Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making

Common neural dysfunction of economic decision-making across psychiatric conditions

Adaptive decision-making, which is often impaired in various psychiatric conditions, is essential for well-being. Recent evidence has indicated that decision-making capacity in multiple tasks could be accounted for by latent dimensions, enlightening the question of whether there is a common disruption of brain networks in economic decision-making across psychiatric conditions. Here, we addressed the issue by combining activation/lesion network mapping analyses with a transdiagnostic brain imaging meta-analysis. Our findings indicate that there were transdiagnostic alterations in the thalamus and ventral striatum during the decision or outcome stage of decision-making. The identified regions represent key nodes in a large-scale network, which is composed of multiple heterogeneous brain regions and plays a causal role in motivational functioning. The findings suggest that disturbances in the network associated with emotion- and reward-related processing play a key role in dysfunctions of decision-making observed in various psychiatric conditions. This study provides the first meta-analytic evidence of common neural alterations linked to deficits in economic decision-making.

A cognitive-computational account of mood swings in adolescence

Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

The neurobehavioral correlates of exploration without learning: Trading off value for explicit, prospective, and variable information gains

Exploration is typically motivated by gaining information, with previous research showing that potential information gains drive a "directed" type of exploration. Yet, this research usually studies exploration in the context of learning paradigms and does not directly manipulate multiple levels of information gain. Here, we present a task that isolates learning from decision-making and controls the magnitude of prospective information gains. As predicted, participants explore more with larger future information gains. Both value gains and information gains, at a trial-by-trial level, engage the ventromedial prefrontal cortex (vmPFC), the ventral striatum (VStr), the amygdala, the dorsal anterior cingulate cortex (dACC), and the anterior insula (aINS). Moreover, individual sensitivities to value gains and information gains modulate the vmPFC, dACC, and aINS, but the amygdala and VStr are modulated only by individual sensitivities to information gains. Overall, we identify the neural circuitry of information-based exploration and its relationship with inter-individual exploration biases.

Depression is associated with blunted affective responses to naturalistic reward prediction errors

BACKGROUND

Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown.

METHODS

Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events - the reveal of midterm exam grades - to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs.

RESULTS

Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes - that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding.

CONCLUSIONS

Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.

Naturalistic reinforcement learning

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

State-transition-free reinforcement learning in chimpanzees (Pan troglodytes)

The outcome of an action often occurs after a delay. One solution for learning appropriate actions from delayed outcomes is to rely on a chain of state transitions. Another solution, which does not rest on state transitions, is to use an eligibility trace (ET) that directly bridges a current outcome and multiple past actions via transient memories. Previous studies revealed that humans (Homo sapiens) learned appropriate actions in a behavioral task in which solutions based on the ET were effective but transition-based solutions were ineffective. This suggests that ET may be used in human learning systems. However, no studies have examined nonhuman animals with an equivalent behavioral task. We designed a task for nonhuman animals following a previous human study. In each trial, participants chose one of two stimuli that were randomly selected from three stimulus types: a stimulus associated with a food reward delivered immediately, a stimulus associated with a reward delivered after a few trials, and a stimulus associated with no reward. The presented stimuli did not vary according to the participants' choices. To maximize the total reward, participants had to learn the value of the stimulus associated with a delayed reward. Five chimpanzees (Pan troglodytes) performed the task using a touchscreen. Two chimpanzees were able to learn successfully, indicating that learning mechanisms that do not depend on state transitions were involved in the learning processes. The current study extends previous ET research by proposing a behavioral task and providing empirical data from chimpanzees.

Interindividual Brain and Behavior Differences in Adaptation to Unexpected Uncertainty

To adapt to a new environment, individuals must alternate between exploiting previously learned "action-consequence" combinations and exploring new actions for which the consequences are unknown: they face an exploration/exploitation trade-off. The neural substrates of these behaviors and the factors that may relate to the interindividual variability in their expression remain overlooked, in particular when considering neural connectivity patterns. Here, to trigger environmental uncertainty, false feedbacks were introduced in the second phase of an associative learning task. Indices reflecting exploitation and cost of uncertainty were computed. Changes in the intrinsic connectivity were determined using resting-state functional connectivity (rFC) analyses before and after performing the "cheated" phase of the task in the MRI. We explored their links with behavioral and psychological factors. Dispersion in the participants' cost of uncertainty was used to categorize two groups. These groups showed different patterns of rFC changes. Moreover, in the overall sample, exploitation was correlated with rFC changes between (1) the anterior cingulate cortex and the cerebellum region 3, and (2) the left frontal inferior gyrus (orbital part) and the right frontal inferior gyrus (triangular part). Anxiety and doubt about action propensity were weakly correlated with some rFC changes. These results demonstrate that the exploration/exploitation trade-off involves the modulation of cortico-cerebellar intrinsic connectivity.

Modulation of ventromedial orbitofrontal cortical glutamatergic activity affects the explore-exploit balance and influences value-based decision-making

The balance between exploration and exploitation is essential for decision-making. The present study investigated the role of ventromedial orbitofrontal cortex (vmOFC) glutamate neurons in mediating value-based decision-making by first using optogenetics to manipulate vmOFC glutamate activity in rats during a probabilistic reversal learning (PRL) task. Rats that received vmOFC activation during informative feedback completed fewer reversals and exhibited reduced reward sensitivity relative to rats. Analysis with a Q-learning computational model revealed that increased vmOFC activity did not affect the learning rate but instead promoted maladaptive exploration. By contrast, vmOFC inhibition increased the number of completed reversals and increased exploitative behavior. In a separate group of animals, calcium activity of vmOFC glutamate neurons was recorded using fiber photometry. Complementing our results above, we found that suppression of vmOFC activity during the latter part of rewarded trials was associated with improved PRL performance, greater win-stay responding and selecting the correct choice on the next trial. These data demonstrate that excessive vmOFC activity during reward feedback disrupted value-based decision-making by increasing the maladaptive exploration of lower-valued options. Our findings support the premise that pharmacological interventions that normalize aberrant vmOFC glutamate activity during reward feedback processing may attenuate deficits in value-based decision-making.

Belief updating in psychosis, depression and anxiety disorders: A systematic review across computational modelling approaches

Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.

Pavlovian-to-Instrumental Transfer across Mental Disorders: A Review

A mechanism known as Pavlovian-to-instrumental transfer (PIT) describes a phenomenon by which the values of environmental cues acquired through Pavlovian conditioning can motivate instrumental behavior. PIT may be one basic mechanism of action control that can characterize mental disorders on a dimensional level beyond current classification systems. Therefore, we review human PIT studies investigating subclinical and clinical mental syndromes. The literature prevails an inhomogeneous picture concerning PIT. While enhanced PIT effects seem to be present in non-substance-related disorders, overweight people, and most studies with AUD patients, no altered PIT effects were reported in tobacco use disorder and obesity. Regarding AUD and relapsing alcohol-dependent patients, there is mixed evidence of enhanced or no PIT effects. Additionally, there is evidence for aberrant corticostriatal activation and genetic risk, e.g., in association with high-risk alcohol consumption and relapse after alcohol detoxification. In patients with anorexia nervosa, stronger PIT effects elicited by low caloric stimuli were associated with increased disease severity. In patients with depression, enhanced aversive PIT effects and a loss of action-specificity associated with poorer treatment outcomes were reported. Schizophrenic patients showed disrupted specific but intact general PIT effects. Patients with chronic back pain showed reduced PIT effects. We provide possible reasons to understand heterogeneity in PIT effects within and across mental disorders. Further, we strengthen the importance of reliable experimental tasks and provide test-retest data of a PIT task showing moderate to good reliability. Finally, we point toward stress as a possible underlying factor that may explain stronger PIT effects in mental disorders, as there is some evidence that stress per se interacts with the impact of environmental cues on behavior by selectively increasing cue-triggered wanting. To conclude, we discuss the results of the literature review in the light of Research Domain Criteria, suggesting future studies that comprehensively assess PIT across psychopathological dimensions.

The (un)learning of social functions and its significance for mental health

Social interactions are dynamic, context-dependent, and reciprocal events that influence prospective strategies and require constant practice and adaptation. This complexity of social interactions creates several research challenges. We propose a new framework encouraging future research to investigate not only individual differences in capacities relevant for social functioning and their underlying mechanisms, but also the flexibility to adapt or update one's social abilities. We suggest three key capacities relevant for social functioning: (1) social perception, (2) sharing emotions or empathizing, and (3) mentalizing. We elaborate on how adaptations in these capacities may be investigated on behavioral and neural levels. Research on these flexible adaptations of one's social behavior is needed to specify how humans actually "learn to be social". Learning to adapt implies plasticity of the relevant brain networks involved in the underlying social processes, indicating that social abilities are malleable for different contexts. To quantify such measures, researchers need to find ways to investigate learning through dynamic changes in adaptable social paradigms and examine several factors influencing social functioning within the three aformentioned social key capacities. This framework furthers insight concerning individual differences, provides a holistic approach to social functioning, and may improve interventions for ameliorating social abilities in patients.

Relationship between nuclei-specific amygdala connectivity and mental health dimensions in humans

There has been increasing interest in using neuroimaging measures to predict psychiatric disorders. However, predictions usually rely on large brain networks and large disorder heterogeneity. Thus, they lack both anatomical and behavioural specificity, preventing the advancement of targeted interventions. Here we address both challenges. First, using resting-state functional magnetic resonance imaging, we parcellated the amygdala, a region implicated in mood disorders, into seven nuclei. Next, a questionnaire factor analysis provided subclinical mental health dimensions frequently altered in anxious-depressive individuals, such as negative emotions and sleep problems. Finally, for each behavioural dimension, we identified the most predictive resting-state functional connectivity between individual amygdala nuclei and highly specific regions of interest, such as the dorsal raphe nucleus in the brainstem or medial frontal cortical regions. Connectivity in circumscribed amygdala networks predicted behaviours in an independent dataset. Our results reveal specific relations between mental health dimensions and connectivity in precise subcortical networks.

Influence of Rat Central Thalamic Neurons on Foraging Behavior in a Hazardous Environment

Foraging entails a complex balance between approach and avoidance alongside sensorimotor and homeostatic processes under the control of multiple cortical and subcortical areas. Recently, it has become clear that several thalamic nuclei located near the midline regulate motivated behaviors. However, one midline thalamic nucleus that projects to key nodes in the foraging network, the central medial thalamic nucleus (CMT), has received little attention so far. Therefore, the present study examined CMT contributions to foraging behavior using inactivation and unit recording techniques in male rats. Inactivation of CMT or the basolateral amygdala (BLA) with muscimol abolished the normally cautious behavior of rats in the foraging task. Moreover, CMT neurons showed large but heterogeneous activity changes during the foraging task, with many neurons decreasing or increasing their discharge rates, with a modest bias for the latter. A generalized linear model revealed that the nature (inhibitory vs excitatory) and relative magnitude of the activity modulations seen in CMT neurons differed markedly from those of principal BLA cells but were very similar to those of fast-spiking BLA interneurons. Together, these findings suggest that CMT is an important regulator of foraging behavior. In the Discussion, we consider how CMT is integrated into the network of structures that regulate foraging.SIGNIFICANCE STATEMENT Foraging entails a complex balance between approach and avoidance alongside sensorimotor and homeostatic processes under the control of multiple cortical and subcortical areas. Although the central medial thalamic nucleus (CMT) is connected to many nodes in this network, its role in the regulation of foraging behavior has not been investigated so far. Here, we examined CMT contributions to foraging behavior using inactivation and unit recording techniques. We found that CMT inactivation abolishes the normally cautious foraging behavior of rats and that CMT neurons show large but heterogeneous changes in firing rates during the foraging task. Together, these results suggest that CMT is an important regulator of foraging behavior.

Differentiating brain function of punishment versus reward processing in conduct disorder with and without attention deficit hyperactivity disorder

OBJECTIVES

Conduct disorder (CD) and attention-deficit/hyperactivity disorder (ADHD) are reported to co-occur in about 30-50% of affected individuals. Research suggests that poor reinforcement-based decision-making may contribute to impaired social functioning in both youths with CD and ADHD. Considering its frequent co-occurrence this raises the question whether decision-making deficits in both disorders have a disorder-specific and/or shared neurobiological basis.

METHODS

138 participants with CD, ADHD, or CD + ADHD, and typically developing controls (TDCs) aged 9-18 years (48% girls) were included in the study. Participants completed a reinforcement-based decision-making task in the fMRI scanner, investigating decision-making capabilities under different reinforcement contingencies (i.e. punishment vs. reward). Whole-brain and ROI analyses were used to test for potential group differences.

RESULTS

For punishment versus reward contingencies, relative to TDCs, youths with CD + ADHD displayed lower brain activity in dorsal striatum (incl. caudate), middle temporal gyrus (MTG), inferior frontal gyrus (IFG) and lateral occipital cortex, and they showed lower activity in dorsal striatum (incl. putamen), orbitofrontal cortex (OFC) and IFG relative to participants with ADHD. All other group comparisons were found to be non-significant.

CONCLUSIONS

Participants with comorbid CD + ADHD are neurobiologically the most severely impaired group regarding reinforcement-based decision-making, particularly in response to punishment.

The effect of apathy and compulsivity on planning and stopping in sequential decision-making

Real-life decision-making often comprises sequences of successive decisions about whether to take opportunities as they are encountered or keep searching for better ones instead. We investigated individual differences related to such sequential decision-making and link them especially to apathy and compulsivity in a large online sample (discovery sample: n = 449 and confirmation sample: n = 756). Our cognitive model revealed distinct changes in the way participants evaluated their environments and planned their own future behaviour. Apathy was linked to decision inertia, i.e., automatically persisting with a sequence of searches for longer than appropriate given the value of searching. Thus, despite being less motivated, they did not avoid the effort associated with longer searches. In contrast, compulsivity was linked to self-reported insensitivity to the cost of continuing with a sequence of searches. The objective measures of behavioural cost insensitivity were clearly linked to compulsivity only in the discovery sample. While the confirmation sample showed a similar effect, it did not reach significance. Nevertheless, in both samples, participants reported awareness of such bias (experienced as "overchasing"). In addition, this awareness made them report preemptively avoiding situations related to the bias. However, we found no evidence of them actually preempting more in the task, which might mean a misalignment of their metacognitive beliefs or that our behavioural measures were incomplete. In summary, individual variation in distinct, fundamental aspects of sequential decision-making can be linked to variation in 2 measures of behavioural traits associated with psychological illness in the normal population.

A neural and behavioral trade-off between value and uncertainty underlies exploratory decisions in normative anxiety

Exploration reduces uncertainty about the environment and improves the quality of future decisions, but at the cost of provisional uncertain and suboptimal outcomes. Although anxiety promotes intolerance to uncertainty, it remains unclear whether and by which mechanisms anxiety relates to exploratory decision-making. We use a dynamic three-armed-bandit task and find that higher trait-anxiety is associated with increased exploration, which in turn harms overall performance. We identify two distinct behavioral sources: first, decisions made by anxious individuals are guided toward reduction of uncertainty; and second, decisions are less guided by immediate value gains. These findings are similar in both loss and gain domains, and further demonstrate that an affective trait relates to exploration and results in an inverse-U-shaped relationship between anxiety and overall performance. Additional imaging data (fMRI) suggests that normative anxiety correlates negatively with the representation of expected-value in the dorsal-anterior-cingulate-cortex, and in contrast, positively with the representation of uncertainty in the anterior-insula. We conclude that a trade-off between value-gains and uncertainty-reduction entails maladaptive decision-making in individuals with higher normal-range anxiety.

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.

Computational modelling of social cognition and behaviour-a reinforcement learning primer

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

Decision-making as transdiagnostic construct for mental health research

Transdiagnostic research and linking behavioral, neural, and symptom measures are important endeavors in clinical neuroscience and biological psychiatry. In this issue of Neuron, Moutoussis et al. (2021) describe a new cognitive construct-decision acuity-that is related to mental health symptoms and distinct resting-state networks.

Maladaptive Avoidance Learning in the Orbitofrontal Cortex in Adolescents With Major Depression

BACKGROUND

Understanding the mechanisms in the brain's incentive network that give rise to symptoms of major depressive disorder (MDD) during adolescence provides new perspectives to address MDD in early stages of development. This functional magnetic resonance imaging study determines whether instrumental vigor and brain responses to appetitive and aversive monetary incentives are altered in adolescent MDD and associated with symptom severity.

METHODS

Adolescents with moderate to severe MDD (n = 30, mean age [SD] = 16.1 [1.4] years) and healthy control subjects (n = 33, mean age = 16.2 [1.9] years) matched for age, sex, and IQ performed a monetary incentive delay task. During outcome presentation, prediction error signals were used to study the response and coupling of the incentive network during learning of cue-outcome associations. A computational reinforcement model was used to assess adaptation of response vigor. Brain responses and effective connectivity to model-derived prediction errors were assessed and related to depression severity and anhedonia levels.

RESULTS

Participants with MDD behaved according to a more simplistic learning model and exhibited slower learning. Effective connectivity analysis of functional magnetic resonance imaging data revealed that impaired loss error processing in the orbitofrontal cortex was associated with aberrant gain control. Anhedonia scores correlated with loss-related error signals in the posterior insula and habenula.

CONCLUSIONS

Adolescent MDD is selectively related to impaired processing of error signals during loss, but not reward, in the orbitofrontal cortex. Aberrant evaluation of loss outcomes might reflect an early mechanism of how negative bias and helplessness manifest in the brain. This approach sheds light on pathomechanisms in MDD and may improve early diagnosis and treatment selection.

Decision-making ability, psychopathology, and brain connectivity

Decision-making is a cognitive process of central importance for the quality of our lives. Here, we ask whether a common factor underpins our diverse decision-making abilities. We obtained 32 decision-making measures from 830 young people and identified a common factor that we call "decision acuity," which was distinct from IQ and reflected a generic decision-making ability. Decision acuity was decreased in those with aberrant thinking and low general social functioning. Crucially, decision acuity and IQ had dissociable brain signatures, in terms of their associated neural networks of resting-state functional connectivity. Decision acuity was reliably measured, and its relationship with functional connectivity was also stable when measured in the same individuals 18 months later. Thus, our behavioral and brain data identify a new cognitive construct that underpins decision-making ability across multiple domains. This construct may be important for understanding mental health, particularly regarding poor social function and aberrant thought patterns.

Contribution of Eye-Tracking to Study Cognitive Impairments Among Clinical Populations

In the field of psychology, the merge of decision-theory and neuroscientific methods produces an array of scientifically recognized paradigms. For example, by exploring consumer’s eye-movement behavior, researchers aim to deepen the understanding of how patterns of retinal activation are being meaningfully transformed into visual experiences and connected with specific reactions (e.g., purchase). Notably, eye-movements provide knowledge of one’s homeostatic balance and gatekeep information that shape decisions. Hence, vision science investigates the quality of observed environments determined under various experimental conditions. Moreover, it answers questions on how human process visual stimuli and use gained information for a successful strategy to achieve certain goals. While capturing cognitive states with the support of the eye-trackers progresses at a relatively fast pace in decision-making research, measuring the visual performance of real-life tasks, which require complex cognitive skills, is tentatively translated into clinical experiments. Nevertheless, the potential of the human eye as a highly valuable source of biomarkers has been underlined. In this article, we aim to draw readers attention to decision-making experimental paradigms supported with eye-tracking technology among clinical populations. Such interdisciplinary approach may become an important component that will (i) help in objectively illustrating patient’s models of beliefs and values, (ii) support clinical interventions, and (iii) contribute to health services. It is possible that shortly, eye-movement data from decision-making experiments will grant the scientific community a greater understanding of mechanisms underlining mental states and consumption practices that medical professionals consider as obsessions, disorders or addiction.

Recent updates of eye movement abnormalities in patients with schizophrenia: A scoping review

AIM

Although eye-tracking technology expands beyond capturing eye data just for the sole purpose of ensuring participants maintain their gaze at the presented fixation cross, gaze technology remains of less importance in clinical research. Recently, impairments in visual information encoding processes indexed by novel gaze metrics have been frequently reported in patients with schizophrenia. This work undertakes a scoping review of research on saccadic dysfunctions and exploratory eye movement deficits among patients with schizophrenia. It gathers promising pieces of evidence of eye movement abnormalities in attention-demanding tasks on the schizophrenia spectrum that have mounted in recent years and their outcomes as potential biological markers.

METHODS

The protocol was drafted based on PRISMA for scoping review guidelines. Electronic databases were systematically searched to identify articles published between 2010 and 2020 that examined visual processing in patients with schizophrenia and reported eye movement characteristics as potential biomarkers for this mental illness.

RESULTS

The use of modern eye-tracking instrumentation has been reported by numerous neuroscientific studies to successfully and non-invasively improve the detection of visual information processing impairments among the screened population at risk of and identified with schizophrenia.

CONCLUSIONS

Eye-tracking technology has the potential to contribute to the process of early intervention and more apparent separation of the diagnostic entities, being put together by the syndrome-based approach to the diagnosis of schizophrenia. However, context-processing paradigms should be conducted and reported in equally accessible publications to build comprehensive models.

Momentary subjective well-being depends on learning and not reward

Subjective well-being or happiness is often associated with wealth. Recent studies suggest that momentary happiness is associated with reward prediction error, the difference between experienced and predicted reward, a key component of adaptive behaviour. We tested subjects in a reinforcement learning task in which reward size and probability were uncorrelated, allowing us to dissociate between the contributions of reward and learning to happiness. Using computational modelling, we found convergent evidence across stable and volatile learning tasks that happiness, like behaviour, is sensitive to learning-relevant variables (i.e. probability prediction error). Unlike behaviour, happiness is not sensitive to learning-irrelevant variables (i.e. reward prediction error). Increasing volatility reduces how many past trials influence behaviour but not happiness. Finally, depressive symptoms reduce happiness more in volatile than stable environments. Our results suggest that how we learn about our world may be more important for how we feel than the rewards we actually receive.

Many people believe they would be happier if only they had more money. And events such as winning the lottery or receiving a large pay rise do make people happy, at least temporarily. But recent studies suggest that the main factor driving happiness on such occasions is not the size of the reward received. Instead, it is how well that reward matches up with expectations. Receiving a 10% pay rise when you were expecting 1% will make you feel happier than receiving 10% when you had been expecting 20%.

This difference between an expected and an actual reward is referred to as a reward prediction error. Reward prediction errors have a key role in learning. They motivate people to repeat behaviours that led to unexpectedly large rewards. But they also enable people to update their beliefs about the world, which is rewarding in itself. Could it be that reward prediction errors are associated with happiness mainly because they help us understand the world a little better than before?

To test this idea, Blain and Rutledge designed a task in which the likelihood of receiving a reward was unrelated to the size of the reward. This study design makes it possible to separate out the contributions of learning versus reward to moment-by-moment happiness.

In the task, volunteers had to decide which of two cars would win a race. In the ‘stable’ condition, one of the cars always had an 80% chance of winning. In the ‘volatile’ condition, one car had an 80% chance of winning for the first 20 trials. The other car then had an 80% chance of winning for the next 20 trials. The volunteers were not told these probabilities in advance, but had to work them out by playing the game. However, on every trial, the volunteers were shown the reward they would receive if they chose either of the cars and that car went on to win. The size of the rewards varied at random and was unrelated to the likelihood of a car winning.

Every few trials, the volunteers were asked to indicate their current level of happiness on a scale. The results showed that volunteers were happier after winning than after losing. On average they were also happier in the stable condition than in the volatile condition. This was especially true for volunteers with pre-existing symptoms of depression. Moreover, happiness after wins did not depend on how large the reward they got was, but instead simply on how surprised they were to win.

These results suggest that how we learn about the world around us can be more important for how we feel than rewards we receive directly. Measuring happiness in various types of environment could help us understand factors affecting mental health. The current results suggest, for example, that uncertain environments may be especially unpleasant for people with depression. Further research is needed to understand why this might be the case. In the real world, rewards are often uncertain and infrequent, but learning may nevertheless have the potential to boost happiness.

Under the Hood: Using Computational Psychiatry to Make Psychological Therapies More Mechanism-Focused

Psychological therapies, such as CBT, are an important part of the treatment of a range of psychiatric disorders such as depression and anxiety. There is a growing desire to understand the mechanisms by which such therapies effect change so as to improve treatment outcomes. Here we argue that adopting a computational framework may be one such approach. Computational psychiatry aims to provide a theoretical framework for moving between higher-level psychological states (like emotions, decisions and beliefs) to neural circuits, by modeling these constructs mathematically. These models are explicit hypotheses that contain quantifiable variables and parameters derived from each individual's behavior. This approach has two advantages. Firstly, some of the variables described by these models appears to reflect the neural activity of specific brain regions. Secondly, the parameters estimated by these models may offer a unique description of a patient's symptoms which can be used to both tailor therapy and track its effect. In doing so this approach may offer some additional granularity in understanding how psychological therapies, such as CBT, are working. Although this field shows significant promise, we also highlight several of the key hurdles that must first be overcome before clinical translation of computational insights can be realized.

Prospection, Perseverance, and Insight in Sequential Behavior

Real-world decisions have benefits occurring only later and dependent on additional decisions taken in the interim. We investigated this in a novel decision-making task in humans (n = 76) while measuring brain activity with fMRI (n = 24). Modeling revealed that participants computed the prospective value of decisions: they planned their future behavior taking into account how their decisions might affect which states they would encounter and how they themselves might respond in these states. They considered their own likely future behavioral biases (e.g., failure to adapt to changes in prospective value) and avoided situations in which they might be prone to such biases. Three neural networks in adjacent medial frontal regions were linked to distinct components of prospective decision making: activity in dorsal anterior cingulate cortex, area 8 m/9, and perigenual anterior cingulate cortex reflected prospective value, anticipated changes in prospective value, and the degree to which prospective value influenced decisions.

Solving the Credit Assignment Problem With the Prefrontal Cortex

In naturalistic multi-cue and multi-step learning tasks, where outcomes of behavior are delayed in time, discovering which choices are responsible for rewards can present a challenge, known as the credit assignment problem. In this review, I summarize recent work that highlighted a critical role for the prefrontal cortex (PFC) in assigning credit where it is due in tasks where only a few of the multitude of cues or choices are relevant to the final outcome of behavior. Collectively, these investigations have provided compelling support for specialized roles of the orbitofrontal (OFC), anterior cingulate (ACC), and dorsolateral prefrontal (dlPFC) cortices in contingent learning. However, recent work has similarly revealed shared contributions and emphasized rich and heterogeneous response properties of neurons in these brain regions. Such functional overlap is not surprising given the complexity of reciprocal projections spanning the PFC. In the concluding section, I overview the evidence suggesting that the OFC, ACC and dlPFC communicate extensively, sharing the information about presented options, executed decisions and received rewards, which enables them to assign credit for outcomes to choices on which they are contingent. This account suggests that lesion or inactivation/inhibition experiments targeting a localized PFC subregion will be insufficient to gain a fine-grained understanding of credit assignment during learning and instead poses refined questions for future research, shifting the focus from focal manipulations to experimental techniques targeting cortico-cortical projections.