About This Project
Flow, aka "the zone", is at the center of peak performance and intrinsic motivation. Despite such benefits of flow, we are far from fully understanding it. In fact, objectively identifying flow has been an issue due to the subjective nature of flow experience and expensive gadgets to measure attentional state like eye trackers. Thus, this project aims to answer the following RQ: TWE is there a quantifiable correlation between attention and flow state?
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What is the context of this research?
The term ‘flow state’, coined by M. Csikszentmihalyi, refers to one’s mental state when immersed in an activity. Colloquially called "being in the zone," flow state can "transform a previously unengaging task into that which is intrinsically motivating.” While further investigation of the nature of flow state is vital, researchers face challenges to objectively identify flow state due to 1) the subjective nature of flow experience, and 2) lack of access to quantitative neurological indicators such as EEG. Despite these challenges, recent studies have proposed an alternative to objectively measure flow state: attention. However, the high costs of such gadgets have been acting as barriers for further research on flow state.
What is the significance of this project?
According to a 10-year McKinsey study, top executives' productivity increased to 500% in flow state. Apart from increased productivity, flow state is known to boost personal development and happiness as Csikszentmihalyi claims that flow state as an essential component for personal happiness. Flow state has even been found to correlate strongly with self-transcendent experiences like awe and heightened meaningfulness in life, suggesting life-changing benefits. Thus, an easily accessible and effective model to objectively measure flow state can advance flow state research and promote its benefits. In addition, the relationship between validated attention indexes and flow state quantification may enhance our understanding of the mechanism of flow state.
What are the goals of the project?
Deep learning models and other AI algorithms will be applied to estimate selected indicators of attention (i.e., blinks, head movement, head rotation) and calculate participant attention indexes via webcam recording, while the Flow State Scale (FSS) will be used as a measure of ground truth for participants' flow state. A software prototype will be developed to store collected data, perform data visualization, and etc. Using the prototype, a behavioral attention experiment will be conducted to calculate the participants' attention index and analyze the correlation between attention and flow state. A research paper that summarizes the procedures and results of this project will be submitted. The data and code will be publicized to promote further research.
Remuneration for experiment participation: To provide incentives for the participants' effort and time, each participant will receive a $15 Amazon gift card. For 24 participants, it will require a total of $360. ($15*24 = $360)
Software costs: Winrar–To run experiment tasks on mac ($46.37) + Parallel Desktop–Allows to run software and files from Windows on Mac ($99.99)
High-Performance Computing (HPC) virtual machine cost: This will be the cost of using AWS HPC solutions in order to improve the deep learning computing speed for my head pose estimation model.
In return of the funding, throughout the duration of the project, the backers will receive the following results/updates on the project:
- Demo videos of the machine learning model for attention estimation
- Screenshots or videos on experiment setup and execution
- Screenshots of data/results from the pilot study and the actual experiment
Also, the backer's name will be listed on the front page of the research paper of this project!
Individual analysis models for visual attention have already been built and tested. However, the slow computing speed of head rotation estimation model is a limiting factor to effectively analyze videos of each experiment participants. When the prototype is completed, the participants will engage in flow-inducing tasks while being recorded. After collecting all of their data, I will post the code for the model and GUI on Github Repository and submit the research paper for publication
Jan 22, 2022
May 30, 2022
Finish research on flow state and practical measurements of flow state
Jul 03, 2022
Finish attention index estimation model
Jul 11, 2022
Finish FSS survey backend connection
Sep 02, 2022
Refine error handling of the attention index estimation index model & develop workflow of the complete model
Meet the Team
Hi! I am a rising senior in Singapore American School with great interests in Cognitive Science, especially about Computational Thinking(CT) and Artificial Intelligence(AI). Apart from academics, I enjoy solving coding questions, reading books, playing basketball, and gazing at the nature.
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