CHI 2014: Modeling Users and Interaction

Model of Visual Search and Selection Time in Linear Menus by Gilles Bailly

  • model to understand human performance for target acquisition in realistic menus
  • novice: scan, skip around; intermediate: directed search with some error; expert: directed search with less error or point directly
  • gaze distribution = f(menu organization, menu size, position of target, absent items, expertise); last item effect – last item is slightly faster to select
  • data collection: 40,000 selections for time, cursor position, and gaze position; cursor follows gaze
  • model handles previous findings about menu usage; accurate describes behavior, not a simple model but has 3×8 parameters for a complex task

Towards Accurate and Practical Predictive Models of Active-Vison-Based Visual Search by David Kieras

  • color is a better cue than size or shape but all contribute; want to build a model to predict human performance; built an EPIC model for this task; very good fit to empirical data; EPIC models are complex and hard to develop; want to develop a GOMS model that then can generate a GLEAN GOMS
  • color can be distinguished in a much wider angle than size and shape; focus model on color alone and comes close enough for many situations; useful for model-based evaluation

Understanding Multitasking Through Parallelized Strategy Exploration and Individualized Cognitive Modeling by Yunfeng Zhang

  • in many tasks, multi-tasking is inevitable; computational cognitive models allow study
  • experiment: multimodal duel task; classification + tracking; sound on or off; peripheral (other display) visible or not
  • result: sound helps when peripheral not visible for both tasks; combine even better
  • EPIC model: explore 72 different microstrategies for task switching, with 12 settings, so 864 models; used parallel computation to speed up the simulations, shortening from 14 hours to 20 minutes
  • basic model follows human data closely; can also compare different strategies; human data averages tracks best strategies closely, but individual performance varies widely
  • individualized models fit data well and could find best strategies by comparing best human performers; average performance leads to a match to bottom performing human

How Does Knowing What You Are Looking For Change Visual Search Behavior by Duncan Brumby

  • 2 types of search: semantic vs known-item search; known-item is faster; why are semantic searches slower?
  • accessing facts in our head takes time; is it reflected in eye movements? no, except when tightly packed
  • instead, it relates to the distance between eye jumps; semantic goes item by item, known-item jumps around

Automated Nonlinear Regression Modeling for HCI by Antti Oulasvirta

  • nonlinear regression models: expressive and white-box, like pointing, learning, foraging; hard to acquire these models
  • exploration is inefficient and laborious, so automate it; using optimization techniques from symbolic programming
  • experiment: 11 existing models in literature using same data; improved 7 of 11 models and nearly the same for 4 others; complex data sets come up with complex models; constrain settings; also works with multiple data sets
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