novel, temporally-sensitive, behavioural proxy for prediction error to show that it is minimised most quickly when variability is low, but also when volatility is high. Further, when human participants report agency, they show steeper prediction error minimisation. We demonstrate broad effects of uncertainty on accuracy of agency judgements, movement, policy selection, and hypothesis switching. Measuring autism traits, we find differences in policy selection, sensitivity to uncertainty and hypothesis switching despite no difference in overall accuracy.

neuroscience↗