The evolution of language An experimental review of the iterated and collaborative learning model

A.H.E. van der Meijden

ANR: 700571

Master’s Thesis

Communication and Information Sciences

Specialization BDM

Tilburg University

Faculty of Humanities

Supervisor: Dr. P.A. Vogt

Second reader: Dr. M. Postma – Nilsenova

July 2014

 

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Acknowledgements

Here it is, my Master’s thesis. When I started with the research for my thesis, I had

little knowledge about the subject, but the more I researched it, the more it interested

me. Now, five months later, I can genuinely say the evolution of language interests

me and that I am proud of the end result.

This thesis would not be here without the help of several people. First of all, I would

like to express my sincere gratitude towards my supervisor, Paul Vogt, for all of his

useful remarks, comments and support throughout the process of writing this thesis.

Furthermore, I want to thank Linda Meijer. Together we have designed and conducted

the experiment for the purposes of this thesis. Without Linda, the experiment could

not have been conducted and it would have been a lot less fun to do so.

Finally, I want to thank my parents, friends and other family members for their

support and patience to hear about the concerns I had while writing this thesis, even

though they had no idea what I was talking about. Special thanks go out to Asta

Aleskute, Chris Williams and Robert van der Meijden.

Anouck van der Meijden

July 2014

 

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Abstract

Research on the evolution of language mainly distinguishes two theories: the iterated

learning model and the collaborative learning model. The iterated learning account

states that a language evolves through the transmission of the language from one

generation to the other, whereas the collaborative account states that a language

evolves through the interaction of people within the same generation. Research

indicates evidence on both theories. However, various studies have been conducted in

a different manner, which makes it difficult to compare the results and provide a clear

view of the differences in the evolving language when the different models are being

applied.

This current study compares the learnability and the structure of an emerging

language supported by both theories in two similar experiments. Results show that a

language becomes increasingly easier to learn in both conditions. However, when

participants interacted with each other (collaborative) the language is easier to learn

than when participants learnt from a previous generation (iterated). Moreover, in both

conditions, a non-random structure emerges which indicates that a language becomes

compositional. However, no differences in structure between the conditions were

found.

 

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Table of Contents

Acknowledgements ..................................................................................................... ii

Abstract ....................................................................................................................... iii

Table of Contents ........................................................................................................ iv

1. Introduction .............................................................................................................. 6

2. Theoretical Framework ........................................................................................... 9

2.1 Iterated learning model ........................................................................................ 9

2.2 Collaborative learning model ............................................................................. 11

2.3 Researching iterated and collaborative learning through computer modelling . 12

2.4 Researching iterated learning and collaborative learning in the laboratory ...... 13

2.5 Hypotheses ......................................................................................................... 15

3. Method .................................................................................................................... 17

3.1 Conditions .......................................................................................................... 17

3.2 Participants ......................................................................................................... 17

3.3 Materials ............................................................................................................ 17

3.4 Procedure ........................................................................................................... 19

3.4.1 General procedure ....................................................................................... 19

3.4.2. Iterated learning condition ......................................................................... 20

3.4.3. Collaborative learning condition ................................................................ 21

3.5 Analysis .............................................................................................................. 22

4. Results ..................................................................................................................... 25

4.1 Learnability of the language .............................................................................. 25

4.1.1. Learnability in the iterated condition ......................................................... 25

4.1.2. Learnability in the collaborative condition ............................................... 27

4.2 Structure of the language ................................................................................... 28

4.2.1. Structure in the iterated condition .............................................................. 28

4.2.2. Structure in the collaborative condition ..................................................... 29

4.3 Qualitative research of the languages ................................................................ 31

5. Discussion ................................................................................................................ 34

5.1 Discussion of results .......................................................................................... 34

 

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5.2 Limitations ......................................................................................................... 37

5.3 Suggestions for further research ........................................................................ 39

6. Conclusion .............................................................................................................. 40

7. References ............................................................................................................... 42

8. Appendices .............................................................................................................. 44

Appendix 1: All possible combinations of entities .................................................. 44

Appendix 2: Feedback form iterated condition ....................................................... 45

Appendix 3: Feedback form collaborative condition ............................................... 46

Appendix 4: Consent form iterated condition .......................................................... 47

Appendix 5: Consent form collaborative condition ................................................. 49

 

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The evolution of language: an experimental review of the iterated and

collaborative learning model

1. Introduction

Research on the evolution of language and human communication systems is a field

that has been studied for centuries. Charles Darwin (1884) was one of the first to note

that dominant languages spread around the world, which leads to the extinction of

other smaller languages and dialects. Darwin (1884) noted that languages that have

easier words and less difficult grammar are stronger and push other languages aside.

He called this survival and preservation of certain words natural selection.

This notion by Charles Darwin states that human communication systems

evolve and change over time. From a traditional perspective, the evolution of a

language is explained by two dynamics that interact with each other: individual

learning and biological evolution (Fay, Garrod, Roberts & Swoboda, 2010).

Children have the ability to learn a language at a very young age, even before

they have other basic skills such as walking. The ease with which children can learn a

language indicates that the structure of a language can be guessed with a fairly high

chance of guessing right (Christiansen & Chater, 2008). From a traditional

perspective, there are two views on this phenomenon. First, the human brain has

adapted itself over a long period of time that makes learning a language easier. A

second view is that the brain mechanisms that deal with language learning have

emerged over a long period of time (Christiansen & Chater, 2008). Christiansen and

Chater (2008) propose a third view on this subject. They do not ask why the brain is

as good at learning a language but instead propose that language has evolved to make

it easier to learn by the human brain.

Dediu and Ladd (2007) state that language learners construct a language based

on the language they hear around them, the language being used by people from the

same cultural group. The grammar that these learners construct is not necessarily

identical to the one they hear and the small changes they make can have large

influences over time. Furthermore, cognitive biases within a population of language

learners might influence the direction in which a language changes over different

populations. This could cause the differences in linguistic changes across different

populations, resulting therefore in different dialects and even different languages.

 

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This indicates that there is a causal relation between genetics and differences in

languages.

It is only in the past ten to fifteen years that this aspect of cultural evolution

has been incorporated into theories of language evolution. This cultural evolution may

account for the changes in languages and communication systems through natural

selection as proposed by Darwin, just as biological changes have helped optimise

individual learning (Fay, Garrod, Roberts & Swoboda, 2010; Kirby, Dowman &

Griffiths, 2007).

Scott-Phillips and Kirby (2010) propose three models of cultural transmission:

linear transmission, the replacement method and the closed group model. The linear

transmission model proposes that humans learn from older generations in a purely

vertical manner. These generations can consist of one person (an agent) or multiple

agents. Agents within the younger generation do not communicate back to the older

generation and do not influence each other. The replacement method proposes that

humans learn a language in a group. Within this group, members are slowly replaced

with new members. These members learn from the rest of the population that results

in both linear horizontal and vertical learning. The last model, the closed group,

proposes that within a population, no new members are introduced to the population;

the group remains the same during the learning process. This population could be a

dyad, where only two agents communicate and learn from each other, or a larger

group, a so-called cultural group. In this method, only horizontal learning occurs. This

means that agents within a specific population do not learn from older generations.

Traditionally, these three models of cultural transmission have been studied

through computer models (Fay, Garrod, Roberts & Swoboda, 2010). In general, two

models have been introduced, the collaborative and individualistic. The collaborative

model includes the closed group method as proposed by Scott-Phillips and Kirby

(2010). The individualistic model includes the linear transmission, and the

replacements method could be considered as a combination of both the individualistic

and collaborative learning model. A study on this combination has been conducted by

Vogt (2007).

The focus of this thesis will be (1) the linear model, or the iterated learning

model (Kirby, 2002; Kirby & Hurford 2002; Smith, Kirby & Brighton, 2003) as a part

of the individualistic learning accounts and (2) the closed group method as a part of

 

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the collaborative learning accounts. Both models will be discussed thoroughly,

followed by a discussion of computer modelling studies on this topic. This will be

concluded with a review of natural laboratory experiments on both the iterated

learning model and collaborative models. This current research will focus on the

distinction between the iterated learning model and the collaborative learning model

and will make a comparison of how the structure of the emerging languages differ

when the different models are applied.

 

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2. Theoretical Framework

2.1 Iterated learning model

The iterated learning model (Kirby & Hurford, 2002) is inspired by models developed

in biological sciences and was initially a computational model. Chomsky (1986)

proposed that language exists and persists through two channels: I-language and E-

language. I-language assumes that language exists through neural connectivity or

grammar. E-language is the actual utterances and symbols that people produce when

speaking. The relation between those two channels is the central idea behind the

iterated learning model (ILM). In order for a language to survive, and therefore be

learned from a previous generation, it must be transferred from I-language to E-

language, which can be done by using the language. It must also be transferred from

E-language back to I-language to be learned.

The ILM is concerned with the structure that exists when meaning is mapped

to actual signals such as utterances. To be able to model this, four components are

necessary: (1) a meaning space, (2) a signal space, (3) language-learning agent(s) and

(4) language-using adult agent(s). In a normal simulation, an adult generation, the

language-using agent, receives a set of random meanings and signals and learns that

set of meanings. The output of the meanings and signals that are learned serves as a

new training set for the language-leaning agents, who can be seen as children. This

mapping from the ‘adult generation’ to the ‘child generation’ is called an iteration

(Kirby & Hurford, 2002). After learning the language, a child grows up and becomes

a language-using agent, who then transfers knowledge to a new language-learning

agent. This is repeated over several generations, after which an analysis can be

conducted on how the structure of the language has developed over time (Fay,

Garrod, Roberts & Swoboda, 2010).

One of the aspects that has been studied thoroughly through the ILM is

compositionality. Compositional signalling can be described as “one in which the

meaning of a sign is a function of the meaning of the parts of that signal and the way

in which they are put together” (Kirby & Hurford, 2002, p. 128). This mapping

should not only be compositional, but should also be repeated in the same manner. By

doing this, after a certain period of time it becomes possible to create numerous

utterances with only few signs or symbols (Kirby & Hurford, 2002). For example,

 

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“green square” is a compositional utterance in which, “green” refers to the colour

and “square” to the shape (Vogt, 2007). However, for compositionality to occur, a

signal such as the word green should have a clear meaning that can be used in

different utterances. De Boer, Sandler & Kirby (2012) make a distinction between

two types of combinatorial structure. The first level, in which meaningless sounds and

syllables are combined into a word and are given meaning, is called the combinatorial

structure. The second level, in which words are combined into an utterance, is called

the compositional structure. Together, they are called the duality of patterning (de

Boer, Sandler & Kirby, 2012). Hackett (1960) defines this duality of patterning a

design feature unique to human language; every human language has this.

Two important aspects that are taken into account with the iterated learning

model are agents' innate learning biases and the linguistic bottleneck (Fay, Garrod,

Roberts & Swoboda, 2010). These aspects are important for creating structure in a

linguistic symbol system.

The first aspect, the learning biases, is based on the assumption that language

learning is done through prior knowledge; however, a child who learns a language has

little prior knowledge about that language. For this reason, scholars assume that this

knowledge is based on the general cognitive system (Gildea & Jurafsky, 1996). This

can be explained by innate learning biases such as the Bayesian inference (Kirby,

Dowman & Griffiths, 2007). With the ILM, learning biases influence the output and

the processing of the linguistic input of a generation. Since in this model, generations

are more or less identical, the preference of an adult agent will be similar to that of a

child agent. For this reason, the language-using agent only has to produce signals such

as utterances that they found easy to produce, as the language-learning agent will

most likely find this easy as well and therefore maximizes the chance that the learner

will comprehend the signal correctly.

The second aspect that is important when researching the ILM is the linguistic

bottleneck that refers to the size of the set of signals that are presented to the first

agent and from which language rules are created. When a given set of signals is small,

for instance 20 sets of meaning – signal pairs, the language will become unstable,

contrary to medium (50 pairs) or large (2.000 pairs) sized sets. However, with a large

sized set, it takes a long time until a language becomes stable (Fay, Garrod, Roberts &

Swoboda, 2010). These findings suggest that there is an optimal number of meanings

 

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- signal pairs, which should be taken into consideration in studies on the evolution of

language.

To summarize, the ILM assumes that a language evolves because children

learn a language from their parents. However, children do not only communicate

unidirectionally with their parents but also with other people from their own

generation. From this perspective, the collaborative learning models might account

for language evolution as well. This model will be thoroughly discussed in the next

chapter.  

2.2 Collaborative learning model

Where the ILM states that language learning occurs unidirectionally, from parent to

child and with no other communication taking place, the collaborative learning model

takes a different approach. This model assumes that language learning occurs through

social interactions instead of prescriptions by the parents. This type of language

learning is bidirectional and horizontal, children communicate with people within

their social group and adapt to the people around them and might even contribute to

the language (Fay, Garrod, Roberts & Swoboda, 2010). In short, the collaborative

learning model (CLM) states that when a language is acquired, this is done by

communication and interaction within a certain population. An example of this is a

child who not only interacts with his or her parents to acquire a language, but also

with siblings, grandparents, teachers and other children.

According to Fitch (2007) a language changes rapidly, something that seems

unlikely when the ILM is applied. In that case, a child learns directly from its parents

with no other interference, making it unlikely that a language can change rapidly.

Fitch (2007) proposes that these changes occur on a microscopic personal level. An

example that Fitch (2007) gives is that many years ago, words that are now used to

describe women with loose morals, were respectable names for women. For a

language to undergo such changes in meaning, interaction between people is

necessary because if language learning would be purely vertical such large changes

would be unlikely to occur.

 

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2.3 Researching iterated and collaborative learning through computer modelling

As stated before, iterated learning models are typically studied through computer

modelling which has shown promising results. As with ILM, support is found for the

collaborative learning model through computer modelling. In this section, studies on

both models and their accompanying results will be discussed.

Before natural studies on the ILM, Smith, Kirby and Brighton (2003)

conducted a study on the iterated learning model through computational simulations.

Based on a mathematical model, which was based on the idea of language as a

culturally transmitted system, they predicted that compositional languages are more

stable than holistic languages. Moreover, they test the idea of a bottleneck on cultural

transmission and that linguistic agents have structured representations of objects.

Based on these predictions, two main questions arise: “What will happen to

languages that are to some extend compositional?” and “Can compositional

languages emerge from a holistic language through cultural transmission?”

In this study, different computational environments were created in which

objects were assigned with meanings. Within this experiment, the environments had

different features. An environment could have few objects, such environment is called

a low-density environment, or many objects, such environment is called a high-

density environment. The meanings of the objects could have been assigned

randomly; in this case the environment is called unstructured. When meanings were

assigned non-random, but in such a way that it minimizes the average inter-meaning

Hamming distance, such environment is called structured. Furthermore, Smith, Kirby

and Brighton (2003) also manipulated the linguistic bottleneck through the presence

of absence of a bottleneck.

Results from their computer simulations show that in case of the absence of a

linguistic bottleneck, the majority of the systems that arise are holistic and are mainly

stable. Moreover, in the absence of a bottleneck, highly compositional systems do not

occur often and only occur when the environment is structured. A simulation that

includes a linguistic bottleneck does show that highly compositional systems occur

more often; this is strengthened when the environment is structured. These results

show that, when there is a limited amount of possible combinations, languages can

become compositional, especially when they are structured.

 

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Steels and Kaplan (2002) studied the CLM through autonomous mobile

robots. They researched which aspects are critical for learning a language. In three

experiments, Steels and Kaplan (2002) programmed the robots with a form of

collaborative learning and manipulated the degree of social interaction with a

mediator: strong interaction, less interaction creating a form of supervised

observational learning, or no interaction with a human mediator. The robot played a

classification instead of a word game, which was found to be very effective to

recreate social and cultural learning (Steels, 2001), since the camera of the robot

could only see one object at a time.

Steels and Kaplan (2002) found that it is critical to have structured social

interactions, where the learner asks for feedback and receives this feedback.

Moreover, a mediator should be present since he or she influences the acquisition of

concepts, and this way, the learner is able to conform to what is needed for a specific

language.

Vogt (2007) conducted a computational research where both the ILM and

CLM were combined. This was derived from an earlier study (Vogt, 2005a) in which

was found that a language does not seem to change as much when it is transmitted

horizontally. In this 2007 study, halfway during the simulation, half of the population

was replaced with new agents, which could be compared with the ILM. In this

computational study, the stimuli materials were colours and shapes.

The results show that when no new agents are introduced in a population, the

rules that are established are divers and could be considered as less optimal, whereas

when the new agents are introduced, the compositional rules combining colours and

shapes take the overhand, which results in a more stable structure. Participants used

these rules more often since they are easier to learn by the new agents because they

are applicable to almost every situation. This way, new combinations can be made

which are coherent with the existing rules.

2.4 Researching iterated learning and collaborative learning in the laboratory

Besides computational research on the evolution of language, in the past fifteen years,

more and more natural research on the evolution of language has been conducted.

Therefore, in this chapter, laboratory research on both the ILM and CLM of language

evolution will be discussed.

 

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A natural experiment that has been conducted is a study by Kirby, Cornish and

Smith (2008) who applied the ILM. They hypothesised that when languages evolve

and become cumulatively adaptive, they become easier to learn over different

generations and a linguistic structure should emerge. In this experiment, 27 possible

combinations could have been made from three categories: a shape, a colour and a

movement. The first participant saw fourteen of those items with random assigned

meanings that he or she learned. After learning these items, the participant completed

a test in which he or she had to assign meaning to each of the 27 combinations. This

output was used as learning material for the next generation. A chain of participants

consisted of ten generations and thus ten participants.

The results of this experiment show that the language developed by the

participants indeed becomes cumulatively adaptive which results in a more structured

language that becomes easier to learn. These results show that participants who did

not know that they were designing a language, created a cumulative and

compositional structure in their language. The participants in this experiment only

tried their best to reproduce the language that was learnt to them, the linguistic

structure that arises can be understood as a response to the fact that participants knew

that they had to transmit the language to a next generation.

Another natural experiment has been conducted by Galantucci (2005) who

studied the emergence of a language and the evolution of this emerged language with

the CLM. In this experiment, two participants played a virtual game where they had

to find each other’s agents in a virtual environment consisting of four rooms. The

participants could only see one room at a time and did not know in which room the

other participant’s agent was. Moreover, they were seated in different rooms and

could not see each other. They could communicate, and therefore create a

communication system, by drawing lines and dots on a magnetic pad.

Results of this research show that most pairs became more accurate after

playing the game for a while since they started to understand the signals they sent to

each other. This indicates that a communication system can emerge in a very short

time. Galantucci (2005) found in this experiment two ways of how a communication

system emerges: learning by using and naming procedures. First of all, when people

learn by using, they use signals that someone has used before in a similar situation to

find out the meaning of that signal. If the other user shows the same behaviour after

 

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that signal, the player knows how to interpret that signal. Furthermore, when learning

by naming procedures, one participant uses what both agents have in common at the

time they have found each other, in this case, feedback is not necessary since both

players know at the time the signal is sent it can only have one meaning.

Furthermore, Galantucci (2005) found three types of communication systems

emerging in this experiment: numeration based, icon based and map based. In the

numeration-based system, participants gave signals with a different number of

features to each of the rooms. In the icon based system, participants made a signal

based on the object that they could see in the rooms and mimicked those. Finally,

participants who created a map based communication system based the meaning of

their signals on the place where the signal was written.

After finding these results, Galantucci (2005) investigated how these

communication systems would further evolve. He assumed that by adding more

rooms to the virtual environment, the need for communication would increase and the

communication system had to become more advanced. The results show that

participants used the same strategy for creating their language as they did before.

Moreover, participants did not lose their ability to find each other, which indicates

that the communication indeed developed, and was adapted by the participants rather

easy.

The different results from the studies that are mentioned above imply that

there are differences between the CLM and the iterated learning model. This current

study aims to find these differences through using a similar learning task for both the

collaborative learning and the ILM. The central question of this thesis is therefore:

“Are there any differences in the learnability and structure of an evolving language

between the application of the iterated learning model and the collaborative learning

model and how might these differences complement each other?”

2.5 Hypotheses

H1: When the iterated learning model is applied, after a certain amount of

generations, a language will become more learnable (Kirby, Cornish and Smith,

2008).

 

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H2: In the iterated condition, participants will find it easier to learn the language than

in the collaborative condition since the rules that will be established are applicable to

more situations (Kirby, Cornish and Smith, 2008; Vogt, 2007).

H3: A non-random structure will occur in a language after a certain amount of

generations when the iterated learning model is applied. (Kirby, Cornish and Smith,

2008).

H4: A non-random structure will occur in a language when the collaborative learning

model is applied (Galantucci, 2005).

H5: When the iterated learning model is applied, it will take longer until

compositionality, and thus structure, arises in the evolving language than when the

collaborative learning model is applied (Fitch, 2007).

 

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3. Method

3.1 Conditions

This current research focussed on three conditions: the photographed iterated learning

condition; the photographed collaborative learning condition and the videotaped

iterated learning condition. This last condition has not been taken into consideration

in this thesis, as another master student will analyse this condition in order to make a

distinction between photographed and videotaped languages. This was done based on

the iterated learning model. The analysis of this current study was done on the basis

of the photographed conditions. In these conditions, the dependent variables were the

degree of learnability and structure that arise in the evolving language. The

independent variables were the iterated learning model and the collaborative learning

model. The study was set up as a between subjects design, all participants could only

participate in one condition.

3.2 Participants

52 participants participated in the experiment in exchange for course credit. Of these

participants, 30 were assigned to the iterated learning condition. From these

participants, 22 where women and eight were men with an average age of respectively

21 (SD = 2.13) years and 23 (SD = 3.00) years. The other 22 participants were

assigned to the collaborative learning condition. Of these participants, seventeen were

women with an average age of 21 (SD = 2.41) and five were men with an average age

of 21 (SD = 2.70). The unequal distribution of men and women was due to the

availability of participants. The participants have been recruited from the subjects

pool of the Communication and Information Sciences program of Tilburg University

in which more women than men are represented.

3.3 Materials

First of all, the stimuli materials consisted of three categories with each three features

that could be combined together, which resulted in a total of 27 combinations. This is

the same amount of features used by Kirby, Cornish and Smith (2008) in their study.

The categories that were used are fruits, colours and movements. The fruit features

were apple, pear and strawberry. The choice for three fruits with a similar shape was

 

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to increase the difficulty of the language task as it is more difficult to signal the

shapes of the fruits iconically. The colours consisted of red, yellow and green and the

movements were bouncing, circling and straight. An example of the possible

combinations is shown in figure 1, all possible combinations are shown in appendix 1.

Figure 1: Three possible combinations of the used features.

Secondly, in the iterated learning condition, the stimuli materials were

presented as a learning set. The 27 combinations were assigned with random signals

that were used by the first participants of a generation in a chain. In the different

chains, the first participant received randomized learning materials. In the

collaborative learning condition, the combinations were not assigned with signals.

The participants were shown the list with the items as an image.

Thirdly, to create the signals, participants were provided with four matches

without heads. This limited amount of matches was chosen so that participants could

not create ‘drawings’ of the stimuli materials and to make it more difficult to convey

the message. Participants were provided with a grid on which they could create the

signals. This grid was used to analyse the results more accurately and to limit the

number of possible signals. The grid is shown in figure 2.

Figure 2: Grid

 

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Moreover, in the iterated learning condition, each participant, after learning

and practicing with the stimuli material, was given a test in order to analyse their

knowledge about the evolving language. This test consisted of all 27 possible

combinations. The test was given on 27 cards that each contained one of the possible

figures. All cards had to be placed one by one next to the grid on which the

participant placed the signal. After placing a signal, a photograph was taken of the

combination of the signal and image; this photograph was taken from above. These

photographs were used as learning input for the next generation. The actual test at the

end of the experiment was the same for the iterated and the collaborative condition.

Finally, at the end of an experiment, participants were handed a short

questionnaire on which they were asked to explain their language in terms of

grammar, signals and how they came to an understanding of their language (Appendix

2 and 3).

3.4 Procedure

In this section the procedure of the experiment will be discussed. First of all, the

general procedure, which was applicable for both conditions, will be explained.

Secondly, the procedures that are different for the iterated and collaborative

conditions will be reviewed.

3.4.1 General procedure

Upon arriving, the participants were welcomed and told that they were participating in

an alien language experiment. The experiment was introduced as a game to make it

more enjoyable for the participants. Participants were instructed that they had to

communicate with other people with four matches and nothing else. They were

invited to sit behind a desk with laptop or computer in front of them. The grid was

taped to the table and participants were instructed to place the image left of the grid

during the tests. A camera was placed on a tripod above their heads, aiming at the

grid. By doing this, only the grid was visible on the videos and photographs and the

participants were not recognizable in the videos and photographs. The camera was

used in all conditions to keep the conditions as equal as possible since it could be

possible that participants behave differently when they are confronted with a camera

than when they are not.

 

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All participants were handed a consent form (Appendix 4 and 5) on which the

goal of the study was stated. The participants also had the opportunity to give or deny

their consent for using their results for other educational and research purposes. All

participants were offered a copy of this consent form. After the experiment,

participants were asked to fill in a questionnaire about their experiences with the

experiment, these questionnaires are added in appendix two and three.

3.4.2. Iterated learning condition

In the iterated learning condition, six chains were created with each five generations.

Participants were told that it was of great importance that they did their best since

their output would be used as learning material for the next participant. Moreover,

they were told that if the entire chain has good scores on the test, all of them would be

rewarded with a small prize. However, the participants did not know what their places

were in the chain. The reason for this was to stimulate the participants to make a good

effort at learning the language. Were this not the case, the results would not have been

reliable.

The language, the set of stimuli, was divided into two sets. This was done

randomly. These sets were randomly divided for all participants. These sets were the

‘seen set’ and the ‘unseen set’. The seen set consisted of fourteen combinations, and

the unseen set of thirteen combinations. The first participant was shown the fourteen

seen items with the randomly assigned signals. Each participant was given two

training rounds before they performed the final test. Each training round consisted of

two exposures to the fourteen random items after which the participants completed a

small test. Each image was shown for eight seconds. The training rounds were filmed

for possible analysis. The training test consisted of half of the seen combinations and

half of the unseen combinations, a total of thirteen images. After two rounds of

training, the participant made the final test that existed of all 27 items. Before this

final test, the participants saw the seen set twice for the last time. The results of this

test were photographed and the output was used as training material for the next

participant. This procedure was repeated until the fifth participant. This procedure is

in line with the procedure done by Kirby, Cornish and Smith (2008).

 

  21  

3.4.3. Collaborative learning condition

In the collaborative learning condition, the experiment was presented to the

participants as an alien language learning game. They played the game with another

participant who was seated in a different room. Each participant was given the

opportunity to practice the game with three simplified images to get familiar with the

idea of communication with matches. After these five minutes the participants were

briefed on how they could make photographs of their signals and send them to the

other participant. This was done with two smartphones and the IM application

WhatsApp.

To be able to keep the conditions as similar as possible, the stimuli sets were

divided randomly into a seen set of fourteen items. This was done randomly for each

dyad. Each participant was given the set of fourteen seen stimuli on a sheet of paper.

This paper contained the images, which were numbered. The game was played with

the use of a PowerPoint presentation on which the images were shown one by one.

The images were divided in sets of four, both players had different PowerPoint

presentations, each set of four images on the PowerPoint of player two contained

images that were already shown on the PowerPoint of player one and two new

images. This meant that player two already received two of the images, which made it

possible to recognize these. Upon starting, player one created a symbol for the first

item in his or hers PowerPoint presentation. This signal had to be photographed, and

sent to the other participant. Player two, with the help of the sheet with the fourteen

possible images, had to guess which combination was signalled and sent this to player

one, player one then told whether this was correct or not, if not correct he or she told

the other participant which item he or she meant. During this IM-conversation,

participants were instructed not to send any other textual messages or scroll back to

the previous images. The game was played in turns; each turn consists of sending or

guessing four images. In the last turn, only two images were sent. A pre-test has

shown that with the limited time of the experiment, it was only possible for one round

of playing. This means that each player sent and guessed all fourteen items only once.

After each participant had signalled all combinations in their PowerPoint, both

participants took a test individually; this test was the same test that was used in the

iterated learning condition. After completing the test, participants were handed a short

 

  22  

questionnaire on which they were asked to explain their language in terms of

grammar, signals and how they came to an understanding of their language.

3.5 Analysis

For the analysis of the results, all signals from the tests made by the participants were

coded. The signals were coded by assigning numbers to each line on the grid, which is

shown in figure 3.

Figure 3: Numbered grid

When coding the signals, the matches that were put down on the grid were translated

into a four-digit string in ascending order. This was done since it did not matter in

which order the matches were placed on the grid. Figure 4 shows two examples of

signals that were put down by a participant, the strings that belong to those signals are

“3478” for A and “2678” for B.

A B

Figure 4: Signals put down by a participant.

 

  23  

The dependent variable to measure learnability is the signal distance. This

distance is calculated through comparing how many positions of the numbers in a

string are not present in the string of numbers in a corresponding image. The

minimum signal distance could be zero, which means there are no differences in the

strings, and the maximum distance could be four, which means that all numbers in

two strings are different. The order of the matches has not been taken into

consideration since this was not of importance of the signals. For the first generation,

there were no signals assigned to the unseen-set since the participant did not see this

set, these images were coded as missing.

To compare the learnability through the generations in the iterated condition,

the mean signal distances between two generations were compared with the previous

generations through a paired t-test. For example, the distances between generations

one and two were compared with the distances between generations zero and one.

This was done for all generations. In the collaborative condition, such a comparison

could not be made since there is only one mean signal distance. This methodology

follows the methodology that was used in the study of Kirby, Cornish and Smith

(2008). However, in their study, the transmission error was used which is similar to

the signal distance. To make a comparison between the iterated and the collaborative

conditions, the average signal distances between generations four and five and the

average signal distances of the dyads were compared through an independent samples

t-test.

The dependent variable to measure the structure in a particular language, e.g.

the language that one participant in a generation has created, is the correlation

between the normalized Levenshtein distance and the normalized Hamming distance.

The difference between the normalized Hamming distance and the normalized

Levenshtein distance is that the Hamming distances calculates on how many places

the digits in a string are different from another string whereas the Levenshtein

distance calculates the number of steps necessary to make two strings identical. Both

of these distances could have a minimum value of zero, which means that there is no

difference and a maximum value of one, which means that the strings are on all

aspects different.

The distances for all pairs of signals for all pairs of images were calculated

using a normalized Levenshtein distance. This resulted in 351 pairs of distances.

 

  24  

Moreover, the distances between all meaning pairs were calculated with a normalized

Hamming distance. This also resulted in 351 pairs of distances. The correlation

between the Levenshtein distance and the normalized Hamming distance indicated the

structure that has emerged in a language. Again, this methodology follows the

methodology used by Kirby, Cornish and Smith (2008). To compare the different

generations with each other in the iterated condition, a paired t-test was used to

compare the correlations. As for learnability, the differences in structure between the

iterated and the collaborative condition were measured using an independent t-test.

This was calculated with the correlations between the normalized Levenshtein

distance and the normalized Hamming distance of the fifth generation in the iterated

conditions and all the correlations of the participants in the collaborative condition.

Furthermore, a qualitative analysis has been conducted to research the

structures in the emerged languages and if participants used any strategies to create

the languages. The languages were analysed by investigating the coded strings of the

created languages and by studying the responses participants gave to questions about

their experiences with the experiment.

 

  25  

4. Results

4.1 Learnability of the language

The results on learnability are discussed separately for the iterated condition and the

collaborative condition. After the separate results, a comparison has been made

between the two conditions.

4.1.1. Learnability in the iterated condition

Table 1 shows the mean signal distances between every two generations of all 27

tested items in the iterated learning condition. The average signal distances are

displayed separately for each chain. The asterisks indicate the level of significance.

Table 1: Mean signal distances between generations (n = 27) for each analysed chain

indicating the transmission error between generations (SD).

The results show that chains two, three and five support hypothesis one

individually, which states that after a certain amount of generations, the language

becomes easier to learn, the other chains do not support the hypothesis. A paired t-test

showed that the mean signal distance between generations three and four in chain two

(M = 1.78, SD = 0.80) are larger than between generations two and three (M = 0.93,

SD = 1.11), t (26) = -3.43, p = .002, d = 0.75. The effect size shows that 75% of the

Chain

Generations

0-1

Generations

1-2

Generations

2-3

Generations

3-4

Generations

4-5

1 1.86 (0.36) 1.70 (0.87) 1.85 (0.60) 1.44 (0.85) 1.48 (0.85)

2 1.43 (1.09) 1.33 (0.96) 0.93 (1.14) 1.78 (0.80)* 1.11 (0.89)*

3 1.93 (0.92) 1.96 (0.76) 1.74 (0.98) 1.74 (1.06) 0.78 (0.70)**

4 1.79 (0.70) 1.67 (0.88) 1.59 (0.80) 1.67 (1.04) 1.52 (0.58)

5 2.14 (1.17) 1.67 (0.92) 1.19 (1.11) 1.52 (1.25) 0.44 (0.89)**

6 1.86 (0.23) 1.63 (0.74) 1.74 (0.86) 1.7 (1.07) 1.52 (0.96)

Signal distances of generations are compared using a paired t-test with the preceding

generations in this table. Asterisks indicate the level of significance with the preceding

generation

* p<.01

** p<.001

 

  26  

distances in generations three and four point in the direction of this effect. This

indicates that it was more difficult for generation four to learn from generation three

than it was for generation three to learn from two, which conflicts with hypothesis

one. However, the mean signal distance of generations four and five (M = 1.11, SD =

0.80) are significantly smaller than between generations three and four (M = 1.78, SD

= 0.80), t (26) = 3.03, p = .005, d = 0.79. This effect size of 79% is high. These

results are in line with hypothesis one.

For chain three, a paired t-test showed that the mean signal distance only

differed between generations four and five (M = 0.78, SD = 0.70) and generations

three and four (M = 1.74, SD = 1.06), t (26) = 3.65, p = .001, which showed a large

effect: d = 0.91. This indicates that it became easier to learn the language for the

participant in generation five than it was for the participant in generation 4. This was

also the case in chain five, the mean signal distance between generations four and five

(M = 0.44, SD = 0.89) is lower than that between generations three and four (M =

1.52, SD = 1.25), t (26) = 3.81, p = .001, d = 0.86, again, this effect size is large.

Table 2: Mean signal distances between generations for all chains combined

indicating the transmission error between generations (SD).

Generations

0-1

Generations

1-2

Generations

2-3

Generations

3-4

Generations

4-5

1.84 (0.23) 1.66 (0.20)* 1.51 (0.37) 1.65 (0.13) 1.14 (0.45)**

Signal distances of generations are compared using a paired t-test with the

preceding generations in this table. Asterisks indicate the level of significance with

the preceding generation

* = p<.000

** = p<.05

Combining all chains together shows a stronger support towards hypothesis

one. Table 2 shows the average signal distances of the combined chains. The asterisks

indicate the level of significance when compared with the preceding generations. In

general it can be assumed that it was easier for participants in generation five to learn

the language (M = 1.14, SD = 0.45) than it was for participants in generation four (M

= 1.65, SD = 0.13), t (5) = 2.62, p = .047, r = 0.76. This effect size could be

 

  27  

considered as large. These results confirm hypothesis one, which states that after a

certain amount of generations a language becomes easier to learn. In this experiment,

the language appears to become easier to learn after the fourth generation.

4.1.2. Learnability in the collaborative condition

Table 3 shows the mean signal distances of the participants in the collaborative

learning condition. The averages indicate that when the participants created the

language together, this language became easy to learn. Especially dyads one and six

showed a very low mean signal distance.

Table 3: Mean signal distances in collaborative condition for each dyad

Dyad Mean (SD)

1 0.04 (0.19)

2 0.48 (0.64)

3 1.07 (0.92)

4 1.48 (0.70)

5 1.26 (0.53)

6 0.15 (0.33)

7 1.56 (0.89)

8 1.52 (0.64)

9 1.67 (1.04)

10 0.41 (0.50)

11 1.30 (0.82)

Average all

dyads 0.66 (0.25)

With an independent t-test, the average signal distances between the iterated

learning condition and the collaborative learning condition are compared. The average

signal distance that was used for the iterated learning condition is the comparison

between generations four and five, since this comparison reveals the lowest mean

signal distance in the chains. This test showed that participants in the collaborative

learning condition (M = 0.65, SD = 0.25) learnt the language more easily than

participants in the iterated learning condition (M = 1.14, SD = 0.45), t (15) = 2.86, p =

.012, the effect size indicated a medium effect: r = 0.59. This result does not support

hypothesis two which states that when the iterated learning model is applied, a

 

  28  

language will become easier to learn than when the collaborative learning model is

applied as the rules that will be established are applicable to more situations.

Table 4: Average proportions of correct answers through the game (SD)

Round 1 Round 2 Round 3 Round 4

.20 (.20) .31 (.30) .32 (.29) .30 (.33)

0 = non correct

1 = all correct

When analysing the learnability throughout the game, it was shown that

participants did not become more accurate while playing the game. Table 4 shows the

average proportions of correct answers for each playing round of the game. There are

no significant differences in the proportion of correct answers between round one (M

= .20, SD = .20) and round two (M = .31, SD = .30), t (21) = -1.44, p = .165. There

are also no differences between round two and round three (M = .32, SD = .29), t (21)

= -0.24, p = .815. Finally, there are no differences in the average proportions of

correct answers between round three and round four (M = .30, SD = .33), t (21) =

0.27, p = .789.

4.2 Structure of the language

The results on structure are discussed separately for the iterated condition and the

collaborative condition. After the separate results, a comparison has been made

between the two conditions.  

 

4.2.1. Structure in the iterated condition

Table 5 shows the correlations between the normalized Hamming distance and the

Levenshtein distance for each generation. As is shown in table 5, most significant

correlations are present in the later generations. Except for the correlation of

generation five in chain six, the correlations are weak. The correlations in generation

five of chain six are strong.

 

  29  

Table 5: Correlations between normalized Hamming distance and normalized

Levenshtein distance

Chain Generation 1 Generation 2 Generation 3 Generation 4 Generation 5

1 -.02 .15** .14* .29** .25**

2 .05 .31** .33** .13* .25**

3 .15** .05 .19** .20** .21**

4 .16** .05 .04 .22** .07

5 .19** -.03 .02 .15** .25**

6 .11 .23** .09 .09 .67**

* p<.05

** p<.01

A paired t-test showed that there are no significant differences between

generations four (M = .18, SD = 0.07) and five (M = .28, SD = 0.20) when it comes to

the emerging structure t (5) = -0.99, p = .364. These findings do not support

hypothesis three that states that a language will become more structured after a certain

amount of generations.  

 

4.2.2. Structure in the collaborative condition

Table 6 shows the correlations between the Levenshtein distance and the Hamming

distance for every dyad in the collaborative learning condition. Most of the

correlations are significant and the strength of the correlations range from weak to

moderate. These results partly support hypothesis five that states that a structure will

emerge when the collaborative learning model is applied. However, it depends on the

dyads how structured the language becomes.

 

  30  

Table 6: Correlations between normalized Hamming distance and Levenshtein

distance (n = 351)

Dyad Participant r

1 1 .47*

2 .46*

2 3 .40*

4 .44*

3 5 .48*

6 .15*

4 7 .36*

8 .05

5 9 .27*

10 .29*

6 11 .40*

12 .44*

7 13 .05

14 .20*

8 15 -.02

16 .20*

9 17 .23*

18 .35*

10 19 .44*

20 .43*

11 21 .31*

22 .21*

* p<.01

To research whether one learning account would lead to a more structured

language, an independent t-test has been conducted. This t-test showed that there is no

difference in whether the iterated learning model (M = .28, SD = .20) is applied or

when the collaborative learning model (M = .30, SD = .15) is applied, t (26) = -0.23, p

= .820. This result does not support hypothesis six which states that with the

collaborative learning model the emerging language will become more structured than

when the iterated learning model is applied.

 

  31  

4.3 Qualitative research of the languages

The previous discussed results show that there are effects of the applied learning

model on the learnability and the emerging structure in languages. However, they do

not show what structure has emerged and if there are any strategies participants have

used to create the language. For this reason, some qualitative findings on the structure

and strategies will be discussed based on examples from the experiment. It should be

noted that all participants stated that they found the experiment to be very difficult.

First of all, in the iterated condition, in the initial generations some

participants indicated that they deliberately made up structures for the signals since

they could not remember the signals that were presented. When analysing these

structures, they are not consistent throughout the entire test. However, subsequent

generations show, in general, a lower mean signal distance that indicates that the

language was easier to learn. The structures that are made up by the participants in

initial generations might be accountable for this.

Secondly, when asking the participants in the subsequent generations such as

two and three if they could identify structures in the languages that were presented to

them, most of them stated that they were not able to. Most participants indicated that

they could not remember the signals that were presented. However, the decreasing

signal distance indicates that the languages do become easier to be learned.

Nevertheless, the low correlations in these generations show that there is little

structure in the languages at that point.

Finally, in the later generations, such as four and five, some participants stated

that they were able to find some structures in the signals that were presented to them.

When analysing the test results of those participants they indeed learnt some structure

from the previous generation for one or two aspects such as shape and movement.

Although those structures were not consistent in all cases, they did make it easier for

the next participant to learn and correlations did become slightly stronger.

When analysing the structures and strategies that have been used in the

collaborative condition it comes to the attention that for some dyads the mean signal

distance is very low, which indicates that the language was easy to be learnt. Besides

some correlations between the Levenshtein distance and the Hamming distance are

also fairly strong. When searching for the strategies that have been used in those

dyads, it is noticeable that those participants did not use all features of the image in

 

  32  

their signals. The language that was created by the first dyad is a good example of

this. Those participants have created a very clear structure for the types of fruit and

movements but did not take colour into consideration; their structure is shown in

figure five.

Figure 5: Signals created by dyad 1 for fruits and movements.

This system has been steadily created throughout the game and has

consistently been used in the test. Only one of the twenty-seven signals that were

tested was not identical. The high test results indicate a very effective system,

however, by leaving out on the aspects of the image, the participants made the

assignment easier for themselves. All dyads that have a low mean signal distance and

relatively high correlation between the Levenshtein distance and the Hamming

distance used this strategy. It can be stated that the participants in those dyads created

a very structured language for themselves, but is did not signal all aspects of the

image.

Another result that can be found in multiple dyads is that a structure did

emerge, but that it is not completely consistent with every match. Some participants

indicated that they had a structure for one of the three aspects or that they had a few

signals for one aspect, which limited the number of possibilities throughout the game.

It could be assumed that when participants have had more time, they might have

created consistent signals for all aspects, which would make it into a compositional

language, something that is not the case in this experiment. This is also the reason for

the findings that participants did not become more and more accurate after playing the

game for a while (table 4).

 

  33  

Because the languages were not optimal, participants had to keep guessing

between two or three options. Even when they did guess correctly, this is not a result

from the emergence of an optimal language, but it was purely based on chance. This

is a result that can be found in all dyads that accomplished to create some structure.

The reason for this is that in most of these cases, colour and movement were not

signalled by the participants.

 

  34  

5. Discussion

In this section, the results will be critically reviewed and discussed on the basis of the

hypotheses and theories that have been discussed earlier in this thesis. Furthermore,

limitations of this current study will be discussed after which suggestions for further

researches are given.

5.1 Discussion of results

The first hypothesis, which states that after a certain amount of generation a language

will become more learnable, is mainly based on the study of Kirby, Cornish and

Smith (2008) on which a large part of the experiment is based. They found results that

indicate that after a certain amount of generations, in their experiment ten generations

were used, a language became easier to be learnt by the participants. The results in

this current study support these findings. However, where Kirby, Cornish and Smith

(2008) used ten generations in their experiment, this current research only uses five

generations. In combination with the difficulty of the experiment, all participants

stated that they found the experiment extremely difficult and sometimes frustrating, it

can be assumed that even when the iterated learning model (ILM) is applied, a

language can evolve very quickly and become more easily adaptable. It could be

assumed that when more generations are added to the chains, the emerged languages

will become even easier to be learned. However, the results in this current research

are less consistent than the results found in the study by Kirby, Cornish and Smith

(2008). A reason for this can be found in the second experiment of their study, all

ambiguous signals were filtered out of the learning material, which was not done in

this current study. This could have made it easier for those participants to learn the

language since no ambiguous signals were shown to them. However, to make any

further statements about this more research is required.

When focusing on the learnability in the collaborative condition, a comparison

can be made with research from Galantucci (2005) who studied the collaborative

learning account (CLM) in the laboratory. In his research, participants had to create a

communication system with other participants in order to communicate which virtual

room they were in. Galantucci (2005) found that during the course of the experiment,

participants became more and more accurate which indicates that they created an

 

  35  

understandable communication system. For this reason, the second hypothesis is:

when the collaborative learning model is applied, a communication system will

emerge that is easily adaptable for participants.

The results of this current research show that an emerging language becomes

easier to adapt when participants create this communication system together. These

results are based on the mean signal distance between two participants in a dyad. This

mean signal distance is calculated by comparing how many matches have been

positioned the same by both participants. However, when looking at the accuracy of

the participants guessing which image was meant during the game, this accuracy is

low. A reason for this is that the communication systems that evolved were only

structured on one or two features of the image. This meant that participants guessed

from two or three possible images which image was signalled; sometimes this was

correct, purely on chance and not on understanding. For this reason, it could be

assumed that when participants have more time to create a communication system,

their accuracy during the game would improve, just as the accuracy between the tests

the participants make. This would be in line with the research by Galantucci (2005),

the duration of that experiment was three hours instead of the duration of one hour in

this current research. However, to make further statements about these speculations, a

follow-up study will be necessary.

An important aspect of this study was to compare the iterated learning model

and the collaborative learning model on the learnability of an emerging language. In

the computational study by Vogt (2005a, 2007), agents interacted through

communicating different colours and shapes. When no new agents were introduced to

the community, and thus when the language is transmitted horizontally, the emerging

language does not change much and the rules that were established were not optimal.

This set-up can be compared to the collaborative learning model. In a follow-up study

(Vogt, 2007), halfway through the simulation, new agents were introduced to the

community. In this case, compositional rules that combined colour and shapes took

the overhand. The agents used these rules more often since they were easier to learn

and applicable to multiple situations.

The results of this current study cannot support these previous results. It was

found that in the collaborative condition, participants found it easier to learn the

language than in the iterated condition. An explanation for these results can be found

 

  36  

in the set-up of the experiment. Where the population of the computational research

consisted of multiple agents, this current research used dyads where participants only

communicated with each other and rules were established quickly and one on one,

which could have made is easier to comprehend the rules. However, to make any

further statements about this, more research is necessary.

Moreover, these current results also contradict results found by Smith, Kirby

and Brighton (2003) who found that when the iterated model is applied and the

language contains a linguistic bottleneck, a language will evolve to become more

compositional which is generally easier to learn. A reason for this can again be found

in the set-up of the experiment; it might be easier for participants to create a language

in together than when the language is transmitted vertically.

When discussing the structure in an emerging language, Kirby, Cornish and

Smith (2008) and Galantucci (2005) found evidence for the iterated and collaborative

learning models. In both studies where the different models were applied a non-

random structure occurred. This current research found evidence for this as well. In

the iterated condition, the correlations between the normalized Hamming distance and

the Levenshtein distance in the fourth and fifth generation are mainly significant.

However, it should be noted that the correlations are not very strong.

Moreover, the dyads in the collaborative condition also showed significant

correlations between the normalized Hamming distance and the Levenshtein distance.

However, these correlations differed in their strength among the different dyads,

ranging from weak to very strong. These results could be explained through the fact

that some dyads found the assignment too difficult and only established rules at the

end of the game.

When comparing these results of the iterated condition and the collaborative

condition, previous research shows different contradicting results. Fitch (2007) states

that languages change very rapidly and in order to make these changes possible,

interaction between people is necessary. These quick changes cannot occur when a

language solemnly evolves through learning from previous generations. However,

Fitch (2007) focuses with his statements on aspects of a language such as how the

meaning and use of words evolve, not the basic structure of a language.

Moreover, Vogt (2007) found results that when language is transmitted

vertically and new agents are introduced to a population, the rules that are established

 

  37  

in a language are more structured and compositional. These rules are also used more

often since they apply to almost every situation.

This current research did not show evidence for the superiority of the ILM or

the collaborative learning model. There were no significant differences between the

correlations of the iterated condition and the collaborative condition. The averages of

both conditions show a trend that could not support the results found by Vogt (2007),

the correlations in the iterated conditions are slightly lower than in the collaborative

condition. However, it must be stated that these averages did not differ significantly

since the correlations for some dyads are indeed higher than for the average chain, but

the low correlations for other dyads even this out.

One aspect of the set-up of the experiment that needs to be discussed is the

modality of the language that was created. Instead of using a spoken or written alien

language, this experiment used a form of sign language in which matches without

heads were used. This was done to make sure that participants in the experiment had

no experience at all with that modality of communication. This type of

communication might be arguable since in real life communication it would not be

effective as it is very static. On the other hand, this modality does contain some

features of spoken language. For instance, meaningless elements such as a match

evolve into meaningful signals through the placement of this match. This could be

compared to how an utterance evolves into a word that has meaning. Moreover,

duality of patterning is possible with this modality. Combining the meaning of one

match with the meanings of other matches, a large amount of signals can be made

with different meanings. As Hackett (1960) stated, this duality of patterning occurs in

every human language and it occurs only in human languages. The advantages and

disadvantages of this modality could be a reason for why the results of this study do

confirm in some matter previous findings whereas other results contradict those.

However, this modality could possibly create a new methodology to study the

evolution of language.

5.2 Limitations

First of all, one of the major limitations of this study was that the collaborative

condition was derived from an experiment that took several hours, which was not an

option in this current experiment. For this reason, the participants in the collaborative

 

  38  

condition saw the seen-set, with which they played the game, only twice. They only

saw each item once when they sent the signal for the item, and once when they

received the signal whereas in the iterated condition, participants were exposed to the

seen-set six times. However, when it comes to the results of learnability, participants

do favour the collaborative condition but when the structure of the language is

analysed, the iterated condition is favoured. These results might have been different

had the participants in the collaborative condition also received more exposures to the

seen-set.

Another limitation of this research has to do with the limited number of

participants. Because of this, the chains in the iterated condition only consisted of five

generations, which is equal to five participants. Kirby, Cornish and Smith (2008) used

chains of ten generations in their experiment which lead to more convincing results.

Yet it is interesting that this current research found similar results with only five

generations in each chain, however, it is unsure if the results would have developed

further when more generations would have been added to the chains. However, where

Kirby, Cornish and Smith (2008) filtered out ambiguous signals in their second

experiment, which meant that participants did not see these signals. This was not

done in this current experiment, which could be a reason for the less consistent

results.

  Finally, the distribution of male and female participants through the

experiment could be a limitation. While all participants were university students, the

female participants outnumbered the male participants. The main reason for this was

that the participants were recruited from the subject-pool of the communication- and

information sciences program of Tilburg University. In this pool, participants sign up

for experiments in exchange for course credit. As researchers, we had little control

over these signups and did not have the opportunity to make restrictions for the

people who signed up for the study due to time constraints.

 

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5.3 Suggestions for further research

The suggestions for further research can be made on the basis of the implications of

this study. First of all, in a follow-up study, the collaborative condition should be as

similar as possible to the iterated condition. In this research, the seen-set was seen

fewer times by the participants in the collaborative condition than the participants in

the iterated condition. By making these exposures equal for both conditions, a

comparison between the conditions would be more reliable.

Secondly, to be able to compare this research to the study of Kirby, Cornish

and Smith (2008) five more generations should be added to the chains. Since the

results that were found only occurred in the fifth generation, it could be assumed,

based on the results found by Kirby, Cornish and Smith (2008), that these results

would continue through the subsequent generations. However, to make statements

about this, more research is necessary.

Finally, the last suggestion for further research is based on the participants.

The division between men and women should be more equal to increase the reliability

of the research. In this research we unfortunately did not have this opportunity.

Moreover, it would be interesting to see if the results would be the same if the

participants were not all university students. Those participants are used to participate

in these types of experiments and therefore might be biased.

 

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6. Conclusion

From a traditional perspective, it is believed that humans can learn a language easily

because the human brain has adapted itself over years to do so. However, in the past

two decades, more and more research has been conducted to explore the idea that it is

not the brain that has developed to make it easier to learn a language but that language

itself has changed to become more learnable. From this perspective of cultural

transmission, two main language-learning models have been proposed, the iterated

and the collaborative learning model.

  The purpose of this current study was to explore the differences between the

iterated learning model, in which is believed that a language evolves through learning

by observing from a previous generation, and the collaborative learning model. The

collaborative learning model proposes that a language evolves through

communication and interaction with others. This notion led to the following research

question:

“Are there any differences in the learnability and structure of an evolving language

between the application of the iterated learning model and the collaborative learning

model and how might these differences complement each other?”

This current experiment was set up to test both models on learnability and

structure in the evolving languages and to be able to compare them with each other. It

was found that when the iterated learning model was applied, after four generations it

became easier for participants to learn the evolving language. However, this effect of

learnability was larger when the collaborative learning model was applied. Therefore

it is possible that humans learn a language more easily through the communication

and interaction with other. Moreover, results showed that when both the iterated and

the collaborative model were applied, a non-random structure arose in the languages;

however, no differences were found between the learning models.

When looking at these results, it is possible to conclude that there are indeed

some differences between the two learning models. When it comes to learning a

language, it appeared to be easier for participants to do this through interaction with

others. When one thinks about how a child learns a language, through practicing and

receiving feedback from others, this sounds logical. However, when looking at the

 

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structure of a language, which makes it easier to learn a language, no differences were

found between both models and therefore it might be possible that both models

complement each other when it comes to structure in an evolving language. To make

any further statements about this, more research will be necessary.

 

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7. References

de Boer, B., Sandler, W., & Kirby, S. (2012). New perspectives on duality of

patterning: Introduction to the special issue. Language and Cognition, 4(04),

251-259.

Chomsky, N. (1986). Knowledge of language: Its nature, origin, and use. Greenwood

Publishing Group.

Christiansen, M. H., & Chater, N. (2008). Language as shaped by the brain.

Behavioral and Brain Sciences, 31(05), 489-509.

Dediu, D., & Ladd, D. R. (2007). Linguistic tone is related to the population

frequency of the adaptive haplogroups of two brain size genes, ASPM and

Microcephalin. Proceedings of the National Academy of Sciences, 104(26),

10944-10949.

Darwin, C. (1874). The descent of man and selection in relation to sex (2nd ed.).

London: John Murray.

Fay, N., Garrod, S., Roberts, L., & Swoboda, N. (2010). The interactive evolution of

human communication systems. Cognitive Science, 34(3), 351-386.

Fitch, W. T. (2007). Linguistics: An invisible hand. Nature, 449(7163), 665-667.

Galantucci, B. (2005). An experimental study of the emergence of human

communication systems. Cognitive science, 29(5), 737-767.

Gildea, D., & Jurafsky, D. (1996). Learning bias and phonological-rule induction.

Computational Linguistics, 22(4), 497-530

Hackett, C. 1960. The origin of speech. Scientific American 203. 88–111.

Kirby, S. (2002). Natural language from artificial life. Artificial life, 8(2), 185-215.

Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in the

laboratory: An experimental approach to the origins of structure in human

language. Proceedings of the National Academy of Sciences, 105(31), 10681-

10686.

Kirby, S., Dowman, M., & Griffiths, T. L. (2007). Innateness and culture in the

evolution of language. Proceedings of the National Academy of Sciences,

104(12), 5241-5245.

Kirby, S., & Hurford, J. (2002). The emergence of linguistic structure: An overview

of the iterated learning model. In A. Cangelosi & D. Parisi (Eds.), Simulating

 

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the evolution of language (pp. 121–148). London: Springer.

Scott-Phillips, T. C., & Kirby, S. (2010). Language evolution in the laboratory.

Trends in cognitive sciences, 14(9), 411-417.

Smith, K., Kirby, S., & Brighton, H. (2003). Iterated learning: A framework for the

emergence of language. Artificial life, 9(4), 371-386.

Steels, L., & Kaplan, F. (2002). Aibos first words: The social learning of language

and meaning. Evolution of communication, 4(1), 3-32.

Steels, L. (2001). Language games for autonomous robots. Intelligent Systems, IEEE,

16(5), 16-22.

Vogt, P. (2007). Variation, competition and selection in the self-organisation of

compositionality. The Mind, the Body, and the World: Psychology After

Cognitivism, 233-256.

Vogt, P. (2005a). The emergence of compositional structures in perceptually

grounded language games. Artificial Intelligence, 167(1-2), 206-242.

 

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8. Appendices

Appendix 1: All possible combinations of entities

 

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Appendix 2: Feedback form iterated condition

1) What did you think of the experiment? How difficult/easy/fun/boring was it?

2) In what way did you try to memorize the combinations of the pictures with their signals to

be seen in the PowerPoint slides?

3) In what way did you try to emulate or create a structure? You can draw if you like.

4) How do you feel about the fact that you had to practice in 3 rounds? How meaningful did

you find that?

5) Do you think that the results that you have made of the final test are just as good as the

combinations to be seen in the PowerPoint slides, or even worse, or even better? Why?

6) How did the translating of the 3 categories go (color, kind of fruit, movement) onto the

grid? Did you have a system for that? How did you arrive at that system?

7) To which degree did you keep in mind that the photos which would be taken of your

results will be seen by subsequent participants? Do you think that they will find your signals

more, less, or equally clear as the signals in the PowerPoint slides?

8) If you had a system, was it perfect, or did you run into problems? Would you have

preferred maybe more sticks? Or fewer sticks? Or fewer lines on the grid maybe?

9) Do you think that this experiment can be called some kind of “language”? If yes, why?

10) Do you have any other remarks/suggestions/questions for us? (Also let us know if you

feel that your results are not representative e.g. because you’ve done a similar experiment

recently or you feel like you haven’t tried your best.)

Thanks for participating! You can still ask us questions

 

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Appendix 3: Feedback form collaborative condition

1) What did you think of the experiment? How difficult/easy/fun/boring was it?

2) Did you enjoy being player 1 or 2? Or would you have preferred being the other player?

3) Was there a match to be found in the way you two laid down the matches? What was/were

the match(es)? (You can draw if you like.)

4) If you had a system, was it perfect, or did you run into problems? Would you have

preferred maybe more sticks? Or fewer matches? Or fewer lines on the grid maybe?

5) Do you think what mostly you were responsible for the development of that structure? Or

the other player? Or do you feel that that is divided equally?

6) What did you think of the 3 categories (colour, type of fruit, movement)? Which was/were

the easiest, and which the hardest?

7) How much did you find yourself scrolling back up into the Whatsapp conversation in order

to see the previous results? Did you look at your own previous results, or those of your

partner?

8) Did you have any trouble with Whatsapp/iPhone (maybe you’re not used to them)?

9) What did you think of the duration of the experiment? Did you find the experiment to be

TOO long?

10) Do you think that this experiment can be called some kind of “language”? If yes, why?

11) Do you have any other remarks/suggestions/questions for us? (Also let us know if you

feel that your results are not representative e.g. because you’ve done a similar experiment

recently or you feel like you haven’t tried your best.)

Thanks for participating! You can still ask us questions!

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Appendix 4: Consent form iterated condition

We are students Communication and Information Sciences and for our master thesis

we will be investigating how people acquire a language.

For this experiment you will be learning an ‘alien’ language. Because the aliens can’t

talk, they use matches as objects. You will learn this language by way of three

practice rounds, after which you will perform a small test. In these practice rounds

you will be shown a picture with its corresponding signal. Each combination will be

shown for 8 seconds. During the small tests you will try to recreate the combinations

as closely as possible. Your hands will be filmed during these tests, however, you will

not be recognizable in any sort of way.

After the practice rounds, you will perform one large test one time. It’s very important

that you will try your best, because your results will be used for the practice rounds of

the next participant! That is why your results will be photographed afterwards. You

won’t be visible in the photo.

All data are confidential and will be secured safely. Only us and our thesis supervisor

will have access to this information. Futhermore, you will be anonymous as a

participant.

In case you have further questions about this research, please contact us at the

following:

Anouck van der Meijden or

Linda Meijer

Permission:

I am aware of the fact that my participation of this experiment will be completely

anonymous. I understand the nature of this research. If, for what reason, wish to quit

the experiment, I can do so without any explanation.

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- I do/do not give any permission for the usage and storage of the photos and

video’s that emerge from my test.

- I do/do not give any permission for the usage of the photos and video’s that

emerge from my test for educational purposes and conferences.

A copy of this form has been offered to me, which I may keep. I have read all of

the above. I am aware of the fact that I can stop at any time, and I am giving my

permission for participation.

__________________ _____________

Participant’s signature Date

_________________

Experiment leader’s signature

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Appendix 5: Consent form collaborative condition

We are students Communication and Information Sciences and for our master thesis

we will be investigating how people acquire a language.

For this experiment you will be learning an ‘alien’ language. Because the aliens can’t

talk, they use matches as objects. You will learn this language by playing a game in

which you have to send pictures. You will be shown an image for which you come up

with a signal. You will make a picture from this signal and send it to the other player.

This players guesses which image you mean and in you will let that player know if

this was correct or not. After playing the game we will ask you to make a test, this test

will be photographed. After this test you are askes to fill in a questionnaire.

All data are confidential and will be secured safely. Only we and our thesis supervisor

will have access to this information. Futhermore, you will be anonymous as a

participant.

In case you have further questions about this research, please contact us at the

following:

Anouck van der Meijden or Linda Meijer

Permission:

I am aware of the fact that my participation of this experiment will be completely

anonymous. I understand the nature of this research. If, for what reason, wish to quit

the experiment, I can do so without any explanation.

- I do/do not give any permission for the usage and storage of the photos that

emerge from my test.

 

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- I do/do not give any permission for the usage of the photos that emerge from

my test for educational purposes and conferences.

A copy of this form has been offered to me, which I may keep. I have read all of

the above. I am aware of the fact that I can stop at any time, and I am giving my

permission for participation.

__________________ _____________

Participant’s signature Date

_________________

Experiment leader’s signature