Bibliographic record
Abstract
Hysteresis is the tendency of a dynamical system to display bias toward a given state or category based on recent history. Here we evaluate hysteresis between shape-defined categories using a multidimensional shape space framework we have previously used to examine categorical perception of fruit (Wilkinson et al, ECVP 2004). The bounding contours of many rounded objects can be described as sums of radial frequency (RF) components of specified amplitude and relative phase and represented as points within multidimensional RF space. Fruit shapes occupy regions of this space, in some cases adjacent (e.g. pear and avocado) and in others, separated by uncommitted regions (e.g. pear and apple). To examine hysteresis both between fruit categories and at the boundaries between fruit regions and non-committed regions of this shape space, 20 participants were tested in 2 conditions on each of 6 fruit continua. In both conditions, participants were first shown exemplars of the two endpoints (e.g. apple and pear), followed by the presentation of the continuum beginning at one end (e.g. apple), with each step along the continuum presented for 500 ms separated by a 250 ms grey screen. In the STOP conditions, participants indicated the point at which the shape “stopped being an apple”; in the CHANGE condition, they indicated when the stimulus had clearly “changed into a pear”. Each of the 6 continua was presented 5 times in each direction in each condition. Significant hysteresis was found between similarly shaped fruit (pear/avocado) and also at the boundaries between fruit categories and uncommitted regions of this space (apple/uncommitted/pear), indicating the involvement of cooperative/competitive mechanisms in establishing and maintaining categorical regions within this space. Our findings will be considered in the context of the recent distinction between dynamical and judgmental hysteresis made by Hock et al (Spatial Vision, 2005).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".