Eeshan Hasan
Contact: eehasan@iu.edu eeshan.hasan@gmail.com
I am a dual PhD Cognitive Science and Psychology student, advised by Jennifer Trueblood at Indiana University. (CV) (Google Scholar)
I combine computational and experimental methods to study cognitive decision-making, focusing on the interplay between perception and attention with decision making. My research explores the interaction between human and machine cognition, aiming to use insights from one to improve the other.
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How do we use collective human intelligence to train machines?
We find that if you put enough novices together, they can classify white blood cell images better than experts. When you crowdsource people from an app, the best approaches model individual decision idiosyncrasies and task features. If you only select the best people to annotate skin lesion medical data, you fail to get a sense of the uncertainty and might miss out on the rarer classes. -
How do we use machine intelligence to understand and improve humans?
We find that one can use the representations from deep neural networks to find and fix inconsistencies in human (novice) decisions. We also find that novices without any training represent complex medical stimuli in similar ways as expert neural networks. -
How do cognitive processes interact with decision making?
We find that in a choice display with three items (ABC), the presentation order (e.g., BAC, CBA, ACB) impacts decisions potentially because one might pay more attention to the differences and compare neighboring options. However, manipulating attention processes by making alternatives salient impacts choice by creating a salience selection bias but does not change comparisons. -
How do we scale experimentation and modeling?
We conducted an exploratory registered report, recruiting more than 2000 individuals and assigning them to one of 144 conditions to conduct a large experiment in multi-attribute choice. In another project, we used a switchboard analysis to test hundreds of wisdom of the crowd models.
See Publications, Projects, Computational Methods, Background, CV Google Scholar