One of the most unique and impressive feats of the human mind is its ability to discover and continuouslyrefine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is verydifficult because changes in cognitive strategies are not directly observable. One important domain in whichstrategies and mechanisms are studied is planning. To enable researchers to uncover how people learn howto plan, we offer a tutorial introduction to a recently developed process-tracing paradigm along with a newcomputational method for inferring people’s planning strategies and their changes over time from the resultingprocess-tracing data. Our method allows researchers to reveal experience-driven changes in people’s choice ofindividual planning operations, planning strategies, strategy types, and the relative contributions of differentdecision systems. We validate our method on simulated and empirical data. On simulated data, its inferencesabout the strategies and the relative influence of different decision systems are accurate. When evaluated on human data generated using our process-tracing paradigm, our computational method correctly detects theplasticity-enhancing effect of feedback and the effect of the structure of the environment on people’s planningstrategies. Together, these methods can be used to investigate the mechanisms of cognitive plasticity and toelucidate how people acquire complex cognitive skills such as planning and problem-solving. Importantly, ourmethods can also be used to measure individual differences in cognitive plasticity and examine how differenttypes (pedagogical) interventions affect the acquisition of cognitive skills.

Many controversies arise from differences in how people resolve moral dilemmas by following deontological moral rules versus consequentialist cost-benefit reasoning (CBR). This article explores whether and, if so, how these seemingly intractable differences may arise from experience and whether they can be overcome through moral learning. We designed a new experimental paradigm to investigate moral learning from consequences of previous decisions. Our participants (N=387) faced a series of realistic moral dilemmas between two conflicting choices: one prescribed by a moral rule and the other favored by CBR. Critically, we let them observe the consequences of each of their decisions before making the next one. In one condition, CBR-based decisions consistently led to good outcomes, whereas rule-based decisions consistently led to bad outcomes. In the other condition, this contingency was reversed. We observed systematic, experience-dependent changes in people's moral rightness ratings and moral decisions over the course of just 13 decisions. Without being aware of it, participants adjusted how much moral weight they gave to CBR versus moral rules according to which approach produced better consequences in their respective experimental condition. These learning effects transferred to their subsequent responses to the Oxford Utilitarianism Scale, indicating genuine moral learning rather than task-specific effects. Our findings demonstrate the existence of rapid adaptive moral learning from the consequences of previous decisions. Individual differences in morality may thus be more malleable than previously thought.

Ecological Momentary interventions (EMIs) open new and exciting possibilities for conducting research and delivering mental health interventions in real-life environments via smartphones. This makes designing psychotherapeutic EMIs a promising step towards cost-effective, scalable digital solutions for improving mental health and understanding the effects and mechanisms of psychotherapy.

Scientific discovery concerns finding patterns in data and creating insightful hypotheses that explain these patterns. Traditionally, each step of this process required human ingenuity. But the galloping development of computer chips and advances in artificial intelligence (AI) make it increasingly more feasible to automate some parts of scientific discovery. Understanding human planning is one of the fields in which AI has not yet been utilized. State-of-the-art methods for discovering new planning strategies still rely on manual data analysis. Data about the process of human planning is often used to group similar behaviors together. Researchers then use this data to formulate verbal descriptions of the strategies which might underlie those groups of behaviors. In this work we leverage AI to automate these two steps of scientific discovery. We introduce a method for the automatic discovery and description of human planning strategies from process-tracing data collected with the Mouselab-MDP paradigm. Our algorithm, called Human-Interpret, uses imitation learning to describe data gathered in the experiment in terms of a procedural formula and then translates that formula to natural language using a pre-defined predicate dictionary. We test our method on a benchmark data set that researchers have previously scrutinized manually. We find that the descriptions of human planning strategies that we obtain automatically are about as understandable as human-generated descriptions. They also cover a substantial proportion of all types of human planning strategies that had been discovered manually. Our method saves scientists' time and effort as all the reasoning about human planning is done automatically. This might make it feasible to more rapidly scale up the search for yet undiscovered cognitive strategies that people use for planning and decision-making to many new decision environments, populations, tasks, and domains. Given these results, we believe that the presented work may accelerate scientific discovery in psychology, and due to its generality, extend to problems from other fields.