## MinExpEntStair

##### >Psychtoolbox>PsychStairCase

Minimum Expected Entropy Staircase

The staircase gives suggestions for which probe value to test next,

choosing the probe that will provide the most information (based on the

principle of minimum entropy = maximally unambiguous probability

distribution). Probes are chosen from a set of possible probe values

provided at staircase init, and their use is evaluated based on the

expected amount of information gain given a space of PSE and slope values

to test over.

See MinExpEntStairDemo for an example and the comments in the method

functions below for use of the different staircase methods.

By default, a psychometric function ranging from 0% to 100% is used, as

is suitable for discrimination experiments with a standard in the middle

of the possible stimulus parameter range. For other paradigms, such as

n-AFC detection tasks, one can set the guessrate input during staircase

init to 1/num_alternatives, e.g. .5 when doing a 2IFC detection task.

This guess rate is thus not the rate at which participants guess instead

of do your task (thats the lapse rate), it the minimum rate of correct

responses as determined by your design. NB: below discussion is based on

the default psychometric function with the full range, but all points are

equally valid for a scaled psychometric function.

It is recommended to have the staircase determine the optimal next probe

based on only a random subset of the response history (see the

‘toggle_use_resp_subset’ and ‘toggle_use_resp_subset_prop’ methods). This

makes its operation more robust for response errors and also avoids probe

oscillations when the fit estimate is converging.

When we are close to convergence, probes will tend to be near the 25% and

75% points. If a probe is 25% and you answer ‘1’ (pedestal faster, which

is likely, because it’s near the correct 25% point), then for the next

trial the peak in expected entropy reduction will generally be the 75%

point, and vice versa. This can lead to undesirable probe sequences where

the correct response alternates 0,1,0,1,0,1. If you choose a random

subset, this will completely eliminate the problem. If the staircase has

converged to where there are two almost equal expected entropy minima,

then small variations due to the selection of subsets will randomly vary

which minimum emerges as lowest.

This strategy does not significantly affect optimal operation of the

staircase. Lots of probe values provide useful information. Therefore, it

is not crucial to have a highly accurate estimate of likelihoods, so

relatively few trials are sufficient (less than are needed to for final

estimates of PSE and DL). Throwing out trials for the staircase

computation yields robustness without much cost.

Another option would be to load a non-uniform prior on the space of

possible location/mean/PSE and dispersion/slope parameters (known as mu

and sigma respectively for a cumulative Gaussian - see the loadprior

method). Probe sampling will then stay reasonable in early trials even if

there were a couple bad responses. But this strategy is not as robust as

using a random subset – bad trials will continue to have an effect

throughout.

In absence of anything to base the optimal probe value on, the first

probe is chosen randomly from the set of possible probes. When a prior

was loaded, a likelihood distribution is available based on which the

optional probe value can be computed. If for any other reason choosing

the next probe based on the measure of minimum expected entropy fails,

the staircase will fall back on the same random probe sampling strategy.

There is an option to set the first probe value to be tested (see the

set_first_value method), which, for the first trial only, will overrule

both of the above probe choice strategies. This can be useful if you want

to be sure that the first trial is an easy one so the participant knows

what to expect.

Another measure for robustness is to choose a small lapse rate. If lapse

would be zero and a response error is made by the observer, immediately a

whole range of mean-slope combinations becomes impossible. If lapse rate

is non-zero, these would still have a non-zero probability and the

staircase can rebound. Therefore a lapse rate of 5% or even more

depending on task difficulty is always recommended. NB: in the default

discrimination setup of the staircase (guessrate is not specified or set

to 0), half of the lapse rate is taken off the bottom of the psychometric

function and half is taken off the top. So if the lapse rate is 0.05, the

psychometric function will range from 0.025 to 0.975. In the setup for a

n-AFC detection experiment when the psychometric function has a lower

bound of 1/num_alternatives, the lapse rate is taken off the top. So when

the guess_rate is set to .5 (2AFC) and the pase rate is set to .05, the

psychometric function will range from 0.05 to 0.95.

Note that the staircase does not support a 0 lapse rate in the first

place as it works with log-probability and we get in trouble if we would

take the log of a 0 probability. Any lapse rate lower than 1e-10 will be

adjusted to 1e-10 upon calling the init method.

If the staircase gets stuck at one of the bounds of the probe set, check

that the sign of the slope space matches the expected sign of the

response. E.g., lets look at an experiment in which you are doing 2IFC

task in which the observer is asked to report which interval contained

the faster motion. If the observer choses the test over the pedestal

interval the response is 1, if the observer chosen the pedestal to be

faster, the response is 0. All slopes in the set would in this case be

positive as the low end of the probe space (slow speeds) is associated

with response 0 and the high end with response 1. If we however asked the

observer to indicate the slower interval, the slopes in our slope set

would not match the task, and the staircase would get stuck at one of the

probe bounds. In this case, the lower end of the probe space is

associated with the response 1 and the higher end with the response

0–we’d thus have a negative slope for the fitted cumulative probability

function.

The staircase currently only supports logistic and cumulative Gaussian

(default) psychometric functions (see the set_psychometric_func method),

but others could easily be implemented. Changes should be needed only to

the private fit_a_point method near the bottom of this mfile, providing

that the function is characterized by two parameters (which do not

necessarily have to be PSE and slope, though that is the terminology

here.

Should you implement such a function, please do send me your code to

dcnieho @at@ gmail.com.

The above discussion assumes that response inputs to the process_resp

method are either 0 or 1 (see note above about their meaning) though in

practice anything larger than 0 is treated as 1 and anything lower than

0, including 0, is treated as 0. the staircase can thus easily be

integrated with programs that use a 1, -1 response scheme.

For actual offline fitting of your data, you would probably want to use a

dedicated toolbox such as Prins, N & Kingdom, F. A. A. (2009) Palamedes:

Matlab routines for analyzing psychophysical data.

http://www.palamedestoolbox.org. instead of using the function parameters

or PSE and DL returned from staircase methods get_fit and get_PSE_DL.

Also note that while the staircase runs far more robust when a small

lapse rate is assumed, it is common to either fit the psychometric

function without a lapse rate, or otherwise with the lapse rate as a free

parameter (possibily varying only over subjects, but not over conditions

within each subject).

References:

Based on the Minimum expected entropy staircase method developed by:

Saunders JA & Backus BT (2006). Perception of surface slant from

oriented textures. Journal of Vision 6(9), article 3

Discussions of conceptually similar staircases can be found in:

Kontsevich LL & Tyler CW (1999). Bayesian adaptive estimation of

psychometric slope and threshold. Vision Res 39(16), pp. 2729-37

Lesmes LA, Lu ZL, Baek J & Albright TD (2010). Bayesian adaptive

estimation of the contrast sensitivity function: The quick CSF method.

Journal of Vision 10(3), article 17

`Psychtoolbox/PsychStairCase/MinExpEntStair.m`