Compare
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?
linkRoitman & 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.
Deltas relative to the first row. Larger σ = shallower slope = lower discriminability. Compare Δσ.
| Finding | μ (bias) | σ (slope) | x at 75% | Δμ | Δσ | Δ threshold |
|---|---|---|---|---|---|---|
| P Roitman JD, Shadlen MN. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 2002, 22(21):9475-9489. (reference) | -0.19 | 5.04 | 5.35 | — | — | — |
| P Palmer J, Huk AC, Shadlen MN. The effect of stimulus strength on the speed and accuracy of a perceptual decision. Journal of Vision, 2005, 5(5):376-404. | -0.06 | 4.11 | 4.48 | 0.14 | -0.94 | -0.87 |
Do humans and macaques show comparable RT-vs-coherence chronometrics on RDM?
linkBoth 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?
linkBrunton 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.
Deltas relative to the first row. Compare slope (σ) and lapse. The London DBS-off curve is one human group estimate; the five Brunton rats are individual rats so the curve spread reflects single-subject variability rather than within-species sample uncertainty.
| Finding | μ (bias) | σ (slope) | x at 75% | Δμ | Δσ | Δ threshold |
|---|---|---|---|---|---|---|
| P Brunton BW, Botvinick MM, Brody CD · A080 (reference) | -0.11 | 5.97 | 11.24 | — | — | — |
| P Brunton BW, Botvinick MM, Brody CD · B075 | 0.04 | 4.40 | 8.73 | 0.15 | -1.57 | -2.51 |
| P Brunton BW, Botvinick MM, Brody CD · B127 | -3.71 | 4.24 | 8.99 | -3.59 | -1.73 | -2.26 |
| P Brunton BW, Botvinick MM, Brody CD · T014 | 2.11 | 5.63 | 13.28 | 2.23 | -0.34 | 2.04 |
| P Brunton BW, Botvinick MM, Brody CD · T074 | -2.65 | 4.63 | 13.48 | -2.53 | -1.34 | 2.24 |
| R London D · dbs=off | -1.89 | 4.19 | 3.33 | -1.77 | -1.78 | -7.91 |
How does prior probability shift the IBL psychometric in mice?
linkThe 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.
Deltas relative to the first row. Δμ across blocks measures the bias shift in % contrast units.
| Finding | μ (bias) | σ (slope) | x at 75% | Δμ | Δσ | Δ threshold |
|---|---|---|---|---|---|---|
| P The International Brain Laboratory et al · p_left=0.2 (reference) | -25.24 | 12.22 | -2.52 | — | — | — |
| P The International Brain Laboratory et al · p_left=0.5 | -12.34 | 8.73 | -2.45 | 12.90 | -3.49 | 0.07 |
| P The International Brain Laboratory et al · p_left=0.8 | -3.13 | 16.31 | 22.65 | 22.11 | 4.09 | 25.18 |
Does the Walsh prior cue act on the DDM starting point or on the drift?
linkWalsh 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.
Lower AIC wins (★). Current Δ AIC: cue-valid favours starting-point bias (Δ ≈ 84), cue-neutral favours drift bias (Δ ≈ 46), cue-invalid is essentially a tie (Δ ≈ 0). The cue-dependent bias-locus is itself the curated result; it suggests the prior-cue effect isn't a single mechanism in this paradigm. Caveats: marginal mean-RT prediction uses the unbiased EZ-DDM closed form and so under-uses RT for distinguishing z-bias vs v-bias.
| Group | ddm-starting-point-bias | ddm-drift-bias | ||
|---|---|---|---|---|
| AIC | params | AIC | params | |
| cue=invalid | ★ 13336.5 | boundary=0.764 · drift_per_unit_evidence=4.59e-3 · non_decision_time=0.649 · starting_point=0.706 | 13336.6 | boundary=0.703 · drift_bias=1.244 · drift_per_unit_evidence=5.01e-3 · non_decision_time=0.745 |
| cue=neutral | 11569.4 | boundary=0.668 · drift_per_unit_evidence=0.107 · non_decision_time=0.705 · starting_point=0.723 | ★ 11523.4 | boundary=0.835 · drift_bias=1.245 · drift_per_unit_evidence=0.088 · non_decision_time=0.705 |
| cue=valid | ★ 34872.2 | boundary=0.763 · drift_per_unit_evidence=0.204 · non_decision_time=0.680 · starting_point=0.651 | 34956.2 | boundary=0.896 · drift_bias=0.834 · drift_per_unit_evidence=0.169 · non_decision_time=0.680 |
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?
linkRoitman 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.
Lower AIC wins (★). All ten fits use the same vanilla DDM (z fixed at 0.5, v0 fixed at 0, lapse fixed at 0). Compare k (drift per unit coherence) across the two species; differences in t0 are partly task-driven (saccade vs button press). The view below renders one strip plot per parameter when all model fits in a comparison share the same variant.
| Group | ddm-vanilla | |
|---|---|---|
| AIC | params | |
| Palmer J, Huk AC, Shadlen MN | ★ 2238.2 | boundary=0.998 · drift_per_unit_evidence=0.239 · non_decision_time=0.409 |
| Roitman JD, Shadlen MN | ★ 4373.4 | boundary=1.057 · drift_per_unit_evidence=0.194 · non_decision_time=0.410 |
| monkey-1 | ★ 1935.0 | boundary=0.925 · drift_per_unit_evidence=0.204 · non_decision_time=0.444 |
| monkey-2 | ★ 2440.1 | boundary=1.208 · drift_per_unit_evidence=0.182 · non_decision_time=0.358 |
| phs-ah | ★ 295.6 | boundary=1.417 · drift_per_unit_evidence=0.271 · non_decision_time=0.357 |
| phs-eh | ★ 468.0 | boundary=1.044 · drift_per_unit_evidence=0.138 · non_decision_time=0.434 |
| phs-jd | ★ 321.8 | boundary=0.915 · drift_per_unit_evidence=0.379 · non_decision_time=0.372 |
| phs-jp | ★ 301.2 | boundary=1.125 · drift_per_unit_evidence=0.325 · non_decision_time=0.398 |
| phs-mk | ★ 332.7 | boundary=0.990 · drift_per_unit_evidence=0.325 · non_decision_time=0.386 |
| phs-mm | ★ 460.2 | boundary=0.867 · drift_per_unit_evidence=0.178 · non_decision_time=0.494 |
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?
linkWalsh 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.
Deltas relative to the first row. Compare Δμ between valid and invalid; if cueing is symmetric, |valid − neutral| ≈ |invalid − neutral|.
| Finding | μ (bias) | σ (slope) | x at 75% | Δμ | Δσ | Δ threshold |
|---|---|---|---|---|---|---|
| P Walsh K, McGovern DP, Dully J, Kelly SP, O'Connell RG · cue=invalid (reference) | -19.59 | 2.06 | — | — | — | — |
| P Walsh K, McGovern DP, Dully J, Kelly SP, O'Connell RG · cue=neutral | -11.92 | 6.63 | -2.85 | 7.67 | 4.57 | — |
| P Walsh K, McGovern DP, Dully J, Kelly SP, O'Connell RG · cue=valid | -1.73 | 1.19 | -0.58 | 17.86 | -0.87 | — |
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.