From Evolved Motives to Everyday Mentation: Evolution, Goals, and Cognition
Bibliographic record
Abstract
INTRODUCTION Walking across a crowding shopping mall, you may see a group of people who vary in their race, gender, attractiveness, clothing style, and demeanor. A similarly complex array of social stimuli confronts us at conferences, airports, farmer's markets, and college campuses. Rarely do we attend equally to all individuals in such complex social environments or to all characteristics of any given individual. Rather, we selectively direct our attention toward a smaller subset of individuals and characteristics. This selective direction of attention often occurs automatically, without conscious intent, and can have important consequences for subsequent thoughts and actions. Who do we attend to, think about, and later remember? And how are the answers to this question linked to our goals at the moment? We recently embarked on a program of research to explore the processes that influence the selective and automatic direction of perceptual and cognitive resources. In this chapter, we present a conceptual framework that begins to articulate the role that fundamental social goals play in governing these processes. We focus, in particular, on the ways in which self-protection and mating goals selectively facilitate attention toward people who have characteristics relevant to those goals. Integrating theory and research on selective attention processes, the influence of goals on social cognition and behavior, and ecological theories of motivation and social cognition, our framework yields some novel hypotheses about how self-protection and mating goals influence attention to, perceptions of, and cognitions about individuals who differ in gender, physical attractiveness, and ethnicity.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".