Flexible structure learning under uncertainty.

Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether uncertainty in dynamic environments affects our ability to learn predictive structures. We exposed participants to sequences of symbols determined by first-order Markov models and asked them to indicate which symbol they expected to follow each sequence. We introduced uncertainty in this prediction task by manipulating the: (a) probability of symbol co-occurrence, (b) stimulus presentation rate. Further, we manipulated feedback, as it is known to play a key role in resolving uncertainty. Our results demonstrate that increasing the similarity in the probabilities of symbol co-occurrence impaired performance on the prediction task. In contrast, increasing uncertainty in stimulus presentation rate by introducing temporal jitter resulted in participants adopting a strategy closer to probability maximization than matching and improving in the prediction tasks. Next, we show that feedback plays a key role in learning predictive statistics. Trial-by-trial feedback yielded stronger improvement than block feedback or no feedback; that is, participants adopted a strategy closer to probability maximization and showed stronger improvement when trained with trial-by-trial feedback. Further, correlating individual strategy with learning performance showed better performance in structure learning for observers who adopted a strategy closer to maximization. Our results indicate that executive cognitive functions (i.e., selective attention) may account for this individual variability in strategy and structure learning ability. Taken together, our results provide evidence for flexible structure learning; individuals adapt their decision strategy closer to probability maximization, reducing uncertainty in temporal sequences and improving their ability to learn predictive statistics in variable environments.

Memory transmission in small groups and large networks: An empirical study.

When people try to remember information in a group, they often recall less than if they were recalling alone. This finding is called collaborative inhibition, and has been studied primarily in small groups because of the difficulty of bringing large groups into the laboratory. To study the dynamics of collaborative inhibition in large groups (Luhmann & Rajaram, Psychological Science, 26, 1909-1917, 2015) constructed an agent-based model that extrapolated from previous laboratory experiments with small groups. The model predicts that collaborative inhibition should increase with group size. Here, we evaluate this model by recruiting a large number of participants using crowdsourcing, allowing us to replace the artificial agents in the model with people to study collaborative memory at larger scales. Our empirical results did not match the model predictions: there was no evidence for an increase in collaborative inhibition with group size, despite substantial power to detect such an effect. These findings motivate further empirical work to elucidate the mechanisms of collaborative memory.

A rational model of people's inferences about others' preferences based on response times.

There's a difference between someone instantaneously saying "Yes!" when you ask them on a date compared to "…yes." Psychologists and economists have long studied how people can infer preferences from others' choices. However, these models have tended to focus on what people choose and not how long it takes them to make a choice. We present a rational model for inferring preferences from response times, using a drift diffusion model to characterize how preferences influence response time, and Bayesian inference to invert this relationship. We test our model's predictions for three experimental questions. Matching model predictions, participants inferred that a decision-maker preferred a chosen item more if the decision-maker spent less time deliberating (Experiment 1), participants predicted a decision-maker's choice in a novel comparison based on inferring the decision-maker's relative preferences from previous response times and choices (Experiment 2), and participants could incorporate information about a decision-maker's mental state of cautious or careless (Experiments 3, 4A, and 4B).

Relationship to CBT outcome and dropout of decision support tools of the written case formulation, list of treatment goals and plot of symptom scores.

Many patients who receive cognitive behavior therapy (CBT) for mood and anxiety disorders fail to respond or drop out of treatment. We tested the hypotheses that therapist use of each of three decision support tools, a written case formulation, a list of treatment goals, and a plot of symptom scores, was associated with improved outcome and reduced dropout in naturalistic CBT provided to 845 patients in a private practice setting. We conducted regression analyses to test the hypotheses that the presence of each tool in the clinical record was associated with lower end-of-treatment scores on the Beck Depression Inventory (BDI) and the Burns Anxiety Inventory (BurnsAI), and lower rates of premature and uncollaborative dropout. We found that the presence of a written case formulation in the clinical record was associated with lower rates of both types of dropout. A list of treatment goals was associated with lower end-of-treatment scores on the BDI and the BurnsAI, and a lower rate of uncollaborative but a higher rate of premature dropout. A plot of symptom scores was associated with lower end-of-treatment scores on the BDI, and lower rates of both types of dropout. Results suggest that therapist use of a written case formulation, list of treatment goals, and a plot of symptom scores can contribute to improved outcome and reduced dropout in CBT.

How to Be Helpful to Multiple People at Once.

When someone hosts a party, when governments choose an aid program, or when assistive robots decide what meal to serve to a family, decision-makers must determine how to help even when their recipients have very different preferences. Which combination of people's desires should a decision-maker serve? To provide a potential answer, we turned to psychology: What do people think is best when multiple people have different utilities over options? We developed a quantitative model of what people consider desirable behavior, characterizing participants' preferences by inferring which combination of "metrics" (maximax, maxsum, maximin, or inequality aversion [IA]) best explained participants' decisions in a drink-choosing task. We found that participants' behavior was best described by the maximin metric, describing the desire to maximize the happiness of the worst-off person, though participant behavior was also consistent with maximizing group utility (the maxsum metric) and the IA metric to a lesser extent. Participant behavior was consistent across variation in the agents involved and  tended to become more maxsum-oriented when participants were told they were players in the task (Experiment 1). In later experiments, participants maintained maximin behavior across multi-step tasks rather than shortsightedly focusing on the individual steps therein (Experiment 2, Experiment 3). By repeatedly asking participants what choices they would hope for in an optimal, just decision-maker, and carefully disambiguating which quantitative metrics describe these nuanced choices, we help constrain the space of what behavior we desire in leaders, artificial intelligence systems helping decision-makers, and the assistive robots and decision-makers of the future.