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Caffeine’s Effect on Appraisal and Mental Arithmetic Performance:
A Cognitive Modeling Approach Tells Us More
Sue E. Kase ([email protected]), Frank E. Ritter ([email protected])
College of Information Sciences and Technology, Pennsylvania State University Michael Schoelles ([email protected])
Cognitive Science Department, Rensselaer Polytechnic Institute Abstract
observations, self-reported appraisal, and performance data, we then developed a cognitive model in the ACT-R cognitive A human subject experiment was conducted to investigate caffeine’s effect on appraisal and performance of a mental architecture of the serial subtraction task. Parametric solution serial subtraction task. Serial subtraction performance data was sets resulting from optimizing the serial subtraction cognitive collected from three treatment groups: placebo, 200 mg model to data from three treatment groups (placebo, 200 mg, caffeine, and 400 mg caffeine. Data were analyzed by average 400 mg) and two task appraisal conditions (challenge and across treatment group and by challenge and threat task threat) provided the first cognitive modeling-derived insights appraisal conditions. A cognitive model of the serial subtraction task was developed and fit to the human performance data. How the model’s parameters change to fit the data suggest how cognition changes across treatments and due to appraisal. Overall, the cognitive modeling and optimization results This section begins with an overview of the human subject suggest that the speed of vocalization is changed the most along experiment where performance and task appraisal data were with some changes to declarative memory. This approach collected and later utilized in the development and promises to offer fine-grained knowledge about the effects of moderators on task performance. optimization of a cognitive model. A detailed description of the cognitive task follows, as well as, the formulation of the Keywords: Caffeine, stress, task appraisal, cognitive arithmetic
self-reported appraisal conditions. Lastly, results and interpretations of the human performance data are suggested. Introduction
As part of a larger project, human subject data was Caffeine is widely consumed throughout the world in collected to study the effects of stress and caffeine on beverages, foods, and as a drug for a variety of reasons, cardiovascular health. The authors collaborated with Dr. including its stimulant-like effects on mood and cognitive Laura Klein and her lab in the Biobehavioral Health performance (for review see Fredholm et al., 1999). Its Department at Penn State University. A mixed experimental positive effects on performance, notably sustained vigilance design was conducted with 45 healthy men 18-30 years of age and related cognitive functions, are well documented when (Klein, Whetzel, Bennett, Ritter, & Granger, 2006). (Men are administered to rested volunteers in doses equivalent to single typically used in these types of studies because we also took servings of beverages (Amendola et al., 1998; Smith et al., additional physiological measures and their systems are 1999). Additionally, its consumption in moderate doses is associated with few, if any, adverse effects (Nawrot et al., All subjects were asked to perform a series of three 2003). Therefore, caffeine has been a strategy examined for cognitive tasks. Subjects individually performed a simple its usefulness to military personnel (Lieberman & Tharion, reaction time (RT) and a working memory (WM) task taking 15 minutes to complete. Then subjects were administered one The majority of caffeine research is conducted through of three doses of caffeine: none (placebo), 200 mg caffeine human experimentation with analysis of the collected (equivalent to 1-2, 8 oz cups of coffee), or 400 mg caffeine performance data. Few studies have attempted to model the (equivalent to 3-4, 8 oz cups of coffee). After allowing effects of caffeine. One such study by Benitez et al. (2009) absorption time, a 20-minute stress session of the mental presented a biomathematical model for describing arithmetic portion of the TSST was performed. Following performance during extended wakefulness with the effect of completion of this stressor, subjects again were asked to complete the RT and WM tasks. Cognitive performance was Likewise, this study takes a modeling approach employing determined by calculating accuracy and response time scores. cognitive modeling and optimization techniques to investigate This paper focuses on one portion of the experiment—the the effects of caffeine on cognitive performance. In particular, TSST. The TSST protocol has been used for investigating we examined the effects of caffeine and task appraisal during psychobiological stress responses in a laboratory setting since the arithmetic portion of the Trier Social Stress Test (TSST), the 1960s (Kirschbaum, Pirke, & Hellhammer, 1993). TSST a mental serial subtraction task. Based on human subject traditionally consists of an anticipation period and a test period in which subjects have to deliver a free speech and resources or reserves to deal with the serial subtraction task perform mental arithmetic in front of an audience. The mental and three focused on the subject’s perception as to how arithmetic portion of the TSST is a mental serial subtraction For all questions the scale was from 1 to 5 with a value of 3 indicating that the subject is neither challenged nor threatened Serial Subtraction Task
by the task. After correcting for the imbalance in questions, a The serial subtraction task utilized in the experiment ratio of perceived stress to perceived coping resources was consisted of four 4-minute blocks of mentally subtracting by created. For example, if a subject’s total appraisal score was 7s and 13s from 4-digit starting numbers. Figure 1 illustrates 1.5 or less, their perceived stress was less than or equal to the serial subtraction task. These were the four starting their perceived ability to cope, which equated to a challenge numbers used to begin the four blocks of subtraction during condition. If a subject’s appraisal score was greater than 1.5, their perceived stress was greater than their perceived ability to cope, which equated to a threat condition. Each treatment group was composed of 15 subjects. The placebo group had approximately the same number of subjects in each appraisal condition (7 challenge, 8 threat). The 200 mg caffeine group had twice as many challenged subjects as threatened subjects (10 challenge, 5 threat). The 400 mg caffeine group contained only 2 challenged subjects with the remainder (13) subjects reporting a threatening appraisal. Results and Discussion
For this investigation, the serial subtraction performance data from the placebo group (PLAC), the 200 mg caffeine group Figure 1: An illustration of the four blocks of the serial (LoCAF), and the 400 mg caffeine group (HiCAF), were analyzed by average across treatment group and by appraisal condition. The performance statistics of primary interest were number of attempted subtraction problems and a percentage Before the task begins the experimenter explains that the correct score. The data are shown in Table 1 where each pair subject’s performance is going to be voice recorded and of values represents number of attempts and percent correct. reviewed by a panel of psychologists for comparison with the The results discussed in this paper apply to data from the first other subjects participating in the experiment. The task is performed mentally with no visual or paper clues. After the task is explained to the subject, a task appraisal questionnaire Table 1: Human performance (average number of attempts is completed, and the subject begins performing the task. It is and percent correct) by treatment group (each N=15) and thought that this anticipation period, for some subjects, appraisal condition (challenge, threat). increases anxiety and worry about poor performance on the Subjects sit in a chair directly in front and near the experimenter who is holding a time keeping device and clipboard of the correct subtraction answers that she checks off as the subject performs the task. Before the task begins the experimenter emphasizes that the task should be preformed as quickly and as accurately as possible. An experimenter tells the subject the starting number; from then on, the subject For all treatment groups the challenge condition showed the speaks the answer to each subtraction problem. When an best performance in both number of attempts and percent incorrect answer was given, the subject was told to “Start correct over the average across treatment and the threat over at <the last correct number>”. At two minutes into each condition. The threat condition showed the worst 4-minute session, subjects were told that “two minutes performance. Performance differences between the challenge remain, you need to go faster”. This prompt enhances the and threat conditions were most pronounced in the LoCAF group with an impressive increase of nearly 25 more attempted subtraction problems and a 13.5% increase in Task Appraisal
subtraction accuracy by challenged subjects over threatened Before and after the serial subtraction stress session, subjects subjects. For the HiCAF group the challenge and threat completed pre- and post-task appraisals based on Lazarus and condition differences were less than LoCAF but still Folkman’s (1984) theory of stress and coping. Each subject substantial: 13 more attempted problems and a 7.7% increase was asked five questions orally: two focused on the subject’s in subtraction accuracy. Differences between the challenge and threat condition were least visible in the PLAC group, 10 In the challenge condition (middle section), HiCAF more attempted problems and only a 5.4% increase in performance does not drop below PLAC, but is approximately equivalent or slightly higher. In both the Figure 2 better illustrates these performance differences average across treatments and the challenge condition, with the treatment groups labeled along the x-axis and the LoCAF performance is well above that of PLAC. This is also plot subdivided into three sections: averages across treatment supported in previous research that low doses of caffeine tend groups (not by appraisal condition) in the leftmost section, to increase performance (Amendola et al., 1998; Smith et al., and averages across treatment groups subdivided by appraisal 1999). In both these cases, the across treatments and condition in the center (challenge) and rightmost sections challenge plots, the effects of caffeine take on characteristics related to level of arousal studies (i.e., Anderson & Revelle, The plot visualizes several interesting trends; some 1982) and appear to follow the Yerkes-Dodson (1908) law supported by existing caffeine and cognition research and that postulates that the relationship between arousal and others not. In the average across treatments plot (leftmost performance follows an inverted U-shape curve. section), the performance of the HiCAF group drops below There is no supporting research for the performance trends that of PLAC for both performance statistics. This supports visible under the threat condition (right section). Threatened findings that large doses of caffeine are occasionally subjects self-reported stress and lack of coping skills to associated with anxiety and disrupt performance (Haishman, adequately perform the serial subtraction task. The threat plot & Henningfield, 1992; Wesensten, Belenky, & Kautz, 2002). shows performance decreases from PLAC to LoCAF (instead Whether a 400 mg dose is considered ‘large’ may be in of increases as observed in the other sections of the plot) with question as some studies administered up to 800 mg doses HiCAF only very slightly higher than LoCAF (+1.4 attempts, (McLellian et al., 2007). Generally, 100 to 300 mg doses are and +0.3% correct). In this case, the U-shape is not inverted, categorized as ‘low’ dosages because 50-300 mg of caffeine is available in a number of forms including tablets, chewing gum, a wide variety of beverages and some food products. Figure 2: Comparing human performance differences in number of attempts and percent correct by treatment group (x-axis) and appraisal condition: treatment groups not accounting for appraisal (leftmost section), and averages across treatment groups divided by appraisal condition, challenge (middle section) and threat (rightmost section). More can be discussed about the human performance data task. The ACT-R cognitive architecture (Anderson, 2007) by way of analysis and interpretation of caffeine’s effect on was chosen to model the serial subtraction task for several appraisal and serial subtraction. However, a more important reasons: it provides a parameter-driven subsymbolic level of question remains: Can these effects be modeled using a processing; it permits the parallel execution of the verbal cognitive architecture and what might be learned from the system with the control and memory systems, and it has parameters and values generating best fits during been used for other models of addition and subtraction optimization of the model? The serial subtraction model performs a block of Modeling Serial Subtraction
subtracting by 7s or 13s in a similar manner to that of the human subjects. The model’s declarative knowledge Theory about how mental arithmetic is performed combined consists of arithmetic facts and goal-related information. with observations gathered during the human subjects’ The model’s procedural knowledge is production rules that performance of serial subtraction laid the foundation for the allow for retrieval of subtraction and comparison facts development of a cognitive model of the serial subtraction necessary to produce an appropriate answer. The model Optimizing to Human Data
performs subtractions by column-by-column. How does cognition change under stress and caffeine? We The model runs under ACT-R 6.0 and utilizes the can explore this question by adjusting theoretically imaginal module and buffer. The imaginal buffer motivated parameters in architecture. The parameters that implements a problem representation capability. In the serial lead to better correspondences suggest how cognition subtraction model the imaginal buffer holds the current 4- changes. This section begins by discussing the architectural digit number being operated on (the minuend) and the parameters selected for adjusting the model’s performance number being subtracted (the subtrahend). The goal module to simulate the human data. This process of fitting the and buffer implement control of task execution by cognitive model to human data is a form of optimization. manipulation of a state slot. ACT-R’s vocal module and The optimization approach to fit the model is briefly buffer verbalize the answer to each subtraction problem as described in the second part of the section. The optimization results, accompanied by interpretations of best fitting The model starts with the main goal to perform a parameter values, is discussed at the end of the section. subtraction and a borrow goal to perform the borrow operation when needed. Both types of goal chunks contain a Architectural Parameters
state slot, the current column indicator, and the current subtrahend. The current problem is maintained in the Three ACT-R architectural parameters appeared important imaginal buffer. This buffer is updated as the subtraction in performing serial subtraction and were selected for problem is being solved. The model begins with an integer adjusting the model’s performance: seconds-per-syllable, minuend of 4-digits. All numbers in the model are chunks of base level constant, and activation noise. The rate the model type integer with a slot that holds the number. The model speaks is controlled by the seconds-per-syllable parameter also contains subtraction and addition fact chunks whose (SYL). The ACT-R default timing for speech is 0.15 slots are the integer chunks described above. This seconds per assumed syllable based on the length of the text representation of the integers and arithmetic facts has been string to speak. There is a default of three characters per syllable controlled by the characters-per-syllable parameter. The model determines if a borrow operation is required The seconds-per-syllable and characters-per-syllable by trying to retrieve a comparison fact that has two slots, a parameters control subsymbolic processes in ACT-R’s vocal greater slot containing the minuend and a lesser slot module. The vocal module gives ACT-R a rudimentary containing the subtrahend. If the fact is successfully ability to speak. It is not designed to provide a sophisticated retrieved then no borrow is necessary, otherwise a borrow simulation of human speech production, but to allow subgoal is created and executed. Borrowing is performed by ACT-R to speak words and short phrases for simulating retrieving the addition fact that represents adding ten to the verbal responses in experiments such as the answers to the minuend. The subtraction fact with the larger minuend is retrieved. The model then moves right one column by The other two parameters affect declarative knowledge retrieving a next-column fact using the current column value access: the base level constant (BLC), and the activation as a cue. If this retrieval fails, there are no more columns so noise parameter (ANS). The BLC parameter and a decay the borrow and the subgoal return back to the main task parameter affect declarative memory retrieval and retrieval goal. If there is a next column and its value is not 0 than 1 is time. The ANS value affects variance in retrieving subtracted from it by retrieval of a subtraction fact. If the declarative information and error rate for retrievals in the value is 0 then the problem is rewritten in the imaginal model. This instantaneous noise value can also represent buffer with a 9 and the model moves to the next column and variance from trial to trial. Other parameters, such as base repeats the steps discussed above, returning to the main task level learning, decay, and the characters-per-syllable parameters were built into the model as modifiable but were The model outputs the answer by speaking the 4-digit left fixed at their default values for this study. The search result. The model has two output strategies. For this paper space for the model optimization was defined by the the data reported are for the calc-and-speak strategy where parameter value boundaries: ANS and SYL 0.1 to 0.9, and the model speaks the answer in parallel with the calculation described above. If the answer is incorrect, the problem is reset to the last correct answer. If the answer is correct, the Optimization Approach
main problem task is rewritten in the imaginal buffer. Because the search space was large and assumed to be After the model has performed a block of subtractions the rather complex a departure from the cognitive modeling number of attempted subtraction problems and percent community’s traditional manual optimization technique was correct, are recorded. The model’s performance can be initiated (Kase, 2008). A new front-end function for the adjusted by varying the values of architectural parameters cognitive model was developed for execution in a parallel associated with specific modules and buffers, and processing environment and the ACT-R parameter values subsymbolic processes within the architecture. (ANS, BLC, and SYL) were passed to multiple instances of running models from a parallel genetic algorithm (PGA). The SYL parameter was chosen for optimization because vocalization of the answer is the most time consuming what would be manually assigned to the model in the aspect of this task. The BLC and ANS parameters were ACT-R modeling community. This could be because the chosen because the task is memory intensive. Other memory nature of the task is stressful (i.e., purposively used to parameters could have been chosen and ongoing work is elicited a stress response). The ANS value range in Table 2 exploring the fitting of other parameters. Normally, the is narrow from the lowest ANS of 0.67 to the highest ANS parameter values are set within the model code before of 0.78, a difference of only 0.11. This hints at the fact that runtime. Using the PGA to search the parameter space for caffeine may not effect this parameter’s role in the model’s promising parameter value sets generating best fits between performance of serial subtraction. ANS values are basically the model and human data saved a substantial amount of equivalent for the PLAC and LoCAF groups for challenge modeler time and computational resources. Model-to-data (0.68) and threat (0.71). In this case, the slightly higher fit was determined by an objective function, or fitness ANS in predicting threatened subjects corresponds to the function, defined as the discrepancy between model lower performance (less attempts and lower accuracy), and performance (number of attempts and percent correct) and the self-reports where subjects do not believe they will the corresponding human performance (e.g., 47.3 – 48.1). perform well. Worrying or embarrassment about their poor The fitness is in terms of error (or cost) with a fitness value performance is a distraction and may interfere with working of 0 representing perfect correspondence between the model memory processes and verbalizing solutions. The greatest variability in ANS values is found in HiCAF. Surprisingly, Employing this type of ‘automated’ optimization the trend reverses with HiCAF challenge predictions approach allowed for 20,000 different sets of parameter yielding a higher ANS value (0.75) than threat predictions value to be tested in a directed manner each time the PGA was executed. Using the approach, the model was optimized The base level constant parameter values (BLC, middle to nine sets of human performance data (see Table 2). value in triple) show a trend of nearly equivalent higher values for LoCAF and HiCAF challenge conditions (2.65 Results and Discussion
and 2.69) then threat conditions (2.48 and 2.35), and also for Table 2 shows the resulting model performance compared to all BLC values under PLAC (2.49, 2.48 and 2.53). In this the human performance data using parameter value solution case, caffeine may be causing a ‘boost’ in the base level sets identified by the PGA that produced the best fits activation value of facts in declarative memory promoting (fitness values less than 1.0) to the human performance, and higher probability of selection in response to a retrieval suggest how cognition changed. Several trends can be request and quicker fact retrieval time. observed within the parameter values producing best fits. The parameter values shown in the table are averaged; Table 2: Optimization results for three treatment groups denoted by the numeric value in parentheses after the (PLAC, LoCAF, HiCAF) and appraisal conditions parameter set values (i.e., ‘(3)’ in the first row means that (CH=challenge, TH=threat) comparing human performance the PGA found 3 parameter sets producing fitness less than and model predictions in number attempts and percent 1.0, and that these values were averaged). Each parameter correct (both rounded), and fitness value associated with set included in the average was run 200 times (i.e., 200 average (over N) of best fitting (less than 1.0) ACT-R Beginning with the seconds per syllable parameter, SYL is shown in the last column and last value in the triple of Table 2. The model predictions indicate that challenged subjects speak a syllable more quickly than threatened subjects. This is true for all treatment groups. LoCAF shows the greatest difference in speech rate with challenge SYL at 0.31 (also lowest SYL overall) and threat SYL at nearly two times slower (0.61). HiCAF differences in SYL are less: challenge 0.40 compared to threat 0.57, a difference of 0.17. PLAC shows a slightly less SYL difference of 0.14. Challenge subjects self-report less stress and are generally confident that they can perform the serial subtraction task well. With less stress and a low dose of caffeine more fluid speech appears to result, or possibly the speech rate acts as a window into the cognitive processes required to complete the subtractions (i.e., fact retrieval, working memory and place-keeping operations, and concatenation of Overall across treatments, the activation noise parameter values (ANS, first value in triple) are high as compared to Conclusion
Kase, S. E. (2008). HPC and PGA optimization of a cognitive model: Investigating performance on a math A cognitive model of the serial subtraction task was stressor task. Unpublished PhD thesis, College of IST, developed and fit to the human performance data from three Penn State University, University Park, PA. caffeine treatments and by challenge and threat appraisal. Klein, L. C., Whetzel, C. A., Bennett, J. M., Ritter, F. E., & This fit suggests that there are systematic changes in Granger, D. A. (2006). Effects of caffeine and stress on cognition due to caffeine and appraisal. Most notable is the salivary alpha-amylase in young men: A salivary speaking rate, but declarative memory retrievals are also biomarker of sympathetic activity. Psychosomatic These results show that using a cognitive model and Kirschbaum, C., Pirke, K. M., & Hellhammer, D. H. (1993). parametric optimization approach can further our The Trier Social Stress Test—A tool for investigating understanding of caffeine beyond a human experimentation psychobiological stress responses in a laboratory setting. approach. Overall, the cognitive modeling and optimization Neuropsychobiology, 28, 76-81. approach was successful. The preliminary modeling results Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal and and interpretations offer insight into the effects of caffeine coping. New York, NY: Springer Publishing. on task appraisal and subsequent performance of the task, Lieberman, H. R., & Tharion, W. J. (2002). Effects of and promise an improved methodology for the study of caffeine, sleep loss, and stress on cognitive performance other behavioral moderators and other cognitive tasks. At and mood during U. S. Navy seal training. this point in our investigation more analysis is needed and Psychopharmacology, 164, 250-261. additional parameter sets should be examined, along with McLellan, T. M., Kamimori, G. H., Voss, D. M., Tate, C., & continued refinement of the serial subtraction model for Smith, S. J. R. (2007). Caffeine effects on physical and predicting the effects of caffeine on cognition. cognitive performance during sustained operations. Aviation, Space, and Environmental Medicine, 78(9), Acknowledgments
This project is partially supported by ONR grant Nawrot, P., Jordan, S., & Eastwood, J. (2003). Effects of N000140310248. Computational resources were provided caffeine on human health. Food Additives and by TeraGrid DAC TG-IRI070000T and run on the NCSA clusters. The authors would like to thank Laura Klein and Smith, A. P., Clark, R., & Gallagher, J. (1999). Breakfast her lab and Jeanette Bennett at the Department of cereal and caffeinated coffee: Effects on working Biobehavioral Health, Penn State University, for collection memory, attention, mood and cardiovascular function. of the human performance data and data analysis assistance. Physiology & Behavior, 67, 9-17. Wesensten, N. J., Belenky, G., & Kautz, M. (2002). References
Maintaining alertness and performance during sleep Amendola, C. A., Gabrieli, J. D. E., & Lieberman, H. R. (1998). Caffeine’s effects on performance and mood are Psychopharmacology, 159, 238-47. independent of age and gender. Nutritional Neuroscience, Yerkes, F. M., & Dodson, J. D. (1908). The relationship of strength of stimulus to rapidity of habit-formation. Anderson, J. R. (2007). How can the human mind occur in Journal of Comparative Neurology and Psychology, 18, the physical universe? New York, NY: Oxford University Anderson, K. J., & Revelle, W. (1982). Impulsivity, caffeine, and proofreading: A test of the Easterbrook hypothesis. Journal of Experimental Psychology: Human Perception and Performance, 8, 614-624. Benitez, P. L., Kamimori, G. H., Balkin, T. J., Greene, A., & Johnson, M. L. (2009). Modeling fatigue over sleep deprivation, circadian rhythm, and caffeine with a minimal performance inhibitor model. Methods in Enzymology, 454, 405-419. Fredholm, B. B., Battig, L., Homen, J., Nehlig, A., & Zvarlaw, E. E. (1999). Actions of caffeine in the brain with special reference to factors that contribute to its widespread use. Pharmacological Reviews, 51, 83-133. Haishman, S. J., & Henningfield, J. E. (1992). Stimulus functions of caffeine in humans: Relation to dependence potential. Neuroscience & Biobehavioral Reviews, 16, 273-287.

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