University of Pittsburgh
The primary research focus of the lab is causal learning - how people learn cause-effect relationships from their experiences (e.g., this new medicine I have been trying seems to work well). We are especially interested in causal learning from time series data, in which the causes and effects exhibit gradual changes over time. Another direction is studying how politically motivated reasoning affects causal judgments.
Over the past 8 years or so, our focus has primarily been on causal learning in everyday life, for example how people test whether a medicine is working or not, try new diets, or assess the efficacy of other lifestyle changes they make. To study this in a controlled way, we have recently conduced a number of smartphone experiments to study causal learning and memory over 3-4 weeks.
One of the primary goals of science is to uncover causal relations. We are interested in understanding how to teach causal inference in research methods classrooms to improve science education. See Resources for more info.
More broadly, we are interested in when and whether human decision makers make judgments that are approximately 'normative' (correct). We have studied the accuracy of decision making with regards to causally-based decisions as well as Bayesian reasoning and medical diagnosis.
Currently, I am exploring a new area of research on how we can provide decision support to people who are trying to make decisions about how to live a more sustainable life. In particular, I am focusing on decisions that people can make regarding ways to reduce energy consuption for their homes. One challenge is that there are hundreds of potential home retrofits; how can we help people decide which ones are relevant to their particular home, fit their budget, and are compatible with other goals such as comfort.
| osRMss | open source Research Methods for the social sciences. An open educational resource that anyone teaching research methods can adapt for their own courses. |
| Causality & Regression | R Shiny app for learning about the relation between Causality and Multiple Regression. |
| PsychCloud | Tutorial and Code for making psychology experiments (or interactive websites more generally) hosted on Google App Engine and Google's Could. This is how we program web experiments. At this point, the code is out of date but is helpful to understand what can be done. |
| Causal Strength | Code for models of causal strength including Rescorla-Wagner (Rescorla & Wagner, 1972), ∆P (Jenkins & Ward, 1965), Power-PC (Cheng, 1997), and Temporal-difference (Sutton & Barto, 1987). |
Significance: Spacing out learning over time is one of the most robust methods to enhance later memory. Yet, much of this work is predicated on repeated exposures to identical information, which is rare in everyday life. To harness these benefits in real-world settings, we first need to characterize the efficacy of the spacing effect in the face of mnemonic variability. For isolated features of memory presented with mnemonic variability, the benefit of spacing was only observed with longer timescales of spacing intervals (hours to days) and not at short intervals (seconds to minutes). However, for more associative forms of memory, variability undermined the effects of spacing. These results challenge the existing notions of how and when spaced learning facilitates long-term memory.
Abstract: The memory benefit that arises from distributing learning over time rather than in consecutive sessions is one of the most robust effects in cognitive psychology. While prior work has mainly focused on repeated exposures to the same information, in the real world, mnemonic content is dynamic, with some pieces of information staying stable while others vary. Thus, open questions remain about the efficacy of the spacing effect in the face of variability in the mnemonic content. Here, in two experiments, we investigated the contributions of mnemonic variability and the timescale of spacing intervals, ranging from seconds to days, to long-term memory. For item memory, both mnemonic variability and spacing intervals were beneficial for memory; however, mnemonic variability was greater at shorter spacing intervals. In contrast, for associative memory, repetition rather than mnemonic variability was beneficial for memory, and spacing benefits only emerged in the absence of mnemonic variability. These results highlight a critical role for mnemonic variability and the timescale of spacing intervals in the spacing effect, bringing this classic memory paradigm into more ecologically valid contexts.