Psychology Of Problem Solving Research Paper

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‘Joint problem solving’ refers to the situation where two or more people work together to solve a shared problem, where agreement on a single solution may or may not be required. The proverb says ‘two heads are better than one,’ but why so, or when so, is not explicitly understood yet. Despite this folk knowledge, studies have shown that joint solving does not always result in better performance than that of the most capable solos. Rather, careful studies of the processes have revealed that there are clear cognitive process gains in the joint endeavor. In joint situations, the participants tended to deepen their conceptual understandings of the topic problem (Miyake 1986), or the subjects generated a greater number of experimental hypotheses and had better chances to duplicate a scientific discovery (Okada and Simon 1997). The reasons for such gains are still under investigation, but research findings suggest that the joint situation allows the participants to naturally externalize their solving processes, and this externalization provides chances for flexible revisions of their trials, leading them to more conceptual understandings and newer ideas.

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1. Gains Of Joint Processes

Joint problem solving has been extensively studied in social psychology, mainly with an interest in whether people in groups, thus in ‘social’ situations, would perform better than solos. Hastie (1986) gives an extensive review, where joint cases are compared against solos on tasks like number estimation, vocabulary tests, and arithmetic brainteasers. Hastie summarizes the field and concludes, ‘these studies tended to find that group performance exceeded the performance of the average individual, [but] for most of these problems groups did not match the performance of their best members’ (p. 150). He also suggests that ‘the critical characteristic of performance on the tasks is solution demonstrability, with higher levels of group performance associated with tasks where correct solutions are easily demonstrated and communicated to group members, and relatively low levels of performance associated with difficult-to-demonstrate solutions’ (p. 150). His interest was mostly in categorizing group performances on tasks and group membership, not on cognitive aspects of the solving processes.

Miyake (1986) tried to clarify the cognitive gains associated with paired performance. She asked three pairs of subjects to understand how a sewing machine made its stitches and analyzed their verbal protocols. The sewing machine problem has interesting characteristics of having different levels of answers. One answer to the question would just mention that the machine uses two intertwined pieces of thread to fasten the cloth. On hearing this, one can further ask about the exact physical configuration of the seam, which turns out to be a loop-in-loop. Discovering this could open up a new puzzle because it is physically impossible to form a loop-in-loop with two threads without free ends. (At a middle point in sewing, for both upper and lower threads, one end of them is in the spindle or in the bobbin, while the other end is tightly sewn to the cloth.) In fact, while the upper thread comes down into the machine, its loop goes entirely around the bobbin. This is a perfect answer until one starts wondering how, then, the bobbin itself is attached to the machine.

This hierarchical nature of the possible answers to the sewing machine problem made it possible to analyze the protocols in terms of when they reached the intermediate understandings and how and when they noticed the insufficiency of their solutions and started to ask the ‘one level deeper’ questions. Miyake found that each of her subjects descended this hierarchy according to his or her individualistic formulation of the question. This often resulted in situations where members of the pair worked at different levels at the same time: while one person may worry about the physical configuration of the seam, the other could try to find how the loop-in-loop configuration is possible with two threads without free ends. Because of this disagreement, the subjects were almost always in conflict, which contributed to push each individual to refine his or her own models so that ‘the other member would understand my points.’ In summary, Miyake argues that the process gain of joint problem solving does not necessarily lie in the performance, but rather in raising the necessity and motivation for deepening and refining each individual’s understandings.

In the 1990s, the amount of research on joint problem solving increased, showing more positive results for both pairs and groups. Conditions for successful collaborations were identified where earlier studies focused on degrees of mutual participation, while the more recent studies identified explicit types of talk that contributed to the success (e.g., Teasley’s ‘transactive’ talks, Teasley 1997). A similar style of research has been extended to cover topics of learning and development (e.g., Rogoff 1990). Hatano and Inagaki (1991), for example, analyzed a science classroom where the children selected among the alternative hypotheses provided by the teacher and debated among them. By analyzing the discussion they identified what they named ‘the partisan motivation,’ shared by the naturally grouped members with the same alternative, that acted as an enhancer for the self-motivated articulation of their explanations.

2. Joint Problem Solving In The Real World

Okada and Simon (1997) took a slightly different course of identifying causes for pairs’ success in an experimental scientific ‘discovery.’ Using a computer simulated task of making a discovery in molecular genetics, paired subjects were found to be superior to single subjects. This success was associated with higher levels of explanatory activities of the pairs, and further, such explanatory activities were facilitated when paired subjects made explicit requests of each other for explanations and testable hypotheses.

This change occurred with the search into more ecologically valid research in the domain of problem solving. Daily problems often get solved in groups, and there are pragmatic needs and interests in finding out how groups should be organized to be productive. This led cognitive problem solving research to take an ‘in vivo’ form (Dunber 1995), where workers and scientists in realistic situations were carefully studied. Dunber observed four laboratories carrying out immunization research and followed their daily scientific activities for a year, with various methods from attending the research meetings and taking notes, reading their lab diaries, to extensive interviews. His findings are diverse, and factors he found positive include frequent exchange of ideas, nonauthoritative membership, positive attitudes toward even negative intermediate results, use of local analogies, etc. In an interview-based study of a real R & D team in a detergent company, Ueda and Niwa (1996) found that having an outsider’s view at the right time was crucial for the team to change the course, which led them to produce a new successful product. All such reported factors tend to confirm the folk expectation of what should contribute to high group performance. Yet how we can coordinate these factors to make them all work simply remains to be discovered. Furthermore, the overall conclusion we could draw from these sets of research is that the quality of group performances depends heavily on the organization and activity control of the groups.

3. Joint Problem Solving From A Cognitive Anthropology Perpective

In contrast, cognitive anthropologists have maintained more positive perspectives. Hutchins (1990) observed a team navigating a large ship and found that there was overlapping knowledge shared among the team members that strongly coordinated the teamwork. In the reported case of a ‘fix,’ or a joint activity of finding the exact location of a ship, at least three roles are involved. The bearing-taker measures the angles between the ship’s direction and selected landmarks using a gyroscope, and calls out the number through a tube to the charting house. Then this number is repeated by the bearing timer-recorder who writes down the number into the log book. The number then gets converted to the angle on the device that allows the plotter to make lines on the chart so that they can pinpoint the location of the ship onto the map. Staff start working in this team as the bearing-taker, who after some experience is promoted to the timer recorder job, who would later move into the plotter position. Thus the plotter knows everything the other two know, and the timer-recorder knows all of what the bearing-taker knows. It is also possible for the lower-rank staffs to closely observe what the staff one rank higher is engaged in. While a cross-sectional, one-shot observation might make this teamwork appear to be highly coordinated across divisions of work, in reality what coordinates this team is the shared knowledge of a longitudinal, historical nature. Laboratory-oriented, puzzle-based experimental studies of joint problem solving have been quite weak on this aspect.

Hutchins’ report maintains one more important observation on how the teamwork is divided and carried out. In the above example of taking a fix, there are three subtasks, each of them producing some intermediate results. Careful inspection reveals that each of them takes quite a different, and redundant form of representation of the intermediate results of a single process. The first outcome of the bearer is by voice, which is then written down in number symbols, which is then again converted into lines on the chart. These different forms, at different times of representation, provide different and redundant forms for people involved to check the process from different perspectives. In this sense this is a real-world documentation of what Hastie called ‘demonstrability,’ which, in realistic situations, does not just provides the information concerning the correctness of the solution but opens up the possibility for other interpretations and possibly flexible redesigning of the process itself.

4. Toward Further Research Into The Mechanisms Of Joint Gain

Paired subjects could, in fact, take advantage of externally shared objects in the course of simpler experimental tasks. Shirouzu et al. (1999) asked subjects to indicate two-thirds of three-quarters of the area of a square, folding paper measuring 15 cm 15 cm. They report that in various conditions people strongly opted (over 90 percent on average) to use external resources to do this arithmetic, either by folding the paper or by marking the edges with their fingers and partitioning, etc. Because this reliance on external resources is quite strong, most of the single subjects did not change the solution method (from folding to calculating, for example) even when they were consecutively asked the second question, i.e., to indicate the area of three-quarters of two-thirds of the same sized paper. The subjects did not change it even after seeing the ‘externalized’ result to be exactly one-half of the paper. They compared this to pair performances and reported that the pairs are a lot more flexible in choosing different solutions to the second question (70 percent of the pairs changed the solution methods while less than 20 percent of the single subjects did so). By analyzing the solving processes and the protocols of such subjects they found that the flexible pairs were in fact gradually responding to the constantly changing externalized forms of the paper while they worked together, rather than each randomly contributing newer ideas at their turns.

As a future course of joint problem-solving research, new learning studies focus on collaborations. As indicated above, joint situations tend to provide the participants with verbal expressions and tangible externalized objects that each member can manipulate to concretely suggest newer, or at least different, ideas. In rapidly changing societies, the goal of learning is not only digesting what is taught. Learners are required to acquire skills to learn, to become self-regulated learners. In order to fulfill this goal, it is necessary to give learners more chances to be reflective than currently enforced, to scrutinize upon and revise what they themselves do and how others do similar tasks. Studies on joint problem-solving situations are expected to provide rich resources to help develop effective learning situations (e.g., Bransford et al. 2000).


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  2. Dunber K 1995 How scientists really reason: Scientific reasoning in real world laboratories. In: Sternberg R J, Davidson J E (eds.) The Nature of Insight. Cambridge University Press, New York
  3. Hastie R 1986 Review essay: Experimental evidence on group accuracy. In: Grofman B, Owen G (eds.) Decision Research, Vol. 1, Information Pooling and Group Decision Making: Proceedings of the Second Conference on Political Economy. University of California, Irvine, CA, pp. 129–57
  4. Hatano G, Inagaki K 1991 Sharing cognition through collective comprehension activity. In: Resnick L B, Levine J M, Teasley S D (eds.) Perspectives on Socially Shared Cognition. APA, Washington, DC
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  7. Okada T, Simon H A 1997 Collaborative discovery in a scientific domain. Cognitive Science 21(2): 109–46
  8. Rogoff B 1990 Apprenticeship in Thinking: Cognitive Development in Social Context. Oxford University Press, Oxford, UK
  9. Shirouzu H, Miyake N Masukawa H 1999 Roles of cognitive externalization for joint problem solving. In: Proceedings of the 2nd International Conference on Cognitive Science and the 16th Annual Meeting of the Japanese Cognitive Science Society Joint Conference, pp. 337–42
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