My name is Trent N. Cash and I am a Postdoctoral Scholar in the Department of Psychology at the University of Waterloo. I earned a Joint Ph.D. from the Department of Psychology and the Department of Social and Decision Sciences at Carnegie Mellon University. My research largely focuses on the role of higher order reasoning - particularly metacognition - in judgment and decision making.
In one line of research, I focus on the role of metacognition in subjective, multi-attribute choice decisions, such as choosing colleges, buying houses, or selecting romantic partners. I am particularly interested in understanding the degree to which decision makers have metacognitive knowledge of the factors that influence these decisions and exploring contexts that may promote or inhibit this knowledge. I primarily assess metacognitive knowledge using a novel paradigm I created, called the Knowledge of Weights (KoW) paradigm. This line of research was the focus of my Dissertation and was funded by a Doctoral Dissertation Research Improvement Grant (#2333553) from the National Science Foundation. During my postdoctoral career, I will be studying how AI can be used to improve metacognitive knowledge in healthcare decisions.
In a second line of research, I study the metacognitive capacities of large language model chatbots (LLMs), such as ChatGPT. To do so, I ask LLMs to complete experimental tasks from the metacognition literature and compare their performance to that of humans. For example, I recently published a series of studies demonstrating that LLMs are capable of making confidence judgments that are about as well-calibrated as those made by humans. I also have an ongoing project comparing the performance of LLMs on the KoW paradigm to that of humans and have published a theoretical piece arguing that LLMs lack access to fluency-based cues that support human metacognition.
In a third line of research, I study student learning and development. Recently, my work in this area has focused on testing the efficacy of interventions - such as small-group discussions and collaborations with LLMs - for promoting metacognitive reasoning skills in the classroom. I also investigate the psychological development of gifted students. In a recently published paper, I shared data from a 4-year longitudinal study (data collected 2019-2022) demonstrating that gifted students were no more resilient, and in some cases more vulnerable, to the negative effects of the COVID-19 pandemic than non-identified students.
In a final, exploratory line of research, I study decisions that individuals make about whether or not to disclose information about themselves. My current work in this space focuses on test-optional college admissions policies, which allow applicants to choose whether or not to submit standardized test scores when applying to college (e.g., ACTs, SATs). Within this context, my present focus is on exploring how individual differences in strategic reasoning influence both applicant disclosure decisions and the inferences that admissions officers make about students who omit their scores.
In one line of research, I focus on the role of metacognition in subjective, multi-attribute choice decisions, such as choosing colleges, buying houses, or selecting romantic partners. I am particularly interested in understanding the degree to which decision makers have metacognitive knowledge of the factors that influence these decisions and exploring contexts that may promote or inhibit this knowledge. I primarily assess metacognitive knowledge using a novel paradigm I created, called the Knowledge of Weights (KoW) paradigm. This line of research was the focus of my Dissertation and was funded by a Doctoral Dissertation Research Improvement Grant (#2333553) from the National Science Foundation. During my postdoctoral career, I will be studying how AI can be used to improve metacognitive knowledge in healthcare decisions.
In a second line of research, I study the metacognitive capacities of large language model chatbots (LLMs), such as ChatGPT. To do so, I ask LLMs to complete experimental tasks from the metacognition literature and compare their performance to that of humans. For example, I recently published a series of studies demonstrating that LLMs are capable of making confidence judgments that are about as well-calibrated as those made by humans. I also have an ongoing project comparing the performance of LLMs on the KoW paradigm to that of humans and have published a theoretical piece arguing that LLMs lack access to fluency-based cues that support human metacognition.
In a third line of research, I study student learning and development. Recently, my work in this area has focused on testing the efficacy of interventions - such as small-group discussions and collaborations with LLMs - for promoting metacognitive reasoning skills in the classroom. I also investigate the psychological development of gifted students. In a recently published paper, I shared data from a 4-year longitudinal study (data collected 2019-2022) demonstrating that gifted students were no more resilient, and in some cases more vulnerable, to the negative effects of the COVID-19 pandemic than non-identified students.
In a final, exploratory line of research, I study decisions that individuals make about whether or not to disclose information about themselves. My current work in this space focuses on test-optional college admissions policies, which allow applicants to choose whether or not to submit standardized test scores when applying to college (e.g., ACTs, SATs). Within this context, my present focus is on exploring how individual differences in strategic reasoning influence both applicant disclosure decisions and the inferences that admissions officers make about students who omit their scores.
To learn more about the research I am working on right now - including some projects that were not described here - check out my Publications and Projects.