scholarly journals Trusting the experts: The domain-specificity of prestige-biased social learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255346
Author(s):  
Charlotte O. Brand ◽  
Alex Mesoudi ◽  
Thomas J. H. Morgan
Keyword(s):  
Social Learning ◽  
Learning Strategy ◽  
Emergent Property ◽  
Domain Specificity ◽  
General Knowledge ◽  
Online Experiment ◽  
Domain Specific ◽  
Cross Domain ◽  
Successful Group ◽  
Group Members

Prestige-biased social learning (henceforth “prestige-bias”) occurs when individuals predominantly choose to learn from a prestigious member of their group, i.e. someone who has gained attention, respect and admiration for their success in some domain. Prestige-bias is proposed as an adaptive social-learning strategy as it provides a short-cut to identifying successful group members, without having to assess each person’s success individually. Previous work has documented prestige-bias and verified that it is used adaptively. However, the domain-specificity and generality of prestige-bias has not yet been explicitly addressed experimentally. By domain-specific prestige-bias we mean that individuals choose to learn from a prestigious model only within the domain of expertise in which the model acquired their prestige. By domain-general prestige-bias we mean that individuals choose to learn from prestigious models in general, regardless of the domain in which their prestige was earned. To distinguish between domain specific and domain general prestige we ran an online experiment (n = 397) in which participants could copy each other to score points on a general-knowledge quiz with varying topics (domains). Prestige in our task was an emergent property of participants’ copying behaviour. We found participants overwhelmingly preferred domain-specific (same topic) prestige cues to domain-general (across topic) prestige cues. However, when only domain-general or cross-domain (different topic) cues were available, participants overwhelmingly favoured domain-general cues. Finally, when given the choice between cross-domain prestige cues and randomly generated Player IDs, participants favoured cross-domain prestige cues. These results suggest participants were sensitive to the source of prestige, and that they preferred domain-specific cues even though these cues were based on fewer samples (being calculated from one topic) than the domain-general cues (being calculated from all topics). We suggest that the extent to which people employ a domain-specific or domain-general prestige-bias may depend on their experience and understanding of the relationships between domains.

scholarly journals Trusting the experts: the domain-specificity of prestige-biased social learning

2021 ◽  
Author(s):  
Charlotte Olivia Brand ◽  
Alex Mesoudi ◽  
Tom Morgan
Keyword(s):  
Social Learning ◽  
Learning Strategy ◽  
Emergent Property ◽  
Domain Specificity ◽  
General Knowledge ◽  
Online Experiment ◽  
Domain Specific ◽  
Cross Domain ◽  
Successful Group ◽  
Group Members

Prestige-biased social learning (henceforth “prestige-bias”) occurs when individuals predominantly choose to learn from a prestigious member of their group, i.e. someone who has gained attention, respect and admiration for their success in some domain. Prestige-bias is proposed as an adaptive social-learning strategy as it provides a short-cut to identifying successful group members, without having to assess each person’s success individually. Previous work has documented prestige-bias and verified that it is used adaptively. However, the domain-specificity and generality of prestige-bias has not yet been explicitly addressed experimentally. By domain-specific prestige-bias we mean that individuals choose to learn from a prestigious model only within the domain of expertise in which the model acquired their prestige. By domain-general prestige-bias we mean that individuals choose to learn from prestigious models in general, regardless of the domain in which their prestige was earned. To distinguish between domain specific and domain general prestige we ran an online experiment (n=397) in which participants could copy each other to score points on a general-knowledge quiz with varying topics (domains). Prestige in our task was an emergent property of participants’ copying behaviour. We found participants overwhelmingly preferred domain-specific (same topic) prestige cues to domain-general (across topic) prestige cues. However, when only domain-general or cross-domain (different topic) cues were available, participants overwhelmingly favoured domain-general cues. Finally, when given the choice between cross-domain prestige cues and randomly generated Player IDs, participants favoured cross-domain prestige cues. These results suggest participants were sensitive to the source of prestige, and that they preferred domain-specific cues even though these cues were based on fewer samples (being calculated from one topic) than the domain-general cues (being calculated from all topics). We suggest that the extent to which people employ a domain-specific or domain-general prestige-bias may depend on their experience and understanding of the relationships between domains.

Copy the In-group: Group Membership Trumps Perceived Reliability, Warmth, and Competence in a Social-Learning Task

2021 ◽  
pp. 095679762110322
Author(s):  
Marcel Montrey ◽  
Thomas R. Shultz
Keyword(s):  
Social Learning ◽  
Cultural Evolution ◽  
Learning Strategy ◽  
Group Membership ◽  
Social Groups ◽  
Learning Task ◽  
Minimal Group Paradigm ◽  
Learning Game ◽  
Group Members ◽  
Cumulative Cultural Evolution

Surprisingly little is known about how social groups influence social learning. Although several studies have shown that people prefer to copy in-group members, these studies have failed to resolve whether group membership genuinely affects who is copied or whether group membership merely correlates with other known factors, such as similarity and familiarity. Using the minimal-group paradigm, we disentangled these effects in an online social-learning game. In a sample of 540 adults, we found a robust in-group-copying bias that (a) was bolstered by a preference for observing in-group members; (b) overrode perceived reliability, warmth, and competence; (c) grew stronger when social information was scarce; and (d) even caused cultural divergence between intermixed groups. These results suggest that people genuinely employ a copy-the-in-group social-learning strategy, which could help explain how inefficient behaviors spread through social learning and how humans maintain the cultural diversity needed for cumulative cultural evolution.

Strong conformity requires a greater proportion of asocial learning and achieves lower fitness than a payoff-based equivalent

2018 ◽  
Vol 26 (6) ◽  
pp. 323-333
Author(s):  
Matt Grove
Keyword(s):  
Social Learning ◽  
Theoretical Analysis ◽  
Environmental Change ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Individual Learning ◽  
Heterogeneous Environments ◽  
The One ◽  
Social Learning Strategies

There is a growing interest in the relative benefits of the different social learning strategies used to transmit information between conspecifics and in the extent to which they require input from asocial learning. Two strategies in particular, conformist and payoff-based social learning, have been subject to considerable theoretical analysis, yet previous models have tended to examine their efficacy in relation to specific parameters or circumstances. This study employs individual-based simulations to derive the optimal proportion of individual learning that coexists with conformist and payoff-based strategies in populations experiencing wide-ranging variation in levels of environmental change, reproductive turnover, learning error and individual learning costs. Results demonstrate that conformity coexists with a greater proportion of asocial learning under all parameter combinations, and that payoff-based social learning is more adaptive in 97.43% of such combinations. These results are discussed in relation to the conjecture that the most successful social learning strategy will be the one that can persist with the lowest frequency of asocial learning, and the possibility that punishment of non-conformists may be required for conformity to confer adaptive benefits over payoff-based strategies in temporally heterogeneous environments.

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

The Rationality of (Over)imitation

2018 ◽  
Vol 13 (6) ◽  
pp. 678-687 ◽  
Author(s):  
Stefanie Keupp ◽  
Tanya Behne ◽  
Hannes Rakoczy
Keyword(s):  
Social Learning ◽  
Learning Strategy ◽  
Social Constraints ◽  
Rational Action ◽  
Cultural Traditions ◽  
Human Capacity ◽  
New Perspective ◽  
Cognitive Foundations ◽  
Different Levels

Imitation is a powerful and ubiquitous social learning strategy, fundamental for the development of individual skills and cultural traditions. Recent research on the cognitive foundations and development of imitation, though, presents a surprising picture: Although even infants imitate in selective, efficient, and rational ways, children and adults engage in overimitation. Rather than imitating selectively and efficiently, they sometimes faithfully reproduce causally irrelevant actions as much as relevant ones. In this article, we suggest a new perspective on this phenomenon by integrating established findings on children’s more general capacities for rational action parsing with newer findings on overimitation. We suggest that overimitation is a consequence of children’s growing capacities to understand causal and social constraints in relation to goals and that it rests on the human capacity to represent observed actions simultaneously on different levels of goal hierarchies.

A Transformer-Based Approach to Multilingual Fake News Detection in Low-Resource Languages

2022 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
Arkadipta De ◽  
Dibyanayan Bandyopadhyay ◽  
Baban Gain ◽  
Asif Ekbal
Keyword(s):  
Language Processing ◽  
English Language ◽  
Neural Model ◽  
Fake News ◽  
Web Content ◽  
Low Resource ◽  
Domain Specific ◽  
Cross Domain ◽  
Current State ◽  
Domain Transfer

Fake news classification is one of the most interesting problems that has attracted huge attention to the researchers of artificial intelligence, natural language processing, and machine learning (ML). Most of the current works on fake news detection are in the English language, and hence this has limited its widespread usability, especially outside the English literate population. Although there has been a growth in multilingual web content, fake news classification in low-resource languages is still a challenge due to the non-availability of an annotated corpus and tools. This article proposes an effective neural model based on the multilingual Bidirectional Encoder Representations from Transformer (BERT) for domain-agnostic multilingual fake news classification. Large varieties of experiments, including language-specific and domain-specific settings, are conducted. The proposed model achieves high accuracy in domain-specific and domain-agnostic experiments, and it also outperforms the current state-of-the-art models. We perform experiments on zero-shot settings to assess the effectiveness of language-agnostic feature transfer across different languages, showing encouraging results. Cross-domain transfer experiments are also performed to assess language-independent feature transfer of the model. We also offer a multilingual multidomain fake news detection dataset of five languages and seven different domains that could be useful for the research and development in resource-scarce scenarios.

scholarly journals Domain Conditioned Adaptation Network

2020 ◽  
Vol 34 (07) ◽  
pp. 11386-11393 ◽  
Author(s):  
Shuang Li ◽  
Chi Liu ◽  
Qiuxia Lin ◽  
Binhui Xie ◽  
Zhengming Ding ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Feature Learning ◽  
Feature Representations ◽  
Domain Specific ◽  
Cross Domain ◽  
Learning Tasks ◽  
Domain Learning ◽  
High Level ◽  
High Level Feature ◽  
Target Data

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.

scholarly journals Distinction of Domain-Specific and Cross-Domain Linkage Types for Engineering Change Management

2019 ◽  
Vol 1 (1) ◽  
pp. 1125-1134
Author(s):  
Robert Wilms ◽  
David Inkermann ◽  
Vadym Finn Cemmasson ◽  
Michael Reik ◽  
Thomas Vietor
Keyword(s):  
Change Management ◽  
Organizational Structures ◽  
Change Propagation ◽  
Engineering Change ◽  
Domain Specific ◽  
Engineering Change Management ◽  
Cross Domain ◽  
Product Models ◽  
Additional Costs ◽  
Engineering Changes

AbstractEngineering Changes (ECs) are substantial elements of the design process of technical products and are in particular relevant for companies due to enormous additional costs and time delays they can cause. In order to better understand ECs and realize efficient Engineering Change Management (ECM), different approaches exist. One aspect of ECM are change propagation analysis, which try to analyze knock-on effects of an EC on other product elements or the development process. How ECs can propagate is in particular difficult to assess for complex products realized within different engineering domains (mechanical, electrical and software engineering). To address this challenge, ECs are classified, strategies to cope with ECs are presented and change propagation approaches are analyzed in this paper. Thereby a lack of indicators for cross-domain propagation is identified. To overcome this issue, the distinction of domain-specific and cross-domain linkage types is proposed and a set of linkage types is presented. Further research is motivated to integrate these linkage types in product models while also considering processes and organizational structures as additional dimensions of ECM.

SECOND LANGUAGE EPISTEMOLOGY

2009 ◽  
Vol 31 (2) ◽  
pp. 291-321 ◽  
Author(s):  
Laurent Dekydtspotter
Keyword(s):  
Second Language ◽  
State Of The Art ◽  
Native Language ◽  
Universal Grammar ◽  
Domain Specificity ◽  
Domain Specific ◽  
Language Knowledge

This article presents evidence that supports the claim that second language (L2) grammars arise in a domain-specific, informationally encapsulated module with contents provided by Universal Grammar and enriched by native language knowledge, as entertained by Schwartz (1986, 1987, 1999) contra Bley-Vroman (1990). I consider state-of-the-art evidence representative of a body of research on the poverty of the stimulus (POS) that argues for the domain-specificity of L2 representations, with a main focus on interpretation. Then I examine interpretive evidence relevant to the role of informational encapsulation and compositionality in SLA. I seek to demonstrate that the acquisition of syntax-linked interpretive properties where the POS is severe provides opportunities for a type of fingerprinting of mental organization that can inform a variety of epistemologically relevant questions.

Outcomes of Organizational and Community Embeddedness: A Conservation of Resources Perspective

2020 ◽  
pp. 105960112096356
Author(s):  
Barjinder Singh ◽  
Margaret Shaffer ◽  
Thirumalai Thattai Rajan Selvarajan
Keyword(s):  
Underlying Mechanism ◽  
Future Research ◽  
Conservation Of Resources ◽  
Mediating Role ◽  
Domain Specific ◽  
Community Embeddedness ◽  
Cross Domain ◽  
Community Outcomes ◽  
Psychological Flourishing

Drawing on Conservation of Resources and spillover theories, we empirically examine work and community outcomes of both organizational and community embeddedness and the underlying mechanism whereby the two forms of embeddedness influence both domain-specific and cross-domain outcomes. With data from 165 matched pairs of employees and their colleagues from a Midwestern US organization, we found that organizational and community embeddedness influence specific individual behaviors both within and across their respective domains. Additionally, we found support for the mediating role of psychological flourishing in the relationships between embeddedness and various organizational and community outcomes. We discuss the theoretical contributions and practical implications of our findings, as well as suggestions for future research.

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