Bibliographic record
Abstract
concept learning, including same/different and matching-to-sample concept learning, provides the basis for many other forms of "higher" cognition.The issue of which species can learn abstract concepts and the extent to which abstractconcept learning is expressed across species is discussed.Definitive answers to this issue are argued to depend on the subjects' learning strategy (e.g., a relational-learning strategy) and the particular procedures used to test for abstract-concept learning.Some critical procedures that we have identified are: How to present the items to-be-compared (e.g., in pairs), a high criterion for claiming abstract-concept learning (e.g., transfer performance equivalent to baseline performance), and systematic manipulation of the training set (e.g., increases in the number of rule exemplars when transfer is less than baseline performance).The research covered in this article on the recent advancements in abstract-concept learning show this basic ability in higher-order cognitive processing is common to many animal species and that "uniqueness" may be limited more to how quickly new abstract concepts are learned rather than to the ability itself.Abstract concepts are said to be the basis of higher order cognition in human, and no concept is more important than the concept of identity.William James (1890/1950) was perhaps the first to note that our "sense of sameness is the very keel and backbone of our thinking."(p.459).Over the past century, the ability to judge whether items are the same or different has been a central focus in cognitive development, cognition, and comparative cognition (e.g., Daehler & Bukatko, 1985;Mackintosh, 2000;Shettleworth, 1998;Thompson & Oden, 2000).For example, abstract thinking is considered to be the basis of equivalence operations in math (e.g., "item I is the same as item J"), conservation tasks, and may be a necessary prerequisite for learning language (e.g., Marcus et al., 1999;Piaget & Inhelder, 1966/1969;Siegler, 1996).This thinking is abstract because it is based on rules which allow subjects' judgments to transcend the training stimuli and is therefore called higher order.Abstract-concept learning is the focus of this article.Issues of testing and verifying abstract-concept learning with animals in same/ different (S/D) and matching-to-sample (MTS) tasks are discussed.In addition, we review some of the research on abstract-concept learning.In cognitive and comparative psychology, abstract-concept learning is, unfortunately, often confused with categorization because both are frequently referred to as concept learning.To clarify, there are two types of categorization, 1) natural concepts, which are accounted for by stimulus generalization based on specific features, and 2) associative concepts, which are accounted for by second-order conditioning.Natural concept learning (also called perceptual concept learning) involves categorizing (sorting) stimuli (e.g., those found in nature like pictures of birds, flowers, people, or artificial ones like shapes) based on stimulus perceptual similar-
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.026 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".