Wagers for work: Decomposing the costs of cognitive effort.

Some aspects of cognition are more taxing than others. Accordingly, many people will avoid cognitively demanding tasks in favor of simpler alternatives. Which components of these tasks are costly, and how much, remains unknown. Here, we use a novel task design in which subjects request wages for completing cognitive tasks and a computational modeling procedure that decomposes their wages into the costs driving them. Using working memory as a test case, our approach revealed that gating new information into memory and protecting against interference are costly. Critically, other factors, like memory load, appeared less costly. Other key factors which may drive effort costs, such as error avoidance, had minimal influence on wage requests. Our approach is sensitive to individual differences, and could be used in psychiatric populations to understand the true underlying nature of apparent cognitive deficits.

Bayesian reinforcement learning: A basic overview.

We and other animals learn because there is some aspect of the world about which we are uncertain. This uncertainty arises from initial ignorance, and from changes in the world that we do not perfectly know; the uncertainty often becomes evident when our predictions about the world are found to be erroneous. The Rescorla-Wagner learning rule, which specifies one way that prediction errors can occasion learning, has been hugely influential as a characterization of Pavlovian conditioning and, through its equivalence to the delta rule in engineering, in a much wider class of learning problems. Here, we review the embedding of the Rescorla-Wagner rule in a Bayesian context that is precise about the link between uncertainty and learning, and thereby discuss extensions to such suggestions as the Kalman filter, structure learning, and beyond, that collectively encompass a wider range of uncertainties and accommodate a wider assortment of phenomena in conditioning.

The choice-wide behavioral association study: data-driven identification of interpretable behavioral components.

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

Clinically significant differences in self-reported pain scores in children with headaches.

OBJECTIVES: Changes in pain scores that represent clinically significant differences in children with headaches are necessary for study design and interpretation of findings reported in studies. We aimed to determine changes in pain scores associated with a minimum clinically significant difference (MCSD), ideal clinically significant difference (ICSD), and patient-perceived adequate analgesia (PPAA) in this population. METHODS: We performed a secondary analysis of two prospective studies of children with headaches presenting to an emergency department. Two serial assessments were performed in children aged 6-17 and 4-17 years who self-reported their pain intensity using the Verbal Numerical Rating Scale (VNRS) and Faces Pain Scale-Revised (FPS-R), respectively. Children qualitatively described any endorsed change in pain score; those who received an analgesic were asked if they wanted additional analgesics to decrease their pain intensity. We used receiver operating characteristic curve-based methodology to identify changes in pain scores associated with "a little less" (MCSD) and "much less" (ICSD) pain and patients declining additional analgesics because they experienced adequate analgesia after treatment (PPAA). RESULTS: We analyzed 105 children: 63.8% were female and the median (IQR) age was 13 (10-15) years. Ninety-eight children were analyzed for the VNRS and 101 were analyzed for the FPS-R. For the VNRS, raw change and percent reductions in pain scores associated with MCSD, ICSD, and PPAA were 2/10 and 25%, 4/10 and 56%, and 3/10 and 50%, respectively, and for the FPS-R, 2/10 and 25%, 4/10 and 67%, and 4/10 and 60%, respectively. The area under the curve (AUC) associated with a MCSD for both scales ranged from 94% to 98%; the AUC associated with an ICSD or PPAA for both scales ranged from 76% to 83%. CONCLUSIONS: We identified changes in pain score associated with patient-centered outcomes in children with headaches suitable for designing trials and assigning clinical significance to changes in pain scores reported in studies.

Dopamine and serotonin in human substantia nigra track social context and value signals during economic exchange.

Dopamine and serotonin are hypothesized to guide social behaviours. In humans, however, we have not yet been able to study neuromodulator dynamics as social interaction unfolds. Here, we obtained subsecond estimates of dopamine and serotonin from human substantia nigra pars reticulata during the ultimatum game. Participants, who were patients with Parkinson's disease undergoing awake brain surgery, had to accept or reject monetary offers of varying fairness from human and computer players. They rejected more offers in the human than the computer condition, an effect of social context associated with higher overall levels of dopamine but not serotonin. Regardless of the social context, relative changes in dopamine tracked trial-by-trial changes in offer value-akin to reward prediction errors-whereas serotonin tracked the current offer value. These results show that dopamine and serotonin fluctuations in one of the basal ganglia's main output structures reflect distinct social context and value signals.

Distributional reinforcement learning in prefrontal cortex.

The prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories center on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in the prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. In the present study, we show that distributional RL also better explains macaque anterior cingulate cortex neuronal responses, suggesting that it is a common mechanism for reward-guided learning.

Delphi Process for Validation of Fluid Treatment Algorithm for Critically Ill Pediatric Trauma Patients.

INTRODUCTION: While intravenous fluid therapy is essential to re-establishing volume status in children who have experienced trauma, aggressive resuscitation can lead to various complications. There remains a lack of consensus on whether pediatric trauma patients will benefit from a liberal or restrictive crystalloid resuscitation approach and how to optimally identify and transition between fluid phases. METHODS: A panel was comprised of physicians with expertise in pediatric trauma, critical care, and emergency medicine. A three-round Delphi process was conducted via an online survey, with each round being followed by a live video conference. Experts agreed or disagreed with each aspect of the proposed fluid management algorithm on a five-level Likert scale. The group opinion level defined an algorithm parameter's acceptance or rejection with greater than 75% agreement resulting in acceptance and greater than 50% disagreement resulting in rejection. The remaining were discussed and re-presented in the next round. RESULTS: Fourteen experts from five Level 1 pediatric trauma centers representing three subspecialties were included. Responses were received from 13/14 participants (93%). In round 1, 64% of the parameters were accepted, while the remaining 36% were discussed and re-presented. In round 2, 90% of the parameters were accepted. Following round 3, there was 100% acceptance by all the experts on the revised and final version of the algorithm. CONCLUSIONS: We present a validated algorithm for intavenous fluid management in pediatric trauma patients that focuses on the de-escalation of fluids. Focusing on this time point of fluid therapy will help minimize iatrogenic complications of crystalloid fluids within this patient population.

Modelling individual aesthetic judgements over time.

Listening to music, watching a sunset-many sensory experiences are valuable to us, to a degree that differs significantly between individuals, and within an individual over time. We have theorized (Brielmann & Dayan 2022 Psychol. Rev. 129, 1319-1337 (doi:10.1037/rev0000337))) that these idiosyncratic values derive from the task of using experiences to tune the sensory-cognitive system to current and likely future input. We tested the theory using participants' (n = 59) ratings of a set of dog images (n = 55) created using the NeuralCrossbreed morphing algorithm. A full realization of our model that uses feature representations extracted from image-recognizing deep neural nets (e.g. VGG-16) is able to capture liking judgements on a trial-by-trial basis (median r = 0.65), outperforming predictions based on population averages (median r = 0.01). Furthermore, the model's learning component allows it to explain image sequence dependent rating changes, capturing on average 17% more variance in the ratings for the true trial order than for simulated random trial orders. This validation of our theory is the first step towards a comprehensive treatment of individual differences in evaluation. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

From Complexity to Precision-Charting Decision-Making Through Normative Modeling.

This Viewpoint synthesizes data-driven and theory-driven approaches to normative modeling.

A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals.

Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.

Stratification of risk for emergent intracranial abnormalities in children with headaches: a Pediatric Emergency Care Applied Research Network (PECARN) study protocol.

INTRODUCTION: Headache is a common chief complaint of children presenting to emergency departments (EDs). Approximately 0.5%-1% will have emergent intracranial abnormalities (EIAs) such as brain tumours or strokes. However, more than one-third undergo emergent neuroimaging in the ED, resulting in a large number of children unnecessarily exposed to radiation. The overuse of neuroimaging in children with headaches in the ED is driven by clinician concern for life-threatening EIAs and lack of clarity regarding which clinical characteristics accurately identify children with EIAs. The study objective is to derive and internally validate a stratification model that accurately identifies the risk of EIA in children with headaches based on clinically sensible and reliable variables. METHODS AND ANALYSIS: Prospective cohort study of 28 000 children with headaches presenting to any of 18 EDs in the Pediatric Emergency Care Applied Research Network (PECARN). We include children aged 2-17 years with a chief complaint of headache. We exclude children with a clear non-intracranial alternative diagnosis, fever, neuroimaging within previous year, neurological or developmental condition such that patient history or physical examination may be unreliable, Glasgow Coma Scale score<14, intoxication, known pregnancy, history of intracranial surgery, known structural abnormality of the brain, pre-existing condition predisposing to an intracranial abnormality or intracranial hypertension, head injury within 14 days or not speaking English or Spanish. Clinicians complete a standardised history and physical examination of all eligible patients. Primary outcome is the presence of an EIA as determined by neuroimaging or clinical follow-up. We will use binary recursive partitioning and multiple regression analyses to create and internally validate the risk stratification model. ETHICS AND DISSEMINATION: Ethics approval was obtained for all participating sites from the University of Utah single Institutional Review Board. A waiver of informed consent was granted for collection of ED data. Verbal consent is obtained for follow-up contact. Results will be disseminated through international conferences, peer-reviewed publications, and open-access materials.

Reframing dopamine: A controlled controller at the limbic-motor interface.

Pavlovian influences notoriously interfere with operant behaviour. Evidence suggests this interference sometimes coincides with the release of the neuromodulator dopamine in the nucleus accumbens. Suppressing such interference is one of the targets of cognitive control. Here, using the examples of active avoidance and omission behaviour, we examine the possibility that direct manipulation of the dopamine signal is an instrument of control itself. In particular, when instrumental and Pavlovian influences come into conflict, dopamine levels might be affected by the controlled deployment of a reframing mechanism that recasts the prospect of possible punishment as an opportunity to approach safety, and the prospect of future reward in terms of a possible loss of that reward. We operationalize this reframing mechanism and fit the resulting model to rodent behaviour from two paradigmatic experiments in which accumbens dopamine release was also measured. We show that in addition to matching animals' behaviour, the model predicts dopamine transients that capture some key features of observed dopamine release at the time of discriminative cues, supporting the idea that modulation of this neuromodulator is amongst the repertoire of cognitive control strategies.

A (Dis-)information Theory of Revealed and Unrevealed Preferences: Emerging Deception and Skepticism via Theory of Mind.

In complex situations involving communication, agents might attempt to mask their intentions, exploiting Shannon's theory of information as a theory of misinformation. Here, we introduce and analyze a simple multiagent reinforcement learning task where a buyer sends signals to a seller via its actions, and in which both agents are endowed with a recursive theory of mind. We show that this theory of mind, coupled with pure reward-maximization, gives rise to agents that selectively distort messages and become skeptical towards one another. Using information theory to analyze these interactions, we show how savvy buyers reduce mutual information between their preferences and actions, and how suspicious sellers learn to reinterpret or discard buyers' signals in a strategic manner.

Anxiety associated with perceived uncontrollable stress enhances expectations of environmental volatility and impairs reward learning.

Unavoidable stress can lead to perceived lack of control and learned helplessness, a risk factor for depression. Avoiding punishment and gaining rewards involve updating the values of actions based on experience. Such updating is however useful only if action values are sufficiently stable, something that a lack of control may impair. We examined whether self-reported stress uncontrollability during the first wave of the COVID-19 pandemic predicted impaired reward-learning. In a preregistered study during the first-wave of the COVID-19 pandemic, we used self-reported measures of depression, anxiety, uncontrollable stress, and COVID-19 risk from 427 online participants to predict performance in a three-armed-bandit probabilistic reward learning task. As hypothesised, uncontrollable stress predicted impaired learning, and a greater proportion of probabilistic errors following negative feedback for correct choices, an effect mediated by state anxiety. A parameter from the best-fitting hidden Markov model that estimates expected beliefs that the identity of the optimal choice will shift across images, mediated effects of state anxiety on probabilistic errors and learning deficits. Our findings show that following uncontrollable stress, anxiety promotes an overly volatile representation of the reward-structure of uncertain environments, impairing reward attainment, which is a potential path to anhedonia in depression.

Radiographic pneumonia in young febrile infants presenting to the emergency department: secondary analysis of a prospective cohort study.

OBJECTIVE: The lack of evidence-based criteria to guide chest radiograph (CXR) use in young febrile infants results in variation in its use with resultant suboptimal quality of care. We sought to describe the features associated with radiographic pneumonias in young febrile infants. STUDY DESIGN: Secondary analysis of a prospective cohort study in 18 emergency departments (EDs) in the Pediatric Emergency Care Applied Research Network from 2016 to 2019. Febrile (≥38°C) infants aged ≤60 days who received CXRs were included. CXR reports were categorised as 'no', 'possible' or 'definite' pneumonia. We compared demographics, clinical signs and laboratory tests among infants with and without pneumonias. RESULTS: Of 2612 infants, 568 (21.7%) had CXRs performed; 19 (3.3%) had definite and 34 (6%) had possible pneumonias. Patients with definite (4/19, 21.1%) or possible (11/34, 32.4%) pneumonias more frequently presented with respiratory distress compared with those without (77/515, 15.0%) pneumonias (adjusted OR 2.17; 95% CI 1.04 to 4.51). There were no differences in temperature or HR in infants with and without radiographic pneumonias. The median serum procalcitonin (PCT) level was higher in the definite (0.7 ng/mL (IQR 0.1, 1.5)) vs no pneumonia (0.1 ng/mL (IQR 0.1, 0.3)) groups, as was the median absolute neutrophil count (ANC) (definite, 5.8 K/mcL (IQR 3.9, 6.9) vs no pneumonia, 3.1 K/mcL (IQR 1.9, 5.3)). No infants with pneumonia had bacteraemia. Viral detection was frequent (no pneumonia (309/422, 73.2%), definite pneumonia (11/16, 68.8%), possible pneumonia (25/29, 86.2%)). Respiratory syncytial virus was the predominant pathogen in the pneumonia groups and rhinovirus in infants without pneumonias. CONCLUSIONS: Radiographic pneumonias were uncommon in febrile infants. Viral detection was common. Pneumonia was associated with respiratory distress, but few other factors. Although ANC and PCT levels were elevated in infants with definite pneumonias, further work is necessary to evaluate the role of blood biomarkers in infant pneumonias.

National Variation in EMS Response and Antiepileptic Medication Administration for Children with Seizures in the Prehospital Setting.

BACKGROUND AND OBJECTIVES: Prehospital Advanced Life Support (ALS) is important to improve patient outcomes in children with seizures, yet data is limited regarding national prehospital variation in ALS response for these children. We aimed to determine the variation in ALS response and prehospital administration of antiepileptic medication for children with seizures across the United States. METHODS: We analyzed children <19 years with 9-1-1 dispatch codes for seizure in the 2019 National Emergency Medical Services Information System dataset. We defined ALS response as ALS-paramedic, ALS-Advanced Emergency Medical Technician, or ALS-intermediate responses. We conducted regression analyses to identify associations between ALS response (primary outcome), antiepileptic administration (secondary outcome) and age, gender, location, and US census regions. RESULTS: Of 147,821 pediatric calls for seizures, 88% received ALS responses. Receipt of ALS response was associated with urbanicity, with wilderness (adjusted odds ratio [aOR] 0.44, 0.39-0.49) and rural (aOR 0.80, 0.75-0.84) locations less likely to have ALS responses than urban areas. Of 129,733 emergency medical service (EMS) activations with an ALS responder's impression of seizure, antiepileptic medications were administered in 9%. Medication administration was independently associated with age (aOR 1.008, 95% confidence interval [CI] 1.005-1.010) and gender (aOR 1.22, 95% CI 1.18-1.27), with females receiving medications more than males. Of the 11,698 children who received antiepileptic medications, midazolam was the most commonly used (83%). CONCLUSION: The majority of children in the US receive ALS responses for seizures. Although medications are infrequently administered, the majority who received medications had midazolam given, which is the current standard of care. Further research should determine the proportion of children who are continuing to seize upon EMS arrival and would most benefit from immediate treatment.

Interactive cognitive maps support flexible behavior under threat.

In social environments, survival can depend upon inferring and adapting to other agents' goal-directed behavior. However, it remains unclear how humans achieve this, despite the fact that many decisions must account for complex, dynamic agents acting according to their own goals. Here, we use a predator-prey task (total n = 510) to demonstrate that humans exploit an interactive cognitive map of the social environment to infer other agents' preferences and simulate their future behavior, providing for flexible, generalizable responses. A model-based inverse reinforcement learning model explained participants' inferences about threatening agents' preferences, with participants using this inferred knowledge to enact generalizable, model-based behavioral responses. Using tree-search planning models, we then found that behavior was best explained by a planning algorithm that incorporated simulations of the threat's goal-directed behavior. Our results indicate that humans use a cognitive map to determine other agents' preferences, facilitating generalized predictions of their behavior and effective responses.

Metacognitive Information Theory.

The capacity that subjects have to rate confidence in their choices is a form of metacognition, and can be assessed according to bias, sensitivity and efficiency. Rich networks of domain-specific and domain-general regions of the brain are involved in the rating, and are associated with its quality and its use for regulating the processes of thinking and acting. Sensitivity and efficiency are often measured by quantities called meta-d' and the M-ratio that are based on reverse engineering the potential accuracy of the original, primary, choice that is implied by the quality of the confidence judgements. Here, we advocate a straightforward measure of sensitivity, called meta-𝓘, which assesses the mutual information between the accuracy of the subject's choices and the confidence reports, and two normalized versions of this measure that quantify efficiency in different regimes. Unlike most other measures, meta-𝓘-based quantities increase with the number of correctly assessed bins with which confidence is reported. We illustrate meta-𝓘 on data from a perceptual decision-making task, and via a simple form of simulated second-order metacognitive observer.

Development of a Model to Identify Febrile Children at Low Risk for Multisystem Inflammatory Syndrome.

OBJECTIVES: The case definition for multisystem inflammatory syndrome in children (MIS-C) is broad and encompasses symptoms and signs commonly seen in children with fever. Our aim was to identify clinical predictors that, independently or in combination, identify febrile children presenting to the emergency department (ED) as low risk for MIS-C. METHODS: We conducted a retrospective single-center study of otherwise healthy children 2 months to 20 years of age presenting to the ED with fever and who had a laboratory evaluation for MIS-C between April 15, 2020, and October 31, 2020. We excluded children with a diagnosis of Kawasaki disease. Our outcome was an MIS-C diagnosis defined by the Centers for Disease Control and Prevention criteria. We conducted multivariable logistic regression analyses to identify variables independently associated with MIS-C. RESULTS: Thirty-three patients with and 128 patients without MIS-C were analyzed. Of those with MIS-C, 16 of 33 (48.5%) had hypotension for age, signs of hypoperfusion, or required ionotropic support. Four variables were independently associated with the presence of MIS-C; known or suspected SARS CoV-2 exposure (adjusted odds ratio [aOR], 4.0; 95% confidence interval [CI], 1.4-11.9) and the following 3 symptoms and signs: abdominal pain on history (aOR, 4.8; 95% CI, 1.7-15.0), conjunctival injection (aOR, 15.2; 95% CI, 5.4-48.1), and rash involving the palms or soles (aOR, 12.2; 95% CI, 2.4-69.4). Children were at low risk of MIS-C if none of the 3 symptoms or signs were present (sensitivity 87.9% [95% CI, 71.8-96.6]; specificity 62.5% [53.5-70.9], negative predictive value 95.2% [88.3-98.7]). Of the 4 MIS-C patients without any of these 3 factors, 2 were ill-appearing in the ED and the other 2 had no cardiovascular involvement during their clinical course. CONCLUSIONS: A combination of 3 clinical symptoms and signs had moderate to high sensitivity and high negative predictive value for identifying febrile children at low risk of MIS-C. If validated, these factors could aid clinicians in determining the need to obtain or forego an MIS-C laboratory evaluation during SARS-CoV-2 prevalent periods in febrile children.