Moving Target Selection: A Cue Integration Model

This paper investigates a common task requiring temporal precision: the selection of a rapidly moving target on display by invoking an input event when it is within some selection window. Previous work has explored the relationship between accuracy and precision in this task, but the role of visual cues available to users has remained unexplained. To expand modeling of timing performance to multimodal settings, common in gaming and music, our model builds on the principle of probabilistic cue integration. Maximum likelihood estimation (MLE) is used to model how different types of cues are integrated into a reliable estimate of the temporal task. The model deals with temporal structure (repetition, rhythm) and the perceivable movement of the target on display. It accurately predicts error rate in a range of realistic tasks. Applications include the optimization of difficulty in game-level design.

[1]  Philippe Pasquier,et al.  Towards a Generic Framework for Automated Video Game Level Creation , 2010, EvoApplications.

[2]  B. Repp,et al.  Sensorimotor synchronization: A review of recent research (2006–2012) , 2013, Psychonomic Bulletin & Review.

[3]  E. Hoffmann Capture of moving targets: a modification of Fitts' Law , 1991 .

[4]  J. Tresilian,et al.  Manual interception of moving targets in two dimensions: Performance and space-time accuracy , 2009, Brain Research.

[5]  Tovi Grossman,et al.  A probabilistic approach to modeling two-dimensional pointing , 2005, TCHI.

[6]  Renaud Blanch,et al.  Semantic pointing: improving target acquisition with control-display ratio adaptation , 2004, CHI.

[7]  Wijnand A. IJsselsteijn,et al.  Dynamic Game Balancing by Recognizing Affect , 2008, Fun and Games.

[8]  Shumin Zhai,et al.  Beyond Fitts' law: models for trajectory-based HCI tasks , 1997, CHI Extended Abstracts.

[9]  Catalin V. Buhusi,et al.  What makes us tick? Functional and neural mechanisms of interval timing , 2005, Nature Reviews Neuroscience.

[10]  Elina M. I. Koivisto,et al.  Playability heuristics for mobile games , 2006, Mobile HCI.

[11]  M. T. Elliott,et al.  Moving in time: Bayesian causal inference explains movement coordination to auditory beats , 2014, Proceedings of the Royal Society B: Biological Sciences.

[12]  Vincent Corruble,et al.  Challenge-sensitive action selection: an application to game balancing , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[13]  Abigail Sellen,et al.  A comparison of input devices in element pointing and dragging tasks , 1991, CHI.

[14]  J. Tresilian,et al.  Perceptual and cognitive processes in time-to-contact estimation: Analysis of prediction-motion and relative judgment tasks , 1995, Perception & psychophysics.

[15]  Zoran Popovic,et al.  Evaluating Competitive Game Balance with Restricted Play , 2012, AIIDE.

[16]  Andrew Nealen,et al.  Exploring Game Space Using Survival Analysis , 2015, FDG.

[17]  Krishna Bharat,et al.  Making computers easier for older adults to use: area cursors and sticky icons , 1997, CHI.

[18]  Sriram Subramanian,et al.  Talking about tactile experiences , 2013, CHI.

[19]  Carl Gutwin,et al.  The Effects of Feedback on Targeting with Multiple Moving Targets , 2004, Graphics Interface.

[20]  Marc O. Ernst,et al.  A Bayesian view on multimodal cue integration , 2006 .

[21]  David N. Lee,et al.  A Theory of Visual Control of Braking Based on Information about Time-to-Collision , 1976, Perception.

[22]  Tovi Grossman,et al.  The bubble cursor: enhancing target acquisition by dynamic resizing of the cursor's activation area , 2005, CHI.

[23]  Luis Augusto Teixeira,et al.  Control of interceptive actions is based on expectancy of time to target arrival , 2009, Experimental Brain Research.

[24]  Yani Widyani,et al.  Game development life cycle guidelines , 2013, 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[25]  Carl F. Craver,et al.  When mechanistic models explain , 2006, Synthese.

[26]  Zhigang Deng,et al.  PADS: enhancing gaming experience using profile-based adaptive difficulty system , 2010, Sandbox '10.

[27]  Manfred Tscheligi,et al.  CHI '04 Extended Abstracts on Human Factors in Computing Systems , 2004, CHI 2004.

[28]  H. Hecht,et al.  Intercepting real and simulated falling objects: What is the difference? , 2009, Journal of Neuroscience Methods.

[29]  J. Tresilian Hitting a moving target: Perception and action in the timing of rapid interceptions , 2005, Perception & psychophysics.

[30]  Mark O. Riedl,et al.  Automatic playtesting for game parameter tuning via active learning , 2019, FDG.

[31]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[32]  Ernest Adams,et al.  Game Mechanics: Advanced Game Design , 2012 .

[33]  George P. Moore,et al.  Timings and interactions of skilled musicians , 2010, Biological Cybernetics.

[34]  Michael Victor Ilich,et al.  Moving Target Selection in Interactive Video , 2009 .

[35]  James Joseph Belisle Accuracy, Reliability, and Refractoriness in a Coincidence-Anticipation Task , 1963 .

[36]  I. Scott MacKenzie,et al.  Extending Fitts' law to two-dimensional tasks , 1992, CHI.

[37]  H. Bülthoff,et al.  Merging the senses into a robust percept , 2004, Trends in Cognitive Sciences.

[38]  R M Church,et al.  Scalar Timing in Memory , 1984, Annals of the New York Academy of Sciences.

[39]  Olivier Chapuis,et al.  DynaSpot: speed-dependent area cursor , 2009, CHI.

[40]  S. K. Card,et al.  The Model Human Processor: A Model for Making Engineering Calculations of Human Performance , 1981 .

[41]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[42]  J. Mates,et al.  Temporal Integration in Sensorimotor Synchronization , 1994, Journal of Cognitive Neuroscience.

[43]  P. Fitts The information capacity of the human motor system in controlling the amplitude of movement. , 1954, Journal of experimental psychology.

[44]  Shumin Zhai,et al.  Human on-line response to target expansion , 2003, CHI '03.

[45]  Vincenzo Maffei,et al.  Extrapolation of vertical target motion through a brief visual occlusion , 2010, Experimental Brain Research.

[46]  Robert E. Mercer,et al.  A methodological approach to identifying and quantifying video game difficulty factors , 2014, Entertain. Comput..

[47]  John Anderson,et al.  An evaluation of techniques for selecting moving targets , 2009, CHI Extended Abstracts.

[48]  Tovi Grossman,et al.  Pointing at trivariate targets in 3D environments , 2004, CHI.

[49]  Susumu Harada,et al.  The angle mouse: target-agnostic dynamic gain adjustment based on angular deviation , 2009, CHI.

[50]  Renaud Blanch,et al.  Object Pointing: A Complement to Bitmap Pointing in GUIs , 2004, Graphics Interface.

[51]  Eve E. Hoggan,et al.  Boxer: a multimodal collision technique for virtual objects , 2017, ICMI.

[52]  B. Repp Sensorimotor synchronization: A review of the tapping literature , 2005, Psychonomic bulletin & review.

[53]  Hiroki Nakamoto,et al.  Experts in fast-ball sports reduce anticipation timing cost by developing inhibitory control , 2012, Brain and Cognition.

[54]  Andy Cockburn,et al.  Improving the Acquisition of Small Targets , 2004 .

[55]  Charlotte Wiberg,et al.  Game Usability Heuristics (PLAY) for Evaluating and Designing Better Games: The Next Iteration , 2009, HCI.

[56]  William Buxton,et al.  A three-state model of graphical input , 1990, INTERACT.

[57]  E R Hoffmann,et al.  Effect of target shape on movement time in a Fitts task. , 1994, Ergonomics.

[58]  Shumin Zhai,et al.  Refining Fitts' law models for bivariate pointing , 2003, CHI '03.

[59]  Stéphane Natkin,et al.  Measuring the level of difficulty in single player video games , 2011, Entertain. Comput..

[60]  Antti Oulasvirta,et al.  Modelling Error Rates in Temporal Pointing , 2016, CHI.

[61]  H. Zelaznik,et al.  Motor-output variability: a theory for the accuracy of rapid motor acts. , 1979, Psychological review.

[62]  D. Meyer,et al.  Conditions for a Linear Speed-Accuracy Trade-Off in Aimed Movements , 1983, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[63]  Ravin Balakrishnan,et al.  Acquisition of expanding targets , 2002, CHI.

[64]  Patrick Baudisch,et al.  Starburst: a target expansion algorithm for non-uniform target distributions , 2008, AVI '08.

[65]  Georgios N. Yannakakis,et al.  Real-time challenge balance in an RTS game using rtNEAT , 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

[66]  Adrian David Cheok,et al.  22nd International Conference on Human-Computer Interaction with Mobile Devices and Services , 2007, Lecture Notes in Computer Science.

[67]  Martin S. Moran,et al.  A Test of Fitts' Law with Moving Targets , 1980, Human factors.

[68]  J Mates,et al.  The Perceptual Centre of a Stimulus as the Cue for Synchronization to a Metronome: Evidence from Asynchronies , 1995, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[69]  Ehud Sharlin,et al.  Predictive interaction using the delphian desktop , 2005, UIST.

[70]  Thomas G. Whisenand,et al.  Some effects of angle of approach on icon selection , 1995, CHI '95.

[71]  Jonathan D. Cohen,et al.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. , 2006, Psychological review.

[72]  Konrad Paul Kording,et al.  Review TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Bayesian decision theory in sensorimotor control , 2022 .

[73]  Heather Desurvire,et al.  Using heuristics to evaluate the playability of games , 2004, CHI EA '04.

[74]  Paul Kabbash,et al.  The “prince” technique: Fitts' law and selection using area cursors , 1995, CHI '95.