Compare

Curated cross-paper comparisons that answer one question each. Each panel embeds the relevant findings overlaid on shared signed-evidence axes and reports fitted parameter deltas computed at build time from a 4-parameter logistic fit to each finding. For an arbitrary paper-pair view, see /compare/papers.

Comparisons are hand-curated; the underlying findings live in findings/ and the complete catalog is on /findings.

How do macaque and human random-dot motion psychometrics compare?

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Roitman & Shadlen 2002 (two macaques) and Palmer-Huk-Shadlen 2005 (six human observers) both ran the same signed motion coherence 2AFC at processed-trial level. They use the same canonical axis but different species and different report modalities (saccade vs button-press), so a slope difference is a candidate signature of perceptual sensitivity at the species level.

Do humans and macaques show comparable RT-vs-coherence chronometrics on RDM?

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Both Roitman & Shadlen 2002 and Palmer-Huk-Shadlen 2005 also report median response time as a function of absolute coherence. The chronometric speed-accuracy tradeoff has been used to argue for shared bounded-accumulation dynamics across species, but the absolute time scales differ.

No 4-parameter logistic fit applies (curve type does not support it, or fits failed). Visual comparison only.

How does evidence accumulation in the Poisson clicks task differ between rats and humans?

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Brunton et al. 2013 trained five rats on the rat auditory clicks task and the London 2018 Mendeley release ran the same Poisson clicks accumulation paradigm in humans (DBS off baseline shown here for like-for-like comparison). Both pin to the same canonical axis — signed click-count difference (right minus left) → p_right — so this is one of the cleanest cross-species comparisons in the atlas. Differences in slope or lapse rate cannot be attributed to differing stimulus families.

How does prior probability shift the IBL psychometric in mice?

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The IBL trainingChoiceWorld biased variant cycles through three blocks of leftward prior probability (0.2 / 0.5 / 0.8). A bias-shift across blocks is the textbook signature of prior integration; the slope (σ) should remain stable.

Does the Walsh prior cue act on the DDM starting point or on the drift?

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Walsh et al. 2024 manipulate prior probability via cues that either match the upcoming target side (cue=valid), give no information (cue=neutral), or actively mislead (cue=invalid). Two canonical ways the cue could enter a DDM are (a) shifting the starting point z toward the favored boundary, or (b) adding a constant drift offset v0 in the favored direction. We fit both four-parameter variants to each pooled per-cue psychometric and compare AICs. Lower AIC = better-supported variant for that cue.

No 4-parameter logistic fit applies (curve type does not support it, or fits failed). Visual comparison only.

How does the per-unit-coherence drift rate k differ between macaque and human random-dot motion?

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Roitman and Palmer fit the same canonical psychometric + chronometric curves on the random-dot motion task, on the same x-axis (signed motion coherence in percent), in different species. Vanilla DDM fits recover three parameters per (paper, subject) — drift per unit evidence (k), boundary separation (a), and non-decision time (t0). This comparison aggregates all 10 fits (Roitman: 1 pooled + 2 macaques; Palmer: 1 pooled + 6 humans) so the spread of k across species is directly visible.

Parameter strip plot · ddm-vanilla

10 fits, all sharing variant ddm-vanilla. Hover a point for fit id, paper, and stratification.

No 4-parameter logistic fit applies (curve type does not support it, or fits failed). Visual comparison only.

Do prior-probability cues bias contrast discrimination in humans?

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Walsh et al. 2024 manipulate trial-by-trial prior cues (valid / neutral / invalid). The prediction is that valid cues produce a bias toward the cued side without changing slope, and invalid cues produce the opposite shift.

These comparisons are pre-baked at build time; the underlying logistic fits are computed by behavtaskatlas site-index using the same model as the interactive fitter on /findings, so values agree. Curate new comparisons by adding a YAML file under comparisons/ in the repository.