Dataset Information

Social learning about construction behaviour via an artefact.

ABSTRACT: One source of public information may be the enduring products of others' behaviour, such as discarded tools or vacated nests. Here, we examined whether observation of a nest affects the material captive zebra finch males prefer when they construct their first nest. It does: for first-time nest construction, males that viewed only an empty cage preferred the colour of material each initially favoured but those males that had observed a pre-built nest of material of their non-preferred colour lost their material-colour preference altogether. Additionally, half of the males that viewed a nest were tested in an environment (the laboratory) different to that in which they were reared (an outdoor aviary). We had expected the aviary-reared (versus laboratory-reared) males would be more uncertain, and thus more likely to select material for their first nest that matched in colour to the colour of the 'demonstrated' nest-but this was not the case. The aviary-reared males did, however, tend to touch first the demonstrated colour of material more than did the laboratory-reared males. Together these results show that both observation of a nest and a change in environment can influence the material choices of novice builders. For naïve animal builders, then, construction artefacts can be information resources for learning about potential construction material.

SUBMITTER: Breen AJ

PROVIDER: S-EPMC6507502 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Social learning about construction behaviour via an artefact.

Breen Alexis J AJ Bonneaud Clémence C CC Healy Susan D SD Guillette Lauren M LM

Animal cognition 20190214 3

One source of public information may be the enduring products of others' behaviour, such as discarded tools or vacated nests. Here, we examined whether observation of a nest affects the material captive zebra finch males prefer when they construct their first nest. It does: for first-time nest construction, males that viewed only an empty cage preferred the colour of material each initially favoured but those males that had observed a pre-built nest of material of their non-preferred colour lost ...[more]

PMID: 30767145

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Project description:ObjectivesThe susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning-based metal artefact reduction (MAR) in quantitative PET/CT imaging.MethodsDeep learning-based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques.ResultsThe evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR.ConclusionThe DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images.Key points• The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images. • The aim of this work is to develop and evaluate a deep learning-based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging. • Deep learning-based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.

Project description:The propensity for a grizzly bear to develop conflict behaviours might be a result of social learning between mothers and cubs, genetic inheritance, or both learning and inheritance. Using non-invasive genetic sampling, we collected grizzly bear hair samples during 2011-2014 across southwestern Alberta, Canada. We targeted private agricultural lands for hair samples at grizzly bear incident sites, defining an incident as an occurrence in which the grizzly bear caused property damage, obtained anthropogenic food, or killed or attempted to kill livestock or pets. We genotyped 213 unique grizzly bears (118 M, 95 F) at 24 microsatellite loci, plus the amelogenin marker for sex. We used the program COLONY to assign parentage. We evaluated 76 mother-offspring relationships and 119 father-offspring relationships. We compared the frequency of problem and non-problem offspring from problem and non-problem parents, excluding dependent offspring from our analysis. Our results support the social learning hypothesis, but not the genetic inheritance hypothesis. Offspring of problem mothers are more likely to be involved in conflict behaviours, while offspring from non-problem mothers are not likely to be involved in incidents or human-bear conflicts themselves (Barnard's test, p = 0.05, 62.5% of offspring from problem mothers were problem bears). There was no evidence that offspring are more likely to be involved in conflict behaviour if their fathers had been problem bears (Barnard's test, p = 0.92, 29.6% of offspring from problem fathers were problem bears). For the mother-offspring relationships evaluated, 30.3% of offspring were identified as problem bears independent of their mother's conflict status. Similarly, 28.6% of offspring were identified as problem bears independent of their father's conflict status. Proactive mitigation to prevent female bears from becoming problem individuals likely will help prevent the perpetuation of conflicts through social learning.

Dataset Information

Social learning about construction behaviour via an artefact.

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Social learning about construction behaviour via an artefact.

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