Grasping the Concept of an Object at a Glance: Category Information Accessed by Brief Dichoptic Presentation
Bibliographic record
Abstract
What type of conceptual information about an object do we get at a brief glance? In two experiments, we investigated the nature of conceptual tokening-the moment at which conceptual information about an object is accessed. Using a masked picture-word congruency task with dichoptic presentations at "brief" (50-60 ms) and "long" (190-200 ms) durations, participants judged the relation between a picture (e.g., a banana) and a word representing one of four property types about the object: superordinate (fruit), basic level (banana), a high-salient (yellow), or low-salient feature (peel). In Experiment 1, stimuli were presented in black-and-white; in Experiment 2, they were presented in red and blue, with participants wearing red-blue anaglyph glasses. This manipulation allowed for the independent projection of stimuli to the left- and right-hemisphere visual areas, aiming to probe the early effects of these projections in conceptual tokening. Results showed that superordinate and basic-level properties elicited faster and more accurate responses than high- and low-salient features at both presentation times. This advantage persisted even when the objects were divided into categories (e.g., animals, vegetables, vehicles, tools), and when objects contained high-salient visual features. However, contrasts between categories show that animals, fruits, and vegetables tend to be categorized at the superordinate level, while vehicles tend to be categorized at the basic level. Also, for a restricted class of objects, high-salient features representing diagnostic color information (yellow for the picture of a banana) facilitated congruency judgments to the same extent as that of superordinate and basic-level labels. We suggest that early access to object concepts yields superordinate and basic-level information, with features only yielding effects at a later stage of processing, unless they represent diagnostic color information. We discuss these results advancing a unified theory of conceptual representation, integrating key postulates of atomism and feature-based theories.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".