Characterizing human habits in the lab

Habits pose a fundamental puzzle for those aiming to understand human behavior. They pervade our everyday lives and dominate some forms of psychopathology but are extremely hard to elicit in the lab. In this Registered Report, we develop novel experimental paradigms grounded in computational models, which suggest that habit strength should be proportional to the frequency of behavior and, in contrast to previous research, independent of value. Specifically, we manipulate how often participants perform responses in two tasks varying action repetition without, or separately from, variations in value. Moreover, we ask how this frequency-based habitization relates to value-based operationalizations of habit and self-reported propensities for habitual behavior in real life

Characterizing human habits in the lab

Habits pose a fundamental puzzle for those aiming to understand human behavior. They pervade our everyday lives and dominate some forms of psychopathology but are extremely hard to elicit in the lab. In this Registered Report, we developed novel experimental paradigms grounded in computational models, which suggest that habit strength should be proportional to the frequency of behavior and, in contrast to previous research, independent of value. Specifically, we manipulated how often participants performed responses in two tasks varying action repetition without, or separately from, variations in value. Moreover, we asked how this frequency-based habitization related to value-based operationalizations of habit and self-reported propensities for habitual behavior in real life. We find that choice frequency during training increases habit strength at test and that this form of habit shows little relation to value-based operationalizations of habit. Our findings empirically ground a novel perspective on the constituents of habits and suggest that habits may arise in the absence of external reinforcement. We further find no evidence for an overlap between different experimental approaches to measuring habits and no associations with self-reported real-life habits. Thus, our findings call for a rigorous reassessment of our understanding and measurement of human habitual behavior in the lab

Predicting non-response in patient-reported outcome measures: results from the Swiss quality assurance programme in cardiac inpatient rehabilitation

Background Quality assurance programmes measure and compare certain health outcomes to ensure high quality care in the health care sector. The outcome health related quality of life (HRQOL) is typically measured by patient-reported outcome measures (PROMs). However, certain patient groups are less likely to respond to PROMs than others. This non-response bias can potentially distort results in quality assurance programmes. Our study aims to identify relevant predictors for non-response during assessment using the PROM MacNew Heart Disease questionnaire in cardiac rehabilitation. Methods This is a cross-sectional study based on data from the Swiss external quality assurance programme. All patients aged 18 years or older who underwent inpatient cardiac rehabilitation in 16 Swiss rehabilitation clinics between 2016 and 2019 were included. Patients’ sociodemographic and basic medical data were analysed descriptively by comparing two groups: non-responders and responders. We used a random intercept logistic regression model to estimate associations of patient characteristics and clinic differences with non-response. Results Of 24 572 patients, there were 33.3% non-responders and 66.7% responders. The mean age was 70; 31.0% were women. The regression model showed that being female was associated with non-response (odds ratio (OR) 1.22; 95% confidence interval (95% CI) 1.14–1.30), as well as having no supplementary health insurance (OR 1.49; 95% CI 1.39–1.59). Each additional year of age increased the chance of non-response by an OR of 1.02 (95% CI 1.02–1.02). Not being a first language speaker of German, French, or Italian increased the chance of non-response by an OR of 6.94 (95% CI 6.03–7.99). Patients admitted directly from acute care had a higher chance of non-response (OR 1.23; 95% CI 1.10–1.38), as well as patients being discharged back into acute care after rehabilitation (OR 3.89; 95% CI 3.00–5.04). Each point on the cumulative illness rating scale (CIRS) total score increased the chance of non-response by an OR of 1.05 (95% CI 1.04–1.05). Certain diagnoses also influenced the chance of non-response. Even after adjustment for known confounders, response rates differed substantially between the 16 clinics. Conclusion We have found significant non-response bias among certain patient groups, as well as across different treatment facilities. Measures to improve response rates among patients with known barriers to participation, as well as among different treatment facilities need to be considered, particularly when PROMs are being used for comparison of providers in quality assurance programmes or outcome evaluation

A dual role for prediction error in associative learning

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla--Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity

Qualität aus Patientenperspektive

PROMs Dank Patient Reported Outcome Measures erhält die Patientenperspektive in der Qualitätsbeurteilung von Gesundheitseinrichtungen und -angeboten ihren festen Platz. Gleichzeitig stellen diese Indikatoren Health Care Professionals, Direktionen und Public-Health-Organisationen vor neue Herausforderungen

Neural arbitration between social and individual learning systems

Decision making requires integrating self-gathered information with advice from others. However, the arbitration process by which one source of information is selected over the other has not been fully elucidated. In this study, we formalised arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modelling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our relative precision definition of arbitration. Functional neuroimaging demonstrated arbitration signals that were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favour of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favour of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioural and neural levels

Two spatiotemporally distinct value systems shape reward-based learning in the human brain

Avoiding repeated mistakes and learning to reinforce rewarding decisions is critical for human survival and adaptive actions. Yet, the neural underpinnings of the value systems that encode different decision-outcomes remain elusive. Here coupling single-trial electroencephalography with simultaneously acquired functional magnetic resonance imaging, we uncover the spatiotemporal dynamics of two separate but interacting value systems encoding decision-outcomes. Consistent with a role in regulating alertness and switching behaviours, an early system is activated only by negative outcomes and engages arousal-related and motor-preparatory brain structures. Consistent with a role in reward-based learning, a later system differentially suppresses or activates regions of the human reward network in response to negative and positive outcomes, respectively. Following negative outcomes, the early system interacts and downregulates the late system, through a thalamic interaction with the ventral striatum. Critically, the strength of this coupling predicts participants’ switching behaviour and avoidance learning, directly implicating the thalamostriatal pathway in reward-based learning

The two-process model of sleep regulation: a reappraisal

In the last three decades the two-process model of sleep regulation has served as a major conceptual framework in sleep research. It has been applied widely in studies on fatigue and performance and to dissect individual differences in sleep regulation. The model posits that a homeostatic process (Process S) interacts with a process controlled by the circadian pacemaker (Process C), with time-courses derived from physiological and behavioural variables. The model simulates successfully the timing and intensity of sleep in diverse experimental protocols. Electrophysiological recordings from the suprachiasmatic nuclei (SCN) suggest that S and C interact continuously. Oscillators outside the SCN that are linked to energy metabolism are evident in SCN-lesioned arrhythmic animals subjected to restricted feeding or methamphetamine administration, as well as in human subjects during internal desynchronization. In intact animals these peripheral oscillators may dissociate from the central pacemaker rhythm. A sleep/fast and wake/feed phase segregate antagonistic anabolic and catabolic metabolic processes in peripheral tissues. A deficiency of Process S was proposed to account for both depressive sleep disturbances and the antidepressant effect of sleep deprivation. The model supported the development of novel non-pharmacological treatment paradigms in psychiatry, based on manipulating circadian phase, sleep and light exposure. In conclusion, the model remains conceptually useful for promoting the integration of sleep and circadian rhythm research. Sleep appears to have not only a short-term, use-dependent function; it also serves to enforce rest and fasting, thereby supporting the optimization of metabolic processes at the appropriate phase of the 24-h cycle

Risk Perception of Climate Change: Empirical Evidence for Germany

The perception of risks associated with climate change appears to be a key factor for the support of climate policy measures. Using a generalized ordered logit approach and drawing on a unique data set originating from two surveys conducted in 2012 and 2014, each among more than 6,000 German households, we analyze the determinants of individual risk perception associated with three kinds of natural hazards: heat waves, storms, and floods. Our focus is on the role of objective risk measures and experience with these natural hazards, whose frequency is likely to be affected by climate change. In line with the received literature, the results suggest that personal experience with adverse events and personal damage therefrom are strong drivers of individual risk perception.Die Wahrnehmung von Risiken, die mit dem Klimawandel einhergehen, wird als wesentlicher Faktor für die Unterstützung von Klimaschutzmaßnahmen in der Bevölkerung erachtet. Auf Basis eines Generalized-Ordered-Logit Ansatzes und eines Datensatzes, der auf zwei Erhebungen aus den Jahren 2013 und 2015 unter jeweils mehr als 6000 Haushalten beruht, werden in diesem Papier die Determinanten der individuellen Risikowahrnehmung von drei Naturereignissen analysiert: Hitzewellen, Stürme und Überschwemmungen. Im Fokus der Analyse befinden sich die Rolle von objektiven Risikomaßen und die persönliche Erfahrung mit solchen Ereignissen. In Übereinstimmung mit der empirischen Literatur deuten unsere Ergebnisse darauf hin, dass persönliche Erfahrung mit diesen Ereignissen und dadurch erlittene Schäden die individuelle Risikoeinschätzung stark beeinflusst