Spelling out the lexeme The Dell model. Outline zConnectionist models zDell (1986) yPhenomena...
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Transcript of Spelling out the lexeme The Dell model. Outline zConnectionist models zDell (1986) yPhenomena...
Spelling out the lexeme
The Dell model
Outline
Connectionist models Dell (1986)
Phenomena Structure of model Accounts of phenomena Evaluation
Dell (1988) Changes and motivation
Connectionist Models
= (artificial) neural networks, ANNs, PDP models Models of cognition, more or less inspired by information
processing in the brain: Massive parallism, using large networks of relatively
simple units. Widely applied in cognitive psychology, robotics, medical
information systems, pattern recognition,...
Example
Node (unit, neurode) with Activation level)
Input connections
Output connections
0.7
A(t) = F [ A(t-1), IN(t-1) ]
Example
Node (unit, neurode) with Activation level)
Input connections (with weights)
Output connections
0.7
A(t) = F [ A(t-1), IN(t-1) ]
IN(t-1) = ∑jwij.Aj(t-1)
0.1 0.10.6
Important Dimensions
Representational assumptions (localized, distributed) Network Structure (Layers, Connectivity) Activational dynamics Learning rule (if any) Biological plausibility
Representational assumptions
Localist or Distributed Localist networks: one-to-one correspondence between nodes
and psychological units (ie. ‘grandmother cells’). (Dell, 1986; McClelland & Rumelhart, 1981; Roelofs, 1997)
Distributed networks (true Parallel Distributed Networks): representations as patterns of activations over many nodes (Seidenberg & McClelland, )
How to deal with serial order? M & C, 1981: 4-letter words only, each letter represented for each position.
Architectural assumptions
How many layers? (linear separability problem) Connectivity between layers: full connectivity or
bounded connectivity?). Types of connections: excitatory, inhibitory, both (Initial) weights: random (PDP-models), fixed
(McClelland & Rumelhart, 1981),uniform (Dell, 1986)
Activational Dynamics
Ai,t = F [ {(Ai(t-1)}, In,i(t-1) ] Ini(t-1) = Gj [ (Aj (t-1), Wij ]
How is input determined? How does input change activation level? How is competition resolved?
‘Lateral’ inhibition => Winner-takes-all ‘Virtual’ inhibition (Roelofs): Luce ratio
Other Dimensions
Learning rules: Supervised (Backpropagation, Delta rule) or Unsupervised (Hebb rule, Self-organizing NN)
Biological plausibility: Constraint or not? (Full connectivity, Dale’s law, ..)
The Dell (1986) model
Phenomenon of interest: phonological speech errors Characteristics & distribution Lexical bias effect Repeated phoneme effect
Data: Corpus Analyses
Substitution errors
57%: Anticipation: Bake my bike [take]
13%: Perseveration She pulled a pantrum [tantrum]
5%: Exchange waple malnut [maple walnut]
Wordshape errors
3% Deletion: _till cheaper [still]
21% Addition: hit frate [rate]
1% Shift back bloxes
Characteristics
Anticipations > Perseverations >> exchanges Additions > Deletions Effects of Speech Rate Phonotactic Constraints respected Effects of Similarity Interacting Elements
Onsets -> onsets, codas-> codas Same stress value Similar phonological features (bilabial, nasal, etc.)
Initialness effect Frequency effects
Characteristics (2)
Lexical Bias Effect Repeated phoneme effects: repeated sounds tend to
trigger misordering of neighbours Speech rate interacts with each of these effects.
Note: See Stemberger (1992): The reliability and and replicability of naturalistic speech error data. In B. Baars (Ed.),: Experimental slips and human error.
Processing
Input to ‘current’ lexeme Activation Spreading through network Spread determined by p (spreading rate) and q (decay
rate) Selection after fixed number of time steps R. Selection at the level of the phoneme, according to the
rule:
SYL -> Onset + Nucleus + Coda
Model Architecture
cat spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spa Lexeme
Syll
Rimecluster
phoneme
feature
Saying Cat: t = 1
spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spacat
Saying Cat: t = 2
spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spacat
Saying Cat: t = 3
spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spacat
Saying Cat: t = 4
spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spacat
Saying Cat: t = 5
spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spacat
Saying Cat: t = 6
spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spacat
Saying Cat: selection
spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spacat
After Selection...
spat
kaet spaet spa
aet aspOn
s p k aea
tnull
fric stop
spacat
Why does the model’s tongue slip?
An error occurs, if another unit than the correct one, is the highest activated onset, nucleus, or coda.
Reasons: There is remaining activation of a previous word There is anticipatory activation of an upcoming word Noise in the model.
What does the model capture (1)?
Effects of speech rate: the less time to selection, the more errors the model makes.
The general distribution of speech errors: Substitution > Non-Substitution Anticipation > Perseveration > Exchange
Note: the ratio of anticipation to perseveration can be pushed around (time-to-selection, decay rate, amount of anticipatory priming). Exchange rate can not be pushed around independently.
What does the model capture (2)? Lexical bias effect
kaet raet
k rae t
What does the model capture (2)? Lexical bias effect
kaet raet
k rae t
What does the model capture (2)? Lexical bias effect
kaet raet
k rae t
What does the model capture (2)? Lexical bias effect
kaet raet
k rae t
What does the model capture (3)? Repeated phoneme effect
kaet raen
k rae t
run
nu
What does the model capture (3)? Repeated phoneme effect
kaet raen
k rae t
run
nu
What does the model capture (3)? Repeated phoneme effect
kaet raen
k rae t
run
nu
What does the model capture (3)? Repeated phoneme effect
kaet raen
k rae t
run
nu
Evaluation
Distribution of speech errors: good global fit. But sensitive to particular parameter settings.
Effects of speech rate: follows naturally from processing assumptions.
Lexical bias: direct result of feedback assumption Repeated phoneme: direct result of feedback
assumption Interaction speech rate X Lex. Bias; speech rate x Rep.
Phoneme: to little time for feedback..
Evaluation (2)
Similarity Effect Position effect (built into the model) Stress effect (not accounted for) Phon. Similarity effect (feedback from feature layer)
Well-formedness effect: built into the model (/ml/ is not a cluster)
Initialness effect: not accounted for Addition bias: Opposite prediction.
Evaluation (3)
Fast running speech: resyllabification. Thus ‘gave it him’, becomes ‘ga-vi-tim’. Model produces only citation forms.
Fixed number of time steps. The model cannot predict naming latencies, only error patterns. Thus, it cannot account for what is going on normally in phonological encoding, but only for derailments of the process.
Dell (1988)
Introduction of explicit representations for phonological structure ‘WordShape Headers’.
Introducing a new selection mechanism: 1. Selection of Wordshape Headers 2. Units to be selected depend on Headers 3. Selection of units can be serial
Revised model (my version)
lopen gaper
WORDS
SYLLABLES
lo:
ga:
lg
p
ro: a:@
CVCVC
p@ p@r
1 21 2
C
C
V
V C
Why this revision?
Solves problem of deletion bias Evidence for role of CV structure in language production
(Costa & Sebastian, 1998; Meijer, 1994; 1996; Sevald, Dell, & Cole, 1995).
Evidence for seriality in phonological encoding: Meyer (1990)