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Cognitive biases, such as the anchoring bias, pose a serious challenge to rational accounts of human cognition. We investigate whether rational theories can meet this challenge by taking into account the mind’s bounded cognitive resources. We asked what reasoning under uncertainty would look like if people made rational use of their finite time and limited cognitive resources. To answer this question, we applied a mathematical theory of bounded rationality to the problem of numerical estimation. Our analysis led to a rational process model that can be interpreted in terms of anchoring-and-adjustment. This model provided a unifying explanation for ten anchoring phenomena including the differential effect of accuracy motivation on the bias towards provided versus self-generated anchors. Our results illustrate the potential of resource-rational analysis to provide formal theories that can unify a wide range of empirical results and reconcile the impressive capacities of the human mind with its apparently irrational cognitive biases.

Expansive social identifications—including with one’s nation and the world—have been increasingly linked to psychological well-being. However, research has yet to examine how these effects interact. Although national and global identification are often assumed to be in conflict, it remains unclear whether strongly holding both diminishes or enhances overall well-being. We investigate this gap using Waves 5 and 6 of the World Values Survey (WVS; N  = 100,650, across developing and developed countries). First, we replicated prior findings via mixed models, finding that national citizenship and national pride (capturing national identity), as well as world citizenship (capturing global identity) each robustly predicted life satisfaction. We then tested interactions and estimated life satisfaction across different combinations of predictor strengths. Despite a negative interaction between national pride and world citizenship, those high on both still reported the highest life satisfaction. Our findings suggest that the joint effects on well-being of national and global identifications, despite their seemingly competing natures, remain additive. We point to the need for future research on the underlying psychological mechanisms and behavioral consequences of these joint identifications.

When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods for improving human decision-making is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new AI-Interpret algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of three large behavioral experiments showed that providing the decision rules generated by AI-Interpret as flowcharts significantly improved people’s planning strategies and decisions across three different classes of sequential decision problems. Moreover, our fourth experiment revealed that this approach is significantly more effective at improving human decision-making than training people by giving them performance feedback. Finally, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems. We conclude that the methods and findings presented in this article are an important step towards leveraging automatic strategy discovery to improve human decision-making. The code for our algorithm and the experiments is available at https://github.com/RationalityEnhancement/InterpretableStrategyDiscovery.

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people's ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. This article introduces a mathematical framework with which competing ideas about the computational quantities indexed by MMN responses can be formalized and tested against single-trial EEG data. This framework was applied to five major theories of the MMN, comparing their ability to explain trial-by-trial changes in MMN amplitude. Three of these theories (predictive coding, model adjustment, and novelty detection) were formalized by linking the MMN to different manifestations of the same computational mechanism: approximate Bayesian inference according to the free-energy principle. We thereby propose a unifying view on three distinct theories of the MMN. The relative plausibility of each theory was assessed against empirical single-trial MMN amplitudes acquired from eight healthy volunteers in a roving oddball experiment. Models based on the free-energy principle provided more plausible explanations of trial-by-trial changes in MMN amplitude than models representing the two more traditional theories (change detection and adaptation). Our results suggest that the MMN reflects approximate Bayesian learning of sensory regularities, and that the MMN-generating process adjusts a probabilistic model of the environment according to prediction errors.

Anxiety disorders are among the most prevalent mental disorders, and stress plays a significant role in their development. Ecological momentary interventions (EMIs) hold great potential to help people manage stress and anxiety by training emotion regulation and coping skills in real-life settings. InsightApp is a gamified EMI and research tool that incorporates elements from evidence-based therapeutic approaches. It is designed to strengthen people's metacognitive skills for coping with challenging real-life situations and embracing anxiety and other emotions. This randomized controlled trial aims to examine the effectiveness of InsightApp in (1) improving individuals' metacognitive strategies for coping with stress and anxiety and (2) promoting value-congruent action. It also evaluates how long these effects are retained. This experiment advances our understanding of the role of metacognition in emotional and behavioral reactivity to stress. We conducted a randomized controlled trial with 228 participants (completion rate: n=197, 86.4%; mean age 38, SD 11.50 years; age range 20-80 years; female: n=101, 52.6%; and White: n=175, 91.1%), who were randomly assigned to either the treatment or the active placebo control group. During the 1-week intervention phase, the treatment group engaged with InsightApp, while participants in the control group interacted with a placebo version of the app that delivered executive function training. We assessed the differences between the 2 groups in posttest and follow-up assessments of mental health and well-being while controlling for preexisting differences. Moreover, we used a multilevel model to analyze the longitudinal data, focusing on the within-participant causal effects of the intervention on emotional and behavioral reactivity to daily stressors. Specifically, we measured daily anxiety, struggle with anxiety, and value-congruent action. The intervention delivered by InsightApp yielded mixed results. On one hand, we found no significant posttest scores on mental health and well-being measures directly after the intervention or 7 days later (all P>.22). In contrast, when confronted with real-life stress, the treatment group experienced a 15% lower increase in anxiety (1-tailed t test, t =-2.4; P=.009) and a 12% lower increase in the struggle with anxiety (t =-1.87; P=.031) than the control group. Furthermore, individuals in the treatment group demonstrated a 7% higher tendency to align their actions with their values compared to the control group (t =3.23; P=.002). After the intervention period, InsightApp's positive effects on the struggle with anxiety in reaction to stress were sustained, and increased to an 18% lower reactivity to stress (t =-2.84; P=.002). As our study yielded mixed results, further studies are needed to obtain an accurate and reliable understanding of the effectiveness of InsightApp. Overall, our findings tentatively suggest that guiding people to apply adaptive metacognitive strategies for coping with real-life stress daily with a gamified EMI is a promising approach that deserves further evaluation. OSF Registries osf.io/k3b5d; https://osf.io/k3b5d.

Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation . How can self-regulation be supported or strengthened in ways that are relevant for everyday work and learning activities? To address this question, we introduce and evaluate a desktop application that helps people stay focused on their work and train self-regulation at the same time. Our application lets the user set a goal for what they want to do during a defined period of focused work at their computer, then gives negative feedback when they get distracted, and positive feedback when they reorient their attention towards their goal. After this so-called focus session, the user receives overall feedback on how well they focused on their goal relative to previous sessions. While existing approaches to attention training often use artificial tasks, our approach transforms real-life challenges into opportunities for building strong attention control skills. Our results indicate that optimal attentional feedback can generate large increases in behavioral focus, task motivation, and self-control—benefitting users to successfully achieve their long-term goals.

The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering--a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN - in terms of latency and amplitude--and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and--more generally--illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.

Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission. In this study, we introduced a principled mathematical method for determining how many points should be awarded or deducted for the enactment or omission of the desired behavior, depending on when and how often the person has succeeded versus failed to enact it in the past. We called this approach optimized gamification of behavior change. As a proof of concept, we designed a chatbot that applies our optimized gamification method to help people build healthy water-drinking habits. We evaluated the effectiveness of this gamified intervention in a 40-day field experiment with 1 experimental group (n=43) that used the chatbot with optimized gamification and 2 active control groups for which the chatbot's optimized gamification feature was disabled. For the first control group (n=48), all other features were available, including verbal feedback. The second control group (n=51) received no feedback or reminders. We measured the strength of all participants' water-drinking habits before, during, and after the intervention using the Self-Report Habit Index and by asking participants on how many days of the previous week they enacted the desired habit. In addition, all participants provided daily reports on whether they enacted their water-drinking intention that day. A Poisson regression analysis revealed that, during the intervention, users who received feedback based on optimized gamification enacted the desired behavior more often (mean 14.71, SD 6.57 times) than the active (mean 11.64, SD 6.38 times; P<.001; incidence rate ratio=0.80, 95% CI 0.71-0.91) or passive (mean 11.64, SD 5.43 times; P=.001; incidence rate ratio=0.78, 95% CI 0.69-0.89) control groups. The Self-Report Habit Index score significantly increased in all conditions (P<.001 in all cases) but did not differ between the experimental and control conditions (P>.11 in all cases). After the intervention, the experimental group performed the desired behavior as often as the 2 control groups (P≥.17 in all cases). Our findings suggest that optimized gamification can be used to make digital behavior change interventions more effective. Open Science Framework (OSF) H7JN8; https://osf.io/h7jn8.

Ecological momentary interventions open up new and exciting possibilities for delivering mental health interventions and conducting research in real-life environments via smartphones. This makes designing psychotherapeutic ecological momentary interventions a promising step toward cost-effective and scalable digital solutions for improving mental health and understanding the effects and mechanisms of psychotherapy. The first objective of this study was to formatively assess and improve the usability and efficacy of a gamified mobile app, the InsightApp, for helping people learn some of the metacognitive skills taught in cognitive behavioral therapy, acceptance and commitment therapy, and mindfulness-based interventions. The app aims to help people constructively cope with stressful situations and difficult emotions in everyday life. The second objective of this study was to test the feasibility of using the InsightApp as a research tool for investigating the efficacy of psychological interventions and their underlying mechanisms. We conducted 2 experiments. In experiment 1 (n=65; completion rate: 63/65, 97%), participants (mean age 27, SD 14.9; range 19-55 years; 41/60, 68% female) completed a single session with the InsightApp. The intervention effects on affect, belief endorsement, and propensity for action were measured immediately before and after the intervention. Experiment 2 (n=200; completion rate: 142/200, 71%) assessed the feasibility of conducting a randomized controlled trial using the InsightApp. We randomly assigned participants to an experimental or a control condition, and they interacted with the InsightApp for 2 weeks (mean age 37, SD 12.16; range 20-78 years; 78/142, 55% female). Experiment 2 included all the outcome measures of experiment 1 except for the self-reported propensity to engage in predefined adaptive and maladaptive behaviors. Both experiments included user experience surveys. In experiment 1, a single session with the app seemed to decrease participants' emotional struggle, the intensity of their negative emotions, their endorsement of negative beliefs, and their self-reported propensity to engage in maladaptive coping behaviors (P<.001 in all cases; average effect size=-0.82). Conversely, participants' endorsement of adaptive beliefs and their self-reported propensity to act in accordance with their values significantly increased (P<.001 in all cases; average effect size=0.48). Experiment 2 replicated the findings of experiment 1 (P<.001 in all cases; average effect size=0.55). Moreover, experiment 2 identified a critical obstacle to conducting a randomized controlled trial (ie, asymmetric attrition) and how it might be overcome. User experience surveys suggested that the app's design is suitable for helping people apply psychotherapeutic techniques to cope with everyday stress and anxiety. User feedback provided valuable information on how to further improve app usability. In this study, we tested the first prototype of the InsightApp. Our encouraging preliminary results show that it is worthwhile to continue developing the InsightApp and further evaluate it in a randomized controlled trial.