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Chapter 10

PSY342 - chapter 10 notes

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University of Toronto St. George
Ari Silburt

Chapter 10Problem Solving and ReasoningThinking is the process of mentally representing the aspects of the world and then transforming those representations to new representations that can prove useful to our goals Thinking is often a conscious process and we are aware of the process of transforming mental representations and we get a chance to reflect on the thoughtsThinking involves problem solving and reasoningProblem Solving is that goaldirected activity involving the application of cognitive processes to overcome the obstacles and achieve a goalReasoning involves the cognitive processes that we use to make inferences from knowledge and draw conclusionsThe Nature of Problem SolvingProblem is a situation where there is no apparent standard or routine way of reaching a goal Often there is difficulty in the pathway to the goal that has to be overcomeProblem solving requires surmounting obstacles to achieve a goalRoutine situations with routine answers are not considered problems There must be novel or nonstandard solution that the problem solver must discover The research on problem solving works to identify the strategies used when confronted with a novel situation and we must decide on a course of action Problem solveri Identifies the problem ii Finds a way to represent it iii Choose a course of action that will make it possible to achieve the goalProblem solving makes use of memory attention and perception The Structure of a ProblemAt a basic level problem comprises of 3 parts i Initial statestart state where you face the problem that has to be solved ii Set of operations that need to be carried out to the destinationgoal iii Goal state where the solution has been attained and problem has been solvedProblems can be welldefined where the initial state goal state and the set of possible moves are clearly defined eg chess Often problems can be illdefined where the initial state operations or even the goal of the problem is not known for sure To solve an illdefined problem it is important to first find the constraints ie restrictions on solutions By removing the constraints we can make the pathway to the goal easierA type of illdefined problem is called insight problem where despite all the unknowns the answer seems to come to mind in a flash second of understanding Eg when solving riddles 1Problem Space Theory Problem space theory involves searching for a solution within a problem space Problem space refers to a set of possible choices that the problem solver comes across at each step in moving from the initial state to the goal state It includes the initial state the goal state and all the possible intermediate states The problem solver moves from state to state by various operations This theory clearly applies to highly constrained situations where there are specific rules and clearly defined initial and goal states For more complex and less constrained problems the problem space theory includes multiple spaces eg in a scientific situation hypothesis space to theory experiment space to design experiments and data space to interpret resultsOur real life problems are more similar to such less constrained problemsStrategies and HeuristicsAlgorithm is a set of procedures for solving a problem that will always produce the correct answer Eg calculating a square root or following a recipe where there are clear steps that have to be followed and following those steps will lead to the desired outcome Algorithms are often timeconsuming and make greater demands on both working memory and longterm memoryHeuristic is a rule of thumb way of solving the problem Such rule gives the correct answer but not always It involves the idea of always moving towards the goal state but often to arrive at the correct solution it is important to move back eg Rubiks cube where it is necessary to move away from the goal state before finally achieving it Random search Generate and test is the simplest cognitively least demanding problemsolving heuristicit involves the process of trial and error where the problem solver randomly picks a move and tests to see if the goal state is achieved or not It may appear inefficient but we often turn to it when nothing else seems to workand at times it may even provide the solution Therefore it is fall back heuristic to use when other heuristics do not work or are cognitively too demandingHill climbing is the problem solving heuristic where the problem solver looks one move ahead and choose the move that most closely resembles the goal state It is relatively more reliable heuristic but it can also lead the problem solver astray since problem solving requires the person to move down the hill at times eg 3 jugs exampleMeansend analysis is more cognitively demanding but a successful strategy where a problem is broken down into subproblems If the subproblem is not solvable at the first stage then it is further broken down into other subproblems until a soluble subproblem is found Eg Tower of Hanoi 2
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