Updating geographical knowledge: Principles of coherence and inertia.
Bibliographic record
Abstract
In 2 experiments, the authors investigated how representations of global geography are updated when people learn new location information about individual cities.Participants estimated the latitude of cities in North America (Experiment 1) and in the Old and New Worlds (Experiment 2).After making their first estimates, participants were given information about the latitudes of 2 cities and asked to make a second set of estimates.Both the first and second estimates revealed evidence for psychologically distinct geographical subregions that were coordinated, in an ordinal sense, across the Atlantic Ocean.Further, the second estimates were affected by the nature of the physical adjacency between regions (e.g., the southern U.S. and Mexico) and by accurate location information about distant, but coordinated, subregions (e.g., the southern U.S. and Mediterranean Europe).The data provide support for a framework for making geographical estimates in which people strike a balance between 2 principles: the need to keep their knowledge base coherent, and the inertial tendency to resist changing the knowledge base unless it is necessary to maintain coherence.People acquire knowledge about the world across the lifespan.This simple fact implies that new knowledge is acquired in the context of prior knowledge and that the content, and perhaps the structure, of the knowledge base changes to reflect this learning.Obviously, the capacity to integrate new knowledge with old is an extremely important one, for without it, we could not adapt to the changing physical, social, and intellectual environment.Yet little is known about how newly acquired facts affect our understanding in complex, real-world domains.The present study was motivated by an interest in this issue, and it represents an attempt to identify principles that determine how and when knowledge changes in response to new information.Specifically, we argue that two principles---coherence and inertia--play a central role in determining how people update real-world knowledge.We present two experiments that use a seeding procedure (Brown & Siegler, 1993, 1996;Friedman & Brown, 2000), in which people are given location information about a small number of cities, to demonstrate these principles at work.In both experiments, participants first estimated the latitude of a set of cities.Then they learned the actual latitudes of two cities and provided a second set of estimates.The comparison between the first and second estimates provides the basis for inferences about the psychological principles and processes underlying the integration of new information with prior knowledge.
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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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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".