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The Psychology of Human-Computer Interaction

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Presentation on theme: "The Psychology of Human-Computer Interaction"— Presentation transcript:

1 The Psychology of Human-Computer Interaction

2 Stuart Card Thomas Moran Allen Newell
Senior Research Fellow at Xerox PARC Bachelors in Physics Ph.D. in Psychology Psychology Artificial Intelligence Computer Science Thomas Moran Engineer at IBM Almaden Research Center and manager at Xerox PARC Founding Editor of Human-Computer Interaction journal Allen Newell Researcher at RAND and at Carnegie Mellon Computer Science Cognitive Psychology Turing Award in 1975 for his work in AI and “the psychology of human cognition” Passed away in 1992 Q: What do HCI and AI have to do with eachother?

3 Human Processor Model “The Human Processor Model draws an analogy between the processing and storage areas of a computer, with the perceptual, motor, cognitive and memory areas of the computer user.” Q: Is this all there is to how we think and interact with the world?

4 GOMS Goals Operators Methods Selection rules

5 GOMS Goals Operators Methods Selection rules
What a user has to accomplish. Operators Action performed in service of a goal. Methods Sequences of operators and subgoals that accomplish a goal. Selection rules Choices between multiple methods that accomplish the same goal.

6 GOMS Variants Keystroke-Level Model (KLM) CMN-GOMS NGOMSL CPM-GOMS

7 Keystroke-Level Model (KLM)
KLM is the simplest GOMS technique, and uses methods in sequence form composed of keystroke-level operators. Useful for most common single-user tasks, but impractical for representing high-level tasks.

8 CogTool CogTool is an open-source tool for KLM analysis developed at Carnegie Mellon.

9 CogTool Comparing two designs

10 CMN-GOMS CMN-GOMS is the first GOMS model by Card, Moran, and Newell, and uses a goal hierarchy of methods in program form. Predicts operator sequence and execution time, and focuses attention on the methods used to accomplish goals.

11 Natural GOMS Language (NGOMSL)
NGOMSL attempts to provide a natural, well-defined, high-level syntax for GOMS, and represents methods in terms of the cognitive complexity theory (CCT) . Predicts learning time as well as execution time, and can represent the user’s memory usage.

12 Q: Do these theoretical mental models really contribute to practical design issues, or are they a distraction? Where is the real “science” in HCI? Can since and design coexists? ~ nada “ The current GOMS models are quite effective because they capture procedural speed and complexity. But other aspects of human performance with an interface are not addressed by the simple cognitive architectures underlying the current GOMS variants. ” BONNIE E. JOHN AND DAVID E. KIERAS THE GOMS FAMILY OF USER INTERFACE ANALYSIS TECHNIQUES: COMPARISON AND CONTRAST. ACM TRANS. COMPUT.-HUM. INTERACT. 3, 4 (DECEMBER 1996),

13 Keystroke-Level Model for Advanced Mobile Phone Interaction
Paul Holleis Research Group Embedded Interaction University of Munich Friederike Otto Heinrich Hußmann Media Informatics Group Albrecht Schmidt Fraunhofer IAIS, Sankt Augustin B-IT, University of Bonn

14 Introduction Goal: Extend KLM by identifying basic interaction elements for mobile phones and give performance estimates derived from user tests. KLM is a precise predictor of expert user performance when comparing designs. Q: Why do we care so much about the expert user? Should we?

15 Operators Original Operators: Keystroke (K) Pointing (P) Drawing (D(nD,lD)) Homing (H) Mental Act (M) Response Time (R(t)) New Operators: Macro Attention Shift (SMacro) Micro Attention Shift (SMicro) Distraction (X) Action (A(t)) Gesture (G) Finger Movement (F) Initial Act (I)

16 User Study 7 studies 9-19 participants per study 50% students
41% women

17 Evaluation Conducted two scenarios for validation: KLM predicted 122 and 174 seconds. Actual result of 117 and 170 seconds. Q: Can GOMS models, like KLM, be applied to any interface? Q: Do you think the models that exist today can still be used for interactions on the future mobile devices? ~ Aditi Kulkarni

18 Conclusion “ We presented models of two different implementations of a real world scenario that also indicate that well grounded design decisions can be reached purely based on the model predictions. ”

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