The Diversity of Conceptual Combination
Bibliographic record
Abstract
Symposium: The Diversity of Conceptual Combination. M ODERATOR Fintan Costello (Fintan.Costello@ucd.ie), Department of Computer Science, University College Dublin, Dublin, Ireland. Fintan Costello (Fintan.Costello@ucd.ie), S PEAKERS Department of Computer Science, University College Dublin, Dublin, Ireland. Zachary Estes (estes@uga.edu), Christina Gagne (cgagne@ualberta.ca), Department of Psychology, University of Alberta, Edmonton, Alberta. Edward Wisniewski (edw@uncg.edu), Psychology Department, University of Georgia, Athens, Georgia. Department of Psychology, University of North Carolina, Greensboro, North Carolina. Introduction A fundamental aspect of everyday language comprehension is the interpretation of novel compound phrases through conceptual combination: a mechanism that is engaged whenever people interpret phrases like sand gun , cactus fish or pet shark . Conceptual combination is a diverse and complex cognitive process: people are able to combine concepts in a variety of different ways (for example, a “sand gun” is a tool that sprays sand, while a “cactus fish” is a fish with prickly spines, and a “pet shark” is a shark which is also a pet). This diversity is reflected in the number of quite different theories of conceptual combination that have recently been proposed by, for example, Wisniewski (Wisniewski, 1997), Gagne (Gagne & Shoben, 1997), Estes (Estes & Glucksberg, 2000), and Costello (Costello & Keane, 2000). The aim of this symposium is to gather current researchers on conceptual combination to discuss both the diversity of ways in which concepts can combine, and the diversity of theories that have been put forward to account for conceptual combination. Diversity of Combination Types Combined concepts are often divided into three types: relational combinations (such as “sand gun”), which assert a relation linking the two concepts being combined; property combinations (such as “cactus fish”), which transfer a property from one concept to the other; and conjunctive combinations (such as “pet shark”), which describe something that is an example of both combining concepts. These types are quite loose, however, and are by no means definitive or all-inclusive. In this symposium, speakers will address questions such as • Why do concepts combine in different ways? • How significant are the different combination types? • Are some combination types more important than others? Relationship between Theories of Combination Recent theoretical accounts of conceptual combination are strikingly different from each other, ranging from Gagne’s CARIN theory (which uses a standard set of 16 relational templates such as X-HAS-Y or X-ABOUT-Y to interpret compound phrases), to Wisniewski’s Dual-Process theory (which suggests that compound interpretation involves both a scenario-construction mechanism and a structural- alignment mechanism similar to that used in analogies), to Costello’s Constraint theory (which describes conceptual combination as a process of constraint satisfaction subject to the pragmatic requirements of communication using compound phrases). Symposium speakers will address questions such as • Why are the various theories of combination so different? • What common ground do these theories share? • How do these theories relate to each other? • Can we come up with an integrating framework to unite these theories? Conclusion By bringing together researchers taking different approaches to conceptual combination, this symposium will give a useful synthesis of the current state of conceptual combination research. By directly addressing the diversity of concept combination, the symposium may provide the basis for a more unified view of this important and fascinating part of human thought and language. References Costello, F. J., & Keane, M. T. (2000). Efficient creativity: Constraint guided conceptual combination. Cognitive Science,24(2). Estes, Z. & Glucksberg, S. (2000). Interactive property activation in conceptual combination. Memory & Cognition, 28, 28-34. Gagne, C. L., & Shoben, E. J. (1997). Influence of thematic relations on the comprehension of modifier-noun combinations. Journal of Experimental Psychology: Learning, Memory and Cognition, 23 (1), 71-87. Wisniewski, E. J. (1997). When concepts combine. Psychnomic Bulletin & Review, 4, 167-183.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".