scholarly journals Foraging Bumblebees Selectively Attend to Other Types of Bees Based on Their Reward-Predictive Value

Insects ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 800
Author(s):  
Jose E. Romero-González ◽  
Amanda L. Royka ◽  
HaDi MaBouDi ◽  
Cwyn Solvi ◽  
Janne-Tuomas Seppänen ◽  
...  
Keyword(s):  
Information Seeking ◽  
Bombus Terrestris ◽  
Social Information ◽  
Relevant Information ◽  
The Novel ◽  
Trial And Error ◽  
Visual Appearance ◽  
Seeking Behavior ◽  
Color Cues ◽  
Error Learning

Using social information can be an efficient strategy for learning in a new environment while reducing the risks associated with trial-and-error learning. Whereas social information from conspecifics has long been assumed to be preferentially attended by animals, heterospecifics can also provide relevant information. Because different species may vary in their informative value, using heterospecific social information indiscriminately can be ineffective and even detrimental. Here, we evaluated how selective use of social information might arise at a proximate level in bumblebees (Bombus terrestris) as a result of experience with demonstrators differing in their visual appearance and in their informative value as reward predictors. Bumblebees were first trained to discriminate rewarding from unrewarding flowers based on which type of “heterospecific” (one of two differently painted model bees) was next to each flower. Subsequently, these bumblebees were exposed to a novel foraging context with two live painted bees. In this novel context, observer bumblebees showed significantly more social information-seeking behavior towards the type of bees that had predicted reward during training. Bumblebees were not attracted by paint-marked small wooden balls (moved via magnets) or paint-marked non-pollinating heterospecifics (woodlice; Porcellio laevis) in the novel context, indicating that bees did not simply respond to conditioned color cues nor to irrelevant social cues, but rather had a “search image” of what previously constituted a valuable, versus invaluable, information provider. The behavior of our bumblebees suggests that their use of social information is governed by learning, is selective, and extends beyond conspecifics.

COVID-19 and health information seeking behavior: Digital health literacy survey amongst university students in Pakistan (Preprint)

2021 ◽  
Author(s):  
Rubeena Zakar ◽  
Sarosh Iqbal ◽  
Muhammad Zakria Zakar ◽  
Florian Fischer
Keyword(s):  
Health Literacy ◽  
University Students ◽  
Information Seeking ◽  
Digital Health ◽  
Relevant Information ◽  
Health Concern ◽  
Information Seeking Behavior ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Seeking Behavior

BACKGROUND Amid to the COVID-19 pandemic, digital health literacy (DHL) has become a significant public health concern. OBJECTIVE This research aims to assess information seeking behavior, as well as the ability to find relevant information and deal with DHL among university students in Pakistan. METHODS An online-based cross-sectional survey, using a web-based interviewing technique, was conducted to collect data on DHL. Simple bivariate and multivariate linear regression was performed to assess the association of key characteristics with DHL. RESULTS The results show a high DHL related to COVID-19 in 54.3% of students. Most of the Pakistani students demonstrated ~50% DHL in all dimensions, except of reliability. Multivariate findings showed that gender, sense of coherence and importance of information were found to be significantly associated with DHL. However, a negative association was observed with students’ satisfaction with information. CONCLUSIONS This led to the conclusion that critical operational and navigations skills are essential to achieve COVID-19 DHL and cope with stress, particularly to promote both personal and community health. Focused interventions and strategies should be designed to enhance DHL amongst university students to combat the pandemic.

scholarly journals COVID-19 and Health Information Seeking Behavior: Digital Health Literacy Survey amongst University Students in Pakistan

2021 ◽  
Vol 18 (8) ◽  
pp. 4009
Author(s):  
Rubeena Zakar ◽  
Sarosh Iqbal ◽  
Muhammad Zakria Zakar ◽  
Florian Fischer
Keyword(s):  
Health Literacy ◽  
University Students ◽  
Information Seeking ◽  
Digital Health ◽  
Relevant Information ◽  
Health Concern ◽  
Information Seeking Behavior ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Seeking Behavior

Amid the COVID-19 pandemic, digital health literacy (DHL) has become a significant public health concern. This research aims to assess information seeking behavior, as well as the ability to find relevant information and deal with DHL among university students in Pakistan. An online-based cross-sectional survey, using a web-based interviewing technique, was conducted to collect data on DHL. Simple bivariate and multivariate linear regression was performed to assess the association of key characteristics with DHL. The results show a high DHL related to COVID-19 in 54.3% of students. Most of the Pakistani students demonstrated ~50% DHL in all dimensions, except for reliability. Multivariate findings showed that gender, sense of coherence and importance of information were found to be significantly associated with DHL. However, a negative association was observed with students′ satisfaction with information. This led to the conclusion that critical operational and navigations skills are essential to achieve COVID-19 DHL and cope with stress, particularly to promote both personal and community health. Focused interventions and strategies should be designed to enhance DHL amongst university students to combat the pandemic.

scholarly journals Trusting and learning from others: immediate and long-term effects of learning from observation and advice

2021 ◽  
Author(s):  
Uri Hertz ◽  
Vaughan Bell ◽  
Nichola Raihani
Keyword(s):  
Reinforcement Learning ◽  
Social Information ◽  
Computational Modelling ◽  
Trial And Error ◽  
Long Term Effects ◽  
Short Term ◽  
Privileged Status ◽  
Error Learning ◽  
Learning From Others

Social learning underpins our species’ extraordinary success. Learning through observation has been investigated in several species but learning from advice – where information is intentionally broadcast – is less understood. We used a pre-registered, online experiment (N=1492) combined with computational modelling to examine learning through observation and advice. Participants were more likely to immediately follow advice than to copy an observed choice but this was dependent upon trust in the adviser: highly paranoid participants were less likely to follow advice in the short-term. Reinforcement learning modelling revealed two distinct patterns regarding the long-term effects of social information: some individuals relied fully on social information whereas others reverted to trial-and-error learning. This variation may affect prevalence and fidelity of socially-transmitted information. Our results highlight the privileged status of advice relative to observation and how assimilation of intentionally-broadcasted information is affected by trust in others.

scholarly journals Social learning in nest-building birds: a role for familiarity

2016 ◽  
Vol 283 (1827) ◽  
pp. 20152685 ◽  
Author(s):  
Lauren M. Guillette ◽  
Alice C. Y. Scott ◽  
Susan D. Healy
Keyword(s):  
Social Learning ◽  
Social Information ◽  
Nest Building ◽  
Zebra Finches ◽  
Trial And Error ◽  
Nest Material ◽  
Conspecific Male ◽  
Error Learning ◽  
First Time

It is becoming apparent that birds learn from their own experiences of nest building. What is not clear is whether birds can learn from watching conspecifics build. As social learning allows an animal to gain information without engaging in costly trial-and-error learning, first-time builders should exploit the successful habits of experienced builders. We presented first-time nest-building male zebra finches with either a familiar or an unfamiliar conspecific male building with material of a colour the observer did not like. When given the opportunity to build, males that had watched a familiar male build switched their material preference to that used by the familiar male. Males that observed unfamiliar birds did not. Thus, first-time nest builders use social information and copy the nest material choices when demonstrators are familiar but not when they are strangers. The relationships between individuals therefore influence how nest-building expertise is socially transmitted in zebra finches.

scholarly journals Trusting and learning from others: immediate and long-term effects of learning from observation and advice

2021 ◽  
Vol 288 (1961) ◽  
Author(s):  
Uri Hertz ◽  
Vaughan Bell ◽  
Nichola Raihani
Keyword(s):  
Reinforcement Learning ◽  
Social Information ◽  
Computational Modelling ◽  
Trial And Error ◽  
Long Term Effects ◽  
Short Term ◽  
Privileged Status ◽  
Error Learning ◽  
Learning From Others

Social learning underpins our species's extraordinary success. Learning through observation has been investigated in several species, but learning from advice—where information is intentionally broadcast—is less understood. We used a pre-registered, online experiment ( n = 1492) combined with computational modelling to examine learning through observation and advice. Participants were more likely to immediately follow advice than to copy an observed choice, but this was dependent upon trust in the adviser: highly paranoid participants were less likely to follow advice in the short term. Reinforcement learning modelling revealed two distinct patterns regarding the long-term effects of social information: some individuals relied fully on social information, whereas others reverted to trial-and-error learning. This variation may affect the prevalence and fidelity of socially transmitted information. Our results highlight the privileged status of advice relative to observation and how the assimilation of intentionally broadcast information is affected by trust in others.

Information Seeking Models in the Digital Age

2018 ◽  
pp. 4515-4527
Author(s):  
Mudasir Khazer Rather ◽  
Shabir Ahmad Ganaie
Keyword(s):  
Information Seeking ◽  
Digital Age ◽  
Relevant Information ◽  
Research Articles ◽  
Online Resources ◽  
Information Seeking Behavior ◽  
Pictorial Representation ◽  
Web Tools ◽  
Database Research ◽  
Seeking Behavior

A model may be defined as a structure for thinking about a problem and may evolve into a statement of the relationships among theoretical propositions. Most models of information behavior are generally the statements, often in the form of diagrams that attempt to explicate an information-seeking activity, the causes and consequences of that activity, or the relations among stages in information-seeking behavior. This chapter explores, introduces and discusses select information seeking models and explains various elements of each modal. Various online resources like database, research articles and other web tools will be accessed to retrieve relevant information related to the select models. Further, the chapter also focuses on the diagrammatic or pictorial representation of each model.

Information Seeking Models in the Digital Age

2019 ◽  
pp. 279-294
Author(s):  
Mudasir Khazer Rather ◽  
Shabir Ahmad Ganaie
Keyword(s):  
Information Seeking ◽  
Digital Age ◽  
Relevant Information ◽  
Research Articles ◽  
Online Resources ◽  
Information Seeking Behavior ◽  
Pictorial Representation ◽  
Web Tools ◽  
Database Research ◽  
Seeking Behavior

A model may be defined as a structure for thinking about a problem and may evolve into a statement of the relationships among theoretical propositions. Most models of information behavior are generally the statements, often in the form of diagrams that attempt to explicate an information-seeking activity, the causes and consequences of that activity, or the relations among stages in information-seeking behavior. This chapter explores, introduces, and discusses select information-seeking models and explains various elements of each model. Various online resources like database, research articles, and other web tools will be accessed to retrieve relevant information related to the select models. Further, the chapter also focuses on the diagrammatic or pictorial representation of each model.

scholarly journals Modeling physician variability to prioritize relevant medical record information

2020 ◽  
Author(s):  
Mohammadamin Tajgardoon ◽  
Gregory F Cooper ◽  
Andrew J King ◽  
Gilles Clermont ◽  
Harry Hochheiser ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Medical Record ◽  
Patient Information ◽  
Information Seeking ◽  
Hierarchical Models ◽  
Relevant Information ◽  
Calibration Error ◽  
Hierarchical Logistic Regression ◽  
Seeking Behavior ◽  
Better Than

AbstractObjectivePatient information can be retrieved more efficiently in electronic medical record (EMR) systems by using machine learning models that predict which information a physician will seek in a clinical context. However, information-seeking behavior varies across EMR users. To explicitly account for this variability, we derived hierarchical models and compared their performance to non-hierarchical models in identifying relevant patient information in intensive care unit (ICU) cases.Materials and MethodsCritical care physicians reviewed ICU patient cases and selected data items relevant for presenting at morning rounds. Using patient EMR data as predictors, we derived hierarchical logistic regression (HLR) and standard logistic regression (LR) models to predict their relevance.ResultsIn 73 pairs of HLR and LR models, the HLR models achieved an area under the ROC curve of 0.81, 95% CI [0.80, 0.82], which was statistically significantly higher than that of LR models (0.75, 95% CI [0.74-0.76]). Further, the HLR models achieved statistically significantly lower expected calibration error (0.07, 95% CI [0.06-0.08]) than LR models (0.16, 95% CI [0.14-0.17]).DiscussionThe physician reviewers demonstrated variability in selecting relevant data. Our results show that HLR models perform significantly better than LR models with respect to both discrimination and calibration. This is likely due to explicitly modeling physician-related variability.ConclusionHierarchical models can yield better performance when there is physician-related variability as in the case of identifying relevant information in the EMR.

scholarly journals COVID-19 and Health Information Seeking Behavior: Digital Health Literacy Survey Amongst University Students in Pakistan

2021 ◽  
Author(s):  
Rubeena Zakar ◽  
Sarosh Iqbal ◽  
Muhammad Zakria Zakar ◽  
Florian Fischer
Keyword(s):  
Health Literacy ◽  
University Students ◽  
Information Seeking ◽  
Digital Health ◽  
Relevant Information ◽  
Health Concern ◽  
Information Seeking Behavior ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Seeking Behavior

Amid to the COVID-19 pandemic, digital health literacy (DHL) has become a significant public health concern. This research aims to assess information seeking behavior, as well as the ability to find relevant information and deal with DHL among university students in Pakistan. An online-based cross-sectional survey, using a web-based interviewing technique, was conducted to collect data on DHL. Simple bivariate and multivariate linear regression was performed to assess the association of key characteristics with DHL. The results show a high DHL related to COVID-19 in 54.3% of students. Most of the Pakistani students demonstrated ~50% DHL in all dimensions, except of reliability. Multivariate findings showed that gender, sense of coherence and importance of information were found to be significantly associated with DHL. However, a negative association was observed with students’ satisfaction with information. This led to the conclusion that critical operational and navigations skills are essential to achieve COVID-19 DHL and cope with stress, particularly to promote both personal and community health. Focused interventions and strategies should be designed to enhance DHL amongst university students to combat the pandemic.

Cross-generational analysis of information seeking behavior of smartphone users

2017 ◽  
Vol 69 (6) ◽  
pp. 721-739 ◽  
Author(s):  
Maayan Zhitomirsky-Geffet ◽  
Maya Blau
Keyword(s):  
Predictive Factors ◽  
Information Seeking ◽  
Social Information ◽  
Information Seeking Behavior ◽  
Content Type ◽  
Seeking Behavior ◽  
Cognitive Information ◽  
Types Of Information ◽  
Behavior Types ◽  
Practical Implications

Purpose The purpose of this paper is to investigate the predictive factors of information seeking behavior of smartphone users from the cross-generational perspective. Based on existing literature, the two most popular types of information seeking behavior of smartphone users were determined: social information seeking behavior; and functional/cognitive information seeking behavior. Design/methodology/approach A questionnaire comprising 66 questions was administered online to 216 smartphone users of three age groups according to three generations: generation X, Y (millennials) and Z. Several predictive factors were examined for each of these information seeking behavior types: generation, gender, personality traits (the Big Five), daily usage time, period of ownership, various application utilization and the level of emotional gain from smartphones. Findings There is a trade-off between the two types of information seeking behavior. Also, men exhibited significantly more functional/cognitive information seeking behavior than women, and younger generations reported significantly higher emotional gain and social information seeking behavior than older generations. Interestingly, significant differences in smartphone apps’ utilization, information seeking behavior types and their predictive factors were found among users from different generations. Extraversion was positively related to social information seeking behavior only for generations X and Y, while WhatsApp usage was one of the strongest predictive factors only for generation Z. Practical implications This research has practical implications for information system design, education, e-commerce and libraries. Originality/value This is a first study that systematically examines predictive factors of the two prominent types of information seeking behavior on smartphones from the cross-generational perspective.

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