Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny
Bibliographic record
Abstract
Humans possess the remarkable capacity to imagine possible worlds and to demarcate possibilities and impossibilities in reasoning. We can think about what might happen in the future and consider what the present would look like had the past turned out differently. We reason about cause and effect, weigh up alternative courses of action and regret our mistakes. In this theme issue, leading experts from across the life sciences provide ground-breaking insights into the proximate questions of how thinking about possibilities works and develops, and the ultimate questions of its adaptive functions and evolutionary history. Together, the contributions delineate neurophysiological, cognitive and social mechanisms involved in mentally simulating possible states of reality; and point to conceptual changes in the understanding of singular and multiple possibilities during human development. The contributions also demonstrate how thinking about possibilities can augment learning, decision-making and judgement, and highlight aspects of the capacity that appear to be shared with non-human animals and aspects that may be uniquely human. Throughout the issue, it becomes clear that many developmental milestones achieved during childhood, and many of the most significant evolutionary and cultural triumphs of the human species, can only be understood with reference to increasingly complex reasoning about possibilities. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.027 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".