Contents

# Overview

The Quest algorithm has been ported directly from the MATLAB sources available with the PsychToolbox. The MATLAB source code was written by Denis Pelli. Thanks to Denis for allowing it to be released under the BSD license to the (Python) world.

This Python version does not depend on the Vision Egg, and may be useful in other contexts.

# Example

```The intensity scale is abstract, but usually we think of it as representing log contrast.
Specify true threshold of simulated observer: 0.5
Estimate threshold: 0.4
Trial   1 at  0.7 is right
Trial   2 at  0.4 is right
Trial   3 at -0.2 is wrong
Trial   4 at  0.7 is right
Trial   5 at  0.4 is right
Trial   6 at  0.4 is wrong
Trial   7 at  1.1 is right
Trial   8 at  0.9 is right
Trial   9 at  0.7 is right
Trial  10 at  0.7 is right
36 ms/trial
Mean threshold estimate is 0.60 +/- 0.38

Quest beta analysis. Beta controls the steepness of the Weibull function.

Now re-analyzing with beta as a free parameter. . . .
logC     sd      beta    sd      gamma
0.62    0.39    3.7     4.5     0.500
Actual parameters of simulated observer:
logC    beta    gamma
0.50    3.5     0.50```

The example above is taken directly from the demo() function of Quest.py and is a direct translation of the demo included in the original MATLAB source:

```   1     print 'The intensity scale is abstract, but usually we think of it as representing log contrast.'
2
3     tActual = None
4     while tActual is None:
5         sys.stdout.write('Specify true threshold of simulated observer: ')
6         input = raw_input()
7         try:
8             tActual = float(input)
9         except:
10             pass
11
12     tGuess = None
13     while tGuess is None:
14         sys.stdout.write('Estimate threshold: ')
15         input = raw_input()
16         try:
17             tGuess = float(input)
18         except:
19             pass
20
21     tGuessSd = 2.0 # sd of Gaussian before clipping to specified range
22     pThreshold = 0.82
23     beta = 3.5
24     delta = 0.01
25     gamma = 0.5
26     q=QuestObject(tGuess,tGuessSd,pThreshold,beta,delta,gamma)
27
28     # Simulate a series of trials.
29     trialsDesired=100
30     wrongRight = 'wrong', 'right'
31     timeZero=time.time()
32     for k in range(trialsDesired):
33         # Get recommended level.  Choose your favorite algorithm.
34         tTest=q.quantile()
35         #tTest=q.mean()
36         #tTest=q.mode()
37
38         tTest=tTest+random.choice([-0.1,0,0.1])
39
40         # Simulate a trial
41         timeSplit=time.time(); # omit simulation and printing from reported time/trial.
42         response=q.simulate(tTest,tActual)
43         print 'Trial %3d at %4.1f is %s'%(k+1,tTest,wrongRight[response])
44         timeZero=timeZero+time.time()-timeSplit;
45
46         # Update the pdf
47         q.update(tTest,response);
48
49     # Print results of timing.
50     print '%.0f ms/trial'%(1000*(time.time()-timeZero)/trialsDesired)
51
52     # Get final estimate.
53     t=q.mean()
54     sd=q.sd()
55     print 'Mean threshold estimate is %4.2f +/- %.2f'%(t,sd)
56     #t=QuestMode(q);
57     #print 'Mode threshold estimate is %4.2f'%t
58
59     print '\nQuest beta analysis. Beta controls the steepness of the Weibull function.\n'
60     q.beta_analysis()
61     print 'Actual parameters of simulated observer:'
62     print 'logC beta    gamma'
63     print '%5.2f        %4.1f   %5.2f'%(tActual,q.beta,q.gamma)
```

# References

• Watson, A. B. and Pelli, D. G. (1983) QUEST: a Bayesian adaptive psychometric method. Percept Psychophys, 33 (2), 113-20.
• Pelli, D. G. (1987) The ideal psychometric procedure. Investigative Ophthalmology & Visual Science, 28 (Suppl), 366.

• King-Smith, P. E., Grigsby, S. S., Vingrys, A. J., Benes, S. C., and Supowit, A. (1994) Efficient and unbiased modifications of the QUEST threshold method: theory, simulations, experimental evaluation and practical implementation. Vision Res, 34 (7), 885-912.