PhD Candidate - Avid Traveler - (Slow) Runner

Computational Modeling of Decision Making

Computational Modeling of Decision Making

Packaging vs. Product: Decomposing the distinction between decisions and judgments. In preparation.

Presented at the following conferences/symposia:

Interdisciplinary Symposium on Decision Neuroscience (ISDN), 2018
Max Planck Summer Institute on Bounded Rationality, 2018
Neuroeconomics, 2018
Society for Judgment and Decision Making (SJDM), 2018

Estimating the dynamic role of attention via random utility. (2019). Journal of the Economic Science Association.

Abstract: When making decisions, people tend to look back and forth between the alternatives until they eventually make a choice. Eye-tracking research has established that these shifts in attention are strongly linked to choice outcomes. A predominant framework for understanding the dynamics of the choice process, and thus the e ects of atten- tion, is sequential sampling of information. However, existing methods for estimat- ing the attention parameters in these models are computationally costly and overly exible, and yield estimates with unknown precision and bias. Here we propose an estimation method that relies on a link between sequential sampling models and random utility models (RUM). This method uses familiar econometric tools (i.e., logistic regression) and yields estimates that appear to be unbiased and relatively precise compared to existing methods, in a small fraction of the computation time. The RUM thus appears to be a useful tool for estimating the e ects of attention on choice.

Gaze amplifies value in decision making. (2019). Psychological Science, 30(1), 116–128.

Abstract: When making decisions, people tend to choose the option they have looked at more. An unanswered question is how attention influences the choice process: whether it amplifies the subjective value of the looked-at option or instead adds a constant, value-independent bias. To address this, we examined choice data from six eye-tracking studies (Ns =39, 44, 44, 36, 20, and 45, respectively) to characterize the interaction between value and gaze in the choice process. We found that the summed values of the options influenced response times in every data set and the gaze-choice correlation in most data sets, in line with an amplifying role of attention in the choice process. Our results suggest that this amplifying effect is more pronounced in tasks using large sets of familiar stimuli, compared with tasks using small sets of learned stimuli.

Presented at the following conferences/symposia:

Center for Cognitive and Brain Sciences Annual Retreat, 2017
Interdisciplinary Symposium on Decision Neuroscience (ISDN), 2017 (2nd place)
Neuroeconomics, 2017
Society for Judgment and Decision Making (SJDM), 2017
Society for Consumer Psychology (SCP), 2018
The Ohio State University Edward F. Hayes Research Forum, 2018

Attention and choice across domains. (2018). Journal of Experimental Psychology: General, 147(12), 1810-1826.

When people are faced with a decision, they tend to choose the option that draws their attention. In recent years, correlations between attention and choice have been documented in a variety of domains. This leads to the question of whether there is a general, stable relationship between attention and choice. Here, we examined choice behavior in tasks with and without risk and social considerations, using food or monetary rewards, within a single experiment. This allowed us to test the consistency of the decision-making process across domains. In the aggregate, we identified remarkable consistency in the attention-choice link. At the individual level, subjects with strong attentional effects in one task were likely to have strong attentional effects in the others. The strength of these effects also correlated with individuals' degree of tunnel vision. Thus, the attention-choice relationship appears to be a stable individual trait that is linked to more general attentional constraints.

Presented at the following conferences/symposia:

Neuroeconomics, 2015
Society for Judgment and Decision Making (SJDM), 2015
Computational Systems Neuroscience (COSYNE), 2016
Interdisciplinary Symposium on Decision Neuroscience (ISDN), 2016
The Ohio State University Edward F. Hayes Research Forum, 2017 (2nd place)
The Ohio State University Graduate Research Forum, 2017 (2nd place)