Optimal Predictions in Illness Cognition

People make accurate predictions for many real world events e.g. human life spans (Griffiths & Tenenbaum, 2006).Accurate predictions are particularly important in the domain of health, where illness knowledge directly influences patientoutcomes. To understand how well peoples’ illness expectations were aligned, we asked participants to estimate durationsfor 9 illnesses, and compared their responses to the real-world distributions. We found that for common acute illnesses (e.g.,the cold) people make accurate predictions, whereas for rare chronic illnesses (e.g., COPD) people make comparatively poorpredictions. Further, we found that participants overestimate the prevalence of every illness, especially for those that are morecommon (e.g., the cold). Taken together, these results suggest that people more accurately estimate the duration of commonacute illnesses, but this may cause them to overestimate the prevalence of these illnesses. Results will be discussed in terms ofimplications for both cognition and behavioral health theory

Time Course of Fidelity and Contributing Factors to Long-Term Memory

Various models have been implemented to explain long-term memory (Brady, et al., 2013; Lew, et al., 2015), withsome being derived from studies of visual working memory (Bays, et al. 2009; Zhang & Luck, 2008). The implicit assumptionis that processes and mechanisms of working memory also exist in long-term memory. However, the findings of fidelity andcontributing factors are highly varied (e.g., Persaud & Hemmer, 2014; Schurgin & Flombaum, 2015) To address what happensto memory traces as they transition from visual working into long-term memory and what factors, such as prior knowledgeand guessing, contribute to the “lifespan” of long-term memory, we implemented three models: the standard remember-guessmodel, a three-component remember-guess model, and a Bayesian mixture model and evaluated these models against data froma continuous recall task. The results clarify the time course of fidelity in long-term memory and pinpoints specific factors thatcontribute to memory

Socially Cognizant Robotics for a Technology Enhanced Society

Emerging applications of robotics, and concerns about their impact, require the research community to put human-centric objectives front-and-center. To meet this challenge, we advocate an interdisciplinary approach, socially cognizant robotics, which synthesizes technical and social science methods. We argue that this approach follows from the need to empower stakeholder participation (from synchronous human feedback to asynchronous societal assessment) in shaping AI-driven robot behavior at all levels, and leads to a range of novel research perspectives and problems both for improving robots' interactions with individuals and impacts on society. Drawing on these arguments, we develop best practices for socially cognizant robot design that balance traditional technology-based metrics (e.g. efficiency, precision and accuracy) with critically important, albeit challenging to measure, human and society-based metrics

Explicit Predictions for Illness Statistics

People’s predictions for real-world events have been shown tobe well-calibrated to the true environmental statistics (e.g.Griffiths and Tenenbaum 2006). Previous work, however, hasfocused on predictions for these events by aggregating acrossobservers, making a single estimate for the total durationgiven a current duration. Here, we focus on assessingpredictions for both the mean and form of distributions in thedomain of illness duration prediction at the individual level.We assess understanding for both acute illnesses for whichpeople might have experience, as well as chronic conditionsfor which people are less likely to have knowledge. Our datasuggests that for common acute illnesses people canaccurately estimate both the mean and form of thedistribution. For less common acute illnesses and chronicillnesses, people have a tendency to overestimate the meanduration, but still accurately predict the distribution form

Lay Understanding of Illness Probability Distributions

Our central question is: how accurate are laypeople’sstatistical intuitions about probability distributions within thedomain of health? Specifically, can participants produceentire probability distributions for the duration of illnesses?While a large body of decision making research has suggestedthat people use a flawed process to arrive at decisions, weposit that participants may be using an optimal process, butwith flawed information. To this end, we assess accuracy interms of both the mean and form of distributions for bothacute illnesses for which people might have experience, andchronic conditions for which people are less likely to haveexperience. We find that participants can accurately estimatethe mean and form of distributions for acute illnesses

Integrating Episodic and Semantic Information in Memory for Natural Scenes

Recall of objects in natural scenes can be influenced not only by episodic but also by semantic memory. To model the statistical regularities that might be encoded in semantic memory, we applied a topic model to a large database of labeled images. We then incorporated the learned topics in a dual route topic model for recall that explains how and why episodic memories are combined with semantic memories. The dual route model was applied to an empirical study in which people recall objects from scenes under varying amounts of study time. The dual route model explains how the trade-off between episodic and semantic memory is affected by study time, output position, and also congruity of the object with the scene context