The pupil dilation response as an indicator of visual cue uncertainty and auditory outcome surprise.

In everyday perception, we combine incoming sensory information with prior expectations. Expectations can be induced by cues that indicate the probability of following sensory events. The information provided by cues may differ and hence lead to different levels of uncertainty about which event will follow. In this experiment, we employed pupillometry to investigate whether the pupil dilation response to visual cues varies depending on the level of cue-associated uncertainty about a following auditory outcome. Also, we tested whether the pupil dilation response reflects the amount of surprise about the subsequently presented auditory stimulus. In each trial, participants were presented with a visual cue (face image) which was followed by an auditory outcome (spoken vowel). After the face cue, participants had to indicate by keypress which of three auditory vowels they expected to hear next. We manipulated the cue-associated uncertainty by varying the probabilistic cue-outcome contingencies: One face was most likely followed by one specific vowel (low cue uncertainty), another face was equally likely followed by either of two vowels (intermediate cue uncertainty) and the third face was followed by all three vowels (high cue uncertainty). Our results suggest that pupil dilation in response to task-relevant cues depends on the associated uncertainty, but only for large differences in the cue-associated uncertainty. Additionally, in response to the auditory outcomes, the pupil dilation scaled negatively with the cue-dependent probabilities, likely signalling the amount of surprise.

[Negative valence systems in the system of research domain criteria : Empirical results and new developments]. / Negative Valenzsysteme im System der Research Domain Criteria : Empirische Resultate und neue Entwicklungen.

BACKGROUND: The research domain criteria (RDoC) domain of negative valence systems can be used to subsume long established and recently developed research approaches, which build upon theoretical knowledge and clinical practice of various psychiatric disorders. OBJECTIVE: This article outlines how the five constructs within the RDoC domain of negative valence systems can contribute to integrating empirical studies into a coherent and differentiated biopsychosocial model. MATERIAL AND METHODS: This is a qualitative review article that summarizes empirical results and discusses new developments on the basis of exemplary studies and selected reviews. RESULTS AND DISCUSSION: The RDoC domain of negative valence systems differentiates in three constructs the time horizon, in which persons need to adequately react to (1) acute, (2) potential, and (3) sustained threats elicited by negative stimuli or situations. These three constructs can be outlined relatively well with specific experimental paradigms and neuronal circuits. Two further constructs focus on the negative consequences of (4) losses and (5) frustrative non-rewards. The former seems to be currently relatively diffusely defined whereas the latter is clearly circumscribed by its relation to specific forms of aggression. Behavioral, physiological, and neuronal reactions to acute and potential threats can be well compared between humans and animals and can be specified with the help of mathematical models. These models can contribute to a better understanding of how healthy and diseased persons process negative stimuli or situations.

A Computational Account of Optimizing Social Predictions Reveals That Adolescents Are Conservative Learners in Social Contexts.

As adolescents transition to the complex world of adults, optimizing predictions about others' preferences becomes vital for successful social interactions. Mounting evidence suggests that these social learning processes are affected by ongoing brain development across adolescence. A mechanistic understanding of how adolescents optimize social predictions and how these learning strategies are implemented in the brain is lacking. To fill this gap, we combined computational modeling with functional neuroimaging. In a novel social learning task, male and female human adolescents and adults predicted the preferences of peers and could update their predictions based on trial-by-trial feedback about the peers' actual preferences. Participants also rated their own preferences for the task items and similar additional items. To describe how participants optimize their inferences over time, we pitted simple reinforcement learning models against more specific "combination" models, which describe inferences based on a combination of reinforcement learning from past feedback and participants' own preferences. Formal model comparison revealed that, of the tested models, combination models best described how adults and adolescents update predictions of others. Parameter estimates of the best-fitting model differed between age groups, with adolescents showing more conservative updating. This developmental difference was accompanied by a shift in encoding predictions and the errors thereof within the medial prefrontal and fusiform cortices. In the adolescent group, encoding of own preferences and prediction errors scaled with parent-reported social traits, which provides additional external validity for our learning task and the winning computational model. Our findings thus help to specify adolescent-specific social learning processes.SIGNIFICANCE STATEMENT Adolescence is a unique developmental period of heightened awareness about other people. Here we probe the suitability of various computational models to describe how adolescents update their predictions of others' preferences. Within the tested model space, predictions of adults and adolescents are best described by the same learning model, but adolescents show more conservative updating. Compared with adults, brain activity of adolescents is modulated less by predictions themselves and more by prediction errors per se, and this relationship scales with adolescents' social traits. Our findings help specify social learning across adolescence and generate hypotheses about social dysfunctions in psychiatric populations.

Human Pavlovian fear conditioning conforms to probabilistic learning.

Learning to predict threat from environmental cues is a fundamental skill in changing environments. This aversive learning process is exemplified by Pavlovian threat conditioning. Despite a plethora of studies on the neural mechanisms supporting the formation of associations between neutral and aversive events, our computational understanding of this process is fragmented. Importantly, different computational models give rise to different and partly opposing predictions for the trial-by-trial dynamics of learning, for example expressed in the activity of the autonomic nervous system (ANS). Here, we investigate human ANS responses to conditioned stimuli during Pavlovian fear conditioning. To obtain precise, trial-by-trial, single-subject estimates of ANS responses, we build on a statistical framework for psychophysiological modelling. We then consider previously proposed non-probabilistic models, a simple probabilistic model, and non-learning models, as well as different observation functions to link learning models with ANS activity. Across three experiments, and both for skin conductance (SCR) and pupil size responses (PSR), a probabilistic learning model best explains ANS responses. Notably, SCR and PSR reflect different quantities of the same model: SCR track a mixture of expected outcome and uncertainty, while PSR track expected outcome alone. In summary, by combining psychophysiological modelling with computational learning theory, we provide systematic evidence that the formation and maintenance of Pavlovian threat predictions in humans may rely on probabilistic inference and includes estimation of uncertainty. This could inform theories of neural implementation of aversive learning.

Maintaining homeostasis by decision-making.

Living organisms need to maintain energetic homeostasis. For many species, this implies taking actions with delayed consequences. For example, humans may have to decide between foraging for high-calorie but hard-to-get, and low-calorie but easy-to-get food, under threat of starvation. Homeostatic principles prescribe decisions that maximize the probability of sustaining appropriate energy levels across the entire foraging trajectory. Here, predictions from biological principles contrast with predictions from economic decision-making models based on maximizing the utility of the endpoint outcome of a choice. To empirically arbitrate between the predictions of biological and economic models for individual human decision-making, we devised a virtual foraging task in which players chose repeatedly between two foraging environments, lost energy by the passage of time, and gained energy probabilistically according to the statistics of the environment they chose. Reaching zero energy was framed as starvation. We used the mathematics of random walks to derive endpoint outcome distributions of the choices. This also furnished equivalent lotteries, presented in a purely economic, casino-like frame, in which starvation corresponded to winning nothing. Bayesian model comparison showed that--in both the foraging and the casino frames--participants' choices depended jointly on the probability of starvation and the expected endpoint value of the outcome, but could not be explained by economic models based on combinations of statistical moments or on rank-dependent utility. This implies that under precisely defined constraints biological principles are better suited to explain human decision-making than economic models based on endpoint utility maximization.

A solid frame for the window on cognition: Modeling event-related pupil responses.

Pupil size is often used to infer central processes, including attention, memory, and emotion. Recent research has spotlighted its relation to behavioral variables from decision-making models and to neural variables such as locus coeruleus activity and cortical oscillations. As yet, a unified and principled approach for analyzing pupil responses is lacking. Here we seek to establish a formal, quantitative forward model for pupil responses by describing them with linear time-invariant systems. Based on empirical data from human participants, we show that a combination of two linear time-invariant systems can parsimoniously explain approximately all variance evoked by illuminance changes. Notably, the model makes a counterintuitive prediction that pupil constriction dominates the responses to darkness flashes, as in previous empirical reports. This prediction was quantitatively confirmed for responses to light and darkness flashes in an independent group of participants. Crucially, illuminance- and nonilluminance-related inputs to the pupillary system are presumed to share a common final pathway, composed of muscles and nerve terminals. Hence, we can harness our illuminance-based model to estimate the temporal evolution of this neural input for an auditory-oddball task, an emotional-words task, and a visual-detection task. Onset and peak latencies of the estimated neural inputs furnish plausible hypotheses for the complexity of the underlying neural circuit. To conclude, this mathematical description of pupil responses serves as a prerequisite to refining their relation to behavioral and brain indices of cognitive processes.

Selectively altering belief formation in the human brain.

Humans form beliefs asymmetrically; we tend to discount bad news but embrace good news. This reduced impact of unfavorable information on belief updating may have important societal implications, including the generation of financial market bubbles, ill preparedness in the face of natural disasters, and overly aggressive medical decisions. Here, we selectively improved people's tendency to incorporate bad news into their beliefs by disrupting the function of the left (but not right) inferior frontal gyrus using transcranial magnetic stimulation, thereby eliminating the engrained "good news/bad news effect." Our results provide an instance of how selective disruption of regional human brain function paradoxically enhances the ability to incorporate unfavorable information into beliefs of vulnerability.

Pupil diameter as an indicator of sound pair familiarity after statistically structured auditory sequence.

Inspired by recent findings in the visual domain, we investigated whether the stimulus-evoked pupil dilation reflects temporal statistical regularities in sequences of auditory stimuli. We conducted two preregistered pupillometry experiments (experiment 1, n = 30, 21 females; experiment 2, n = 31, 22 females). In both experiments, human participants listened to sequences of spoken vowels in two conditions. In the first condition, the stimuli were presented in a random order and, in the second condition, the same stimuli were presented in a sequence structured in pairs. The second experiment replicated the first experiment with a modified timing and number of stimuli presented and without participants being informed about any sequence structure. The sound-evoked pupil dilation during a subsequent familiarity task indicated that participants learned the auditory vowel pairs of the structured condition. However, pupil diameter during the structured sequence did not differ according to the statistical regularity of the pair structure. This contrasts with similar visual studies, emphasizing the susceptibility of pupil effects during statistically structured sequences to experimental design settings in the auditory domain. In sum, our findings suggest that pupil diameter may serve as an indicator of sound pair familiarity but does not invariably respond to task-irrelevant transition probabilities of auditory sequences.

Social preferences and psychopathy in a sample of male prisoners-a pilot study.

Social decisions are influenced by a person's social preferences. High psychopathy is defined by antisocial behaviour, but the relationship between psychopathy and social preferences remains unclear. In this study, we used a battery of economic games to study social decision-making and social preferences in relation to psychopathy in a sample of 35 male prison inmates, who were arrested for sexual and severe violent offenses (mean age = 39 years). We found no evidence for a relationship between social preferences (measured with the Dictator and Ultimatum Games, Social Value Orientation, and one-shot 2 × 2 games) and psychopathy (measured by the overall Hare Psychopathy Checklist-Revised score and both factors). These results are surprising but also difficult to interpret due to the small sample size. Our results contribute to the ongoing debate about psychopathy and social decision-making by providing crucial data that can be combined with future datasets to reach large sample sizes that can provide a more nuanced understanding about the relationship between psychopathy and social preferences.

Positively biased processing of self-relevant social feedback.

Receiving social feedback such as praise or blame for one's character traits is a key component of everyday human interactions. It has been proposed that humans are positively biased when integrating social feedback into their self-concept. However, a mechanistic description of how humans process self-relevant feedback is lacking. Here, participants received feedback from peers after a real-life interaction. Participants processed feedback in a positively biased way, i.e., they changed their self-evaluations more toward desirable than toward undesirable feedback. Using functional magnetic resonance imaging we investigated two feedback components. First, the reward-related component correlated with activity in ventral striatum and in anterior cingulate cortex/medial prefrontal cortex (ACC/MPFC). Second, the comparison-related component correlated with activity in the mentalizing network, including the MPFC, the temporoparietal junction, the superior temporal sulcus, the temporal pole, and the inferior frontal gyrus. This comparison-related activity within the mentalizing system has a parsimonious interpretation, i.e., activity correlated with the differences between participants' own evaluation and feedback. Importantly, activity within the MPFC that integrated reward-related and comparison-related components predicted the self-related positive updating bias across participants offering a mechanistic account of positively biased feedback processing. Thus, theories on both reward and mentalizing are important for a better understanding of how social information is integrated into the human self-concept.

Striatal neurones have a specific ability to respond to phasic dopamine release.

The cAMP/protein kinase A (PKA) signalling cascade is ubiquitous, and each step in this cascade involves enzymes that are expressed in multiple isoforms. We investigated the effects of this diversity on the integration of the pathway in the target cell by comparing prefrontal cortical neurones with striatal neurones which express a very specific set of signalling proteins. The prefrontal cortex and striatum both receive dopaminergic inputs and we analysed the dynamics of the cAMP/PKA signal triggered by dopamine D1 receptors in these two brain structures. Biosensor imaging in mouse brain slice preparations showed profound differences in the D1 response between pyramidal cortical neurones and striatal medium spiny neurones: the cAMP/PKA response was much stronger, faster and longer lasting in striatal neurones than in pyramidal cortical neurones. We identified three molecular determinants underlying these differences: different activities of phosphodiesterases, particularly those of type 4, which strongly damp the cAMP signal in the cortex but not in the striatum; stronger adenylyl cyclase activity in the striatum, generating responses with a faster onset than in the cortex; and DARPP-32, a phosphatase inhibitor which prolongs PKA action in the striatum. Striatal neurones were also highly responsive in terms of gene expression since a single sub-second dopamine stimulation is sufficient to trigger c-Fos expression in the striatum, but not in the cortex. Our data show how specific molecular elements of the cAMP/PKA signalling cascade selectively enable the principal striatal neurones to respond to brief dopamine stimuli, a critical process in incentive learning.

A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders.

Social deficits are among the core and most striking psychiatric symptoms, present in most psychiatric disorders. Here, we introduce a novel social learning framework, which consists of neuro-computational models that combine reinforcement learning with various types of social knowledge structures. We outline how this social learning framework can help specify and quantify social psychopathology across disorders and provide an overview of the brain regions that may be involved in this type of social learning. We highlight how this framework can specify commonalities and differences in the social psychopathology of individuals with autism spectrum disorder (ASD), personality disorders (PD), and major depressive disorder (MDD) and improve treatments on an individual basis. We conjecture that individuals with psychiatric disorders rely on rigid social knowledge representations when learning about others, albeit the nature of their rigidity and the behavioral consequences can greatly differ. While non-clinical cohorts tend to efficiently adapt social knowledge representations to relevant environmental constraints, psychiatric cohorts may rigidly stick to their preconceived notions or overly coarse knowledge representations during learning.

Acceleration of inferred neural responses to oddball targets in an individual with bilateral amygdala lesion compared to healthy controls.

Detecting unusual auditory stimuli is crucial for discovering potential threat. Locus coeruleus (LC), which coordinates attention, and amygdala, which is implicated in resource prioritization, both respond to deviant sounds. Evidence concerning their interaction, however, is sparse. Seeking to elucidate if human amygdala affects estimated LC activity during this process, we recorded pupillary responses during an auditory oddball and an illuminance change task, in a female with bilateral amygdala lesions (BG) and in n = 23 matched controls. Neural input in response to oddballs was estimated via pupil dilation, a reported proxy of LC activity, harnessing a linear-time invariant system and individual pupillary dilation response function (IRF) inferred from illuminance responses. While oddball recognition remained intact, estimated LC input for BG was compacted to an impulse rather than the prolonged waveform seen in healthy controls. This impulse had the earliest response mean and highest kurtosis in the sample. As a secondary finding, BG showed enhanced early pupillary constriction to darkness. These findings suggest that LC-amygdala communication is required to sustain LC activity in response to anomalous sounds. Our results provide further evidence for amygdala involvement in processing deviant sound targets, although it is not required for their behavioral recognition.

Incorporating social knowledge structures into computational models.

To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture the complexities of social learning processes are currently lacking. In this study, we specify and test potential strategies that humans can employ for learning about others. Standard Rescorla-Wagner (RW) learning models only capture parts of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalize two social knowledge structures and implement them in hybrid RW models to test their usefulness across multiple social learning tasks. We name these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results from model comparisons and statistical analyses indicate that participants efficiently combine the concepts of granularity and reference points-with the specific combinations in models depending on the people and traits that participants learned about. Overall, our experiments demonstrate that variants of RW algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.

Social motives in a patient with bilateral selective amygdala lesions: Shift in prosocial motivation but not in social value orientation.

Humans hold social motives that are expressed in social preferences and influence how they evaluate and share payoffs. Established models in psychology and economics quantify social preferences such as general social value orientation, which captures people's tendency to be prosocial or individualistic. Prosocials further differ by how much they maximize joint gains or minimize inequality. Functional neuroimaging studies have linked increased amygdala activity in prosocials to payoff inequality between self and other. However, it is unclear whether amygdala lesions alter social motives. We used two tasks to test a patient with selective bilateral amygdala lesions and three healthy samples (a priori matched control sample N = 20, online sample N = 603, student sample N = 40), which allowed us to assess and model social motives across a relatively large number of participants. In a social value orientation task, the patient was categorized as prosocial and her social value orientation score did not differ from healthy participants. Importantly, the patient differed in prosocial motivation by maximizing joint gains rather than minimizing payoff inequality. In a joint payoff evaluation task, Bayesian model comparisons revealed that participants' evaluations were best described by models, which link participants' evaluations to the payoff magnitude and to inequality. Overall, amygdala lesions did not seem to alter general social value orientation but shifted prosocial motivation toward maximizing joint gains.

L-DOPA modulates activity in the vmPFC, nucleus accumbens, and VTA during threat extinction learning in humans.

Learning to be safe is central for adaptive behaviour when threats are no longer present. Detecting the absence of an expected threat is key for threat extinction learning and an essential process for the behavioural treatment of anxiety-related disorders. One possible mechanism underlying extinction learning is a dopaminergic mismatch signal that encodes the absence of an expected threat. Here we show that such a dopamine-related pathway underlies extinction learning in humans. Dopaminergic enhancement via administration of L-DOPA (vs. Placebo) was associated with reduced retention of differential psychophysiological threat responses at later test, which was mediated by activity in the ventromedial prefrontal cortex that was specific to extinction learning. L-DOPA administration enhanced signals at the time-point of an expected, but omitted threat in extinction learning within the nucleus accumbens, which were functionally coupled with the ventral tegmental area and the amygdala. Computational modelling of threat expectancies further revealed prediction error encoding in nucleus accumbens that was reduced when L-DOPA was administered. Our results thereby provide evidence that extinction learning is influenced by L-DOPA and provide a mechanistic perspective to augment extinction learning by dopaminergic enhancement in humans.

Neurocognitive Mechanisms of Social Inferences in Typical and Autistic Adolescents.

BACKGROUND: Many of our efforts in social interactions are dedicated to learning about others. Adolescents with autism have core deficits in social learning, but a mechanistic understanding of these deficits and how they relate to neural development is lacking. The present study aimed to specify how adolescents with and without autism represent and acquire social knowledge and how these processes are implemented in neural activity. METHODS: Typically developing adolescents (n = 26) and adolescents with autism spectrum disorder (ASD) (n = 20) rated in the magnetic resonance scanner how much 3 peers liked a variety of items and received trial-by-trial feedback about the peers' actual preference ratings. In a separate study, we established the preferences of a new sample of adolescents (N = 99), used to examine population preference structures. Using computational models, we tested whether participants in the magnetic resonance study relied on preference structures during learning and how model predictions were implemented in brain activity. RESULTS: Typically developing adolescents relied on average population preferences and prediction error updating. Importantly, prediction error updating was scaled by the similarity between items. In contrast, preferences of adolescents with ASD were best described by a No-Learning model that relied only on the participant's own preferences for each item. Model predictions were encoded in neural activity. Typically developing adolescents encoded prediction errors in the putamen, and adolescents with ASD showed greater encoding of own preferences in the angular gyrus. CONCLUSIONS: We specified how adolescents represent and update social knowledge during learning. Our findings indicate that adolescents with ASD rely only on their own preferences when making social inferences.

Risk perception and optimism during the early stages of the COVID-19 pandemic.

Slowing the spread of COVID-19 requires people to actively change their lives by following protective practices, such as physical distancing and disinfecting their hands. Perceptions about the personal risk of COVID-19 may affect compliance with these practices. In this study, we assessed risk perception and optimism about COVID-19 in a multinational (UK, USA and Germany), longitudinal design during the early stages of the pandemic (16 March 2020; 1 April 2020; 20 May 2020). Our main findings are that (i) people showed a comparative optimism bias about getting infected and infecting others, but not for getting severe symptoms, (ii) this optimism bias did not change over time, (iii) optimism bias seemed to relate to perceived level of control over the action, (iv) risk perception was linked to publicly available information about the disorder, (v) people reported adhering closely to protective measures but these measures did not seem to be related to risk perception, and (vi) risk perception was related to questions about stress and anxiety. In additional cross-sectional samples, we replicated our most important findings. Our open and partly preregistered results provide detailed descriptions of risk perceptions and optimistic beliefs during the early stages of the COVID-19 pandemic in three Western countries.

Minimizing threat via heuristic and optimal policies recruits hippocampus and medial prefrontal cortex.

Jointly minimizing multiple threats over extended time horizons enhances survival. Consequently, many tests of approach-avoidance conflicts incorporate multiple threats for probing corollaries of animal and human anxiety. To facilitate computations necessary for threat minimization, the human brain may concurrently harness multiple decision policies and associated neural controllers, but it is unclear which. We combine a task that mimics foraging under predation with behavioural modelling and functional neuroimaging. Human choices rely on immediate predator probability-a myopic heuristic policy-and on the optimal policy, which integrates all relevant variables. Predator probability relates positively and the associated choice uncertainty relates negatively to activations in the anterior hippocampus, amygdala and dorsolateral prefrontal cortex. The optimal policy is positively associated with dorsomedial prefrontal cortex activity. We thus provide a decision-theoretic outlook on the role of the human hippocampus, amygdala and prefrontal cortex in resolving approach-avoidance conflicts relevant for anxiety and integral for survival.

The framing effect in a monetary gambling task is robust in minimally verbal language switching contexts.

Decision-making biases, in particular the framing effect, can be altered in foreign language settings (foreign language effect) and following switching between languages (the language switching effect on framing). Recently, it has been suggested that the framing effect is only affected by foreign language use if the task is presented in a rich textual form. Here, we assess whether an elaborate verbal task is also a prerequisite for the language switching effect on framing. We employed a financial gambling task that induces a robust framing effect but is less verbal than the classical framing paradigms (e.g., the Asian disease problem). We conducted an online experiment ( n = 485), where we orthogonally manipulated language use and language switching between trials. The results showed no effects of foreign language use or language switching throughout the experiment. This online result was confirmed in a laboratory experiment ( n = 27). Overall, we find that language switching does not reduce the framing effect in a paradigm with little verbal content and thus that language switching effects seem contingent on the amount of verbal processing required.