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Record W2566724153 · doi:10.1093/eurheartj/ehw479

Wisdom and heart rate

2016· article· en· W2566724153 on OpenAlexaff
Igor Grossmann

Bibliographic record

VenueEuropean Heart Journal · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineCardiologyHeart rateInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Although wisdom can be difficult to define, people generally recognize it when they encounter it ‘Knowing yourself is the beginning of all wisdom.’ Aristotle Most psychologists agree wisdom involves an integration of knowledge, experience and deep understanding that incorporates tolerance for the uncertainties of life as well as its ups and downs. That said, researchers now believe wisdom is a matter of both heart and mind, touting that fluctuations in our heartbeats may, in fact, affect our wisdom. According to Igor Grossmann, Professor of Psychology at University of Waterloo, Ontario and colleagues, their study breaks new ground in wisdom research by identifying conditions under which psychophysiology impacts wise judgment. Human heart rate fluctuates even during steady-state conditions, such as sitting. This study is the first to show that the variability of heart rate during low physical activity is related to less biased, wiser judgment. In this study, researchers assessed behavioural processes reflecting wisdom-related judgment in two ways. First, they asked participants to reason about societal issues and examined their narratives for multiple aspects of wisdom-related reasoning strategies. Second, researchers asked participants to reflect on desirable/undesirable acts committed by another person and tested whether their judgments of those acts relied on biased dispositional explanations or more balanced situation-sensitive explanations. Throughout the study, researchers measured the resting electrophysiological signature of the heart—heart rate variability (HRV)—obtaining a range of time- and frequency-domain HRV indicators. To examine how self-distancing moderates the relationship between wisdom-related judgment and HRV, participants were randomly assigned to adopt a self-distanced as compared to the self-immersed perspective when reflecting on the social issue. As hypothesized, in the self-distanced condition, each HRV indicator was positively related to prevalence of wisdom-related reasoning (e.g. recognition of limits of one's knowledge, recognition that the world is in flux/change, consideration of others' opinions and search for an integration of these opinions) and to balanced vs. biased attributions (recognition of situational and dispositional factors vs. focus on dispositional factors alone). In contrast, there was limited evidence of the relationship between these variables in the self-immersed condition. The researchers found people with more varied heart rates were able to reason in a wiser, less biased fashion about societal problems when they were instructed to reflect on a social issue from a third-person perspective. But, when the study participants were instructed to reason about the issue from a first-person perspective, no relationship between heart rate and wisdom-related reasoning (and a much weaker relationship between HRV and balanced attributions) emerged. According to Grossmann, it has been known that people with greater variation in their heart rates show superior performance in the brain's executive functioning such as working memory. However, that does not necessarily mean these people are wiser; in fact, some people may use their cognitive skills to make unwise decisions. To channel their cognitive abilities for wiser judgment, people with greater HRV first need to overcome their egocentric viewpoints. Indeed, Grossmann’s other research has shown strategies that can effectively attenuate such egocentric viewpoints and boost wise judgment. The present study opens the door for further exploration of wise judgment at the intersection of physiological and cognitive research. Cardio Pulse contact: Andros Tofield, Managing Editor. Email: docandros@bluewin.ch

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.304
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractno

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