A pinboard by
Pele Schramm

PhD student, University of California, Irvine


Although I have many academic interests, my primary research area is in Cognitive Psychometrics. I develop probabilistic models that treat observable behavior as a stochastic system, with probabilities for certain outcomes being defined according to unknown specified properties of people's individual cognitive processes as well as experimental stimuli. With these probabilistic models and behavioral data, I use approaches borrowed from modern computational statistics and machine learning to infer these unknown properties of interest, utilizing their probabilistic connection with the observable data. For example, I often work with paired comparison data. Such a behavioral task often takes the form of preference questions, where people are asked whether item a or item b is preferable to them. When you have a set of items, and different people state their preferences for each pair of items, you can deduce quantitative estimates of properties such as the value of each item for the group, individual people's deviations from the average group preference, and the level of disagreement about each item. Making these sorts of theory-based measurements of cognitive phenomena that are not directly observable is the nature of my work. For the conference, I'm presenting my application of this kind of methodology specifically to the study of probabilistic and temporal monetary discounting. Temporal discounting refers to the devaluation of a monetary reward when there is a known time delay, whereas probabilistic discounting refers to the devaluation of a monetary reward when the known probability of receiving the reward gets smaller. Questions such as "would you prefer $1000 now or $1200 in a year", or "would you prefer $1000 or a 50% chance to win $2100" can help study these things. A popular theory behind temporal and probabilistic discounting is that the two are related in that they both can be thought of a choice between two different frequencies of monetary reward. Using new methodology and probabilistic modeling, I've worked out a way to efficiently measure discounting functions for both time and probability for a wide range of monetary amounts in the same people, along with people's difficulty in assessing value across monetary amounts, probabilities, and time delays. Doing so in the way I've formulated can further enhance our understanding of the connection between the two discounting domains. I will present my results at the conference.


How hierarchical models improve point estimates of model parameters at the individual level

Abstract: Publication date: August 2016 Source:Journal of Mathematical Psychology, Volume 73 Author(s): Kentaro Katahira Computational models have been used to analyze the data from behavioral experiments. One objective of the use of computational models is to estimate model parameters or internal variables for individual subjects from behavioral data. The estimates are often correlated with other variables that characterize subjects in order to investigate which computational processes are associated with specific personal or physiological traits. Although the accuracy of the estimates is important for these purposes, the parameter estimates obtained from individual subject data are often unreliable. To solve this problem, researchers have begun to use hierarchical modeling approaches to estimate parameters of computational models from multiple-subject data. It is widely accepted that the hierarchical model provides reliable estimates compared to other non-hierarchical approaches. However, how and under what conditions the hierarchical models provide better estimates than other approaches has yet to be systematically investigated. This study attempts to investigate these issues, focusing on two measures of estimation accuracy: the correlation between estimates of individual parameters and subject trait variables and the absolute measures of error (root mean squared error, RMSE) of the estimates. An analytical calculation based on a simple Gaussian model clarifies how the hierarchical model improves the point estimates of these two measures. We also performed simulation studies employing several realistic computational models based on the synthesized data to confirm that the theoretical properties hold in realistic situations.

Pub.: 11 May '16, Pinned: 03 Jul '17