Transcript Timescales of linguistic evolution

Language Evolution and Change
Presented by Brianna Conrey
Complex Adaptive Systems Seminar
February 27, 2003
Timescales of language evolution
– (Kirby & Hurford, 2002; Parisi & Cangelosi,
2002)
– Ontogeny
• Learning/Language development in individual
– Glossogeny
• Cultural evolution/Historical change
– Phylogeny
• Biological/species-level evolution
Constraints on language
evolution
• “Language as an organism”
– “Whereas humans can survive without
language, the opposite is not the case. Thus,
language is more likely to have adapted itself to
its human hosts than the other way around”
(Christiansen et al., 2002).
Constraints on language
evolution
• Speaker capabilities
– Cognitive: perception, memory, learning
– Motor/physiological: production/articulation
• Speaker interactions
– Space, both physical and social
Models of language evolution
and change
– Usually focus on one “level” of language (phonology,
syntax, or lexicon) or try to bridge gaps between levels
– Either an ontogenetic or glossogenetic timescale
– Typically minimize number of agents interacting and/or
role of space
– Adults often don’t exhibit language change, which is
assumed to take place mostly at the time of language
acquisition
Some specific models…
• Naming games: Language learning
– Steels (1997; 2002)
• Evolution of compositional language “in a
community”
– Kirby & Hurford (2002); Parisi & Cangelosi (2002)
• Emergence of dialects
– Nettle (1997); Livingstone (2002)
• Self-organization of vowel systems
– de Boer (2000; 2002)
Iterated Learning Model (ILM)
• Kirby & Hurford, 2002; Kirby, 2001
• I-language and E-language (Chomsky, 1986)
• Components of model:
– Meaning and signal spaces (here both 8-bit binary)
– Language-learning and language-using (adult) agents
• Unidirectional networks map signals to meanings
ILM, continued
• Each iteration has one learner and one adult
– At end of cycle, learner becomes adult for new cycle
• Initially no I-language for adult
• “Obverter learning strategy” for signal production
– Find signal that maximizes hearer’s chance of
understanding intended meaning; assume hearer’s
mapping approximates own
• Training through backpropagation
“8-bit” results
• Type of behavior depends on training-set size:
– Small: inexpressive, unstable
– Large: completely expressive and stable
– Medium: also completely expressive and stable, but reaches this
state more quickly
• Languages from large training sets have essentially
random mappings, but medium training sets have highly
structured mappings
– Why?
– Is this significant for real language?
• Nowak, Komarova, & Niyogi (2001) have a similar result
– Number of input sentences necessary to learn “correct” grammar is also
medium
» Accuracy is too low with small number, and learning period is too long
with large number
Emergence of recursive
compositionality
• Simulations use predicate logic for representing
meanings; strings of characters for signals
– Ex.: loves(mary,john) <-> marylovesjohn
• Heuristic-drive inductive learning algorithm
– First incorporate rule, then search for generalizations
over pairs of rules
• “Random invention” for new strings
– Both speaker and hearer add invented strings to
linguistic knowledge
Emergence of recursive
compositionality (syntax)
Modeling irregularity
• Languages are not completely compositional, but
have some irregularities
• “Principle of least effort”
– Shortest string produced for a given meaning
– Small probability of dropping characters from utterance
• Frequency
– Use non-uniform probability distribution over meaning
space inspired by Zipf’s law (word usage is inversely
proportional to its frequency rank)
Frequency correlates with
irregularity
Frequency correlates with
irregularity
• This is also what happens in natural
languages
– English verb frequency example
• In simulation, irregular forms only persist
when they have high frequency
Kirby & Hurford’s conclusions
• “Bottleneck” at point of language transmission
means that generalizations have transmission
advantage historically
• Importance of “obverter property”
• Cool result: when agents generalized part of time
and rote-memorized the rest of the time, general
rules still fixed in language
– A regular and consistent E-language does not imply that
I-language is as clean…
ILM Issues
• Ecological validity
– “Community”?
– Cognitive and motor constraints not really
considered (I-language and E-language are
implemented fairly abstractly)
– What linguistic levels (phonology, morphology,
lexicon, syntax) are being modeled?
• Compositionality as an evolutionary
advantage to “language itself”?
Self-organization of vowel
systems
• de Boer (2000, 2002)
• Concerned with actual linguistic data on vowel
systems
• Attempts to account for “universal” characteristics
of vowel systems through functional explanations
– E.g., articulatory ease, acoustic distinctiveness, process
of learning
• “Optimization” as an emergent property of the
system rather than a property of individual
speakers
Vowel systems model
• Each agent has three parts (S, D, V)
– S: synthesis function
• Mapping from possible articulations to possible acoustic
signals; includes some noise
• Output is formant frequencies of vowels, which are what is
exchanged during communication
– V: vowel prototype set
• Initially empty; not fixed in size
• Based on idea of categorical perception of speech sounds
– D: function calculating distance between heard sound
A and each vowel prototype
• Recognized vowel is one that minimizes D
Development of system
• Imitation game
– Two agents picked at random from population
(N=20) to be initiator and imitator
– Initiator produces sound to be imitated
– Imitator finds closest prototype to this sound
– Initiator communicates (“non-verbally”)
whether this was the intended sound
– Imitator can then alter vowel inventory
Changes an agent can make
Model results
• Conforms well to data from human
languages
Dotted line = emerged systems; solid line = real systems
Emergent five-vowel systems
Vowel system model issues
• Good for isolated vowels, but what about
more complex signals? other aspects of
language?
• Agent interactions still limited
• Convergence on one vowel system within a
community, but in reality dialects of a single
language often vary most in their vowel
systems, even in number of vowels
The emergence of dialects
• Livingstone, 2002
• Phenomenon of dialect continua
• Goal of model to show that linguistic diversity could emerge even
without social motivation, which other models (e.g. Nettle, 1997) have
assumed to be necessary
– Spatial organization only
The emergence of dialects
• Agents in a single line
• Implementation of de Boer’s model of
vowel systems, with additional constraint of
communication only within neighborhood
(neighborhood size is a model parameter)
• Results in formation of dialect continua
Dialect emergence model issues
• Spatial organization is not very complex
• Spatial factors are important, but social
factors do also seem to play a role in
linguistic diversity
– E.g., AAVE; Labov’s study of Martha’s
Vineyard
General points for discussion
• What kinds of issues do these models
address well? fail to address?
– Interaction of social and spatial factors in
language change
– Continuous language change: adult language
changes, too!
– Idiolects and their relationship to overall
notions of “dialect” and “language”
General points for discussion
• Bridge between phonology and syntax?
– Linguistic “levels” (phonology, morphology, syntax)
– Nowak & Komarova (2001)
• Phonology and syntax are two combinatorial levels
– de Boer (2002)
• Ability to learn temporal sequences may be connection
between speech (i.e. phonology) and syntax
– Kirby & Hurford (2002; Kirby, 2001)
• Hard to pinpoint what “level” their frequency effects describe
– Bybee (2002)
• Frequency effects: articulatory reductions found across
morpheme boundaries; word-chunking