720 LRDC
University of Pittsburgh
3939 O'Hara Street
Pittsburgh, PA 15260

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]


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, I investigate how people acquire such knowledge in light of beliefs about the way causal processes unfold over time. I'm also interested in the application of this research in applied contexts; e.g. understanding how patients and doctors understand the effects of drugs, and how this affects adherence.

Decisions from experience

Another thread of my work investigates how people 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 strategies? How do our (skewed) perceptions of outcomes and probabilities influence choice behavior?


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. (2018). Causal Strength Induction From Time Series Data. Journal of Experimental Psychology: General, 147(4), 485-513. [ pdf ] [ data ]

One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends – that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data.

Soo, K. W. & Rottman, B. M. (2018). Switch Rates Do Not Influence Weighting of Rare Events in Decisions from Experience, but Optional Stopping Does. Journal of Behavioral Decision Making, 31(5), 644-661. [ pdf ] [ data ]

The current research investigates how people decide which of two options produces a better reward by repeatedly sampling from the options. In particular, it investigates the roles of two features of search, optional stopping and switch rate, on participants’ final judgments of which option is better. First, in two studies, we found evidence for a new optional stopping effect; when participants stopped sampling right after experiencing a rare outcome, they made decisions as if they overweighted the rare outcome. Second, we investigated an effect proposed by Hills and Hertwig (2010), that people who frequently switch between options when sampling are more likely to make decisions consistent with underweighting rare outcomes. We conducted a theoretical analysis examining how switch rate can influence underweighting, and how the type of decision problem moderates this effect. Informed by the theoretical analysis, we conducted four studies designed to test this effect with high power. None of the studies produced significant effects of switch rate. Lastly, the studies replicated a prior finding that optional stopping and switch rate are negatively correlated. In sum, this research elaborates a fuller understanding of the relation between search strategies (switch rate and optional stopping) on how people decide which option is better, and their tendency to over- vs. underweight rare outcomes.

Soo, K. W. & Rottman, B. M. (2018). Causal Learning From Trending Time Series. In T. T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 2521-2526). Austin, TX: Cognitive Science Society. [ pdf ]

Two studies investigated how people learn the strength of the relation between a cause and an effect in a time series setting in which both variables exhibit temporal trends. In prior research, we found that people control for temporal trends by focusing on transitions, how variables change from one observation to the next in a trial-by-trial presentation (Soo & Rottman, 2018). In Experiment 1, we replicated this effect, and found further evidence that people rely on transitions when there are extremely strong temporal trends. In Experiment 2, we investigated how people infer causal relations from time series data when presented as time series graphs. Though people were often able to control for the temporal trends, they had difficulty primarily when the cause and effect exhibited trends in opposite directions and there was a positive causal relationship. These findings shed light on when people can and can’t accurately learn causal relations in time-series settings.

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). Austin, TX: Cognitive Science Society. [ 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). Austin, TX: Cognitive Science Society. [ 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). Austin, TX: Cognitive Science Society. [ 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 exploring any data that I can get my hands on.
I try to post write-ups from these projects on my blog.

Malaysia's 2018 General Election

I founded DataTarik, a Malaysian data-journalism blog providing analysis of Malaysia's 2018 elections.
I created interactive visualizations for Pulang Mengundi, an effort to help match voters who needed/offered rides across Malaysia to vote on May 9, 2018.

2016 US Presidential Election

Since the 2016 US Presidential election season, I've written code to scrape and analyze electoral data.
I've published a few posts about my analyses on my blog.

American Football

I've only recently become interested in football, upon realizing how rich the statistics are for this sport.
I've written some code for scraping football statistics, and will post analyses when I have the time.
If you're a football fan and have ideas for some questions that my data can help answer, I'd love to talk!
In the meantime, I use my analyses to inform my fantasy football decisions. (Update, January 2018: I won my league!)


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 some analyses.

Research Methods Dojo

Cory Derringer and I built an interactive tutorial for Ben Rottman's Research Methods class.
The Resarch Methods Dojo uses visualization and simulation to teach research methods concepts.