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Building Stick Tasks Experiment - Essay Example

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The paper "Building Stick Tasks Experiment" discusses that individual actions are determined through task features, environment features, and success in using certain strategies. Remarkably, this adaptation process takes place without awareness of the procedures or strategies being applied…
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Building Stick Tasks Experiment
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Building Stick Tasks Experiment This lab report concerns the Building Stick Tasks (BST) experiment. The Building stick Task experiment explains the Dynamic Decision Making (DDM) process, which is used in problem solving. Dynamic Decision making involves interdependent decision making process that occurs in an environment which changes with time because of the previous decision maker’s actions, or because of events that beyond the decision maker’s control (Turkle, 1984). Dynamic decisions are usually more complex, and they occur in real time and entail the level to which individuals are able to utilize their experience in controlling certain complex systems; even the systems that result in better decisions with time. The BST experiment attempts to study how individuals integrate and use the two sources of information (Current information and past experience) when deciding which strategies to adopt in solving problems (Gonzalez et al, 2005). The BST experiment entails participants selecting between two operators or strategies to build a stick: Overshoot operator entails a participant selecting a stick larger than desired stick, and immediately thereafter cutting the stick to desired length. Undershoot operator entails a participant selecting a smaller stick than desired stick, and then adding smaller sticks to reach the desired length. Previous research indicates that the two information sources participants on the influence on the choices of strategies to use in solving a particular problems. The experiment clarifies two independent variables representing information: Distance to goal variable, dictates ability of people to analyze current problems when deciding operator to use. History of success variable refers to past experience of people in using the operator (Brehmer & Allard, 1991). The Building Stick Tasks (BST) Experiment In performing a task or problem solving, several methods are used. People choose between different alternative actions available, that effectively directs towards intended goals. The alternative actions are referred to as operators in psychological or experimental terms (Gonzalez & Vrbin, 2007). Deciding on the most effective action or operator involves cognitive process which involves the analysis of several information sources. For instance, the past behaviors or actions which enables people to pursue an effective course of action in effective problem solving, strongly influences the same people to perform the same action in related situations or environments (Berry & Broadbent, 1984). On the other hand, choosing a course of action or operator may involve careful analysis of current situation and environment in relation to the problem; the analysis choice should be seen as effectively leading towards the achievement of the desired goals in problem solving process. However, the experiment process indicates that the sources of information are interdependent (not mutually exclusive) and it is possible for individuals to use both the information sources when choosing the operator or action to pursue when solving a given problem (Lurchins & Lurchins, 1959). A lot of researches have been conducted in the history of success and the distance to goal, approaches to problem solving. In their 1996 experiment Lovett and Anderson illustrates two pieces of research. The first research conducted by Luchins in 1942, illustrates capability of past experiences (for example the history of success) in the issue of the problem solving set; Luchins referred to the problem solving set as the Einstellung effect. The second research conducted by Atwood and Polson in 1976 illustrates the different heuristic types that individuals use in solving problems; for instance the distance to goal method. These two research processes apply the different methods of the water jug problem in analyzing their appropriate phenomena (Dianes & Fahey, 1995). The Building Stick Tasks experiment, conducted by Lovett and Anderson, is an isomorph like the water jug problem of Luchin. Therefore they have the same procedure or structure. The main purpose of the 1996 experiment by Lovett and Anderson is to illustrate computational and mathematical description of the phenomena in the structure of their own cognition theory; referred to as ACT-R. Lovett and Anderson, therefore, make several predictions or hypotheses on course of action selection, through the assumptions of previous research results and their model. The two developed hypotheses are: The distance to goal hypothesis entails; participants are more able to select a given operator if the action will closer take the current problems towards the goal state (Martin et al, 2004). The history of success hypothesis illustrates; participants are more able to select operators which have effective and successful past experiences. During the experiment duration, Lovett and Anderson continuously changed the history of success and the distance to goal variables to monitor if these changes affected on the operator choice by the participants. There experiment conditions differed based on the problem type presented on the trials 9 – 11. The subjects, in the separate condition experienced 15 problems. The 15 problems were separated into three parts. The initial 8 trials illustrated the training phase, and all the three conditions were the same. The 9-11 trial illustrated the variable phase, and all the three conditions were different. The trials 12-15 represented the test phase, and all the three conditions remained the same. In the test phase, three trails were selected and then repeated in the training phase. Method Participants The Building Sticks Task experiment was conducted with the help of 320 participants; data on this year’s experiment was added to previous year’s data to give a total of 320. The participants voluntarily agreed to be part of the experiment. 104 participants were involved in condition A, 126 participants were involved in condition B, and 90 participants were involved in condition C. the participants were of mixed gender, and they reportedly had good vision to enable effective measurements. In order for the participants to take part in the experiment, they must have achieved the age of the majority, which is 18 years. Equipments A computer was the main equipment used in this experiment. The computer was a HP model with dual processor. The monitor comprised a Dell TFT screen, with medium resolution and 17 inches monitor size. The experiment screen was a black background measuring 350mm by 190mm (Kugler & DeNisi, 1996). The Building stick Tasks experiment was done using a computer program referred to as Cognition Laboratory Experiment (CLE). Basic stationery like pencils and paper were also used in the experiment. Stimuli In the Building Stick Tasks experiment, participants are required to build sticks of desired length through subtracting and adding sticks from infinite resources of three different sticks length (Cronin et al, 2009). Lengths of the resource sticks are different in all the trials. Participants utilize to strategies of solving tasks. These strategies are not inherently correlated with the features of the sticks, therefore, success base rates of each strategy is independent of perceptual problem features (Gonzalez, 2005b). In this experiment the participants solved twenty problems; base rate of success was varied across the trials. For instance, participants got 5 blocks of the trials using one strategy mostly, and thereafter the same five blocks of trial with the applied strategy being less frequently used. Generally, the choices of the participants studied the variable success rates of the two alternative strategies. Design When a person conducts the BST experiment, the computer program generates and output file resembling the one indicated in table 1. On each and every trial, the program indicates the trial number, the condition of the participant, the initial building stick selected by the participant (a, b and c showing building sticks from left to the right), the strategy that the identified building stick represents, the time a participant takes in selecting a building stick and, the outcome of selecting the strategy (s and f for success and failure respectively). Only the information on the first strategy selection is recorded for each trial (Gonzalez, 2004). Table 1. Stick lengths for problems Trial a b c Desired Stick Problem Solution 1 15 (110) 250 (125) 55 (70) 125 b-a-2c or 2c+a 2 10(91) 155(54) 22(79) 101 b-a-2c 3 14(98) 200(88) 37(75) 112 b-a-2c 4 22(92) 200(86) 32(82) 114 b-a-2c 5 10(112) 243(84) 37(122) 159 b-a-2c 6 22(51) 175(102) 40(33) 73 b-a-2c 7 15(122) 250(113) 49(88) 137 b-a-2c 8 10(95) 179(74) 32(73) 105 b-a-2c 9 20(72) 196(104) 42(50) 92 b-a-2c 10 14(73) 203(116) 51(36) 87 b-a-2c 11 12(71) 155(72) 30(53) 83 b-a-2c 12 14(107) 237(116) 51(70) 121 b-a-2c 13 22(92) 200(86) 32(82) 114 b-a-2c 14 14(98) 200(88) 37(75) 112 b-a-2c 15 15(110) 250(125) 55(70) 125 b-a-2c or 2c+a Procedure In the Building Sticks Task experiment; two dependent variables or measures are collected from the participants: a) the initial building sticks selected in each trial and, b) the response time utilized by the participants in selecting a stick (Mckenna, 1991). Each and every building stick indicates a given operator. Sticks a and c shows undershoot operator; and they are shorter than the required stick length. Stick c shows the overshoot operator, hence longer than the required stick length (Horswill and Mckenna, 2004). The response time was measured from the initial appearance of the trial on the screen, up to the time the participant clicks the building stick with the cursor. Results and Discussion In BST experiment, the distance to goal is referred to as the difference between length of desired stick and length of current stick, after selecting first building stick (Endsley, 2006). Each problem’s goal is to ensure the current stick has the same length as desired stick. If the participants use the distance to goal strategy in selecting operator, then they will select operator that ensures the current stick is closest to desired stick’s length. This is calculated through comparing lengths of building sticks and selecting the stick that has the minimal difference in the current and desired stick (Brehmer, 1992). For example, participants will choose stick c for trial 1 (undershoot operator), and stick b (overshoot operator) for trial 2. History of success hypothesis testing is done through analyzing the success and failure experience with a given operator (Lovett, 1996). The last column in Table 1 illustrates solutions for each trial. Solution b-a-2c shows the overshoot strategy, and 2c+a shows the undershoot strategy. During the training phase, the participants in the three conditions are given similar history for operators that they choose. Problems 1 is solved through overshoot or undershoot method; but problems 2-8 can be solved only through the overshoot method. Hence all the participants who use the undershoot method for trial 2-8 will fail, hence they are required to use overshoot method (Gibson et al, 1997). Dynamic decisions making are employed in problem solving at individual or organizational levels. This decision making process occurs in situations that change over time because of previous actions of the person making decisions, or events that are beyond the decision maker’s control. Dynamic decisions are complex and takes place in real time; it involves people using past experiences in formulating better decisions; hence leading to effective problem solving process. Knowledge of the Building Stick experiment enables us to solve problems in everyday life situations. For example, a business organization which is currently making losses, must study factors that ensured past profitability. Also, effective firefighting relies heavily on past methods that proved to be effective. Dynamic decision making illustrates the extent of experience in controlling given system. Organizations underperformance problems can be solved through implementation of past best practices. Conclusion This research paper has provided further evidence that individual action are determined through task features, environment features, and success in using certain strategies (Sterman, 1989). Remarkably, this adaptation process takes place without awareness of the procedures or strategies being applied, success of a strategy, or base rates of stimuli in the environment (Rigas et al, 2002). Some of these strategic choices have been illustrated as behavior that depends on metacognitive control. References Berry, B. C., & Broadbent, D. E. (1984). On the relationship between task performance and associated verbalizable knowledge. Quarterly Journal of Experimental Psychology, 36A, 209–231 Brehmer, B. (1992). Dynamic decision making: Human control of complex systems. Acta Psychologica, 81(3), 211–241. Brehmer, B., & Allard, R. (1991). Real–time dynamic decision making: Effects of task complexity and feedback delays. In J. Rasmussen, B. Brehmer & J. Leplat (Eds.), Distributed decision making: Cognitive models for cooperative work. Chichester: Wiley. Cronin, M., & Gonzalez, C., & Sterman, J. D. (2009). Why dont well-educated adults understand accumulation? A challenge to researchers, educators and citizens. Organizational Behavior and Human Decision Processes, 108(1), 116–130. Dienes, Z., & Fahey, R. (1995). Role of specific instances in controlling a dynamic system. Journal of Experimental Psychology: Learning, Memory and Cognition, 21(4), 848–862. Endsley, M. R. (2006). Expertise and situation awareness. In K. A. Ericcson, N. Charness, P. J. Feltovich & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 633–651). Cambridge: Cambridge University Press. Gibson, F. P., Fichman, M., & Plaut, D. C. (1997). Learning in dynamic decision tasks: Computational model and empirical evidence. Organizational Behavior and Human Decision Processes, 71(1), 1–35. Gonzalez, C. (2004). Learning to make decisions in dynamic environments: Effects of time constraints and cognitive abilities. Human Factors, 46(3), 449–460. Gonzalez, C. (2005b). The relationship between task workload and cognitive abilities in dynamic decision making. Human Factors, 47(1), 92–101. Gonzalez, C., & Vrbin, C. (2007). Dynamic simulation of medical diagnosis: Learning in the medical decision making and learning environment MEDIC. In A. Holzinger (Ed.), Usability and HCI for medicine and health care: Third symposium of the workgroup human-computer interaction and usability engineering of the Austrian Computer Society, USAB 2007 (Vol. 4799, pp. 289–302). Germany: Springer. Gonzalez, C., Vanyukov, P., & Martin, M. K. (2005). The use of microworlds to study dynamic decision making. Computers in Human Behavior, 21(2), 273–286. Horswill, M. S., McKenna, F. P. (2004). Drivers hazard perception ability: Situation awareness on the road. In S. Banbury & S. Tremblay (Eds.), A cognitive approach to situation awareness: Theory and application (pp. 155–175). Aldershot, England: Ashgate. Kluger, A. N. & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284. Lovett, M. C., & Anderson, J. R. (1996). History of success and current context in problem solving: Combined influences on operator selection. Cognitive Psychology, 31, 168–217. Lurchins, A. S., & Lurchins, E. H. (1959). Rigidity of behaviour: A variational approach to the effects of Einstellung. Eugene, OR: University of Oregon Books. Martin, M. K., Gonzalez, C., & Lebiere, C. (2004). Learning to make decisions in dynamic environments: ACT-R plays the beer game. In M. C. Lovett, C. D. Schunn, C. Lebiere & P. McKenna, F. P, & Crick, J. (1991). Experience and expertise in hazard perception. In G. B. Grayson & J. F. Lester (Eds.), Behavioral Research in Road Safety (pp. 39–45). Crowthorne, UK: Transport and Road Research Laboratory. Rigas, G., Carling, E., & Brehmer, B. (2002). Reliability and validity of performance measures in microworlds. Intelligence, 30(5), 463–480. Sterman, J. D. (1989). Misperceptions of feedback in dynamic decision making. Organizational Behavior and Human Decision Processes, 43(3), 301–335. Turkle, S. (1984). The second self: Computers and the human spirit. London: Granada. Read More
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