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The effects of pre-task appraisals and caffeine on cognition: Data and models

Presented at the ONR Cognitive Architectures 2005 Workshop. 3t w q b. 3a p q. 1 d c. 9h j z. The effects of pre-task appraisals and caffeine on cognition: Data and models. Frank E. Ritter Laura C. Klein, Andrew Reifers, Courtney Whetzel Mike Schoelles, Karen Quigley

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The effects of pre-task appraisals and caffeine on cognition: Data and models

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  1. Presented at the ONRCognitive Architectures 2005 Workshop 3t w q b 3a p q 1 d c 9h j z The effects of pre-task appraisals and caffeine on cognition: Data and models Frank E. Ritter Laura C. Klein, Andrew Reifers, Courtney WhetzelMike Schoelles, Karen Quigley IST/Y, BBH @ Penn State, RPI, DVA/UMDNJ frank.ritter@psu.edu This project was supported by the US Office of Naval Research, award N00014-03-1-0248 and N00014-02-1-0021, and the GCRC NIH grant. Marsha Lovett provided the MODS WM task, and Baris provided the MODS software. The views expressed in this article do not necessarily reflect the positions or the policies of the U.S. Government, and no official endorsement should be inferred.

  2. Overview of Presentation • Impacts • The effects of stress and caffeine on cognition • Stress, caffeine, & cortisol w/implications for health • Lessons on testing large scale theories • Overview of our research line • Blascovich, Lazarus, and other work, short review • Tasks, models, and data, CafeNav Project • 1. Task and model suite [BRMIC paper?] • Approach/initial fits/analyses (Ritter, Ceballos, Reifers, Klein, in prep.) • 2. Large study, CafeNav (N=45 x 3 tasks) • CafeNav-SS study [update, preview of results] • CafeNav-Argus study [update: 2/45 + 1?] • Implication for the Navy: Caffeine moderate dosages • 3. Overlays, theories of stress on cognition a. Appraisal & Stress Overlays b. Caffeine review -  Implication for the Navy, Caffeine low dosages • 4. How to do the data comparison • Models analyses based on power [ paper] • Conclusions and future work

  3. Overview Motivation for Studying Moderators • Behavioral moderators appear to influence cognition(maybe they don’t, we just remember things differently!) • heat • affect • stress (multiple causes) • Important for understanding aspectsof human-computer, human-object interactions • Language can be muddled: affect, emotions, moods, arousal • Work in this area has not combined physiology and cognition that often (e.g., performance on cognitive-stressor not recorded)

  4. Overview Motivation for Modeling Moderators • Modeling behavioral moderators that influence architecture processing • Development • Affect • Stress (multiple causes) • Important for modeling aspectsof human-computer interactions • Extending applied models from Quake to ModSAF • Example validated model near affect

  5. Overview Previous Approaches to Stress/emotions and Cognition • Physiology studies • Examples:Blascovich,Klein,Lazarus,Lieberman • AI & Cognitive Science • Examples: Sloman, Picard, Seif-El Nessar, Norling & Ritter • Human Factors • Examples: Woods, Hancock, and in overlays • Cognitive Science • Belavkin, Gunzelmann, Chong, Jongman • Perhaps need for several approaches

  6. Overview Our Approach  (acs.ist.psu.edu/ACT-R_AC ; Ritter, Avraamides, & Councill, 2002) • Cognitive architecture (e.g., ACT-R, COJACK)  (Ritter, 2004) • Biopsychology models and data • Validation of model’s behavior • Specifically • Task appraisal (“Challenging” or “Threatening”) • Caffeine • Displays to explain model to • analysts • readers

  7. Overview Lessons so far • Work with both physiology and cognition • Anova vs. regression and modeling • Different assumptions than previous work

  8. 3t w q b 3a p q 1 d c 9h j z Task/model suite 1. CafeNav Measures and Tasks (N=45/135) • Heart rate, BP/3 min., Cortisol, Am, DHEA, TimeE [Taatgen], mood, appraisal • Visual signal detection task [Reifers, task, model 5, d, ] • Simple reaction time task[Reifers, task, model 5, simple RT] • Working memory task (MODS)[Reder-Lovett-Lebiere, task, model 4, W] (a) Serial subtraction task[Reifers, voice task & keyboard task, model in 5, RT, errors] (b) Argus Prime [Schoelles, task, model in 5, about 6 measures] (c) Argus Prime - Dual task

  9. 1. Task & Model Suite

  10. Task/model suite AC T-R (5) Model of Subtraction • Create goal to serial subtract • Subgoal to do current column • Two strategies: count-down and subtract • Get column answer • Repeat across columns • Report result • 28 rules • 15 state chunks + 230 math facts (~250 total chunks)

  11. Task/model suite Predicted and Actual on Serial Subtractions(Tomaka et al., 1993)(Ritter, Avraamides, & Councill, 2002; Ritter, Reifers, Klein, Quigley, & Schoelles, 2004)

  12. Task/model suite Cycling Study (N=56) • NSF study hormone levels on stress response, modified IRB 2001 (wrt ONR), completed 2004 • BP, HR, hormones, not reported here • Repeated serial subtraction (7’s) • 47.0 (17.1, 8-106) attempts/4 min. block • 40.0 (18.6, 6-105) correct • 82% (14%, 43-100%) correct • Errors at 2 min. interuption = 52% (max 5) • Repeated serial subtraction (13’s) • 36.3 (15.1, 9-78) attempted/4 min. • 29.6 (15.7, 3-77) correct • 77% (17%, 31-100%) correct • Errors at 2 min. interuption = 46% (max 8) • Error types not available

  13. Task/model suite Lessons from 1st Model • Need complete data • Need more overlay theories • Other lessons not reported here(see HFES paper) • Other models will need the same testing • Recording User Input (RUI) software (Kukreja & Ritter, accepted, BRMIC)

  14. CafeNav CafeNav Block 1: Serial Subtraction • Physiology effects of stressor • Performance on tasks • Effects of stressor on performance • Effects of caffeine on performance • Effects of caffeine x stressor interaction

  15. CafeNav Subject Yield • 3 applications (IRB, Biosafety, GCRC) • 10 pages of screening conditions for health behavior and condition (e.g., nicotine, caffeine, drug use) • Screener, scheduler, nurse, RA, Exp2 (+ physician) • First block ended 6 may 05; 2/45 in next block Exp. Psych studies

  16. CafeNav • - 3 x 3 x 2 design • - Caffeine: 0, 200, 400 mg (N=15 men per condition) • Task: Serial sub, driving, Argus • - Appraisal: Median split (3 approvals in hand)

  17. CafeNav Heart Rate Results

  18. CafeNav Effects of Stress on Blood Pressure

  19. CafeNav EffectsofStressonHormoneMeasures

  20. CafeNav Application: Caffeine and Cortisol A similar graph for caffeine and cortisol in non-normal subjects Suggests: that caffeine and stress may have a disadvantagious interaction for long term health impacts of high caffeine doses. Lieberman, H. R., Tharion, W. J., Shukitt-Hale, B., Speckman, K. L., & Tulley, R. (2002). Effects of caffeine, sleep loss, and stress on cognitive performance and mood during U.S. Navy SEAL training. Psycho-pharmacology, 164, 250-261. [online publication first] DOI 10.1007/s00213-002-1217-9 or link.springer.de/link/service/journals/00213/contents/02/01217/paper/s00213-002-1217-9ch000.html.

  21. 200 mg 400 mg CafeNav Solid - pre-stress Dashed - post-stress The effect of Caffeine on VSDT

  22. CafeNav • 7’s easier than 13’s • Practice effect • Inverted U-shaped curve • 0 < 200 > 400

  23. CafeNav Lessons from Café Nav I • More control and care of subjects • Tasks work, cognition, stress, caffeine effects • Reuse, because we have toReuse: BP, HR, cortisol, mood, time-task, time-data, time-model?, working memory task, model?, Argus task and model, ACT-R, /PMNew: vigilance-task & model, serial-sub model, overlays • Moderate caffeine may be more helpful • Caffeine and stress effect on cortisol needs to be kept in mind

  24. 3a. Overlays Summary of Stress Theories

  25. Summary of Stress theories • Stress theories are incomplete—do not touch enough mechanisms (or does tunneling arise through WM?) • Many affect the central processor • Few affect periphery processors and processesE.g., No motor • The trick will be making this dynamic • And then analysing dynamic data • These theories are unlikely to be complete e.g., how will mental arithmetic be influenced by perceptual narrowing theory? Where is tremor? • Might be combinable • To test them, will need • Multiple tasks • Physiological data (HR, BP, cortisol,….) • Experimental psych data • Pointer to overlay chapter(Ritter & Reifers, in prep., Integrated Models of Cognitive Systems, Wayne Gray (ed.), OUP)

  26. 3b. Overlay: Caffeine (Morgan, Ritter, Stine, & Klein, submitted?) 22 stdies

  27. Caffeine Caffeine: Summary(Morgan, Ritter, Stine, & Klein, submitted) • Caffeine influences • RT -7% (not WM, not DM) • Self-reports on attention and alertness, 20-100% • Vigilance stays good up to 3-6 hours (flat or +15%v-30%a) • 50-80 mg looks good for most effects • Have a reusable overlay as a review • Reusable by CoJACK (DMSO, MoD project) • Suggests several caffeine studies • Suggests adding appraisal and fatigue to ACT-R(cf. Gratch & Marsalla, 2004; Gunzelmann et al., in press)

  28. Testing models 4. How to test the models - Theory • Have a series of comparisons, model to data • Have a large cross-model comparisoninputs: WM capacity, processing speed, caffeine, stress measuresto predict: VSDT, time, and serial subtraction measures • Overlays for pre-task appraisal, caffeine • Thought about how many times to run model

  29. CafeNav Analyses Outputs An understanding of: what changes in physiology, IDs RT, d’, lambda RT WM setting RT, strategy choice, error types, variance Models Overlay settings by appraisal and caffeine-level SD model Simple RT model WM model <model x 3> Inputs Sex Caffeine-level BP HR Cortisol Signal Detection Task Simple RT task WM task (MODS) <SSub, Argus’, Argus’’>

  30. 4. How to test the models - Theory II Run the model as much as you can But can’t run oo times, and don’t want 1 or N Power calculations are available, N=100 for power to find 0.5 effects >0.99 (Ritter, Quigley, Klein, submitted)

  31. Testing models 4. How to test the models - Engineering • Thus, will need to run models multiple times • Worse case: 145 S x 2 models x 10 runs x 5 min x 10 overlays = 100 days • Problems resolved with previous supercomputer (use PSU’s!)

  32. Summary • CafeNav Suit: Set of tasks used by subjects and models, and models • Headed towards detailed data set • Biopsychology + cognitive • Ready for model comparisons • Overlays for pre-task appraisal & caffeine • Suggestions for all cognitive architectures • Physio effects, Appraisal effects, Vigilence effects, Strategies • May be a problem fitting the data • Caffeine, low doses may be as good cognitively, and high doses bad physiologically

  33. Future Work • Studies • Self-report study on caffeine use (why, how much) • Caffeine dosage-response curves • Study without caffeine users, on caffeine • Models and overlays • Finish packaging • Move MODS to ACT-R 5 • Consider different ways to compute best W (MSE vs. correlation computations of W) • Finish overlays for pre-task appraisal • Fitting the data/develop the models • Implication for the Navy: • High caffeine and stress leads to cortisol • CafeNav data suggests 200 mg is more than enough • Review suggests lower (50 mg) is enough

  34. 3t w q b 3a p q 1 d c 9h j z The Effects of task appraisals and caffeine on cognition: Tasks, data and ModelsFrank.Ritter@psu.edu • Reusable task and model suite • Effects of task appraisal (stress on cognition and physiology • Effects of caffeine on cognition and physiology • Can test theories of stress • Low caffeine may be efficacious and safer

  35. References at acs.ist.psu.edu/papers  Kukreja, U., & Ritter, F. E. (accepted pending revisions, March, 2005). RUI—Recording User Input from interfaces under Windows. Behavior Research Methods, Instruments, and Computers.  Morgan, G. P., Ritter, F. E., Stine, M., Klein, L. C. (submitted). The effects of caffeine on cognition.  Ritter, F. E. (2004). Choosing and getting started with a cognitive architecture to test and use human-machine interfaces. MMI-Interaktiv-Journal, 7, 17-37. useworld.net/mmiij/musimms.  Ritter, F. E., Ceballos, R., Reifers, A. L., & Klein, L. C. (in prep.). Measuring the effect of dental work as a stressor on cognition.  Ritter, F. E., Quigley, K. S., & Klein, L. C. (submitted). Determining the number of model runs: Treating user models as theories by not sampling their behavior.  Ritter, F. E., Reifers, A., Klein, L. C., Quigley, K., & Schoelles, M. (2004). Using cognitive modeling to study behavior moderators: Pre-task appraisal and anxiety. In Proceedings of the Human Factors and Ergonomics Society. 2121-2125. Santa Monica, CA: Human Factors and Ergonomics Society. • Tomaka, J., Blascovich, J., Kelsey, R. M., & Leitten, C. L. (1993). Subjective, physiological, and behavioral effects of threat and challenge appraisal. Journal of Personality and Social Psychology, 65(2), 248-260.

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