Full results of castleCSF 5-folds fitting on 18 datasets

Fitting error

Dataset Fitting error Sensitivity adjustment
castleCSF castleCSF
Average training 3.50 ± 0.02 [dB] N/A
Average testing 3.59 ± 0.12 [dB] N/A
modelfest 2.67 [dB] 1.075
hdrvdp_csf 2.29 [dB] 1.334
rovamo1993 2.92 [dB] 1.549
laird2006 5.84 [dB] 0.942
snowden1995 4.15 [dB] 1.054
robson1966 2.55 [dB] 1.029
virsu1979 4.76 [dB] 1.253
virsu1982 3.19 [dB] 0.957
wright1983 2.51 [dB] 0.819
colorfest 2.94 [dB] 0.933
hdr_csf 3.78 [dB] 1.000
kim2013 3.82 [dB] 1.130
five_centres 3.34 [dB] 1.201
lucassen2018 2.96 [dB] 0.603
hdr_csf_disc 3.19 [dB] 1.564
kong2018 3.78 [dB] 1.150
vanderHorst1969_b 3.37 [dB] 0.689
hansen2009 6.43 [dB] 0.820

Model parameters

castleCSF
M_lms2acc = 1.0000 1.0000 0 1.0000 -2.3112 0 -1.0000 -1.0000 50.9875 p.rg.sigma_sust = 16.505; p.rg.beta_sust = 1.15244; p.rg.ch_sust.S_max = [ 603.119 39.1484 0.469632 ]; p.rg.ch_sust.f_max = 0.0252664; p.rg.ch_sust.bw = 2.29106; p.rg.A_0 = 2576.1; p.rg.f_0 = 0.0728586; p.rg.ecc_drop = 0.0591377; p.rg.ecc_drop_nasal = 2.89541e-05; p.rg.ecc_drop_f = 2.04986e-69; p.rg.ecc_drop_f_nasal = 0.172617; p.yv.sigma_sust = 8.23433; p.yv.beta_sust = 1.03276; p.yv.ch_sust.S_max = [ 153.288 67.8963 0.404577 ]; p.yv.ch_sust.f_max = 0.00493065; p.yv.ch_sust.bw = 2.65931; p.yv.A_0 = 2.81739e+07; p.yv.f_0 = 0.000630395; p.yv.ecc_drop = 0.00356433; p.yv.ecc_drop_nasal = 5.85804e-141; p.yv.ecc_drop_f = 0.00805084; p.yv.ecc_drop_f_nasal = 0.00966713; p.ach.ach_sust.S_max = [ 55.5604 9.40644 0.135689 5.4066e-07 1.0999e+10 ]; p.ach.ach_sust.f_max = [ 1.91067 136.387 0.253104 ]; p.ach.ach_sust.bw = 0.000213295; p.ach.ach_sust.a = 0.0463763; p.ach.ach_sust.A_0 = 157.103; p.ach.ach_sust.f_0 = 0.702338; p.ach.ach_trans.S_max = [ 0.178535 2862.69 ]; p.ach.ach_trans.f_max = 0.000334353; p.ach.ach_trans.bw = 2.64717; p.ach.ach_trans.a = 0.000241177; p.ach.ach_trans.A_0 = 4.21031; p.ach.ach_trans.f_0 = 2.93507; p.ach.sigma_trans = 0.0853318; p.ach.sigma_sust = 10.9068; p.ach.omega_trans_sl = 2.52869; p.ach.omega_trans_c = 4.58131; p.ach.ecc_drop = 0.026348; p.ach.ecc_drop_nasal = 0.0427916; p.ach.ecc_drop_f = 0.0185086; p.ach.ecc_drop_f_nasal = 0.006635; Parameters for Ach component: p.ach_sust.S_max = [ 55.5604 9.40644 0.135689 5.4066e-07 1.0999e+10 ]; p.ach_sust.f_max = [ 1.91067 136.387 0.253104 ]; p.ach_sust.bw = 0.000213295; p.ach_sust.a = 0.0463763; p.ach_trans.S_max = [ 0.178535 2862.69 ]; p.ach_trans.f_max = 0.000334353; p.ach_trans.bw = 2.64717; p.ach_trans.a = 0.000241177; p.ach_trans.A_0 = 4.21031; p.ach_trans.f_0 = 2.93507; p.sigma_trans = 0.0853318; p.sigma_sust = 10.9068; p.omega_trans_sl = 2.52869; p.omega_trans_c = 4.58131; p.ecc_drop = 0.026348; p.ecc_drop_nasal = 0.0427916; p.ecc_drop_f = 0.0185086; p.ecc_drop_f_nasal = 0.006635; Parameters for RG component: p.ch_sust.S_max = [ 603.119 39.1484 0.469632 ]; p.ch_sust.f_max = 0.0252664; p.ch_sust.bw = 2.29106; p.A_0 = 2576.1; p.f_0 = 0.0728586; p.sigma_sust = 16.505; p.beta_sust = 1.15244; p.ecc_drop = 0.0591377; p.ecc_drop_nasal = 2.89541e-05; p.ecc_drop_f = 2.04986e-69; p.ecc_drop_f_nasal = 0.172617; Parameters for YV component: p.ch_sust.S_max = [ 153.288 67.8963 0.404577 ]; p.ch_sust.f_max = 0.00493065; p.ch_sust.bw = 2.65931; p.A_0 = 2.81739e+07; p.f_0 = 0.000630395; p.sigma_sust = 8.23433; p.beta_sust = 1.03276; p.ecc_drop = 0.00356433; p.ecc_drop_nasal = 5.85804e-141; p.ecc_drop_f = 0.00805084; p.ecc_drop_f_nasal = 0.00966713;

↸CSF model: castleCSF

L+M
L-M
S-(L+M)

Color mechanisms
Sustained and transient response
Peak sensitivity

Legend

To keep the plots legible, only up to 3 models are plotted.

↸Dataset: [modelfest] ModelFest

Achromatic CSF as a function of frequency

↸Dataset: [hdrvdp_csf] HDR-VDP CSF

Achromatic CSF as a function of frequency

Achromatic CSF as a function of size

↸Dataset: [rovamo1993] Rovamo et al. 1993

CSF as the funcation of stimulus area

CSF as the function of spatial frequency

↸Dataset: [laird2006] Laird et al. 2006

Achromatic CSF as a function of temporal frequency for different spatial frequencies

↸Dataset: [snowden1995] Snowden et al. 1995

Temporal contrast sensitivity at different spatial frequencies and luminance levels

↸Dataset: [robson1966] Robson 1966

Spatial CSF for different temporal frequencies

Temporal CSF for different spatial frequencies

↸Dataset: [virsu1979] Virsu & Rovamo 1979

Contrast sensitivity of central and peripheral vision as a function of spatial frequency and eccentricity

↸Dataset: [virsu1982] Virsu et al. 1982

Contrast sensitivity as the function of frequency

↸Dataset: [wright1983] Wright and Johnson 1983

CSF as function of eccentricity

↸Dataset: [colorfest] ColorFest

Chromatic CSF as a function of frequency

↸Dataset: [hdr_csf] High Dynamic Range CSF

CSF as the function of frequency at different luminance levels (fixed number of cycles)

CSF for different number of cycles

↸Dataset: [kim2013] Chromatic CSF [Kim et al. 2013]

Chromatic CSF as a function of frequency

↸Dataset: [five_centres] Five centres [Xu et al. 2020]

Chromatic CSF as ellipses

Chromatic CSF as a function of frequency

↸Dataset: [lucassen2018] Lucassen et al. 2018

Lucassen2018 CSF sensitivity vs spatial frequency

↸Dataset: [hdr_csf_disc] High Dynamic Range Disc CSF

CSF as the function of size at different luminance levels

↸Dataset: [kong2018] Kong et al. 2018

Chromatic CSF as a function of temporal frequency (9 background colors and 4 color directions in uv space

↸Dataset: [vanderHorst1969_b] Van der Horst & Bouman 1969

Static chromatic contrast thresholds as a function of spatial frequency

Static chromatic contrast thresholds as function of luminance

Chromatic contrast thresholds for travelling sine waves as function of spatial frequency

Chromatic contrast thresholds for travelling sine waves as function of temporal frequency

↸Dataset: [hansen2009] Hansen et al. 2009

CSF as the function of eccentricity