156A/ Psych Ling 150: Psychology of Language Learning
Lecture 12 Learning Biases
Announcements
Be working on HW3 (due: 2/24) Pick up previous HWs and midterm if you haven't done so Reminder: Sean's office hours this week are on Friday, 10am - 12pm
Summary from last time: Poverty of the Stimulus and Learning Strategies
Poverty of the stimulus: Children will often be faced with multiple generalizations that are compatible with the language data they encounter. In order to learn their native language, they must choose the correct generalizations.
Items in English Items Encountered
Items not in English
Summary from last time: Poverty of the Stimulus and Learning Strategies
Claim of prior (innate) knowledge: Children only seem to make the right generalization. This suggests something biases them to make that generalization over other possible generalizations. Importantly, that something isn't available in the data itself. It is knowledge they must already know to succeed at learning language.
Items in English Items Encountered
Items not in English
Summary from last time: Poverty of the Stimulus and Learning Strategies
One Learning Bias: Experimental research on artificial languages (Gerken 2006) suggests that children prefer the more conservative generalization compatible with the data they encounter.
data
less general more general
Learning Biases
"Innate capacities may take the form of biases or sensitivities toward particular types of information inherent in environmental events such as language, rather than a priori knowledge of grammar itself." - Seidenberg (1997)
Example: Children seem able to calculate transitional probabilities across syllables (Saffran, Aslin, & Newport 1996). Example: Adults seem able to calculate transitional probabilities across grammatical categories (Thompson & Newport 2007)
But is it always just statistical information of some kind?
Gambell & Yang (2006) found that tracking transitional probabilities across syllables yields very poor word segmentation on realistic English data. Other learning strategies like the Unique Stress Constraint and algebraic learning did far better. These other learning strategies were not statistical in nature - they did not use probabilistic information available in the data.
Pea et al. 2002: Experimental Study
Goal: examine the relation between statistical learning mechanisms and non-statistical learning mechanisms (like algebraic learning).
Adult learners' task on artificial language: (1) word segmentation (2) generalization about words in the language (somewhat similar to categorization)
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Pea et al. 2002: Experimental Study
Goal: examine the relation between statistical learning mechanisms and non-statistical learning mechanisms (like algebraic learning).
Adult learners' task on artificial language: (1) word segmentation (2) generalization about words in the language (somewhat similar to categorization)
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Pea et al. 2002: Experimental Study
Goal: examine the relation between statistical learning mechanisms and non-statistical learning mechanisms (like algebraic learning).
Adult learners' task on artificial language: (1) word segmentation (2) generalization about words in the language (somewhat similar to categorization)
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BE__GA is a type of word in this language
Pea et al. 2002: Experimental Study
The artificial language: "AXC language" Syllables: A, X, C Generalization: A perfectly predicts C: A_C is a word in the language pu_ki, be_ga, ta_du Intervening syllable X: _ra_, _li_, _fo_ pu ra ki be li ga ta fo du puand a ki ta li du be ra ga ... QuickTime fo
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Pea et al. 2002: Experimental Study
The artificial language: "AXC language" Note: transitional probability information is not informative.
TrProb = 1/3 = .333...
pu ra ki be li ga ta fo du pu fo ki ta li du be ra ga ... QuickTimeZ and a
decompressor are needed to see this picture.
Pea et al. 2002: Experimental Study
The artificial language: "AXC language" Note: transitional probability information is not informative.
TrProb = .5
pu ra ki be li ga ta fo du pu fo ki ta li du be ra ga ... QuickTimeOE and a
decompressor are needed to see this picture.
Pea et al. 2002: Experimental Study
The artificial language: "AXC language" Note: transitional probability information is not informative.
TrProb is actually higher at word boundaries...
.333 .333
.5 .333 .333.5
pu ra ki be li ga ta fo du pu fo ki ta li du be ra ga ... QuickTime^ and a
decompressor are needed to see this picture.
Pea et al. 2002: Experimental Study
The artificial language: "AXC language" Note: transitional probability information is not informative. Only non-adjacent syllables are informative about what words are in the language.
Non-adjacent syllable probability = 1
pu ra ki be li ga ta fo du pu fo ki ta li du be ra ga ... QuickTimeZ and a
decompressor are needed to see this picture.
First Question: Good word segmentation?
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10 minute familiarization period Can adults recognize words from part-words? Remember: transitional probability won't help - it'll bias them the wrong way.
word: pu ra ki TrProb (pura) = 1/3, TrProb (raki) = 1/3, TrProb (puraki) = TrProb(pura)*TrProb(raki) = 1/3*1/3 = 1/9 part-word: ra ki be TrProb(raki) = 1/3, TrProb (kibe) = 1/2 TrProb (rakibe) = TrProb(raki)*TrProb(kibe) = 1/3*1/2 = 1/6 (higher than 1/9)
First Question: Good word segmentation?
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Adults prefer real words to part-words that they actually heard. This means they can unconsciously track the non-adjacent probabilities of the AXC language and identify the words.
Next Question: Good generalization about words?
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Do adults make the generalization about which words belong in the language (ex: that words of the language can take the form of pu_ki ) Compare: rakibe
Part-word heard during training
vs.
pubeki
Word that follows the pattern, but is not a word heard during training
Next Question: Good generalization about words?
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Adults have no preference between part-words that they actually heard and real words that follow the generalization about words in the language, but which they didn't actually hear. This means they can't use the non-adjacent probabilities of the AXC language to identify properties of the words in general.
What's going on?
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X
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"We conjecture that this reflects the fact that the discovery of components of a stream and the discovery of structural regularities require different sorts of computations...the process of projecting generalizations...may not be statistical in nature." - Pea et al. (2002)
Prediction for Different Types of Computation
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X
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"...it is the type signal of being processed rather than the amount of familiarization that determines the type of computation in which participants will engage...changing a signal even slightly may induce a change in computation." Pea et al. (2002) Types of computation: statistical, algebraic
New Stimuli: Stimulating Algebraic Computation?
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10 minute familiarization period with 25ms (subliminal) gaps after each word If word segmentation is already accomplished, subjects will be free to engage their algebraic computation. This should allow them to succeed at identifying the properties of words in the artificial language (e.g. pu_ki, be_ga, ta_du), since this kind of structural regularity is hypothesized to be found by algebraic computation.
Question: Good generalization about words?
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Adults prefer real words that follow the generalization about words in the language, but which they didn't actually hear, over part-words they did hear. This means they can use the non-adjacent probabilities of the AXC language to identify properties of the words in general. They make the structural generalization.
Prediction: Algebraic vs. Statistical
Idea: Subjects are really using a different kind of computation (algebraic) because of the nature of the input. Specifically, the input is already subliminally segmented for them, so they don't need to engage their statistical computation abilities to accomplish that. Instead, they are free to (unconsciously) notice more abstract properties via algebraic computation. Prediction 1: If the words are not segmented subliminally, statistical computation will be invoked. It doesn't matter if subjects hear a lot more data. Their performance on preferring a real word they didn't hear over a part-word they did hear will not improve.
Question: Good generalization about words?
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If given 30 minutes of training on unsegmented artificial language, do adults fail to make the generalization even though they have a lot more data?
Compare:
rakibe
Part-word heard during training a lot!
vs.
pubeki
Word that follows the pattern, but is not a word heard during training
Question: Good generalization about words?
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If given 30 minutes of training on unsegmented artificial language, adults really prefer part-words that they actually heard over real words that follow the generalization about words in the language, but which they didn't actually hear. They can't make the generalization: prediction 1 seems true.
Prediction: Algebraic vs. Statistical
Idea: Subjects are really using a different kind of computation (algebraic) because of the nature of the input. Specifically, the input is already subliminally segmented for them, so they don't need to engage their statistical computation abilities to accomplish that. Instead, they are free to notice more abstract properties via algebraic computation. Prediction 2: If the words are segmented subliminally, algebraic computation will be invoked. It doesn't matter if subjects hear a lot less data. They will still prefer a real word they didn't hear over a part-word they did hear.
Question: Good generalization about words?
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If given 2 minutes of training on segmented artificial language, do adults make the generalization even though they have a lot less data?
Compare:
rakibe
Part-word heard during training
vs.
pubeki
Word that follows the pattern, but is not a word heard during training
Question: Good generalization about words?
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If given 2 minutes of training on segmented artificial language, adults really prefer real words that follow the generalization about words in the language, but which they didn't actually hear, over part-words that they actually heard. They still make the generalization: prediction 2 seems true.
Pea et al. (2002): Summary
While humans may be able to compute powerful statistical relationships among the language data they're exposed to, this may not be enough to capture all the linguistic knowledge humans come to possess...