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The impact of learning opportunities on the development of learning and decision-making: an experiment with passerine birds.

ABSTRACT: Developmental context has been shown to influence learning abilities later in life, namely through experiments with nutritional and/or environmental constraints (i.e. lack of enrichment). However, little is known about the extent to which opportunities for learning affect the development of animal cognition, even though such opportunities are known to influence human cognitive development. We exposed young zebra finches (Taenopygia guttata) (n = 26) to one of three experimental conditions, i.e. an environment where (i) colour cues reliably predicted the presence of food (associative learning), (ii) a combination of two-colour cues reliably predicted the presence of food (conditional learning), or (iii) colour cues were non-informative (control). After conducting two different discrimination tasks, our results showed that experience with predictive cues can cause increased choice accuracy and decision-making speed. Our first learning task showed that individuals in the associative learning treatment outperformed the control treatment, while task 2 showed that individuals in the conditional learning treatment had shorter latencies when making choices compared with the control treatment. We found no support for a speed-accuracy trade-off. This dataset provides a rare longitudinal and experimental examination of the effect of predictive versus non-predictive cues during development on the cognition of adult animals. This article is part of the theme issue 'Life history and learning: how childhood, caregiving and old age shape cognition and culture in humans and other animals'.

SUBMITTER: Rojas-Ferrer I

PROVIDER: S-EPMC7293162 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundVaccine hesitancy is a significant threat to global health. A key part of addressing hesitancy is to ensure that public health messaging prioritises information that is considered important to the public. This study aimed to examine how different vaccine characteristics affect public preferences for vaccines in New Zealand, what trade-offs they are willing to make between different vaccine characteristics, and how their preferences are affected by their vaccine-related conspiracy beliefs and COVID-19 vaccination status.MethodsAn online discrete choice experiment (DCE) was designed to elicit individual preferences about vaccines using the 1000minds platform. Members of the general population of New Zealand aged ≥ 18 years were invited to complete the DCE. Participants were asked to indicate their preference between two options showing different combinations of vaccine characteristics. Data on sociodemographic characteristics were collected. Beliefs were measured using the vaccine conspiracy beliefs scale (VCBS) with scores ≥ 19 indicating strong vaccine-related conspiracy beliefs. The DCE was analysed using the PAPRIKA method (Potentially All Pairwise RanKings of all possible Alternatives) and preferences compared between respondents with high versus low VCBS scores and vaccinated versus unvaccinated respondents for COVID-19.ResultsA total of 611 respondents from 15 regions completed the DCE. Mean (SD) age was 45.9 (14.7) years with most having had 2 or more doses of the coronavirus vaccine (86%). Mean (SD) VCBS score was 18.5 (12.4) indicating moderate vaccine-related conspiracy beliefs. Risk of severe adverse effects was the most highly valued vaccine characteristic, followed by vaccine effectiveness and duration of protection. Vaccine origin and route of administration were ranked least important. Respondents scoring high on the VCBS placed less value on the effectiveness of vaccines but greater value on development time and total number of doses (p < 0.001). COVID-19 unvaccinated respondents ranked development time and total number of doses more highly than those vaccinated respondents (p < 0.001).ConclusionsRisk of severe adverse effects, vaccine effectiveness and duration of protection were rated by the New Zealand public as the top three most important vaccine characteristics. This information is important for informing public health messaging to promote vaccine uptake and inform vaccine decision-making.

Project description:BackgroundSurgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.MethodsCochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.ResultsRequirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.ConclusionTraditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

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The impact of learning opportunities on the development of learning and decision-making: an experiment with passerine birds.

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