Aakriti Kumar


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I am a Ph.D. student in the Department of Cognitive Sciences and a Noyce fellow at UC Irvine where I am work with by Dr. Mark Steyvers. My interests lie at the intersection of cognitive science and human-computer interaction.

In my research, I combine computational models and behavioral experiments to better understand different aspects of human cognition, especially when humans interact with AI agents. I work on understanding how humans assess AI ability, how they respond to AI advice, what biases they display when working with AI, and what affects their trust in AI systems.


Research

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Explaining Algorithm Aversion with Metacognitive Bandits
Aakriti Kumar, Trisha Patel, Aaron Benjamin, Mark Steyvers
We develop metacognitive bandits, a computational model of a human's advice-seeking behavior when working with an AI. The model describes a person's metacognitive process of deciding when to rely on their own judgment and when to solicit the advice of the AI. We illustrate that the metacognitive bandit makes decisions similar to humans in a behavioral experiment. We also demonstrate that algorithm aversion, a widely reported bias, can be explained as the result of a quasi-optimal sequential decision-making process.

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Trust between Humans and AI: A Short Review
In this review, I discuss work on human-machine interaction with a focus on understanding how and when humans trust machines. I identify and summarize factors that may affect a human’s trust in a machine. These factors can be categorized as relating to properties of the different components of this collaboration: 1. the human, 2. the machine, 3. the task or context in which the human and machine collaborate, and 4. the interaction between the human and the machine.

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Why Most Studies of Individual Differences With Inhibition Tasks Are Bound To Fail
Jeffrey N. Rouder, Aakriti Kumar, Julia M. Haaf
It has been observed that people’s performances across inhibition tasks show very low correlation. An example is the lack of correlation among performances on the Stroop task and the flanker task. In this paper, we ask whether these low correlations reflect excessive measurement noise or truly unrelated processing.

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Learning and Retention in Large-scale Cognitive Training Datasets
In preparation
Leveraging large-scale data from Lumosity games to analyze the learning trajectories across individuals and cognitive tasks over time.

           

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