Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood.
ABSTRACT: Pavlovian influences are important in guiding decision-making across health and psychopathology. There is an increasing interest in using concise computational tasks to parametrise such influences in large populations, and especially to track their evolution during development and changes in mental health. However, the developmental course of Pavlovian influences is uncertain, a problem compounded by the unclear psychometric properties of the relevant measurements. We assessed Pavlovian influences in a longitudinal sample using a well characterised and widely used Go-NoGo task. We hypothesized that the strength of Pavlovian influences and other 'psychomarkers' guiding decision-making would behave like traits. As reliance on Pavlovian influence is not as profitable as precise instrumental decision-making in this Go-NoGo task, we expected this influence to decrease with higher IQ and age. Additionally, we hypothesized it would correlate with expressions of psychopathology. We found that Pavlovian effects had weak temporal stability, while model-fit was more stable. In terms of external validity, Pavlovian effects decreased with increasing IQ and experience within the task, in line with normative expectations. However, Pavlovian effects were poorly correlated with age or psychopathology. Thus, although this computational construct did correlate with important aspects of development, it does not meet conventional requirements for tracking individual development. We suggest measures that might improve psychometric properties of task-derived Pavlovian measures for future studies.
Project description:BACKGROUND:Schizophrenia is a complex disorder in which the causal relations between risk genes and observed clinical symptoms are not well understood and the explanatory gap is too wide to be clarified without considering an intermediary level. Thus, we aimed to test the hypothesis of a pathway from molecular polygenic influence to clinical presentation occurring via deficits in reinforcement learning. METHODS:We administered a reinforcement learning task (Go/NoGo) that measures reinforcement learning and the effect of Pavlovian bias on decision making. We modelled the behavioural data with a hierarchical Bayesian approach (hBayesDM) to decompose task performance into its underlying learning mechanisms. Study 1 included controls (n = 29, F|M = 0.81), At Risk Mental State for psychosis (ARMS, n = 23, F|M = 0.35) and FEP (First-episode psychosis, n = 26, F|M = 0.18). Study 2 included healthy adolescents (n = 735, F|M = 1.06), 390 of whom had their polygenic risk scores for schizophrenia (PRSs) calculated. RESULTS:Patients with FEP showed significant impairments in overriding Pavlovian conflict, a lower learning rate and a lower sensitivity to both reward and punishment. Less widespread deficits were observed in ARMS. PRSs did not significantly predict performance on the task in the general population, which only partially correlated with measures of psychopathology. CONCLUSIONS:Reinforcement learning deficits are observed in first episode psychosis and, to some extent, in those at clinical risk for psychosis, and were not predicted by molecular genetic risk for schizophrenia in healthy individuals. The study does not support the role of reinforcement learning as an intermediate phenotype in psychosis.
Project description:Motor decision-making is an essential component of everyday life which requires weighing potential rewards and punishments against the probability of successfully executing an action. To achieve this, humans rely on two key mechanisms; a flexible, instrumental, value-dependent process and a hardwired, Pavlovian, value-independent process. In economic decision-making, age-related decline in risk taking is explained by reduced Pavlovian biases that promote action toward reward. Although healthy ageing has also been associated with decreased risk-taking in motor decision-making, it is currently unknown whether this is a result of changes in Pavlovian biases, instrumental processes or a combination of both. Using a newly established approach-avoidance computational model together with a novel app-based motor decision-making task, we measured sensitivity to reward and punishment when participants (n = 26,532) made a 'go/no-go' motor gamble based on their perceived ability to execute a complex action. We show that motor decision-making can be better explained by a model with both instrumental and Pavlovian parameters, and reveal age-related changes across punishment- and reward-based instrumental and Pavlovian processes. However, the most striking effect of ageing was a decrease in Pavlovian attraction towards rewards, which was associated with a reduction in optimality of choice behaviour. In a subset of participants who also played an independent economic decision-making task (n = 17,220), we found similar decision-making tendencies for motor and economic domains across a majority of age groups. Pavlovian biases, therefore, play an important role in not only explaining motor decision-making behaviour but also the changes which occur through normal ageing. This provides a deeper understanding of the mechanisms which shape motor decision-making across the lifespan.
Project description:Motivation exerts control over behavior by eliciting Pavlovian responses, which can either match or conflict with instrumental action. We can overcome maladaptive motivational influences putatively through frontal cognitive control. However, the neurocomputational mechanisms subserving this control are unclear; does control entail up-regulating instrumental systems, down-regulating Pavlovian systems, or both? We combined electroencephalography (EEG) recordings with a motivational Go/NoGo learning task (N = 34), in which multiple Go options enabled us to disentangle selective action learning from nonselective Pavlovian responses. Midfrontal theta-band (4 Hz-8 Hz) activity covaried with the level of Pavlovian conflict and was associated with reduced Pavlovian biases rather than reduced instrumental learning biases. Motor and lateral prefrontal regions synchronized to the midfrontal cortex, and these network dynamics predicted the reduction of Pavlovian biases over and above local, midfrontal theta activity. This work links midfrontal processing to detecting Pavlovian conflict and highlights the importance of network processing in reducing the impact of maladaptive, Pavlovian biases.
Project description:The dynamic modulation of instrumental behaviour by conditioned Pavlovian cues is an important process in decision-making. Patients with major depressive disorder (MDD) are known to exhibit mood-congruent biases in information processing, which may occur due to Pavlovian influences, but this hypothesis has never been tested directly in an unmedicated sample. To address this we tested unmedicated MDD patients and healthy volunteers on a computerized Pavlovian-Instrumental Transfer (PIT) task designed to separately examine instrumental approach and withdrawal actions in the context of Pavlovian appetitive and aversive cues. This design allowed us to directly measure the degree to which Pavlovian cues influence instrumental responding. Depressed patients were profoundly influenced by aversive Pavlovian stimuli, to a significantly greater degree than healthy volunteers. This was the case for instrumental behaviour both in the approach condition (in which aversive Pavlovian cues inhibited 'go' responses), and in the withdrawal condition (in which aversive Pavlovian cues facilitated 'go' responses). Exaggerated aversive PIT provides a potential cognitive mechanism for biased emotion processing in major depression. This finding also has wider significance for the understanding of disrupted motivational processing in neuropsychiatric disorders.
Project description:BACKGROUND:Aversive stimuli in the environment influence human actions. This includes valence-dependent influences on action selection, e.g., increased avoidance but decreased approach behavior. However, it is yet unclear how aversive stimuli interact with complex learning and decision-making in the reward and avoidance domain. Moreover, the underlying computational mechanisms of these decision-making biases are unknown. METHODS:To elucidate these mechanisms, 54 healthy young male subjects performed a two-step sequential decision-making task, which allows to computationally model different aspects of learning, e.g., model-free, habitual, and model-based, goal-directed learning. We used a within-subject design, crossing task valence (reward vs. punishment learning) with emotional context (aversive vs. neutral background stimuli). We analyzed choice data, applied a computational model, and performed simulations. RESULTS:Whereas model-based learning was not affected, aversive stimuli interacted with model-free learning in a way that depended on task valence. Thus, aversive stimuli increased model-free avoidance learning but decreased model-free reward learning. The computational model confirmed this effect: the parameter lambda that indicates the influence of reward prediction errors on decision values was increased in the punishment condition but decreased in the reward condition when aversive stimuli were present. Further, by using the inferred computational parameters to simulate choice data, our effects were captured. Exploratory analyses revealed that the observed biases were associated with subclinical depressive symptoms. CONCLUSION:Our data show that aversive environmental stimuli affect complex learning and decision-making, which depends on task valence. Further, we provide a model of the underlying computations of this affective modulation. Finally, our finding of increased decision-making biases in subjects reporting subclinical depressive symptoms matches recent reports of amplified Pavlovian influences on action selection in depression and suggests a potential vulnerability factor for mood disorders. We discuss our findings in the light of the involvement of the neuromodulators serotonin and dopamine.
Project description:BACKGROUND:Changes in reflexive emotional responses are hallmarks of depression, but how emotional reflexes make an impact on adaptive decision-making in depression has not been examined formally. Using a Pavlovian-instrumental transfer (PIT) task, we compared the influence of affectively valenced stimuli on decision-making in depression and generalized anxiety disorder compared with healthy controls; and related this to the longitudinal course of the illness. METHOD:A total of 40 subjects with a current DSM-IV-TR diagnosis of major depressive disorder, dysthymia, generalized anxiety disorder, or a combination thereof, and 40 matched healthy controls performed a PIT task that assesses how instrumental approach and withdrawal behaviours are influenced by appetitive and aversive Pavlovian conditioned stimuli (CSs). Patients were followed up after 4-6 months. Analyses focused on patients with depression alone (n = 25). RESULTS:In healthy controls, Pavlovian CSs exerted action-specific effects, with appetitive CSs boosting active approach and aversive CSs active withdrawal. This action-specificity was absent in currently depressed subjects. Greater action-specificity in patients was associated with better recovery over the follow-up period. CONCLUSIONS:Depression is associated with an abnormal influence of emotional reactions on decision-making in a way that may predict recovery.
Project description:Neural mechanisms of decision-making and reward response in adolescent cannabis use disorder (CUD) are underexplored.Three groups of male adolescents were studied: CUD in full remission (n=15); controls with psychopathology without substance use disorder history (n=23); and healthy controls (n=18). We investigated neural processing of decision-making and reward under conditions of varying risk and uncertainty with the Decision-Reward Uncertainty Task while participants were scanned using functional magnetic resonance imaging.Abstinent adolescents with CUD compared to controls with psychopathology showed hyperactivation in one cluster that spanned left superior parietal lobule/left lateral occipital cortex/precuneus while making risky decisions that involved uncertainty, and hypoactivation in left orbitofrontal cortex to rewarded outcomes compared to no-reward after making risky decisions. Post hoc region of interest analyses revealed that both control groups significantly differed from the CUD group (but not from each other) during both the decision-making and reward outcome phase of the Decision-Reward Uncertainty Task. In the CUD group, orbitofrontal activations to reward significantly and negatively correlated with total number of individual drug classes the CUD patients experimented with prior to treatment. CUD duration significantly and negatively correlated with orbitofrontal activations to no-reward.The adolescent CUD group demonstrated distinctly different activation patterns during risky decision-making and reward processing (after risky decision-making) compared to both the controls with psychopathology and healthy control groups. These findings suggest that neural differences in risky decision-making and reward processes are present in adolescent addiction, persist after remission from first CUD treatment, and may contribute to vulnerability for adolescent addiction.
Project description:Adapting behavior to changes in the environment is a crucial ability for survival but such adaptation varies widely across individuals. Here, we asked how humans alter their economic decision-making in response to emotional cues, and whether this is related to trait anxiety. Developing an emotional decision-making task for functional magnetic resonance imaging, in which gambling decisions were preceded by emotional and non-emotional primes, we assessed emotional influences on loss aversion, the tendency to overweigh potential monetary losses relative to gains. Our behavioral results revealed that only low-anxious individuals exhibited increased loss aversion under emotional conditions. This emotional modulation of decision-making was accompanied by a corresponding emotion-elicited increase in amygdala-striatal functional connectivity, which correlated with the behavioral effect across participants. Consistent with prior reports of 'neural loss aversion', both amygdala and ventral striatum tracked losses more strongly than gains, and amygdala loss aversion signals were exaggerated by emotion, suggesting a potential role for this structure in integrating value and emotion cues. Increased loss aversion and striatal-amygdala coupling induced by emotional cues may reflect the engagement of adaptive harm-avoidance mechanisms in low-anxious individuals, possibly promoting resilience to psychopathology.
Project description:Information sampling is often biased towards seeking evidence that confirms one's prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled ("positive evidence approach"), the selection of which information to sample ("sampling the favorite"), and the interaction between information sampling and subsequent choices ("rejecting unsampled options"). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.
Project description:According to dual-system accounts, instrumental learning is supported by both a goal-directed and a habitual system. Although behavioral control by the goal-directed system, through outcome-action associations, dominates with moderate training, stimulus-response associations are thought to form concurrently in the habit system. It is therefore challenging to isolate the neural substrate of the goal-directed system in neuroimaging research with healthy human volunteers. Recently, however, de Wit et al. (2007) developed an instrumental discrimination task that distinguishes between goal-directed and habit-based responding. In this task, cues are congruent, unrelated, or incongruent with subsequent outcomes. Whereas performance on congruent and control trials can be supported by both the goal-directed and habitual system, performance on the incongruent discrimination relies solely on the habit system. In the present study, we used this task with healthy participants undergoing functional magnetic resonance imaging to demonstrate that engagement of the goal-directed system during learning is reflected in increased activity in the ventromedial prefrontal cortex. Moreover, using a subsequent outcome devaluation manipulation, we show that this area is involved in guiding decision making when goal values change, even in the absence of external cues to guide performance. We can therefore exclude a purely Pavlovian account of ventromedial prefrontal function and unequivocally demonstrate its involvement in the acquisition as well as deployment of goal-directed knowledge.