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Adaptive Methods. Research Methods Fall 2010 Tamás Bőhm. Adaptive methods. Classical (Fechnerian) methods: stimulus is often far from the threshold inefficient A daptive methods: accelerated testing Modifications of the method of constant stimuli and method of limits. Adaptive methods.

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adaptive methods

Adaptive Methods

Research Methods

Fall 2010

Tamás Bőhm

adaptive methods2
Adaptive methods
  • Classical (Fechnerian) methods: stimulus is often far from the thresholdinefficient
  • Adaptive methods: accelerated testing
    • Modifications of the method of constant stimuli and method of limits
adaptive methods3
Adaptive methods
  • Classical methods: stimulus values to be presented are fixed before the experiment
  • Adaptive methods: stimulus values to be presented depend critically on preceding responses
adaptive methods4
Adaptive methods
  • Constituents
    • Stepping rule: which stimulus level to use next?
    • Stopping criterion: when to finish the session?
    • What is the final threshold estimate?
  • Performance
    • Bias: systematic error
    • Precision: related to random error
    • Efficiency: # of trials needed for a specific precision; measured by the sweat factor
notations
Notations

Xn stimulus level at trial n

Zn response at trial n

Zn= 1 detected / correct

Zn = 0 not detected / incorrect

φtarget probability

absolute threshold: φ = 50%

difference threshold: φ = 75%

2AFC: φ = 50% + 50% / 2 = 75%

4AFC: φ = 25% + 75% / 2 = 62.5%

xφ threshold

adaptive methods6
Adaptive methods
  • Classical methods: stimulus values to be presented are fixed before the experiment
  • Adaptive methods: stimulus values to be presented depend critically on preceding responses

Xn+1 = f(φ, n, Zn, Xn, Zn-1, Xn-1,…, Z1, X1)

adaptive methods7
Adaptive methods
  • Nonparametric methods:
    • No assumptions about the shape of the psychometric function
    • Can measure threshold only
  • Parametric methods:
    • General form of the psychometric function is known, only its parameters (threshold and slope) need to be measured
    • If slope is also known: measure only threshold
nonparametric adaptive methods
Nonparametric adaptive methods
  • Staircase method (aka. truncated method of limits, simple up-down)
  • Transformed up-down method
  • Nonparametric up-down method
  • Weighted up-down method
  • Modified binary search
  • Stochastic approximation
  • Accelerated stochastic approximation
  • PEST and More Virulent PEST
staircase method
Stepping rule:Xn+1 = Xn - δ(2Zn - 1)

fixed step size δ

if response changes:direction of steps is reversed

Stopping criterion:after a predetermined number of reversals

Threshold estimate: average of reversal points(mid-run estimate)

Converges to φ = 50% cannot be used for e.g. 2AFC

Staircase method
transformed up down method
Improvement of the simple up-down (staircase) method

Xn+1 depends on 2 or more preceding responses

E.g.1-up/2-down or 2-step rule:

Increase stimulus level after each incorrect response

Decrease only after 2 correct responses

φ = 70.7%

Threshold:mid-run estimate

8 rules for 8 different φ values(15.9%, 29.3%, 50%, 70.7%, 79.4%, 84.1%)

Transformed up-down method

reversal points

nonparametric up down method
Nonparametric up-down method
  • Stepping rule: Xn+1= Xn- δ(2ZnSφ - 1)
    • Sφ: random number p(Sφ=1) = 1 / 2φ p(Sφ=0) = 1 – (1 / 2φ)
    • After a correct answer: stimulus decreased with p = 1 / 2φ stimulus increased with p = 1 - (1 / 2φ)
    • After an incorrect answer: stimulus increased
  • Can converge to any φ≥ 50%
weighted up down method
Weighted up-down method
  • Different step sizes for upward (δup) and downward steps (δdown)
modified binary search
‘Divide and conquer’

Stimulus interval containing the threshold is halved in every step(one endpoint is replaced by the midpoint)

Stopping criterion: a lower limit on the step size

Threshold estimate:last tested level

Heuristic, no theoreticalfoundation

Modified binary search

Figure from Sedgewick & Wayne

stochastic approximation
Stochastic approximation
  • A theoretically sound variant of the modified binary search
  • Stepping rule:
    • c: initial step size
    • Stimulus value increases for correct responses,decreases for incorrect ones
    • If φ = 50%: upward and downward steps are equal; otherwise asymmetric
    • Step size (both upward and downward) decreases from trial to trial
  • Can converge to any φ
accelerated stochastic approximation
Accelerated stochastic approximation
  • Stepping rule:
    • First 2 trials: stochastic approximation
    • n > 2:step size is changed only when response changes (mreversals: number of reversals)
  • Otherwise the same as stochastic approximation
  • Less trials than stochastic approximation
parameter estimation by sequential testing pest
Parameter Estimation by Sequential Testing (PEST)
  • Sequential testing:
    • Run multiple trials at the same stimulus level x
    • If x is near the threshold, the expected number of correct responses mc after nx presentations will be around φnx the stimulus level is changed if mcis not in φnx ± w
    • w: deviation limit; w=1 for φ=75%
  • If the stimulus level needs to be changed:step size determined by a set of heuristic rules
  • Variants: MOUSE, RAT, More Virulent PEST
adaptive methods20
Adaptive methods
  • Nonparametric methods:
    • No assumptions about the shape of the psychometric function
    • Can measure threshold only
  • Parametric methods:
    • General form of the psychometric function is known, only its parameters (threshold and slope) need to be measured
    • If slope is also known: measure only threshold
parametric adaptive methods
Parametric adaptive methods
  • A template for the psychometric function is chosen:
    • Cumulative normal
    • Logistic
    • Weibull
    • Gumbel
parametric adaptive methods22
Parametric adaptive methods
  • Only the parameters of the template need to be measured:
    • Threshold
    • Slope
fitting the psychometric function
Fitting the psychometric function
  • Linearization (inverse transformation)of data points
    • Inverse cumulative normal (probit)
    • Inverse logistic(logit)
fitting the psychometric function24
Fitting the psychometric function
  • Linear regression
  • Transformation of regression line parameters

X-intercept & linear slope

Threshold & logistic slope

contour integration experiment26
Contour integration experiment

5-day perceptual learning

adaptive probit estimation
Adaptive probit estimation
  • Short blocks of method of constant stimuli
  • Between blocks: threshold and slope is estimated (psychometric function is fitted to the data) and stimulus levels adjusted accordingly
    • Assumes a cumulative normal function probit analysis
  • Stopping criterion: after a fixed number of blocks
  • Final estimate of threshold and slope: re-analysis of all the responses
adaptive probit estimation28
Adaptive probit estimation
  • Start with an educated guess of the threshold and slope
  • In each block: 4 stimulus levels presented 10 times each
  • After each block: threshold ( ) and slope ( ) is estimatedby probit analysis of the responses in block
  • Stimulus levels for the next block are adjusted accordingly
    • Estimated threshold and slopeapplied only through correctionfactors  inertia
measuring the threshold only
Function shape (form & slope) is predetermined by the experimenter

Only the position along the x-axis (threshold) needs to be measured

Iteratively estimating the threshold and adapting the stimulus levels

Two ways to estimate the threshold:

Maximum likelihood (ML)

Bayes’ estimation

QUEST, BEST PEST, ML-TEST, Quadrature Method, IDEAL, YAAP, ZEST

Measuring the threshold only
maximum likelihood estimation
Maximum likelihood estimation
  • Construct the psychometric function with each possible threshold value
  • Calculate the probability of the responses with each threshold value (likelihood)
  • Choose the threshold value for which the likelihood is maximal (i.e. the psychometric function that is the most likely to produce such responses)

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- - + +

- + + +

bayes estimation
Bayes’ estimation
  • Prior information is also used
    • Distribution of the threshold in the population(e.g. from a survey of the literature)
    • The experimenter’s beliefs about the threshold

values of the psychometric functions at the tested stimulus levels

a priori distribution of the threshold

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