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AppealingXenon1045

Uploaded by AppealingXenon1045

City University of Hong Kong

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problem-solving cognitive psychology problem solving strategies human problem solving

Summary

This document discusses problem-solving, including its components like start state, end state, and operators. It explores different approaches, such as the Gestalt approach and the information-processing approach. It also discusses different types of problems and factors that affect problem-solving.

Full Transcript

Recap: Problem-solving Problems arise when people do not see immediately how to get from where they are to where they want to be. 3 key components of a problem: 1. Start state - the position you begin with (the situation before you have started to try to solve the problem)....

Recap: Problem-solving Problems arise when people do not see immediately how to get from where they are to where they want to be. 3 key components of a problem: 1. Start state - the position you begin with (the situation before you have started to try to solve the problem). 2. End state - the state you want to achieve (the situation you want to end up in once the problem has been solved). 3. Operators - different types of actions or processes that can take you from one state to another (the things you can try in order change the state to move closer to solving the problem). Typically something only counts as a problem if the process of going from the start state to the goal state cannot be achieved in a single step - there are intermediate states or stages. Knowledge-lean problems - do not require much prior knowledge in order to be solved - solutions to these kinds of problems tend to be domain-general (strategies used to find solutions to such problems can be applied to a variety of problems). Knowledge-rich problems - require prior knowledge in order to be solved - solutions to these kinds of problems tend to be domain-specific(strategies used to find solutions to such problems tend to be applicable for only one particular type of problem and not others). Well-defined problems - the start and goal states are clearly identified and the operators that are allowed or prohibited are all clearly defined. Ill-defined problems - one or more of the parameters (start state, goal state, allowed operators, prohibited operators) are not known. Non-adversarial problems - an individual or group is required to find the solution to the problem but there is no competition with others - such problems are typically well-defined but knowledge-lean and domain-general. Adversarial problems - involve competition with others and the goal is to defeat your opponent(s) - such problems are typically less well-defined, knowledge-rich, and domain-specific. Approaches to the Study of Problem-solving Gestalt Approach: This approach takes the perspective that problem-solving requires an understanding of the overall structure of a problem rather than focusing on each element of the problem separately. According to this approach problem-solving consists of the following 2 parts: 1. insight - occurs when a person is suddenly aware of the answer - it appears suddenly as if our of nowhere rather than the person gradually working towards it. 2. restructuring - the structure of the problem space is altered (often by the person realising that there are operators available that they were previously unaware of) - occurs after the person has had the key insight for solving the problem. Reproductive thinking - involves the use of previous experience of problem-solving to solve new problems - the approach can be useful but can also lead to sub-optimal performance if it leads people to ignore the structure of the problem and possibly miss simpler available solutions. Productive thinking - involves an understanding of the underlying structure of a problem - more likely to result in insight and lead to restructuring of the problem. Problem-solving set - one possible negative effect of past experience and reproductive thinking - a person learns to solve a series of problems in a specific manner so that the solution becomes a habit (or mental set) - this leads them to use this habit for solving those kinds of problems even in cases where a simpler solution is available (they miss the simpler solution because of the habit). Functional fixedness - a tendency to focus on the normal function of an object and therefore miss that it could have alternative uses. The Gestalt Approach is still very popular and has been highly influential, with the use of their method of verbal protocols still being used in a lot of research designed to study problem-solving. Some problems have nevertheless been identified: 1. the concepts of insight and restructuring have remained only vaguely defined - there is also little explanation for why they occur or fail to occur. 2. the concepts of insight and restructuring seem intuitively accurate but do not provide an explanation for the processes underlying problem-solving. Information-processing Approach: One of the key principles underlying this approach is that like computers, humans can only solve problems by analysing and manipulatinginformation. Problem space - a person's mental representation of the problem - the mental path we take from the start state to the goal state using the operators. General Problem Solver (GPS) - a computer program designed to solve problems based on studies of how humans solve problems. The GPS was designed to implement two problem-solving strategies: 1. algorithmic method - involves a systematic search of all possible solutions to a problem until the correct answer is identified - can take a lot of time and effort but are guaranteed to find the correct solution. 2. heuristic method - involves using rules of thumb that act as short cuts and enable a more selective search for the most likely solutions - speed up the process and often work, but are not guaranteed to find the correct solution. Means-ends analysis - a heuristic identified in human problem-solving and implemented in the GPS - the problem space is divided into a series of sub-goals that are tackled one at a time - the solution for each sub-goal should reduce the difference between the start state and the goal state (i.e., solving a sub-goal should bring you closer to a solution to the overall problem). The information-processing approach has made fundamental contributions to cognitive theory and to our understanding of problem-solving, nevertheless there are a few problems with the approach: 1. it only applies to well-defined problems - most problems in everyday life tend to be ill-defined. 2. the GPS was designed to solve one problem at a time - in everyday life people tend to be faced with multiple problems at the same time. 3. information-processing approaches have difficulty explaining problems that require insight - they are fundamentally designed to deal with problems that require step-by-step solutions - feeling-of-knowing (FOK) refers to a person's estimate of how close they are to solving a problem - FOK gradually increases for step-by-step problems but for insight problems it stays low until just before the moment of insight, after which the problem is solved - this is hard to account for in the information-processing framework. Insight-type problems require a search for an appropriate problem space, whereas non-insight problems require a search through a problem space for a solution. Use of Analogy in Problem-solving Analogy refers to a heuristic device where solutions (or aspects of solutions) from similar problems one has solved in the past are adapted to be applied to the current problem. Factors that affect whether or not analogies are used: 1. degree of similarity of the analogous problems - people tend to focus on the superficial content (surface features) of the problem rather than the underlying similarities (structural features) or the principles of the problem solution. 2. solutions to problems are often context-bound - a solution learned in one context is often not easily transferred to another context. Factors that improve the use of analogies in problem-solving: 1. give participants explicit instructions to compare problems. 2. show participants several structurally similar problems before giving them the one you want them to solve that is also similar. 3. give participants a hint that the solution to an earlier problem may be helpful. 4. ask/encourage participants to study the structure of the problem rather than the surface features. These conditions do not exist in everyday life, hence there is some doubt about the relevance of analogies in solving real-life problems. Problem-solving in Everyday Life The problems we encounter in everyday life are not well defined and often we have only a vague idea about the goal state and a poor understanding of the possible processes (operators) to achieve it. Another difference from problem-solving in the laboratory is that we often need to find solutions to problems while dealing with various sources of distraction. People tend to be more adaptive and inventive in real life problem-solving than in laboratory studies. Real life problems tend to have more relevance for people and/or bring more tangible rewards.

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