Contact

720 LRDC
University of Pittsburgh
3939 O'Hara Street
Pittsburgh, PA 15260
kevin.soo@pitt.edu

Kevin Soo

Hello! I'm a PhD student studying cognitive psychology at the University of Pittsburgh, based in the Learning Research & Development Center. I work with Benjamin Rottman in the Causal Learning & Decision Making Lab.

I use a combination of behavioral experiments and computational modelling to investigate human learning, reasoning, and decision-making. If you have any questions about my research or share similar interests, I'd love to hear from you.

[Download CV]

Links

My GitHub contains code for running experiments and analyses.

Cory Derringer (another student in our lab) does some pretty cool research, too.

I formerly worked with David Lagnado at UCL studying similar stuff.

For recreation, I dabble in photography.

Research interests

Causal cognition

I'm interested in how people think about causation. My research has investigated how people learn about causal direction (e.g., given two related variables, X and Y, is X or Y the cause?) and causal strength (e.g., how strong is the influence of a drug?). In particular, how do people acquire such knowledge in light of beliefs about how causal processes unfold over time? I'm also interested in the intersection between human causal learning, the philosphy of causality, and formal methods of causal discovery.

Decisions from experience

Another thread of my work investigates how people make evaluate options in the world based on past experience when making decisions. What strategies do we use to search for information prior to making a decision? Do our beliefs about the structure of the environment (e.g., the presence of temporal trends) influence our decision-making?

Publications

Electronic versions are provided as a professional courtesy to ensure timely dissemination of academic work for individual, noncommercial purposes. Copyright (and all rights therein) resides with the respective copyright holders, as stated within each paper. These files may not be reposted without permission.

Soo, K. W., & Rottman, B. M. (2016). Causal Learning with Continuous Variables over Time. In A. Papafrogou, D. Grodner, D. Mirman, & J.C. Trueswell (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 153-158). [ pdf ]

When estimating the strength of the relation between a cause (X) and effect (Y), there are two main statistical approaches that can be used. The first is using a simple correlation. The second approach, appropriate for situations in which the variables are observed unfolding over time, is to take a correlation of the change scores - whether the variables reliably change in the same or opposite direction. The main question of this manuscript is whether lay people use change scores for assessing causal strength in time series contexts. We found that subjects' causal strength judgments were better predicted by change scores than the simple correlation, and that use of change scores was facilitated by naturalistic stimuli. Further, people use a heuristic of simplifying the magnitudes of change scores into a binary code (increase vs. decrease). These findings help explain how people uncover true causal relations in complex time series contexts.

Soo, K. W., & Rottman, B. M. (2015). Elemental Causal Learning from Transitions. In R. Dale, C. Jennings, P. Maglio, T. Matlock, D. Noelle, A. Warlaumont, & J. Yoshimi (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 2254-2259). [ pdf ]

Much research on elemental causal learning has focused on how causal strength is learned from the states of variables. In longitudinal contexts, the way a cause and effect change over time can be informative of the underlying causal relationship. We propose a framework for inferring the causal strength from different observed transitions, and compare the predictions to existing models of causal induction. Subjects observe a cause and effect over time, updating their judgments of causal strength after observing different transitions. The results show that some transitions have an effect on causal strength judgments over and above states.
Code for models used to compute causal strength from longitudinal data.

Soo, K. W., & Rottman, B. M. (2014). Learning Causal Direction from Transitions with Continuous and Noisy Variables. In P. Bello, M. Guarin, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1485-1490). [ pdf ]

Previous work has found that one way people infer the direction of causal relationships involves identifying an asymmetry in how causes and effects change over time. In the current research we test the generalizability of this reasoning strategy in more complex environments involving ordinal and continuous variables and with noise. Participants were still able to use the strategy with ordinal and continuous variables. However, when noise made it difficult to identify the asymmetry participants were no longer able to infer the causal direction.

Side projects

In my spare time, I enjoy scraping, visualizing, and analyzing any data that I can get my hands on.
I try to post write-ups from these projects on my blog.
The code used in these projects are posted below (organized by topic).


American football

I've only recently become interested in football upon realizing how rich datasets can shed light on some really interesting questions!
I've written some code for scraping football statistics, and will post some analyses when I have the time!

Soccer

I'm regularly playing, watching, and thinking about soccer (or football, as the world calls it).
I'm currently trying to scrape and compile datasets for fun analyses I plan to do!

Politics

Coming soon!

Test

Research Methods Dojo

Cory Derringer and I built an interactive tutorial for students in Ben Rottman's Research Methods class.
The Resarch Methods Dojo utilizes data visualization and simulations to teach topics like within vs. between subjects designs, carryover, practice, and fatigue effects.