Open drbenvincent opened 5 years ago
w(p) = exp(-(-(ln(p))^gamma ) for 0<gamma<=1 The 1 parameter version has:
w(p) = exp(-(-(ln(p))^gamma )
0<gamma<=1
Todo:
w(p) = exp(-alpha(-(ln(p))^gamma ) for 0<gamma<=1 and 0<s The 2-parameter version has:
w(p) = exp(-alpha(-(ln(p))^gamma )
0<s
chooser.py
Prelec, D. (1998). The probability weighting function. Econometrica, 66, 497–527.
1 parameter version
w(p) = exp(-(-(ln(p))^gamma )
for0<gamma<=1
The 1 parameter version has:Todo:
2 parameter version
w(p) = exp(-alpha(-(ln(p))^gamma )
for0<gamma<=1
and0<s
The 2-parameter version has:Todo:
finish up tasks
chooser.py
in the PsychoPy demoReference
Prelec, D. (1998). The probability weighting function. Econometrica, 66, 497–527.