I am currently investigating the cognitive mechanisms underlying quitting behavior, using computational models and behavioral experiments. My work aims to bridge theoretical frameworks with real-world quitting scenarios. I take inspiration from phenomenology to approach quitting as a phenomenal experience first and aim to provide a mechanistic framework for it. To accomplish this, I begin by first approaching how the decision-making process towards quitting is described by individuals in a particular context. These phenomenological descriptions are then used to formulate computational models involving cognitive processes that can predict quitting behavior. Based on these model predictions, I formulate research questions that are realized as observational studies across different contexts.
Quitting in Online Chess players
My first project looked at the quitting behavior of online chess players. Using the the open access data from lichess.org, I analyzed chess players' propensity to quit in 'Classical' chess matches. Using a combination of game factors and custom quitting factors, I quantified the quitting behavior of player with a statistical hazard of quitting. I also showed evidence for tilting in chess players, that occurs as a consquence of quitting a chess match. These results were contextualized as repesenting a metacognitive failure of the effort allocation process on the task. A specualtive account of this process is provided in the recent Cognitive Science paper I published (with Nisheeth).
Quitting in Long Distance Runners
My second project looks at process-tracing the thoughts of stopping that long-distance runners experience during a run. In order to accomplish this, I conducted field experiments where long-distance runners ran around a track with a wearbale device at a pre-specified pace catered to each runner's endurance levels. Runners self-reported their stopping thoughts using the device. I study the time series of stopping thoughts in order to explicate how the thoughts impact the runner's decision to completely stop the run. This project has been one of my favourites due to the wide range of runners that i have met along the way who have shared their stories with me.
Being Unable to Quit when you want to
In my third project, I approached quitting decisions through the lens of disrupted gaming behavior in video gamers. I collected post session descriptions from gamers across the span of a month. I focused on descriptions about the gamer's experiences of wanting to stop the gaming session but being unable to. These qualitative descriptions were then analyzed for thematic features that are present across gamers. This data is being used to inform better decsriptive models of quitting and also, developing an algroithm that tracks play engagement in order to predict "tilting" behavior that might indicate the inability to quit even when one wants to.