Collective induction: Mutual group and individual influence by exchange of hypotheses and evidence

Laughlin, Patrick R.; McGlynn, Richard P.

doi:10.1016/0022-1031(86)90051-x

Cited by 29 publications

(37 citation statements)

References 9 publications

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“…This indicates that groups tend to rule out false hypotheses by discussing individual hypotheses and by selecting the group hypothesis predominantly from the plausible individual hypotheses. This is in line with the notion by Laughlin and colleagues (e.g., Laughlin & Futoran, 1985;Laughlin & McGlynn, 1986) that groups are superior in hypothesis evaluation compared to individuals. However, groups did not produce more correct responses than individuals.…”

Section: Group Processessupporting

confidence: 83%

Effects of amount of evidence and range of rule on the use of hypothesis and target tests by groups in rule-discovery tasks

Hoffmann

Crott

2004

Thinking & Reasoning

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This experiment investigated the use of positive and negative hypothesis and target tests by groups in an adaptation of the 2-4-6 Wason task. The experimental variables were range of rule (small vs large), amount of evidence (low vs high), and trial block (1 vs 2). The results were in accordance with Klayman and Ha's (1987) analysis of base rate probabilities of falsification and with additional theoretical considerations. Base rate probabilities were more descriptive of participants' behaviour in target than in hypothesis tests, under low than under high amount of evidence, and at the beginning of the process than at its end. The percentage of positive tests was higher under small than large range of rule. More falsifications than verifications resulted from hypothesis tests than would be expected by a random process. When evidence is richly available, the relative importance of falsification seems to decrease. An analysis of the group compositions before and after group discussion by the PCD model (Crott, Werner, & Hoffmann, 1996) revealed that the normative weight was approximately twice as large as the informational. Groups produced fewer false answers than their members individually.

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Section: Group Processessupporting

confidence: 83%

Effects of amount of evidence and range of rule on the use of hypothesis and target tests by groups in rule-discovery tasks

Hoffmann

Crott

2004

Thinking & Reasoning

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“…A single worker might not have the complete skill set to effectively perform review tasks. In contrast, a group of workers acting together are more likely to collectively have the skills needed to effectively review other workers' work [17].…”

Section: Strategies For Organizing Peer Reviewersmentioning

confidence: 85%

“…In other words, individuals in the nominal groups never see their group members' answers, so they never have the opportunity to get struck by the insight. One benefit of interactive teams is that individuals who know the correct answer can influence the whole team through discussions [17]. Specifically, participants who understood the mathematics problem could convince teammates and influence the final judgment, even when they are in the minority.…”

Section: Interactive Teams Judged Mathematics Problem Bestmentioning

confidence: 99%

Reviewing versus doing

Zhu

Dow

Kraut

et al. 2014

Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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In modern crowdsourcing markets, requesters face the challenge of training and managing large transient workforces. Requesters can hire peer workers to review others' work, but the value may be marginal, especially if the reviewers lack requisite knowledge. Our research explores if and how workers learn and improve their performance in a task domain by serving as peer reviewers. Further, we investigate whether peer reviewing may be more effective in teams where the reviewers can reach consensus through discussion. An online between-subjects experiment compares the tradeoffs of reviewing versus producing work using three different organization strategies: working individually, working as an interactive team, and aggregating individuals into nominal groups. The results show that workers who review others' work perform better on subsequent tasks than workers who just produce. We also find that interactive reviewer teams outperform individual reviewers on all quality measures. However, aggregating individual reviewers into nominal groups produces better quality assessments than interactive teams, except in task domains where discussion helps overcome individual misconceptions.

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“…The closest is that of Zhu et al [62] who studied how to improve productivity and quality of crowd worker output, motivated by concerns about overhead in coordinating workers (also known as process loss [42]) and the potential for groups to be swayed toward an undesirable outcome by factors relating to social influence [31,23].…”

Section: Group Work In Crowdsourcingmentioning

confidence: 99%

Structuring Interactions for Large-Scale Synchronous Peer Learning

Coetzee

Lim

Fox

et al. 2015

Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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This research investigates how to introduce synchronous interactive peer learning into an online setting appropriate both for crowdworkers (learning new tasks) and students in massive online courses (learning course material). We present an interaction framework in which groups of learners are formed on demand and then proceed through a sequence of activities that include synchronous group discussion about learnergenerated responses. Via controlled experiments with crowdworkers, we show that discussing challenging problems leads to better outcomes than working individually, and incentivizing people to help one another yields still better results. We then show that providing a mini-lesson in which workers consider the principles underlying the tested concept and justify their answers leads to further improvements. Combining the mini-lesson with the discussion of the multiple-choice question leads to significant improvements on that question. We also find positive subjective responses to the peer interactions, suggesting that discussions can improve morale in remote work or learning settings.

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Collective induction: Mutual group and individual influence by exchange of hypotheses and evidence

Cited by 29 publications

References 9 publications

Effects of amount of evidence and range of rule on the use of hypothesis and target tests by groups in rule-discovery tasks

Effects of amount of evidence and range of rule on the use of hypothesis and target tests by groups in rule-discovery tasks

Reviewing versus doing

Structuring Interactions for Large-Scale Synchronous Peer Learning

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