Insights about the common generative rule underlying an information foraging task can be facilitated via collective search

Naito, Aoi; Katahira, Kentaro; Kameda, Tatsuya

doi:10.1038/s41598-022-12126-3

Cited by 9 publications

(15 citation statements)

References 46 publications

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“…We suggest the superior performance of groups relative to individuals and agents to be contingent on dialogical meta-cognition (Bang et al, 2014;Frith, 2012;Heyes, Bang, Shea, Frith, & Fleming, 2020;Naito et al, 2022;Shea et al, 2014), and its implications for the nature of participants' resulting problem representations, which, we claim, are more flexible and generalizable. Individuals solving the alien task on their own might be inclined to represent distinctive alien features mainly as visual traits (Smith & Sloman, 1994).…”

Section: Discussionmentioning

confidence: 80%

“…What are the mechanisms driving these effects? We suggest that the critical element lies in the dialogical integration of group members’ varied experiences giving rise to more abstract category representations (in this case, rule‐induction, R. Fusaroli et al., 2014; Naito et al., 2022; Olsen & Tylén, 2023; Toader & Kessler, 2018). And these appear to be particularly critical when encountering tasks of increasing complexity (Almaatouq et al., 2021).…”

Section: Discussionmentioning

confidence: 96%

“…We argue that the advantage of groups in these contexts might be due to the way social interaction promotes processes of rule‐induction and abstraction (Naito, Katahira, & Kameda, 2022; Olsen & Tylén, 2023). When group members explicitly share and negotiate their problem representations in order to make joint decisions, they can become aware of variance and even discrepancies between their individual problem representations that, for instance, originate from differences in experience, perspectives, and cognitive style (Bang & Frith, 2017; Hong & Page, 2004; Page, 2008; Shea et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

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The Social Route to Abstraction: Interaction and Diversity Enhance Performance and Transfer in a Rule‐Based Categorization Task

Tylén,

Fusaroli,

Østergaard

et al. 2023

Cognitive Science

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Capacities for abstract thinking and problem‐solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in fact be contingent on human‐specific modes of collaboration, dialogue, and shared attention. In an experimental study, we test the hypothesis that social interaction enhances cognitive processes of rule‐induction, which in turn improves problem‐solving performance. Through three sessions of increasing complexity, individuals and groups were presented with a problem‐solving task requiring them to categorize a set of visual stimuli. To assess the character of participants’ problem representations, after each training session, they were presented with a transfer task involving stimuli that differed in appearance, but shared relations among features with the training set. Besides, we compared participants’ categorization behaviors to simulated agents relying on exemplar learning. We found that groups performed superior to individuals and agents in the training sessions and were more likely to correctly generalize their observations in the transfer phase, especially in the high complexity session, suggesting that groups more effectively induced underlying categorization rules from the stimuli than individuals and agents. Crucially, variation in performance among groups was predicted by semantic diversity in members’ dialogical contributions, suggesting a link between social interaction, cognitive diversity, and abstraction.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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The Social Route to Abstraction: Interaction and Diversity Enhance Performance and Transfer in a Rule‐Based Categorization Task

Tylén,

Fusaroli,

Østergaard

et al. 2023

Cognitive Science

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“…A recent study [33] focused on similar situations and investigated whether and how knowledge of the old environment could be generalized to new environments in terms of an information search by social interactions. This study demonstrated that, through computational analyses of participants' behaviors in a multi-armed bandit task, paired participants performed a more efficient information search than solo participants.…”

Section: Fig 1 Hypothetical Examples Of the Wisdom Of Crowds By A Maj...mentioning

confidence: 99%

Can individual subjective confidence in prior questions predict group performance in future questions?

Shirasuna¹,

Honda²

2022

Preprint

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When people have to solve many tasks, they can aggregate diverse individuals’ judgments using the majority rule, which often improves the accuracy of judgments (wisdom of crowds). When aggregating individuals’ judgments, people sometimes decide whose judgments they should accept based on their subjective confidence. However, can confidence in one task set predict performance not only in the same (prior) task set, but also in another (future) task set? We examined this issue through computer simulations using behavioral data obtained from binary-choice experimental tasks. In our simulations, we developed a “training-test” approach: we split the questions used in behavioral experiments into “training questions” (as prior questions) and “test questions” (as future questions), similar to the cross validation method in machine learning. We found that (i) through analyses of behavioral data, confidence in one question could predict accuracy for the same question, but not always well in another question. (ii) Through a computer simulation of the accordance of two individuals’ judgments, individuals rated with high confidence in one training question tended to make less diverse judgments between each other in test questions. (iii) Through a computer simulation of group judgments, the accuracy of groups constructed by individuals with high confidence in the training question(s) sometimes largely decreased in the test questions, especially when only one training question was available. These results suggest that if people aggregate diverse individuals in terms of subjective confidence in prior questions, they will likely avoid decreasing performance in future questions. We believe that our simulations following a “training-test” approach provide practical implications for keeping groups’ ability to solve future tasks and emphasize the importance of a diversity of confidence in a group.

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“…A recent study [ 33 ] focused on similar situations, investigating whether and how knowledge of the old environment could be generalized to new environments in terms of an information search through social interactions. This study demonstrated that, through computational analyses of participants’ behaviors in a multi-armed bandit task, paired participants performed a more efficient information search than solo participants.…”

Section: Introductionmentioning

confidence: 99%

Can individual subjective confidence in training questions predict group performance in test questions?

Shirasuna

Honda

2023

PLoS ONE

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When people have to solve many tasks, they can aggregate diverse individuals’ judgments using the majority rule, which often improves the accuracy of judgments (wisdom of crowds). When aggregating judgments, individuals’ subjective confidence is a useful cue for deciding which judgments to accept. However, can confidence in one task set predict performance not only in the same task set, but also in another? We examined this issue through computer simulations using behavioral data obtained from binary-choice experimental tasks. In our simulations, we developed a “training-test” approach: We split the questions used in the behavioral experiments into “training questions” (as questions to identify individuals’ confidence levels) and “test questions” (as questions to be solved), similar to the cross-validation method in machine learning. We found that (i) through analyses of behavioral data, confidence in a certain question could predict accuracy in the same question, but not always well in another question. (ii) Through a computer simulation for the accordance of two individuals’ judgments, individuals with high confidence in one training question tended to make less diverse judgments in other test questions. (iii) Through a computer simulation of group judgments, the groups constructed from individuals with high confidence in the training question(s) generally performed well; however, their performance sometimes largely decreased in the test questions especially when only one training question was available. These results suggest that when situations are highly uncertain, an effective strategy is to aggregate various individuals regardless of confidence levels in the training questions to avoid decreasing the group accuracy in test questions. We believe that our simulations, which follow a “training-test” approach, provide practical implications in terms of retaining groups’ ability to solve many tasks.

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Insights about the common generative rule underlying an information foraging task can be facilitated via collective search

Cited by 9 publications

References 46 publications

The Social Route to Abstraction: Interaction and Diversity Enhance Performance and Transfer in a Rule‐Based Categorization Task

The Social Route to Abstraction: Interaction and Diversity Enhance Performance and Transfer in a Rule‐Based Categorization Task

Can individual subjective confidence in prior questions predict group performance in future questions?

Can individual subjective confidence in training questions predict group performance in test questions?

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