Perceptual Learning, Roving and the Unsupervised Bias

Perceptual Learning, Roving and the Unsupervised Bias

By Aaron Clarke, Henning Sprekeler, Wolfram Gerstner and Michael Herzog

Brain Mind InstituteÉcole Polytechnique Fédérale De Lausanne

Switzerland

Talk Outline

• Perceptual Learning & Roving• The Unsupervised Bias• Critical Experiment

Perceptual Learning

Perceptual Learning

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Talk Outline

• Perceptual Learning & Roving• The Unsupervised Bias• Critical Experiment

Roving

1200”1200”

Learning Task 1

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Roving

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Learning Task 1 Learning Task 2

Roving

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Non-Roved

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Roved

1200"1800"

Adapted from Tartaglia, Bamert, Mast & H. Herzog (2009)

Hypotheses

• Roving may disrupt memory-trace-buildup for the roved stimuli (Yu et al., 2004).

• Roving may diminish the stimuli’s predictability (Adini et al., 2004).

• Roving may prevent the participants from conceptually tagging each stimulus type in order to switch their attention to the appropriate perceptual template (Zhang et al., 2008).

Roving

1200”1200” 1800” 1800”

Learning Task 1 Learning Task 2

Hypotheses

• Roving may disrupt memory-trace-buildup for the roved stimuli (Yu et al., 2004).

• Roving may diminish the stimuli’s predictability (Adini et al., 2004).

• Roving may prevent the participants from conceptually tagging each stimulus type in order to switch their attention to the appropriate perceptual template (Zhang et al., 2008).

Talk Outline

• Perceptual Learning & Roving• The Unsupervised Bias• Critical Experiment

Talk Outline

• Perceptual Learning & Roving• The Unsupervised Bias• Critical Experiment

Model Predictions

SupervisedUnsupervised Reward-Based

• No feedback • Trial by trial feedback

• Error feedback• Teacher signal

Output Desired Output

Error

Input

Output Desired Output

Error

Reward

• Feedback after many trials• Error feedback• Teacher signal

i

j

InputInput

Δwij = prei × eij Δwij = Cov(R,wij) + ‹R› ‹wij›Δwij = prei × postj

Δwij = prei × eij

Model Predictions

Unsupervised Reward-Based

• No feedback

Input

Output Desired Output

Error

Reward

• Feedback after many trials• Error feedback• Teacher signal

i

j

Supervised

• Trial by trial feedback

• Error feedback• Teacher signal

Input

Output Desired Output

Error

Herzog & Fahle (1998)

Feedback improves performance.

Learning is possible without feedback

Δwij = Cov(R,wij) + ‹R› ‹wij›Δwij = prei × postj

Reward-Based Learning

Δwij = Cov(R,wij) + ‹R› ‹wij›

weight change Covariation between reward weight change

Average reward

Averages of past trials Reward & current activations

Reward-Based Learning

Δwij = Cov(R,wij) + ‹R› ‹wij›

weight change Covariation between reward weight change

Average reward

= 0

Averages of past trials Reward & current activations

Reward-Based Learning

Δwij = Cov(R1+R2,wij) + ‹R1+R2› ‹wij›

weight change Covariation between reward weight change

Average reward

Averages of past trials

• Learning is impossible with two stimuli.

Reward & current activations

Roving

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Non-Roved

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Roved

1200"1800"

Adapted from Tartaglia, Bamert, Mast & H. Herzog (2009)

Talk Outline

• Perceptual Learning & Roving• The Unsupervised Bias• Critical Experiment

Hypothesis

• Roving impairs perceptual learning when the average reward for the two learned stimuli differs significantly.– This kind of situation occurs when the two roved

tasks differ in their difficulty levels.

Roving

1200”1200” 1800” 1800”

Learning Task 1 Learning Task 2

Results

H0: Mean Hard Slopes = 0:t(7) = -1.115, p = 0.151

1200”

1800”

H0: Mean Easy Slopes = 0:t(7) = -0.222, p = 0.415

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EasyHard

Results

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EasyHard

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H0: Mean Non-Roved Slopes = 0:t(7) = 2.144, p = 0.035

Summary• There are three types of learning models: supervised,

unsupervised and reward-based.• Only reward-based learning withstands empirical

falsification, and it suffers from the unsupervised bias.• When roving two tasks, easy and hard, learning fails, as can

be shown mathematically. And that is why roving occurs empirically.

• A strange prediction from this is that roving a hard and a very easy task should deteriorate performance. Roving two hard tasks might make learning easier than roving a hard and an easy task, and this has actually been shown in other studies.

Thank for your attention.

When is Learning During Roving Successful?

Vs.

Vs.

150 ms 500 ms

Vs.

Experiment• Used two stimuli: 1800” and

1200”.• Measured pre-training

thresholds for both stimuli in isolation.

• Trained subjects with fixed offsets (easy = 1.5 × pre-training threshold, hard = 0.9 × pre-training threshold).

• In 20 blocks of 80 trials.• Roved stimuli.

1800”

Easy

1200”

Hard

Easy

1200”

Other Hypotheses

• Roving may interact with the participants’ initial performance levels where worse initial performers learn more than high initial performers.

• Roving might cause low-level interference between stimulus types (Tartaglia et al., 2009; Zhaoping, Herzog, & Dayan, 2003).