Neuroscience, Wellbeing, and Urban Design: Our Universal Attraction to Vitality
Bibliographic record
Abstract
Although urban planners and architects have understood that there is a relationship between the design of a setting and our thoughts and emotions, it is only recently that we have had the tools to properly dissect this relationship. New methods for measuring brain states in field settings in immersive virtual reality have generated a host of novel findings, but a theme that connects many of these findings together is the idea that human beings have a deep affinity for vitality at every level from the interior of a home to an urban streetscape. Not only this, but recent evidence suggests that we respond to the vitality of scenes almost immediately, even after exposures as brief as 50 milliseconds, possibly using ambient visual processing mechanisms that rely on our peripheral visual field. Further, when we sense and respond to vitality, positive affect increases, which in turn promotes affiliation and buffers us against urban loneliness. I will present findings from experiments both in the laboratory and in the field that show the power of vitality to effect behavioural change, and I will argue that harnessing this power is one of the keys to building a psychologically sustainable city.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.022 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".