186 Crosby & Ikehara Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Section III Human Factors
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Perceptual Multimedia: A Cognitive Style Perspective 187 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Chapter IX Perceptual Multimedia: A Cognitive Style Perspective Gheorghita Ghinea, Brunel University, UK Sherry Y. Chen, Brunel University, UK Abstract In this chapter, we describe the results of empirical studies which examined the effect of cognitive style on the perceived quality of distributed multimedia. We use two dimensions of Cognitive Style Analysis, Field Dependent/Independent and Verbaliser/ Visualiser, and the Quality of Perception metric to characterise the human perceptual experience. This is a metric which takes into account multimedia s infotainment (combined informational and entertainment) nature, and comprises not only a human s subjective level of enjoyment with regards to multimedia content quality, but also his/ her ability to analyse, synthesise and assimilate the informational content of such presentations. Results show that multimedia content and dynamism are strong factors influencing perceptual quality.
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Using Real-Time Physiological Monitoring for Assessing Cognitive States 185 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Ikehara, C., & Crosby, M. (2005, January 6-9). Assessing cognitive load with physiological sensors. Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS 38), Kona, Hawaii. Lange K ttner, C. (1998). Perceptual and Motor Skills, 86 (3 Pt 2), 1299 310. Mayer, R., & Sims, V. (1994). The role of spatial ability in learning with computer generated animations. Journal of Educational Psychology, 86(3), 389-401. Nordbotten, J., & Crosby, M. (1997). Individual user differences in data model comprehension. In M. Smith, G. Salvendy, & R. Koubek (Eds.), Design of computing systems: Social and ergonomic considerations: Vol. 21-B (pp. 663-670). Amsterdam: Elsevier Science Nordbotten, J., & Crosby, M. (1999). The effect of graphic style on data model interpretation. Information Systems Journal, 9, 139 - 155. Picard, R. (1999). Affective computing for HCI. In H. J. Bullinger & J. Ziegler (Eds.), Human-computer interaction: Ergonomics and user interfaces (pp. 829-833). London: Lawrence Erlbaum Associates. Quiroga, L., & Crosby, M. (2004). Information filtering. In W. S. Bainbridge (Ed.), Berkshire encyclopedia of human-computer interaction (pp. 351-355). National Science Foundation; Berkshire Publishing Group LLC: Great Barrington, MA. Sheldon, E. (2001). Virtual agent interactions. Doctoral dissertation, University of Central Florida, Orlando. Sein, M., & Bostrom, R. (1989). Individual differences and conceptual models in training novice users. Human-Computer Interaction, 4, 197-229. Sein, M., Olfman, L., Bostrom, R., & Savis, S. (1993). The importance of visualization ability in predicting learning success. International Journal of Man-Machine Studies, 39(4), 599-620. Sophian, C., & Crosby, M. (1999). A picture is worth more than two lines. In H. J. Bullinger & J. Ziegler (Eds.), Human-computer interaction: Ergonomics and user interfaces: Vol. 1 (pp. 376-380). London: Lawrence Erlbaum Associates. St. John, M., Kobus, D. A., & Morrison, J. G. (2003). DARPA augmented cognition technical integration experiment (Rep. No. TR-1905, Contract Number: N66001- 99-D-0050, Pub.) Pacific Science and Engineering Group Inc. Retrieved from http:/ /handle.dtic.mil/100.2/ADA420147 Vincente, K., Hayes, B., & Williges, R. (1987). Assaying and isolating individual differences in searching a hierarchical file system. Human Factors, 29, 647-668.
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184 Crosby & Ikehara Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Crosby, M., Ikehara, C., & Chin, D. (2002, August 8-10). Measures of real-time assessment to use in adaptive augmentation. Proceedings of the 24th Annual Meeting of the Cognitive Science Society, VA. Crosby, M., & Sophian, C. (2003). Processing spatial configurations in computer interfaces. In J. Hyona, R. Radach, & H. Deubel (Eds.), The mind s eye: Cognitive and applied aspects of eye movement research (pp. 517-530). Amsterdam: North Holland-Elsevier Science. Crosby, M., & Stelovsky, J. (1995). From multimedia instruction to multimedia evaluation. Journal of Educational Multimedia and Hypermedia, 4(2/3), 147-162. Crosby, M., Stelovsky, J., & Ashworth, D. (1994). Hypermedia as a facilitator for retention: A case study using Kanji City. Computer Assisted Language Learning, 7(1), 3-13. Lisse: Swets & Zeitlinger. Crosby, M, Stelovsky, J., & Ashworth, D. (1996). Predicting language proficiency based on the use of multimedia interfaces for transcription tasks. Computer Assisted Language Learning, 9(2-3), 251-262. Lisse: Swets & Zeitlilnger. Dryer, D. C. (1993). Multi-dimensional and discriminant function analyses of affective state data. Unpublished manuscript, Stanford University. Dumais, S., & Wright, A. (1986). Reference by name vs. location in a computer filing system. Proceedings of the Human Factors Society (pp. 824-929). Egan, D. (1988). Individual differences in human-computer interaction. In M. Helender (Ed.), Handbook of human-computer interaction (pp. 543-580). New York: Elsevier (North Holland). Egan, D., & Gomez, L. (1985). Assaying, isolating, and accommodating differences. In R. Dillon (Ed.), Individual differences in cognition (pp. 174-216). Academic Press. Ekstrom, R., French, J., & Harman, H. (1976). Manual for kit of factor-referenced cognitive tests. Princeton, NJ: Educational Testing Service. Friedhoff, R., & Benzon, W. (1989). The second computer revolution: Visualization. New York: Harry W. Abrams. Gomez, L., Egan, D., Wheeler, E., Sharma, D., & Gruchaz, A. (1983). How interface design determines who has difficulty learning to use a text editor. Proceedings of Computer-Human Interaction (CHI) 83 (pp. 176-181). Howard, D. L., & Crosby, M. (1993). Snapshots from the eye: Towards strategies for viewing bibliographic citations. In G. Salvendy & M. Smith (Eds.), Advances in human factors/ergonomics: Human-computer interaction: Software and hardware interfaces: Vol. 19-B (pp. 488-493). Amsterdam: Elsevier Science. Ikehara, C., Chin, D., & Crosby, M. (2004, January 5-8). Modeling and implementing an adaptive human-computer interface using passive biosensors. Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS 37), Kona, Hawaii. Ikehara, C., & Crosby, M. (2003, Jan. 3-6). User identification based on the analysis of the forces applied by a user to a computer mouse. Proceedings of HICSS 36, Kona, Hawaii.
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Using Real-Time Physiological Monitoring for Assessing Cognitive States 183 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. References Andreassi, J. L. (1995). Psychophysiology: Human behavior and physiological response (3rd ed.). Hillsdale, NJ: Lawrence Erlbaum. Andreassi, J. L. (2000). Psychophysiology: Human behavior and physiological response (4th ed.). Hillsdale, NJ: Lawrence Erlbaum. Ark, W., Dryer, D., & Lu, D. (1999). The emotion mouse. In H. J. Bullinger & J. Ziegler (Eds.), Human-computer interaction: Ergonomics and user interfaces (pp. 818- 823). London: Lawrence Erlbaum Associates. Aschwanden, C. (2001). Investigating user comprehension of Web-based educational applications. Masters thesis, Dept. EE, ETH Zurich, Switzerland. Card, S., Moran, T., & Newell, A. (1986). The model human processor. In K. Boff, L. Kaufman, & J. Thomas (Eds.), Handbook of perception and human performance: Vol. 2 (pp. 45-1 - 45-35). New York: Wiley. Charleton, S. (2002). Measurement of cognitive states in testing and evaluation. In S. G. Charleton & T. C. O Brien (Eds.), Handbook of human factors and evaluation (pp. 97-126). Crosby, M., Auernheimer, B., Aschwanden, C., & Ikehara, C. (2001). Physiological data feedback for application in distance education. PUI 2001 Proceedings, Orlando FL. Crosby, M., & Chin, D. (1997). Evaluating multi-user interfaces (EMI). In M. Smith, G. Salvendy, & R. Koubek (Eds.), Design of computing systems: Social and ergonomic considerations: Vol. 21-B (pp. 675-678). Amsterdam: Elsevier Science. Crosby, M., & Chin, D. (1999). Investigating user comprehension of complex multi-user interfaces. In H. J. Bullinger & J. Ziegler (Eds.), Human-computer interaction: ergonomics and user interfaces: Vol. 1 (pp. 856-860). London: Lawrence Erlbaum Associates. Crosby, M., & Iding, M. (1997a). A comparison of two individual differences measures and performance on a multimedia tutor for learning physics, Computers and Education, Pergamon, 29(23), 127-136. Crosby, M., & Iding, M. (1997b). The influence of cognitive styles on the effectiveness of a multimedia tutor. Computer Assisted Language Learning, 10(4), 375-386. Lisse: Swets & Zeitlinger. Crosby, M., Iding, M., & Chin, D. (2003, January 3-6). Research on task complexity as a foundation for augmented cognition. Proceedings of HICSS 36, Kona, Hawaii (pp. 1-9). Crosby, M. E., Iding, M. K., & Chin, D. N. (2001). Visual search and background complexity: Does the forest hide the trees? In M. Bauer, P. J. Gmytrasiewicz, & J. Vassileva, (Eds.), User modeling 2001 (pp. 225-227). Berlin; Heidelberg; New York: Springer-Verlag. Crosby, M., & Ikehara, C. (2004, April 12-16). Continuous identity authentication using multi-modal physiological sensors. Proceedings of the International Society for Optical Engineering (SPIE), Defense and Security Symposium, Orlando, FL.
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182 Crosby & Ikehara Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. to optimize the user s cognitive ability to achieve the short-term goal of maximum performance and the long-term goal of maintaining a high level of cognitive ability. In Figure 7, Cognitive Load #1 (Computation) has a graded vertical scale. The top of that scale represents the maximum cognitive ability. When the user is performing the MTF task, each sub-goal (i.e., S#1, S#2, S#3, & S#4) increases the total computational cognitive load (i.e., Load #1 ). The solid arrow next to Load #1 shows the current computational cognitive load. The dashed arrow indicates a desired cognitive load value. The two-headed arrow denotes the desired computational cognitive load range. The figure shows the computational cognitive load is currently above the desired range. In the same figure, Cognitive Load #2 (Visual/Spatial) shows the visual/spatial cognitive load to be less than the desired range. The adaptive information filter shifts cognitive load from computational to visual to maintain the computational cognitive load within the desired range. Summary Our research investigated ways to instantaneously assess cognitive state and cognitive capability. The suite of physiological sensors to assess cognitive states is shown to be a viable alternative to task performance measures when performance measures are not available. Also, our research has shown that, in some cases, these physiological measures can be a more sensitive measure of cognitive state than task performance measures. These cognitive state factors can then be used to improve task performance through adaptive filtering and other techniques. Using real-time assessment of the users cognitive states, we were able to design adaptive multimedia systems; then, depending on the situation and task, we could then determine if the system helped the user meet the desired performance improvements of making more accurate, faster, or appropriate decisions. Results from these studies provide support that we can create effective ways to adapt to a person s cognition in real-time and thus facilitate real-world tasks. Acknowledgments This research was supported in part by the Office of Naval Research grants no. N000149710578 and N000140310135 and DARPA grant no. NBCH1020004.
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Using Real-Time Physiological Monitoring for Assessing Cognitive States 181 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 1. To calibrate the measurement of cognitive load, a test with tasks of varying cognitive load is presented to the user. Upper and lower limits are extracted, and the range of cognitive load is established for the user. Cognitive load is not a single measure, but can be partitioned as shown in Figure 7. Once the cognitive state of the user is assessed, presentation changes are made by an adaptive information filter to improve task performance. We collect biosensor information from the user so that the adaptive information filtering program can, in real-time, optimize the presentation of information. The MTF task requires the user to elicit several important abilities including: hand-eye coordination, visual search, mathematical computation, fraction estimation, strategy selection, learning, memory, and motivation. Manipulation of the presentation to the user is designed Figure 6. Model of how the adaptive information filter changes the display of the moving target fractions (MTF) task when biosensor information indicates excessive cognitive load Moving Fractions Task System Adaptive Information Filter Biosensors User System Figure 7. The adaptive information filter changes the MTF display depending on the computational cognitive load derived from the biosensors; in this figure, the adaptive filter shifts cognitive load S#1 and S#3 from computation to visual/spatial Load #1 S#3 S#2 S#1 S#4 Range Cognitive Load #1 (Computation) Max Load #2 S#1 Range Cognitive Load #2 (Visual/Spatial) Visual/ Spatial S#3 Max Biosensor Data Adaptive Information Filter MTF Display Input MTF Display Output
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Using Real-Time Physiological Monitoring for Assessing Cognitive States 179 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Figure 4. The composite view of over 80 mouse clicks of a subject who has practiced the task dozens of times and finds the task easy Figure 5. The composite view of over 80 mouse clicks of a subject who is performing the task for the first time
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180 Crosby & Ikehara Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. front is the most recent click. The task increases the difficulty of clicking a moving square on a computer screen. Figure 4 represents a person who has practiced the task dozens of times and has no difficulty with the task. Note the deep consistent click pattern. Figure 5 shows the clicks of a participant who is performing the task for the first time. Note that the initial clicks, when the task is easiest, in the rear of the valley are relatively consistent. The clicks in the front of the valley show the greatest inconsistency when the task is most difficult. Adaptive Information Filter Model Information filtering is an effective method of enhancing performance on cognitive tasks, but can be suboptimal when it does not respond to changes in the user s cognitive ability (Quiroga & Crosby, 2004). The adaptive augmented cognition research at our laboratory is targeted at collecting biosensor information from the user so that the information filtering program can optimize the presentation of information in real-time and achieve learning and performance goals. The MTF task requires the user to elicit several important abilities. These abilities include: hand-eye coordination, visual searching, mathematical problem computation, fraction estimation, strategy selection, learning and motivation. All these requirements of the task affect the user s cognitive ability and constrain cognitive load to different degrees. Manipulation of the presentation to the user is designed to control the user s cognitive load and optimize the user s cognitive ability to achieve the short-term goal of maximum performance and the long-term goal of maintaining a high level of cognitive ability. Adaptive information filtering can be presented to the user by a combination of three methods: emphasis, de-emphasis, and deletion. Information filtering using de-emphasis is the preferred method since it will allow an incremental change in the task difficulty changing the number of filtered targets based on the user s computational cognitive load. Cognitive load is shifted from computation to visual/spatial load. The following sections discuss a potential solution using our suite of real-time passive physiological sensors to assess the cognitive ability of the user and control an adaptive information filter. Figure 6 shows the system model used in our research. The user, Moving Target Fractions (MTF) task and adaptive information filter, are subsystems of the testbed system. What makes this system model unique is that it includes biosensors (i.e., passive physiological sensors) to monitor the user and provide input to the adaptive information filter. We examined the dynamics of how people extract information from moving images, especially as they progressed from novice to expert performers. We investigated several methods to instantaneously assess cognitive load and people s cognitive capability in order to create effective ways to augment cognition as the cognitive load increased (Ikehara, Chin, & Crosby, 2004). We are presently performing experiments to determine which combination of sensor data can assess the user s cognitive states listed in Table
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178 Crosby & Ikehara Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. significantly distinguish between the different numbers of moving targets of unknown location. These results supported our results testing the eye-tracking measures with more dynamic visual scenes where task performance information is not available in real time (Ikehara & Crosby, 2003). We examined the dynamics of how people extract information from moving images, especially as they progressed from novice to expert performers. We investigated several methods to instantaneously assess the users cognitive states and their cognitive capability (Ikehara, Chin, & Crosby, 2004). We are investigating effective ways to augment cognition, taking into account the users current cognitive state. We found that measures from the eye movement monitor significantly distinguished between the different numbers of moving targets of unknown location. These results supported our results testing the eye-tracking measures with more dynamic visual scenes where task performance information is not available in real-time. Andreassi, in his summary of muscle activity measured by electromyography (EMG), noted that muscle tension increased with task difficulty (Andreassi, 2000, pp. 181). We asked if this would also be true for the finger pressure applied when clicking a computer mouse. A pilot experiment with participants doing a task of increasing difficulty showed that when task difficulty increased, individual click pattern pressures became more variable. Shown are the composite views for two subjects of mouse click pressure. Each composite view contains over 80 clicks. The rear of the valley is where the first click occurs and the Figure 3. Targets crossing paths Figure 2. Target clusters A B C D Figure 2. Target A B ? ?
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