Probabilistic Learning Task Research Articles

Pharmacological modulation of dopamine receptors reveals distinct brain-wide networks associated with learning and motivation in non-human primates.

D1 and D2 receptors are heavily implicated in cognitive and motivational processes, as well as in a number of psychiatric disorders. Despite this, little is known about how selective manipulation of these different receptors impacts cognition through changing activity across brain-wide intrinsic networks. Here, we examined the acute behavioral and brain-wide effects of D1 and D2 receptor-selective antagonists, SCH-23390 and haloperidol, in macaques performing a probabilistic learning task. SCH administration diminished, and haloperidol improved, animals' task performance. Mirroring these effects on behavior, SCH reduced, and haloperidol increased, the resting-state functional connectivity across brain-wide networks, most notably in the cortico-striatal areas. Thus, our results highlight the opposing effects of D1 and D2 receptor modulation on the brain and behavior.

Optogenetic activation of mesencephalic projections to the nucleus accumbens shell impairs probabilistic reversal learning by disrupting learning from negative reinforcement.

Cognitive flexibility, the capacity to adapt behaviour to changes in the environment, is impaired in a range of brain disorders, including schizophrenia and Parkinson's disease. Putative neural substrates of cognitive flexibility include mesencephalic pathways to the ventral striatum (VS) and dorsomedial striatum (DMS), hypothesized to encode learning signals needed to maximize rewarded outcomes during decision-making. However, it is unclear whether mesencephalic projections to the ventral and dorsal striatum are distinct in their contribution to flexible reward-related learning. Here, rats acquired a two-choice spatial probabilistic reversal learning (PRL) task, reinforced on an 80%|20% basis (correct|incorrect responses) that assessed the flexibility of behaviour to repeated reversals of response-outcome contingencies. We report that optogenetic stimulation of projections from the ventral tegmental area (VTA) to the nucleus accumbens shell (NAcS) in the VS significantly impaired reversal learning when optical stimulation was temporally aligned with negative feedback (i.e., reward omission). VTA → NAcS stimulation during other phases of the behavioural task was without significant effect. Optogenetic stimulation of projection neurons from the substantia nigra (SN) to the DMS, aligned either with reward receipt or omission or prior to making a choice, had no significant effect on reversal learning. These findings are consistent with the notion that increased activity in the VTA → NAcS pathway disrupts behavioural flexibility by impairing learning from negative reinforcement.

Transitive inference as probabilistic preference learning.

Transitive inference (TI) is a cognitive task that assesses an organism's ability to infer novel relations between items based on previously acquired knowledge. TI is known for exhibiting various behavioral and neural signatures, such as the serial position effect (SPE), symbolic distance effect (SDE), and the brain's capacity to maintain and merge separate ranking models. We propose a novel framework that casts TI as a probabilistic preference learning task, using one-parameter Mallows models. We present a series of simulations that highlight the effectiveness of our novel approach. We show that the Mallows ranking model natively reproduces SDE and SPE. Furthermore, extending the model using Bayesian selection showcases its capacity to generate and merge ranking hypotheses as pairs with connecting symbols. Finally, we employ neural networks to replicate Mallows models, demonstrating how this framework aligns with observed prefrontal neural activity during TI. Our innovative approach sheds new light on the nature of TI, emphasizing the potential of probabilistic preference learning for unraveling its underlying neural mechanisms.

Chronic Ethanol Exposure Produces Sex-Dependent Impairments in Value Computations in the Striatum.

Value-based decision-making relies on the striatum, where neural plasticity can be altered by chronic ethanol (EtOH) exposure, but the effects of such plasticity on striatal neural dynamics during decision-making remain unclear. This study investigated the long-term impacts of EtOH on reward-driven decision-making and striatal neurocomputations in male and female rats using a dynamic probabilistic reversal learning task. Following a prolonged withdrawal period, EtOH-exposed male rats exhibited deficits in adaptability and exploratory behavior, with a preference for value updating based on rewards rather than omissions. These behavioral changes were linked to altered neural encoding in the dorsomedial striatum (DMS), where EtOH increased outcome-related signals and decreased choice-related signals. In contrast, female rats showed minimal behavioral changes with distinct EtOH-evoked alterations of neural signals, revealing significant sex differences in the impact of chronic EtOH. Our findings underscore the profound impact of chronic EtOH exposure on adaptive decision-making, revealing enduring changes in neurocomputational processes in the striatum underlying cognitive deficits that differ by sex.

Expectation to rewards modulates learning emotional words: Evidence from a hierarchical Bayesian model.

In language acquisition, individuals learn the emotional value of words through external feedback. Previous studies have used emotional words as experimental materials to explore the cognitive mechanisms underlying emotional language processing, but have failed to recognize that languages are acquired in changing environments. To this end, this study aims to combine reinforcement learning with emotional word learning, using a probabilistic reversal learning task to explore how individuals acquire the valence of emotional words in a dynamically changing environment. Computational modeling on both behavioral and event-related potential (ERP) data revealed that individuals' expectations to rewards modulated the learning speed and temporal processing of emotional words, demonstrating a clear negative bias. Specifically, as the expected value increased, individuals responded faster and exhibited higher amplitudes for negative emotional words. These findings shed light on the neural mechanisms of emotional word learning in a volatile environment, highlighting the crucial role of expectations in this process and a preference for learning negative information.

Ventrolateral prefrontal cortex in macaques guides decisions in different learning contexts.

Flexibly adjusting our behavioral strategies based on the environmental context is critical to maximize rewards. Ventrolateral prefrontal cortex (vlPFC) has been implicated in both learning and decision-making for probabilistic rewards, although how context influences these processes remains unclear. We collected functional neuroimaging data while rhesus macaques performed a probabilistic learning task in two contexts: one with novel and another with familiar visual stimuli. We found that activity in vlPFC encoded rewards irrespective of the context but encoded behavioral strategies that depend on reward outcome (win-stay/lose-shift) preferentially in novel contexts. Functional connectivity between vlPFC and anterior cingulate cortex varied with behavioral strategy in novel learning blocks. By contrast, connectivity between vlPFC and mediodorsal thalamus was highest when subjects repeated a prior choice. Furthermore, pharmacological D2-receptor blockade altered behavioral strategies during learning and resting-state vlPFC activity. Taken together, our results suggest that multiple vlPFC-linked circuits contribute to adaptive decision-making in different contexts.

Associations of Reversal Learning Performance With Personality Disorder Profile and Drug Abuse History in a Sample of Prison Inmates.

Prison inmate samples present a high prevalence of impulsivity- and compulsivity-related behavioral problems. The Probabilistic Reversal Learning Task (PRLT) is a useful tool to assess decision-making, and we explore its associations with inmates' personality disorder (antisocial personality disorder, APD; obsessive-compulsive personality disorder, OCPD; or both) and history of drug abuse. Mixed-effects methods were used to model acquisition and reacquisition curves across PRLT, in a sample of 275 prison inmates diagnosed with OCPD, APD, or both. Two aspects were assessed: general discrimination learning and decision-making inflexibility. Participants with a mixed personality disorder profile showed a clear pattern of decisional inflexibility. A history of drug abuse was associated with a general poorer performance but not with decision-making inflexibility. Inability to adapt to changing contingencies, and thus to adverse consequences of previously rewarded choices, was not linked to compulsivity, as hypothesized to be present in OCPD and substance use disorders, but to the mixed APD/OCPD profile.

B - 93 The Role of ex-Gaussian Parameters and Apathy in Reward Sensitivity Using a Modified Probabilistic Learning Task

Abstract Objective The current study examined the associations of apathy with attentional processes, indicated by ex-Gaussian (Ex-G) parameters of sensorimotor response speed and reward learning. Methods One hundred college-aged participants (M age = 20.32; SD = 3.69; 69 female) completed self-report measures of depression, apathy, and attentional control. Laboratory tasks included a choice reaction time (RT) task and a modified probabilistic learning task to assess sensorimotor processing and reward sensitivity. In the probabilistic learning task, participants selected the symbol associated with a high probability of gaining a monetary reward. Choice RT data were fit with Ex-G models, from which the parameter τ (bootstrapped sampling, 1000 iterations) was derived and used as the index of attentional lapses. Results A non-linear relationship was found between apathy and reward learning. Hierarchical regression was used to examine linear and higher-order relationships between apathy and behavioral indices (Ex-G τ and reward sensitivity). Results show that apathy explained none of the variance in reward learning (β = −0.005, p = 0.792, [bootstrapCI = −0.037, 0.026]). Incorporating apathy2 into the model led to significant R2 change (0.086), F(2, 89) = 0.4.142, p = 0.019, suggesting that as apathy increases, reward learning increases (β =0.006, p = 0.002, bootstrapCI = 0.002, 0.010]). Moreover, when controlling for depression, τ significantly contributes to the model, F(3, 89) = 4.142, p = 0.019, R2 = 0.17. Interactive effects revealed that at higher levels of apathy, higher Ex-G τ significantly decreases reward sensitivity (β = −0.003, p = 0.037, 95% bootstrapCI = [−0.006, −002]). Conclusions Higher levels of apathy and attentional lapses jointly reduced reward sensitivity, highlighting the role of task disengagement in apathy.

Reinforcement learning processes as forecasters of depression remission

BackgroundAspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness. MethodsWe applied support vector machines to investigate whether blood‑oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit. ResultsBoth nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status. LimitationsOur sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling). ConclusionsResults support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare.

Photonic probabilistic machine learning using quantum vacuum noise

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element – a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~1 Gbps and energy consumption of ~5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.

Altered learning from positive feedback in adolescents with anorexia nervosa.

Anorexia nervosa (AN) is characterized by severe restriction of calorie intake, which persists despite serious medical and psychological sequelae of starvation. Several prior studies have identified impaired feedback learning among individuals with AN, but whether it reflects a disturbance in learning from positive feedback (i.e., reward), negative feedback (i.e., punishment), or both, and the extent to which this impairment is related to severity and duration of illness, has not been clarified. Participants were female adolescents with AN (n = 76) and healthy teen volunteers (HC; n = 38) between the ages of 12-18 years who completed a probabilistic reinforcement learning task. A Bayesian reinforcement learning model was used to calculate separate learning rates for positive and negative feedback. Exploratory analyses examined associations between feedback learning and duration of illness, eating disorder severity, and self/parent reports of reward and punishment sensitivity. Adolescents with AN had a significantly lower rate of learning from positive feedback relative to HC. Patients and HC did not differ in learning from negative feedback or on overall task performance measures. Feedback learning parameters were not significantly associated with duration of illness, eating disorder severity, or questionnaire-based reports of reward and punishment sensitivity. Adolescents with AN showed a circumscribed deficit in learning from reward that was not associated with duration of illness or reported sensitivity to reward or punishment. Subsequent longitudinal research should explore whether differences in learning from positive feedback relate to course of illness in youth with AN.

Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

Subjective cognitive decline in healthy older adults is associated with altered processing of negative versus positive feedback in a probabilistic learning task.

Older adults who worry about their own cognitive capabilities declining, but who do not show evidence of actual cognitive decline in neuropsychological tests, are at an increased risk of being diagnosed with dementia at a later time. Since neural markers may be more sensitive to early stages of cognitive decline, in the present study we examined whether event-related potential responses of feedback processing, elicited in a probabilistic learning task, differ between healthy older adults recruited from the community, who either did (subjective cognitive decline/SCD-group) or did not report (No-SCD group) worry about their own cognition declining beyond the normal age-related development. In the absence of group differences in learning from emotionally charged feedback in the probabilistic learning task, the amplitude of the feedback-related negativity (FRN) varied with feedback valence differently in the two groups: In the No-SCD group, the FRN was larger for positive than negative feedback, while in the SCD group, FRN amplitude did not differ between positive and negative feedback. The P3b was enhanced for negative feedback in both groups, and group differences in P3b amplitude were not significant. Altered sensitivity in neural processing of negative versus positive feedback may be a marker of SCD.

College students with depression symptom are more sensitive to task difficulty in reinforcement learning

BackgroundDepression is usually characterized by impairments in reward function, and shows altered motivation to reward in reinforcement learning. This study further explored whether task difficulty affects reinforcement learning in college students with and without depression symptom. MethodsThe depression symptom group (20) and the no depression symptom group (26) completed a probabilistic reward learning task with low, medium, and high difficulty levels, in which task the response bias to reward and the discriminability of reward were analyzed. Additionally, electrophysiological responses to reward and loss feedback were recorded and analyzed while they performed a simple gambling task. ResultsThe depression symptom group showed more response bias to reward than the no depression symptom group when the task was easy and then exhibited more quickly decrease in response bias to reward as task difficulty increased. The no depression symptom group showed a decrease in response bias only in the high-difficulty condition. Further regression analyses showed that, the Feedback-related negativity (FRN) and theta oscillation could predict response bias change in the low-difficulty condition, the FRN and oscillations of theta and delta could predict response bias change in the medium and high-difficulty conditions. LimitationsThe electrophysiological responses to loss and reward were not recorded in the same task as the reinforcement learning behaviors. ConclusionsCollege students with depression symptom are more sensitive to task difficulty during reinforcement learning. The FRN, and oscillations of theta and delta could predict reward leaning behavior.

Fecal microbiota transplantation of patients with anorexia nervosa did not alter flexible behavior in rats.

Patients with anorexia nervosa (AN) are often anxious, display inflexible behavior and disrupted reward processing. Emerging evidence suggests that gut dysbiosis in patients contributes to the disease phenotype and progression. In a preclinical study, we explored whether AN-derived microbiota impacts cognitive flexibility, anxiety, and dopamine signaling using fecal microbiota transplantation (FMT) in tyrosine hydroxylase-cre rats. We performed probabilistic reversal learning task (PRLT) at the baseline, after antibiotic treatment, and following FMT from patients with AN and controls. We assessed flexible behavior, task engagement, and ventral tegmental area (VTA) dopamine signaling during and in the absence of reward. Furthermore, anxiety-like behavior was evaluated with open field (OF) and elevated plus maze (EPM) tests. Neither antibiotic-induced dysbiosis nor AN FMT led to significant alterations in the number of reversals or lever press strategies after reinforced or nonreinforced lever presses (win and lose-stay) in the PRLT. However, the number of initiated trials decreased after antibiotic treatment while remaining unchanged after FMT. No significant differences were observed in VTA dopamine activity, anxiety measures in the OF and EPM tests. Microbiome analysis revealed limited overlap between the microbiota of the donors and recipients. No evidence was found that the microbiota of patients compared to controls, nor a depleted microbiome impacts cognitive flexibility. Nonetheless, antibiotic-induced dysbiosis resulted in reduced task engagement during the PRLT. The relatively low efficiency of the FMT is a limitation of our study and highlights the need for improved protocols to draw robust conclusions in future studies. While our study did not reveal direct impacts of AN-associated gut microbiota on cognitive flexibility or anxiety behaviors in our preclinical model, we observed a decrease in task engagement after antibiotic-induced dysbiosis, underscoring that the presence of a gut microbiome matters. Our findings underscore the need for further refinement in FMT protocols to better elucidate the complex interplay between gut microbiota and behaviors characteristic of anorexia nervosa.

Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning

The placebo-reward hypothesis postulates that positive effects of treatment expectations on health (i.e., placebo effects) and reward processing share common neural underpinnings. Moreover, experiments in humans and animals indicate that reward uncertainty increases striatal dopamine, which is presumably involved in placebo responses and reward learning. Therefore, treatment uncertainty analogously to reward uncertainty may affect updating from rewards after placebo treatment. Here, we address whether different degrees of uncertainty regarding the efficacy of a sham treatment affect reward sensitivity. In an online between-subjects experiment with N = 141 participants, we systematically varied the provided efficacy instructions before participants first received a sham treatment that consisted of listening to binaural beats and then performed a probabilistic reinforcement learning task. We fitted a Q-learning model including two different learning rates for positive (gain) and negative (loss) reward prediction errors and an inverse gain parameter to behavioral decision data in the reinforcement learning task. Our results yielded an inverted-U-relationship between provided treatment efficacy probability and learning rates for gain, such that higher levels of treatment uncertainty, rather than of expected net efficacy, affect presumably dopamine-related reward learning. These findings support the placebo-reward hypothesis and suggest harnessing uncertainty in placebo treatment for recovering reward learning capabilities.

Neurocomputational Mechanisms Underlying Differential Reinforcement Learning From Wins and Losses in Obesity With and Without Binge Eating

BackgroundBinge-eating disorder (BED) is thought of as a disorder of cognitive control, but evidence regarding its neurocognitive mechanisms is inconclusive. Key limitations of previous research include a lack of consistent separation between effects of BED and obesity and a disregard for self-report evidence suggesting that neurocognitive alterations may emerge primarily in loss- or harm-avoidance contexts. MethodsTo address these gaps, in this longitudinal study we investigated behavioral flexibility and its underlying neurocomputational processes in reward-seeking and loss-avoidance contexts. Obese participants with BED, obese participants without BED, and healthy normal-weight participants (n = 96) performed a probabilistic reversal learning task during functional imaging, with different blocks focused on obtaining wins or avoiding losses. They were reinvited for a 6-month follow-up assessment. ResultsAnalyses informed by computational models of reinforcement learning showed that unlike obese participants with BED, obese participants without BED performed worse in the win than in the loss condition. Computationally, this was explained by differential learning sensitivities in the win versus loss conditions in the groups. In the brain, this was echoed in differential neural learning signals in the ventromedial prefrontal cortex per condition. The differences were subtle but scaled with BED symptoms, such that more severe BED symptoms were associated with increasing bias toward improved learning from wins versus losses. Across conditions, obese participants with BED switched more between choice options than healthy normal-weight participants. This was reflected in diminished representation of choice certainty in the ventromedial prefrontal cortex. ConclusionsOur study highlights the importance of distinguishing between obesity with and without BED to identify unique neurocomputational alterations underlying different styles of maladaptive eating behavior.

Lesions to the mediodorsal thalamus, but not orbitofrontal cortex, enhance volatility beliefs linked to paranoia

Beliefs-attitudes toward some state of the environment-guide action selection and should be robust to variability but sensitive to meaningful change. Beliefs about volatility (expectation of change) are associated with paranoia in humans, but the brain regions responsible for volatility beliefs remain unknown. The orbitofrontal cortex (OFC) is central to adaptive behavior, whereas the magnocellular mediodorsal thalamus (MDmc) is essential for arbitrating between perceptions and action policies. We assessed belief updating in a three-choice probabilistic reversal learning task following excitotoxic lesions of the MDmc (n = 3) or OFC (n= 3) and compared performance with that of unoperated monkeys (n= 14). Computational analyses indicated a double dissociation: MDmc, but not OFC, lesions were associated with erratic switching behavior and heightened volatility belief (as in paranoia in humans), whereas OFC, but not MDmc, lesions were associated with increased lose-stay behavior and reward learning rates. Given the consilience across species and models, these results have implications for understanding paranoia.

Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in Decision-Making.

In patients with mood disorders, negative affective biases - systematically prioritising and interpreting information negatively - are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced preference for a high reward under ambiguous decision-making conditions. The precise mechanisms underscoring this bias are, however, not yet understood. We therefore developed a set of measures to probe the underlying source of the behavioural bias by testing its relationship to a participant's reward sensitivity, value sensitivity and reward learning rate. One-hundred-forty-eight participants completed three online behavioural tasks: the original ambiguous-cue decision-making task probing negative affective bias, a probabilistic reward learning task probing reward sensitivity and reward learning rate, and a gambling task probing value sensitivity. We modelled the learning task through a dynamic signal detection theory model and the gambling task through an expectation-maximisation prospect theory model. Reward sensitivity from the probabilistic reward task (β = 0.131, p = 0.024) and setting noise from the probabilistic reward task (β = -0.187, p = 0.028) both predicted the affective bias score in a logistic regression. Increased negative affective bias, at least on this specific task, may therefore be driven in part by a combination of reduced sensitivity to rewards and more variable responses.

Belief Updating, Childhood Maltreatment, and Paranoia in Schizophrenia-Spectrum Disorders.

Exposure to childhood maltreatment-a risk factor for psychosis is associated with paranoia-may impact one's beliefs about the world and how beliefs are updated. We hypothesized that increased exposure to childhood maltreatment is related to volatility-related belief updating, specifically higher expectations of volatility, and that these relationships are strongest for threat-related maltreatment. Additionally, we tested whether belief updating mediates the relationship between maltreatment and paranoia. Belief updating was measured in 75 patients with schizophrenia-spectrum disorders and 76 nonpsychiatric controls using a 3-option probabilistic reversal learning (3PRL) task. A Hierarchical Gaussian Filter (HGF) was used to estimate computational parameters of belief updating, including prior expectations of volatility (μ03). The Childhood Trauma Questionnaire (CTQ) was used to assess cumulative maltreatment, threat, and deprivation exposure. Paranoia was measured using the Positive and Negative Syndrome Scale (PANSS) and the revised Green et al. Paranoid Thoughts Scale (R-GPTS). Greater exposure to childhood maltreatment is associated with higher prior expectations of volatility in the whole sample and in individuals with schizophrenia-spectrum disorders. This was specific to threat-related maltreatment, rather than deprivation, in schizophrenia-spectrum disorders. Paranoia was associated with both exposure to childhood maltreatment and volatility priors, but we did not observe a significant indirect effect of volatility priors on the relationship between maltreatment and paranoia. Our study suggests that individuals with schizophrenia-spectrum disorders who were exposed to threatening experiences during childhood expect their environment to be more volatile, potentially facilitating aberrant belief updating and conferring risk for paranoia.