Our work pursues an understanding of cognitive and neural processes, especially those related to decision-making. As part of this effort, we develop computational models and other tools that can be applied to a variety of problems spanning topic areas in psychology and other sciences.
Approaching problems from a mathematical and statistical understanding of behavior allows us to construct more complete models of cognition, leveraging different sources of data to gain insight into shared underlying processes. As such, our research is often interdisciplinary, involving tools and collaborators from other areas like economics, biology, computer science, physics, and statistics.
Despite the prevalence of multi-alternative and continuous selection tasks in the real world – pricing, spatial navigation, confidence, numerosity, estimation tasks – models of the decision process have focused primarily on selections between 2-3 alternatives. Much of our cognitive modeling work has striven to extend both our theoretical and empirical understanding of decisions that offer many possible response alternatives. Our previous and ongoing work investigates and models phenomena in perception as well as higher-level cognitive processes, including judgments of color, orientation, magnitude, price, timing, and confidence.
One of the most interesting (and frustrating!) aspects of multi-alternative and continuous responses is the importance of similarity: how does support for one option affect support for others? Similar options share support (if deciding between drinks, we may also like Pepsi if we like Coke), and dissimilar options fight over support (if we want a car with good gas mileage, that provides support for a hybrid and support against a gas guzzler). Understanding how these similarities in our psychological representations of choice options affect decision making is a key part of understanding human decision making in general.
Reynolds, A., Garton, R., Kvam, P. D., Sauer, J. D., Osth, A., & Heathcote, A. (2020). A dynamic model of deciding not to choose. Journal of Experimental Psychology: General, Online First Publication. Link, PDF
Reynolds, A., Kvam, P. D., Osth, A. F., \& Heathcote, A. (2020). Correlated racing evidence accumulator models.Journal of Mathematical Psychology 96, 102331. Link
A great deal of thought and work has gone into understanding how we make decisions -- what information we consider, how we search for relevant information, when we stop and decide. Each of these is affected by what our goals are: do we want to pick A>B or B>A? Or do we want to rate each option we have in terms of how much we like them? The patterns of behavior we observe in either situation can diverge wildly, and indeed the desire to make up our mind (especially on political issues) can lead us to consider extreme information and form polarized views of the world. Our reseach on choice goals examines how decision strategies drive people to become polarized on new issues, how extremists are created, and potential strategies for reducing polarization & extremism.
Of course, behavior doesn't end with a decision either. Once we decide, we have to follow up on it and experience the consequences. The mere fact that we have made a decision can affect how we think about our world, what we learn and remember, and how we act in subsequent choice scenarios. Much of our work examines what happens after a choice is made -- what do we learn from the consequences (reinforcement learning), what information do we remember and share with others (social networks), and how does choice determine the course of later changes in beliefs and preferences (dynamic decision making).
Kvam, P. D., Busemeyer, J. R., & Pleskac, T. (2021). Temporal oscillations in preference strength provide evidence for an open system model of constructed preference. Scientific reports, 11(1), 1-15. Link
Busemeyer, J. R.*, Kvam, P. D.*, & Pleskac, T. J.* (2020). Comparison of quantum versus Markov dynamics for modeling human evidence accumulation. WIREs Cognitive Science, 11(4), e1526. [*all authors contributed equally to this work] Link
Kvam, P.D., Pleskac, T.J., Yu, S., & Busemeyer, J R. (2015). Interference effects of choice on confidence: Quantum characteristics of evidence accumulation. Proceedings of the National Academy of Sciences, 112(34), 10645--10650. Link, PDF
Our work seeks to go beyond simply describing behavior and instead venture into why people behave how they do. To do this, we develop models of what we think is going on in the mind / brain, quantifying what we observe (choices, judgments, neural activity) in terms of processes like learning, attention, information search, and changes in beliefs and preferences. Comparing different models and the diverging predictions they make allows us to come up with the best explanations we can for human behavior.
This approach to studying psychology demands rigorous statistical and computational tools. Much of what we do involves developing better models, better tools for creating and fitting models, or model-based ways to put different sources of data together. The model-based measures we develop are more reliable than simple summaries of behavior (such as accuracy, choice proportions, mean response times) and help us connect the behavior we observe in the laboratory to important outcomes like substance use, mental health, and disordered decision making. We think the results are worth the trouble!
Haines, N., Kvam, P. D., Irving, L., Smith, C. T., Beauchaine, T. P., Pitt, M. A., Ahn, W-Y., & Turner, B. M. (2020). Learning from the reliability paradox: How theoretically informed generative models can advance the social, behavioral, and brain sciences. Preprint.
Kvam, P. D., Romeu, R. J., Turner, B. M., Vassileva, J., & Busemeyer, J. R. (2020). Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks. Psychological Methods, 26(1), 18-37. Link, PDF
Molloy, M. F., Romeu, R. J., Kvam, P. D., Finn, P. M., Busemeyer, J. R., & Turner, B. M. (2020). Hierarchical Bayesian methods to correct estimation errors in hyperbolic discounting models of intertemporal choice. Decision 7(3), 212--224.PDF
Rahnev, D., Desender, K., Lee, A. L. F., Adler, W. T., ..., Kvam, P. D., et al. (2020). The confidence database. Nature Human Behaviour, 4(3), 317--325. Link
A critical consideration when implementing models of cognition is whether a proposed explanation is plausible given what we know about human physiology and ancestry. We know that the nervous system is composed of neural circuitry that has evolved over generations as well as shifted over lifetimes with new learning. Connectionist networks and evolutionary algorithms allow researchers to explore how particular models might be instantiated in the brain. Studying these models lets us study the neural mechanisms we think underlie behavior, and test whether different theories are plausble in light of what we know about the structure and organization of the brain.
The increasing prevalence of artificial intelligence also offers the opportunity to develop new tools for studying psychology and cognition. We are currently using deep neural networks to develop more accessible tools for model fitting, Gaussian processes to model relationships between spatially and temporally correlated outcomes, agent-based models to understand social dynamics, genetic algorithms to model the evolution of cognitive capacities like working memory, and natural language processing to understand the similarities in psychological representation between different concepts. Each of these approaches leverages a different type of AI, but all of them promise to expand our understanding of human cognition.
Kvam, P. D., Hintze, A., Pleskac, T. J., & Pietraszewski, D. (2019). Computational Evolution and Ecologically Rational Decision Making. In R. Hertwig, T.J. Pleskac, T. Pachur, & The Center for Adaptive Rationality, Taming Uncertainty. MIT Press.
Kvam, P. D. \& Hintze, A. (2018). Rewards, risks, and reaching the right strategy: Evolutionary paths from heuristics to optimal decisions. Evolutionary Behavioral Sciences, 12(3), 177--190.
Hintze, A., Edlund, J. A., Olson, R. A., Knoester, D. B., Schossau, J., ..., Kvam, P. D., ..., \& Adami, C. (2017). Markov Brains: A Technical Introduction. Technical report, arXiv:1709.05601 [cs.AI].
Kvam, P.D., Cesario, J., Schossau, J., Eisthen, H., & Hintze, A. (2015) Computational evolution of decision-making strategies. In D.C. Noelle, R. Dale, A.S. Warlaumont, J. Yoshimi, T. Matlock, C.D. Jennings, & P.P. Maglio (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (pp. 1225--1230). Austin, TX: Cognitive Science Society. Link, PDF