The importance of rapid cultural convergence in
the evolution of learned symbolic communication
Kenny Smith
Language Evolution and Computation Research Unit,
Department of Theoretical and Applied Linguistics, The University of Edinburgh,
Adam Ferguson Building, 40 George Square, Edinburgh EH8 9LL
[email protected]
Abstract. Oliphant [6, 7] contends that language is the only naturally-
occurring, learned symbolic communication system, because only humans can accurately observe meaning during the cultural transmission
of communication. This paper outlines several objections to Oliphant's
argument. In particular, it is argued that learning mechanisms necessary
to support learned symbolic communication may be unlikely to evolve in
non-human species, irrespective of their capacity for observing meaning.
This is due to a delay between the emergence of the appropriate learning
mechanism and a tness payo to individuals possessing that learning
mechanism. Two factors which reduce this delay and increase the likelihood of learned symbolic communication emerging are investigated.
These factors emphasise the important role played by cultural processes
in gene-culture coevolution during the evolution of communication.
1 Introduction
Language is unique among the communication systems of the natural world it is culturally transmitted, the relationship between basic lexical tokens and
their meanings is arbitrary and those basic lexical tokens are combined to form
structured forms which are used to communicate complex structured meanings.
How did language come to be as it is and why is it unique?
Much recent work in the eld has focused on the evolution of syntactic communication. Explanations of the human capacity for syntax have placed emphasis
on two contrasting adaptive processes:
Genetic adaptation of the genetically-encoded human language acquisition device
to support syntactic communication due to tness advantages oered by
syntactic communication (e.g. [5, 8]). If this is the key adaptive process, the
uniqueness of language can be explained in terms of a unique set of selection
pressures experienced by the ancestors of modern humans.
Cultural adaptation of language in favour of compositionality, due to cultural
selection resulting from language learner biases during cultural transmission
of communication (e.g. [1, 3]). Such models are not primarily concerned with
the origin of the language learner's biases. If cultural adaptation is the key
2
adaptive process, there are at least two possible explanations for the uniqueness of human language - only our ancestors underwent the mutation or set
of mutations which equipped them with the necessary mental apparatus, or
only the unique set of circumstances experienced by our ancestors resulted
in selection for the mental apparatus required for the cultural evolution of
language to begin.
Recent work by Oliphant [6, 7], building on pioneering work by Hurford [2], focuses on the more basic issue of the emergence of arbitrary and conventionalised
word meaning (see [4] for an alternative approach to a similar issue). Oliphant
works within the cultural adaptation framework and makes two claims. Firstly,
human language is the only learned symbolic communication system. Secondly,
language is unique in this respect due to the human capacity to read the communicative intentions of other language users. Once this capacity is established
optimal, learned symbolic communication reliably follows through cultural evolution of communication systems.
This paper is primarily concerned with integrating the genetic and cultural
adaptation styles of explanation and applying this integrated approach to an
investigation of Oliphant's second claim.
2 Oliphant's argument
Oliphant [6, 7] argues that human language is the only learned symbolic communication system occurring in the natural world and that this fact can be explained
in terms of the problem of observing meaning during cultural transmission of
communication systems. In order to learn a mapping from a signal to its meaning a learner must observe the meaning-signal pairs used by other individuals.
While observing a signal may be fairly straightforward, observing the meaning
that signal is intended to convey may not be - the relationship between meaning
and signal is arbitrary in a symbolic communication system. Oliphant argues
that only humans have mastered the trick of guessing which meaning a signal
is intended to convey during acquisition of arbitrary meaning-signal mappings only humans can observe meaning reliably.
Oliphant [7] supports this claim with a computational model. Bidirectional
associative networks capable of mapping from meanings to signals are used to
model individual communicative agents and Oliphant considers how dierent
weight-update rules for such networks will inuence the cultural development of
communication systems within the framework of iterated cultural transmission
within a population of such agents, if the ability to accurately observe meaning
is assumed.
Oliphant denes a measure of communicative accuracy for these agents,
which calculates the probability of any agent using a signal s to communicate
a meaning m and having s interpreted as m by another agent. A population's
communicative accuracy has a maximum of 1.0, representing the case where any
meaning can be successfully communicated by any agent to any other agent in
3
the population. Such populations are said to possess an optimal communication
system.
Oliphant evaluates weight-update rules in the context of this model with
respect to three criteria:
Acquisition: Can a network using the rule acquire an optimal communication
system?
Maintenance: Can a network using the rule acquire an optimal system against
reasonable levels of noise?
Construction: Can a network using the rule improve a non-optimal system in
such a way that a population of such agents will eventually construct an
optimal system?
Rules capable of only acquisition will be termed learners. Rules capable of
only maintenance and acquisition will be termed maintainers and rules capable of construction, maintenance and acquisition will be termed constructors.
Oliphant shows that a simple weight-update rule implementing a form of Hebbian learning is capable of constructing optimal systems. He argues that this
type of rule is well within the learning capabilities of non-human species, suggesting that learned symbolic communication systems should be common in the
natural world. According to Oliphant the rarity of such systems can therefore
best be explained in terms of the diculty of observing meaning during cultural
transmission of communication systems.
3 Objections to Oliphant's conclusions
There are three main objections to Oliphant's argument that the uniqueness
of human language is best explained in terms of a uniquely human ability to
observe meaning. Firstly, as illustrated in [9], a closer analysis of constructor
weight-update rules shows that they are strongly biased in favour of one-toone mappings between meanings and signals. This kind of learning bias may be
specic to humans - even if other species were capable of observing meaning,
they do not possess a constructor-type learning bias and therefore would be
incapable of developing a learned symbolic communication system.
Secondly, constructor weight-update rules are unlikely to evolve in simulated
populations, even under apparently ideal conditions, due to a delay between the
emergence of constructor rules and a tness advantage to individuals using those
rules. This suggests that the capacity to observe meaning does not inevitably
lead to the emergence of optimal learned communication systems. This argument
is more closely considered in Section 3.1.
Finally, work in progress suggests that learned symbolic communication systems can be maintained, and to a lesser extent constructed, even if the capacity
of learners to observe meanings is diminished - the ability to observe meaning
accurately is not a necessary precondition for the emergence of learned communication.
4
3.1 Unreliable emergence
If it is the case, as argued in [9], that only humans possess the constructor-type
bias in favour of one-to-one mappings between meanings and signals, a modied
version of Oliphant's argument seems appealing - only humans possess the ability to observe meaning, and once this trick has been mastered natural selection
quickly identies the learning biases which will support symbolic communication.
But do constructor-type learning biases reliably emerge given a pre-existing ability to observe meaning? A modied version of Oliphant's model suggests that
natural selection may have diculty in identifying constructor learning biases,
even under apparently ideal conditions. The model consists of three elements:
Communication systems: As in Oliphant's model, unanalysed meanings and unstructured signals are modelled using unit vectors. Communication systems
therefore consist of a mapping from meaning vectors to signal vectors.
Communicative agents: As in Oliphant's model, individual agents are modelled
using bidirectional associative networks (see Figure 1)1 . The mapping between meanings and signals in such networks is determined by the connection weights in the network. Prior to learning the networks have uniform
weights across all their connections - the agents have no preference for any
particular communication system. During training these agents are presented
with meaning-signal pairs (accurate observation of meaning is assumed) and
adjust their network connection weights according to a weight-update rule.
Whereas in Oliphant's model the weight-update rule used by each agent is
determined by the experimenter, in the new model each individual's weightupdate rule, and therefore their learning bias, is genetically encoded 2 .
To map from a meaning to a signal, or from a signal to a meaning, the
appropriate input vector is presented to the network and the output unit
receiving the highest activation is activated, while the other output units
remain inactive. This is a winner-take-all strategy. Given multiple output
units receiving equally high activation, one winner is randomly selected.
Population model: A population consists of 100 agents. At each time step one
agent is selected and removed. A new agent is introduced and receives three
exposures to the communication systems of other members of the population.
1
10 by 10 networks are used for all models outlined in this paper.
2
The weight-update rule used by each agent is specied in a 4-locus genome corresponding to the phenotype rule (OnOn OnO OOn OO), where the value in the
OnOn slot indicates the change to be made to the connection weight between coactive units, OnO is the change to be made when the meaning unit is on and the
signal unit is o and so on. There are 3 possible alleles for each locus: +1, 0 or ?1,
corresponding to 3 possible changes in connection strength in the phenotype network
- increase connection strength, leave connection strength unaltered or decrease connection strength. Each genotype therefore encodes one of 81 possible weight update
rules. Of these, 9 can be classied as constructors, 9 can be classied as maintainers,
13 can be classied as learners and 50 can be classied as non-learners, incapable
of acquiring an optimal system. The initial population in all simulations consists of
agents with random genotypes.
5
Meaning Layer
The new agent adjusts their connection weights according to their weightupdate rule. This replacement process is repeated at each time step. Whereas
in Oliphant's model removal from the population (death) is entirely random
and there was no notion of breeding, in the new model selection pressures on
death and breeding are introduced - less successful communicators are more
likely to be removed from the population and more successful communicators
are more likely to breed to produce new ospring3 , to who they transmit their
genetically encoded weight-update rule4. The population is therefore under
natural selection for communicative success.
Signal Layer
Fig. 1. The associative network. Dashed lines indicate the omission of further nodes.
In common with Oliphant's model, the agents were assumed to be able to
observe meaning during the cultural transmission process. This model therefore
seems perfect for evolving learning biases conducive to communicative success
- agents can observe meaning, weight-update rules are genetically encoded and
there is strong selection pressure for communicative accuracy. Do populations
under these conditions reliably converge on constructor rules and develop optimal, learned symbolic communication?
At each time step the population are evaluated for communicative accuracy according to the measure described in [7]. Each individual is evaluated for their ability
to successfully transmit and receive 20 randomly selected meanings with randomly
selected members of the population. Tournament selection is used for both death
and breeding. For death, a three-agent tournament is held, with the agent whose
communicative accuracy is rated lowest being removed. For breeding, two parents
are selected. Each is selected using a three-agent tournament, with the agent whose
communicative accuracy is rated highest being allowed to breed.
4
One-point crossover occurs during breeding with probability 0 95. Mutation occurs
at each locus on the ospring genotype with probability 0.01. Mutation results in
replacement of the allele at the mutated locus with one of the other two possible
alleles, randomly selected.
3
:
6
Figure 2 illustrates the progress of a run which converged on a (very near)
optimal communication system. This is a typical example of a successful run.
However, only 5 of 100 runs were successful in this respect - optimal communication is unlikely to emerge, even under this apparently ideal set of circumstances.
1
Average communicative accuracy
Constructor proportion
Maintainer proportion
Learner proportion
Non-learner proportion
0.8
0.6
0.4
0.2
0
0
500
1000
1500
2000
2500
Generation
3000
3500
4000
4500
5000
Fig. 2. A successful run.
Why does natural selection have such diculty in identifying weight-update
rules which lead to optimal communication? The problem is that constructors
need time to converge on an optimal system - cultural selection over repeated
time steps gradually moves their communication systems into increasingly optimal overlapping areas of communication system space until they are all converged on an optimal system. In the new model, where weight-update rules are
genetically transmitted, in the early stages of this construction process individuals using constructor rules have little tness advantage over other individuals.
As a consequence the genetic transmission process will be essentially random the population will undergo genetic drift. In successful runs, such as that shown
in Figure 2, genetic drift preserves constructors, by chance, in sucient numbers
for sucient time to allow the construction process to get well under way. Constructors then show increased communicative accuracy which leads to steady
selection for constructor genes, constructor numbers in the population increase
and the population's average communicative accuracy increases sharply. This
switch from genetic drift to directed genetic change occurs at around generation
2000 in the run shown in Figure 2.
Interestingly, when the population's communicative accuracy nears optimal
levels, selection for constructors cuts out - the population enters a second stage
of genetic drift, where constructor numbers uctuate randomly. This is due to
the fact that maintainers, and to a lesser extent learners, are capable of putting
the nishing touches on a communication system and maintaining that system
once it is established, given a little assistance by selective removal of poorly-
7
communicating agents. Constructors loose their advantage over maintainers and
learners and genetic transmission becomes semi-random once more. However,
non-learners never drift back into the population - they are incapable of learning
an optimal system and suer a severe tness penalty. This switch from selection
to drift occurs around generation 3000 in the run shown in Figure 2. This threestage drift-selection-drift pattern is common over all successful runs.
These simulation results suggest that the key factor in explaining the uniqueness of human language is not necessarily the human capacity for observing
meaning. Even if other species could observe meaning accurately the evolution
of a learning mechanism which supports optimal learned symbolic communication would not necessarily follow, due to the delay between the emergence of
constructor-type learning biases and an increase in communicative accuracy for
those suitably biased individuals.
4 Speeding up cultural convergence
The simulation results outlined above suggest that if it were possible to speed up
the construction process then agents with constructor weight-update rules would
receive a tness payo more rapidly, reducing the vulnerability of the population
to genetic drift and allowing optimal learned communication systems to emerge
more reliably. The aim of the research outlined in this section is to identify
what kind of modications to the basic model might speed up the construction
process. The question of whether these modications correspond to any uniquely
human state of aairs can then be addressed. If so, the uniqueness of language
may be explicable in terms of these factors. Two methods of speeding up the
construction process are outlined below.
4.1 Cultural subpopulations
Smaller populations of constructor agents converge on optimal learned systems of
communication more rapidly [7]. However, in the context of the simulation model
used in Section 3.1, reducing the size of the simulated populations will have two
eects. Firstly, cultural convergence will be speeded up. Secondly, genetic drift
will be more pronounced in smaller populations. Since both genetic drift and
speed of cultural convergence play a role in the emergence of optimal learned
communication systems in this model, if we wish to isolate the inuence of speed
of cultural convergence on the emergence of communication we must decouple
cultural population size and genetic population size.
This aim is achieved by introducing spatial organisation into the population
in the arena of cultural transmission and use of communication systems, but
not in the arena of genetic transmission of weight-update rules5. The parameter d determines the level of spatial organisation of the population. For small
5
For the purposes of evaluation of communicative accuracy, which determines removal
from the population and opportunity to breed, the population is assumed to be organised in a ring. Agents are evaluated for their ability to communicate with other
8
d the population is eectively split into several small subpopulations during
communication and cultural transmission, but consists of a single homogeneous
population in genetic terms. For large d (d 20) the population behaves as a
homogeneous whole for both cultural and genetic purposes.
Does splitting the population into several smaller cultural subpopulations
speed up cultural convergence, therefore making optimal communication systems more likely to emerge? Figure 3 plots the number of successful runs (out
of 100) where the population's average communicative accuracy (ca) over the
last 500 generations of a 5000 generation run exceeds a certain threshold, for
various values of d. There is a clear relationship between d and the number of
successful runs, with low d and rapid cultural convergence leading to more reliable emergence of optimal communication systems. It is also clear that there
is little dierence in the number of successful runs regardless of whether \success" is dened as ca > 0:9, ca > 0:5 or ca > 0:1 - emergence of successful
communication appears to be an all-or-nothing process.
70
ca > 0.9
ca > 0.5
ca > 0.1
60
no. runs
50
40
30
20
10
0
0
2
4
6
8
10
d
12
14
16
18
20
Fig. 3. Number of runs achieving a given level of communicative accuracy for various
levels of cultural spatial organisation.
4.2 More learning
Increasing the number of exposures each new agent receives to the communication systems of other members of the population also speeds up cultural
agents picked from a normal distribution around the position they occupy, with a
standard deviation of slots. Similarly, cultural transmission is assumed to be spatially organised, with learners observing the communicative behaviour of individuals
picked from a normal distribution around the learner's position, again with standard
distribution . However, there is no restriction on the spatial relationship between
breeding pairs and their ospring.
d
d
9
convergence. In all previous simulations outlined in this paper, each agent observes the communication systems of three other members of the population and
adjusts their network connection weights according to their weight-update rule.
In the simulations outlined in this section, each agent observes and learns from
the communicative behaviour of n other agents. All other details of the model
are the same as the model outlined in Section 3.1. Figure 4 plots the number of
successful runs (out of 100) for various values of n. There is a clear relationship
between n and the number of runs achieving a given level of communicative
accuracy, with larger values of n resulting in more reliable emergence of nearoptimal communication systems due to faster cultural convergence, although the
eect becomes less marked for n > 15.
70
60
no. runs
50
40
30
20
10
ca > 0.9
ca > 0.5
ca > 0.1
0
0
5
10
15
n
20
25
30
Fig. 4. Number of runs achieving a given level of communicative accuracy for various
amounts of learning.
5 Conclusions
Oliphant [7] argues that optimal, learned symbolic communication will trivially
emerge given a capacity to observe meaning during cultural transmission and
a commonly-occurring learning bias, and that language is the only naturally
occurring instance of such a system because only humans have the ability to observe meanings accurately. The rst part of this paper raises three objections to
Oliphant's proposal. Firstly, the learning bias necessary to construct an optimal,
learned communication system, a bias in favour of one-to-one mappings between
meanings and signals, may be specic to humans. Secondly, even if other species
were capable of observing meaning, the correct learning bias might be unlikely
to evolve due to a delay between the emergence of the bias and a tness payo to individuals possessing it. Thirdly, near-optimal communication systems
10
may emerge and be maintained in populations of agents incapable of accurately
observing meaning.
In the second part of this paper two factors inuencing the speed of cultural
convergence of populations were considered - organisation of the population into
cultural subpopulations and increase of the amount of learning done during cultural transmission. An increase in the speed of cultural convergence due to either
of these factors results in more reliable emergence of genetically-encoded constructor weight-update rules, due to a reduced delay between emergence of such
rules and a tness payo to individuals possessing them. It is not the intention
of this paper to argue that increased learning or cultural spatial organisation
characterise crucial phenomenon in human evolution and therefore explain why
only humans evolved the appropriate learning bias to support learned symbolic
communication, although parallels could be drawn between increased learning
in the model and the lengthy immature period in humans. Rather, these results
should be taken as emphasising the rather complex and unpredictable interactions between genetic and cultural adaptation in models which do not discount
one adaptive process as a starting assumption. Specically, even if a particular learning bias results in the emergence of optimal communication systems
through processes of cultural selection we cannot assume that natural selection
will reliably nd that bias.
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