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Perceptual Learning, Roving and the Unsupervised Bias. By Aaron Clarke, Henning Sprekeler, Wolfram Gerstner and Michael Herzog Brain Mind Institute École Polytechnique Féd é rale De Lausanne Switzerland. Talk Outline. Perceptual Learning & Roving The Unsupervised Bias - PowerPoint PPT Presentation
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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
0 5 10 15 20 25 30 35 40 45 500
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3.5
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4.5
Block Number
d'
Talk Outline
• Perceptual Learning & Roving• The Unsupervised Bias• Critical Experiment
Roving
1200”1200”
Learning Task 1
0 5 10 15 20 25 30 35 40 45 500
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4.5
Block Number
d'
Roving
1200”1200” 1800” 1800”
Learning Task 1 Learning Task 2
Roving
0 5 10 15 20 250.5
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1.5
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2.5
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3.5
4
Block Number
d'
Non-Roved
0 5 10 15 20 250.5
1
1.5
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2.5
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3.5
4
Block Number
d'
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
0 5 10 15 20 250.5
1
1.5
2
2.5
3
3.5
4
Block Number
d'
Non-Roved
0 5 10 15 20 250.5
1
1.5
2
2.5
3
3.5
4
Block Number
d'
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
0 5 10 15 200
0.5
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1.5
2
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Block Number
d'
EasyHard
Results
0 5 10 15 200
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4.5
Block Number
d'
EasyHard
0 5 10 15 200
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4.5
Block Number
d'
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).