Can iterated learning explain the emergence of graphical symbols?

Interaction Studies ( ), – . /is. . gar– / – © John Benjamins Publishing Company

Can iterated learning explain the emergence of graphical symbols?

Simon Garrod1,2, Nicolas Fay2, Shane Rogers2, Bradley Walker2 & Nik Swoboda3

1University of Glasgow/2University of Western Australia/3Universidad Politecnia de Madrid

!is paper contrasts two in"uential theoretical accounts of language change and evolution – Iterated Learning and Social Coordination. !e contrast is based on an experiment that compares drawings produced with Garrod et al.’s (2007) ‘pictionary’ task with those produced in an Iterated Learning version of the same task. !e main #nding is that Iterated Learning does not lead to the systematic simpli#cation and increased symbolicity of graphical signs produced in the standard interactive version of the task. A second #nding is that Iterated Learning leads to less conceptual and structural alignment between participants than observed for those in the interactive condition. !e paper concludes with a comparison of the two accounts in relation to how each promotes signs that are e$cient, systematic and learnable.

Keywords: Graphical communication, Interaction, Iterated learning, Evolution of communication

Introduction

In recent years there has been a resurgence of interest in the science of language evolution, as re"ected in publications in major international journals such as Science and Nature (e.g., Atkinson et al., 2008; Lieberman, 2007). Much of the theory behind this work comes from computer modelling of language evolution assuming a process of either cultural evolution or genetic-cultural co-evolution. One in"u-ential model assumes an evolutionary principle analogous to iterated learning in which the language is transmitted vertically down generations of speakers. Other accounts attribute evolution to processes of social coordination within commu-nities of communicators. In this paper we report an experiment that contrasts the two types of account using a communication task that tracks the evolution of graphical signs. !e main #nding from this experiment is that certain phenomena

© 2010. John Benjamins Publishing CompanyAll rights reserved

Simon Garrod, Nicolas Fay, Shane Rogers, Bradley Walker & Nik Swoboda

associated with the rapid evolution of graphical signs in an interactive setting do not manifest themselves in an iterated learning version of the task.

First, we brie"y discuss the principal theoretical approaches to the evolu-tion of language. !en we consider recent experimental approaches that inves-tigate the evolution of complex non-linguistic communication systems which highlight the importance of interactive communication. !en we report the experiment itself.

!eoretical approaches to the evolution of language

By analogy with biological evolution, languages may evolve as a result of genetic (evolutionary) changes which then affect language learning processes (Pinker, 1994). For example, Dedieu and Ladd (2007) show that the marked geographical distribution of tone languages is a result of the distribution of recently evolved alleles of the brain growth and development genes ASPM and Microceph-alin. !ey suggest that this re"ects genetic in"uences on the ability of their owners to easily acquire the tone languages. However, genetic change can only a%ect the evolution of language over very long time-scales and recent modelling work indi-cates that it is insu$cient to account for most historical changes that have occurred in human languages (Chater, Reali & Christiansen, 2009; Berwick, 2009). !is has led to consideration of a di%erent kind of mechanism termed cultural evolution (Christiansen & Kirby, 2003; Kirby, Dowman, & Gri$ths, 2007). Just as biological adaptation optimizes individual learning, cultural evolution (through the transmis-sion of language across and within generations of speakers) may re#ne language systems by linguistic selection.

!ere are two in"uential modelling approaches to cultural evolution of language. !e #rst assumes that languages evolve as a consequence of vertical transmission from adults to children. For example, the Iterated Learning Model (Kirby, 2002; Kirby & Hurford, 1997, 2002), assumes that as language is transmitted from generation to generation it is incrementally in"uenced by agents’ innate learning biases and constraints on transmission until the language reaches an equilibrium that re"ects these prior linguistic biases. Simulations indicate that under the right conditions iterated learning across many generations will lead to the emergence of structured languages.

A typical simulation starts out with a small population of agents with two agents in the system at any one time. One represents an adult ‘speaker’ the other a child ‘learner’. !e learner is exposed to strings of characters produced by the speaker and acquires mappings between these signals and their meanings – pairs of features that can take di%erent values. Initially, the signal mappings produced by the speaker are holistic and unstructured. However, the learner can use this data (signal-meaning


Can iterated learning explain the emergence of graphical symbols

mappings) to induce their own representation of the language system which they subsequently produce as adult speakers for the next generation of child learners. Because learners are only exposed to a sub-set of the adult’s productions the ‘languages’ change during transmission across generations of speaker-learners.

!is data bottleneck turns out to be crucial for the emergence of structured languages. If it is too wide (e.g., exposure to all the adults productions) the lan-guages do not change and remain essentially random. If it is too narrow the languages become highly unstable. However, if it is neither too wide nor too narrow the languages start to become compositional and the signals decompose into smaller units representing di%erent aspects of meaning. !is is presumably because compositional expressions are more likely to ‘survive’ intergenerational transmission than holistic expressions. In other words, constraints on the learn-ing process lead to an adaptation of the language. In this way iterated learn-ing together with an appropriate data bottleneck can simulate the emergence of structure in language.

In addition to computer simulations there is recent experimental evidence with human participants that also shows that iterated learning (under the right circumstances) can lead to the emergence of compositional arti#cial languages (Kirby, Cornish & Smith, 2008: see also Cornish this issue). Notice that this account does not depend upon any interaction between agents, it only depends upon one-way vertical transmission.

!e second kind of model of cultural evolution assumes that communica-tion systems arise through horizontal transmission among communities of lan-guage users. For example, Steels and colleagues’ social coordination simulations (Steels, 1999; Steels, Kaplan, McIntyre, & Van Looveren, 2002) show that when meaning is locally negotiated, a dominant representation propagates horizontally among a population of interacting agents until the entire community converges upon a shared communication system (see also Barr, 2004). Crucially, this model depends upon interaction and feedback between computer agents.

!is paper sets out to contrast the two accounts, but in the context of the evolution of graphical as opposed to linguistic communication systems. In particular we con-centrate on a feature of the evolution of complex graphical communication systems which relates to the emergence of arbitrary symbolic graphical signs. To illustrate the issue we turn next to work on graphical communication and show how this can be used to address questions about the evolution of human communication systems.

Experiments on the evolution of graphical communication

Graphical communication tasks can be especially useful in studying the emergence and evolution of communication systems. !is is because such tasks allow us to



look at how complex communication systems evolve in the absence of a pre-established system of signs.

For example, Galantucci (2005) had pairs of participants communicate their positions on a grid using only graphical means. He found that with time most participants were able to do this even though the graphical interface did not allow them to produce any kind of conventional signs. In other studies partici-pants attempted to communicate one of two pieces of music using purely graphi-cal means (Healy, Swoboda, Umata & King, 2007), or speci#c items from a list (Garrod, Fay, Lee, Oberlander & McLeod, 2007) by drawing on a whiteboard. In all of these studies participants were not permitted to use conventional language, either spoken or written. Hence, they needed to create a novel communication system from scratch. A consistent #nding across studies was that participants rap-idly established complex communication systems, and that this process depended crucially on interaction and feedback.

!e communication paradigm that we concentrate on in this paper was devel-oped by Garrod et al. (2007) and was based on the parlour game ‘Pictionary’. !ey had participants communicate a series of easily confusable items (e.g., Art gallery, Museum, Drama, Soap Opera) by drawing on a standard whiteboard. Like the game Pictionary, participants were not allowed to use spoken or written language. In one condition pairs communicated a series of recurring items, alternating between drawing and identifying roles from one game to the next. !e changing form of the signs used to convey ‘Clint Eastwood’ across six games of the task are shown in Figure 1. What begins as an iconic depiction of the character evolves into

Game 1 Game 2 Game 3


Figure 1. Drawing re#nement and convergence for the concept ‘Clint Eastwood’ across six games between a pair of interlocutors playing the Pictionary task (adapted from Garrod et al., 2007)



a simpli#ed symbolic form (outline of hat plus triangle). Crucially, such re#nement only occurred when partners were allowed to interact graphically (even if this only involved placing a tick next to the drawing to indicate comprehension). In a control condition in which the participants simply repeated their drawings for an imaginary audience the drawings became more complex and retained their iconic character. A second #nding was that with extended interaction communicators’ drawings became increasingly similar, or convergent (See Figure 1).

!us, in this task graphical communication systems emerged and evolved through a process of interactive grounding. Speci#cally, Garrod et al. (2007) argued that icons rapidly evolve into symbols via interaction; icons help ground shared sign systems and interaction promotes a shi& in the locus of information from the sign to the users’ memory of the sign’s usage. !is shi& in information facilitates the evolution of increasingly simple abstract signs that are easy for communicators to produce and interpret. So this graphical communication experiment might tell us something about how communicators overcome the symbol grounding prob-lem (i.e., how language systems might have emerged out of meaningless symbols; Harnad, 1990). !e basic argument is that the symbols evolve through the com-bination of convergence on increasingly simple forms together with alignment on their meaning.

However, this kind of dyadic communication task only addresses the emer-gence of local communication systems developed by isolated pairs of communica-tors. To generalise the task Fay, Garrod, Roberts and Swoboda (in press; See also Fay, Garrod & Roberts, 2008) devised a community based version of the game (see Garrod & Doherty, 1994 for a community language version). !ey created four 8-person laboratory communities, or microsocieties, via the one-to-one interac-tions of partners drawn from the same pool. Participants played six consecutive games with a partner, where each game contained the same to-be-communicated items (16 targets plus 4 distracters, presented in a di%erent random order on each game) that were known to both partners. As in the previous example, drawing and identifying roles alternated from game to game. Participants then switched part-ners and played a further 6 games with a new partner, and continued to do so until they had interacted with each of the other community members. In other words they eventually constituted a completely connected communication network.

!is Community condition was contrasted with an Isolated Pair condition, in which participants interacted with the same partner over the same number of games (i.e., 42 games). !e task was administered using a virtual whiteboard tool (Healy, Swoboda, & King, 2002), with each participant seated at a computer ter-minal and drawing input and item selection made via a standard mouse. Crucially, participants were unaware of the identity of their partner in any round. Figure 2 illustrates the global and local evolution of the sign representing ‘Brad Pitt’ within a single Community and a corresponding number of Isolated Pairs.



Community drawings at Round 1 Community drawings at Round 7

Pair drawings at Round 1 Pair drawings at Round 7

Figure 2. Drawing re#nement and convergence for the concept ‘Brad Pitt’ among a Community and between Isolated Pairs at Round 1 and Round 7 of the Pictionary task (from Fay et al., in press). Participant numbers are given in bold on the top right of the drawing

!e #rst drawings of ‘Brad Pitt’ (Round 1) illustrate the diversity of graphical signs; some indicate his American origins, others his frequent casting as a ladies man, while others use the rebus principle to represent part of the test item (Com-munity members 5 and 6 draw a large hole in the ground to convey a ‘pit’, whereas Isolated Pair member 4 draws an arrow pointing at an arm ‘pit’). Drawing diversity at Round 1 in the Community condition contrasts sharply with drawing uniformity



at Round 7, where all Community members have globally converged on a re#ned version of person 5’s initial ‘pit’ drawing. Unlike Community members, Isolated Pairs locally converged on a shared sign system, but globally diverged across games. Note that in both conditions groups arrived at a series of signs of equal visual complexity.

To summarise, experiments using the pictionary task demonstrate how graph-ical communication systems (i.e., systems of graphical signs) naturally evolve as a result of interactive communication. In particular, the signs change from complex iconic representations to much simpler symbolic representations as a consequence of grounding. Furthermore this evolutionary process is apparent both within iso-lated pairs of graphical communicators and within micro-societies who interact like a small linguistic community. !is evolution is particularly interesting because it o%ers one explanation for how communicators overcome the symbol ground-ing problem. Next, we ask whether this kind of semiotic evolution can arise as a consequence of vertical transmission and iterated learning in the same way that it occurs with horizontal transmission and interactive grounding.

Comparing iterated learning with interactive communication

One prediction from iterated learning models is that as information is passed down over generations of teachers and learners so its representation converges on the dominant representation for that population. !is has been clearly shown in func-tion learning tasks. For example, Kalish, Gri$ths and Lewandowsky (2007) had people learn one of four mathematical functions (positive linear, negative linear, U-shaped and random) by giving them corrective feedback a&er they had guessed the y-value associated with a given x-value. !e participants then completed a test phase (a combination of prior training values and novel values, without corrective feedback) whose output then served as training data for the next generation. !is procedure continued across nine generations. Irrespective of the data seen by the #rst learner on each transmission chain, a&er only a few generations there was a marked tendency for all participants to converge on a positive linear function. !us, the dominant prior for positive linear functions was ampli#ed across gen-erations until all agents eventually converged on it.

!is iterative evolution process might explain the evolution of graphical rep-resentations in the pictionary task. As representations are repeatedly generated by di%erent members of the community so those representations should tend to converge on the form that re"ects the strongest prior for that population of com-municators. On the assumption that the dominant prior is a simple e$cient form, this would lead to the emergence over iterations of simpler forms. In other words, the simpli#cation process might occur in the absence of interactive feedback, even



though this did not occur when a single drawer repeatedly produced drawings for an imaginary audience.

To test this alternative account we devised an iterated di%usion chain version of the pictionary task to compare the drawings produced in this version of the task with those produced by matched interacting pairs.

Experiment

!e experiment contrasted situations in which pairs performed the standard pic-tionary task with matching chains of participants in an iterated learning version of the task.

Participants

One hundred and #ve undergraduate students participated in exchange for partial course credit. Participants were randomly allocated to one of two conditions: (1) interacting pairs (15 pairs) or (2) iterated learning chain (15 5-person chains).

Task and procedure

!e experimental task is a graphical analogue of a verbal referential communica-tion task (Krauss & Weinheimer, 1964, 1967). !e goal for each participant was to graphically communicate a series of confusable items (e.g., Arnold Schwarzenegger, Brad Pitt, Russell Crowe) in such a way that their partner could identify their intended referent. Like the game Pictionary, participants were prohibited from using letters or numbers in their drawings. !e director would draw each item from their ordered list (16 targets plus 4 distracters; see Table 1 for a complete listing) and their partner, the matcher, tried to identify each item from their unor-dered list of the same items.

In the interacting pair condition (interactive) participants played 6 consecu-tive games of the Pictionary-like task with the same partner, using the same item set on each game (the same target and distracter items were presented in a di%er-ent random order on each game). In this condition participants alternated between directing and matching roles from game to game (i.e., one participant was the director on games 1, 3 and 5 and the matcher on games 2, 4 and 6). Importantly, irrespective of directing or matching role, participants were able to graphically interact within any trial. !us, a matcher might provide feedback to the director by annotating part of their depiction or by o%ering a graphical alternative.

In the iterated learning condition (chain), participants’ graphical signs were passed along a 6-generation di%usion chain. Each di%usion chain was initiated with the #rst drawing of each experimental item produced by directors in the interacting pair condition (i.e., graphical signs produced by directors from pairs



1 to 15 at game 1). !us, at game/generation 1 the interacting pair and di%usion chain condition was comparable. Generation 2 members then tried to identify each drawing before graphically communicating each item (in a di%erent ran-dom order) for generation 3. !is continued across 6 generations in the di%usion chain condition. Unlike participants in the interaction condition, who were able to graphically interact across six games of the task, di%usion chain participants attempted to identify the referent of each graphical sign once and produced each graphical sign once before it was passed along the chain. Furthermore, there was no opportunity for interaction between adjacent members of di%usion chains (all drawing activity was recorded using a virtual whiteboard tool and played back to the next member of the di%usion chain).

Table 1. !e set of items that directors communicated to matchers (distracter items given in italic). Target and distracter items were #xed across conditions and throughout the experiment

P P E

Art Gallery Arnold Schwarzenegger

Cartoon Computer Monitor

Homesick

Parliament Brad Pitt Drama Microwave LoudMuseum Hugh Grant Sci-Fi Refrigerator Poverty!eatre Russell Crowe Soap Opera Television Sadness

!e task was administered using a virtual whiteboard tool (Healy, Swoboda, & King, 2002). Each participant sat at a computer terminal where drawing input and item selection was made via a standard mouse. For the director, each to-be-depicted item was highlighted in white text. Holding down the le& mouse button initiated drawing. Director drawing was restricted to black ink, whereas matcher drawing was restricted to green ink (in order to distinguish original drawing from feedback). By clicking an erase button on the interface participants were able to erase parts of their own drawing and their partner’s drawing (only in the interacting pair condi-tion). All drawing and erasing activity was displayed simultaneously on the director and matcher’s shared whiteboards. When the matcher believed they had identi#ed the director’s intended referent they clicked a ‘Got It’ button on the interface. Doing so activated the list of competing referents, allowing the matcher to make their selec-tion. Item selection brought the current trial to an end and initiated the next trial. With the exception of the feedback option the drawing and identi#cation process was identical for participants in both the interaction and chain conditions. Participants were given no explicit feedback with regard to their communication success in either condition. Finally, having participants communicate remotely across networked computers ensured they were unaware of the identity of their partner.



Results

Examples of drawings of ‘parliament’ are shown in Figure 3 for one pair in the interactive condition (3a) and the matched sequence of participants in the iter-ated learning chain condition (3b). As can be seen in the #gure whereas there is a marked reduction in drawing complexity for the interactive pair there is no such


Game 4 Game 5 Game 6(a)

Generation 1 Generation 2 Generation 3

Generation 4 Generation 5 Generation 6(b)

Figure 3a and b. Drawing re#nement and convergence for the concept ‘Parliament’ (a) across six games between an interacting pair and (b) across six generations in a di%usion chain. Participant numbers are given in bold on the top right of each drawing



reduction across the iterated learning chain. Notice also how the drawings in the interactive condition converge over time in a way that those in the iterated learning chain do not (e.g., compare drawings from games 5 & 6 in both conditions). Below we consider the results in more detail, in terms of identi#cation accuracy, drawing re#nement and convergence of drawings over trials.

Identi!cation accuracy!e #rst analysis looked at identi#cation accuracy between pairs of interacting part-ners (interactive) and non-interacting members of iterated learning chains (chain). !e graph in Fig. 4 shows the change in identi#cation accuracy (%) over games.

!e results clearly show a steady increase in identi#cation accuracy across games for both the interacting pairs and the iterated learning chains. !ese obser-vations are con#rmed by analysis of variance (ANOVA). Because the interactive condition contains 6 data points (game 1–6) and the chain condition contains only 5 data points (generations 1–5) the ANOVA was run across game 1 to 5 for both task conditions.

50556065707580859095

100

1 2 3 4 5 6Game

Acc

urac

y (%

)

InteractiveChain

Figure 4. Identi#cation accuracy for players in the interactive pictionary task (Interactive) compared to accuracy of players in the iterated learning chain (Chain) across games (1–6 for interactive; 1–5 for chain)

For simplicity the data were analyzed by item (whereas items remain constant between interactive and chain conditions participants do not). Percent cor-rect scores were entered into a within items design Linear Trends ANOVA with Condition (interactive, chain) and Game (1–5) as factors. !is returned a main



e%ect of Condition [F1,15 = 30.58, "p2 = 0.67, p < .05] and Game [F1,15 = 49.86,

"p2 = 0.77, p < .05] but no Condition by Game interaction [F<1]. !e results indi-

cate increasing semantic alignment across games in both conditions, but greater overall alignment in the interactive condition as compared to chain condition. !e greater semantic alignment seen in the interactive condition presumably re"ects the bene#t of graphical feedback.

Complexity!e next analysis looked at the complexity of the graphical signs produced for each task condition. !is was done using Pelli et al.’s (2006) information theo-retic measure of perimetric complexity [Perimetric complexity = (inside + out-side perimeter)2/ink area.] Previous work indicates that this is an e%ective scale free measure of graphical complexity (See Garrod et al., 2007; Fay et al., in press). Graphical complexity scores for drawings produced in the interacting pair and transmission chain conditions are shown in Fig. 5.

0

1000

2000

3000

4000

5000

6000

7000

8000

1 2 3 4 5 6Game

Peri

met

ric C

ompl

exity

InteractiveChain

Figure 5. Perimetric complexity of drawings across games 1–6 for interacting pairs (Interactive) and matched iterated learning chains (Chain)

As can be seen in Fig. 5 complexity for the drawings from the interactive condition reduces systematically across games replicating Garrod et al.’s (2007) #ndings. However, for the iterated learning chain the drawings remain equally complex over games 2 to 6. !ese observations are con#rmed with ANOVA.

!e perimetric complexity scores were entered into a within items design Linear Trends ANOVA with Condition (interactive, chain) and Game (2–6) as factors. !is returned a main e%ect of Condition [F1,15 = 173.61, "p

2 = 0.92, p < .05] and



Game [F1,15 = 9.70, "p2 = 0.39, p < .05] that was mediated by a reliable Condition by

Game interaction [F1,15 = 45.33, "p2 = 0.75, p < .05]. As can be seen from Figure 5,

the interaction is explained by the decreasing complexity of drawings in the inter-active condition [F1,15 = 78.20, "p

2 = 0.84, p < .05] and no change in complexity of drawings in the chain condition [F < 1].

!ese #ndings indicate that drawings produced in the di%usion chain like those drawn repeatedly by a single participant (the solo condition in Garrod et al. 2007) do not become less complex over time.

Convergence of signs!e #nal analysis compared the degree of drawing convergence (i.e., the extent to which drawings of the same experimental item become similar) over games. We carried out the convergence analysis for two reasons.

First, we wanted to compare the convergence rate between interactive pairs and di%usion chain members. Here, we expected to #nd greater overall convergence between interactive pairs and at a greater rate than for members of the di%usion chain. To check this pairs of drawings from each condition (at game/generation 1&2, 2&3, 3&4, 4&5, 5&6) were presented on a computer screen and rated for similarity (BW1). Drawings were rated on a scale from 0–9, where 0= very dis-similar and 9= very similar. In total 2400 drawings were rated for similarity2 (pre-sented in a random order). A subset of paired drawings (320 drawings sampled from 2 interacting pairs and 2 transmission chains), were rated for similarity by a second judge (NF). Comparison of the two ratings showed strong inter-coder agreement (r = 0.79).

Secondly, we needed to check that in the pictionary version of the iterated learning task participants were in fact learning from their neighbours in the way that they learned arti#cial languages in previous experiments (e.g., Kirby, Cornish & Smith, 2008). To test for this we compared the similarity of drawings within chains to that measured across chains for the same items. If transmission occurs within the di%usion chains then drawings of the same item from neighboring players within a chain should be judged more similar than those from pseudo neighbors across chains. To do this we created 15 pseudo chains by randomly selecting matched drawings produced by di%erent chains at adjacent chain positions (e.g., drama by chain 1 at position 1, drama by chain 4 at position 2, drama by chain 8 at posi-tion 3, drama by chain 11 at position 4, drama by chain 14 at position 5, drama by chain 2 at position 6). Twelve hundred pairs of matched drawings (16 items at games 1&2, 2&3, 3&4, 4&5, 5&6 for 15 pseudo chains) were then presented on a computer monitor (random order) and rated for similarity (0–9 where 0 = very dissimilar and 9 = very similar) as in the analysis reported above.

!e mean similarity scores across games for drawings produced by inter-acting pairs, members of transmission chains and members of pseudo chains is



illustrated in Figure 6 (Interactive, Chain, Pseudo Chain). !e horizontal dotted line indicates the neutral point (4.5), with values above that indicating similar drawings and values below indicating drawing dissimilarity. !e #gure shows that drawings become increasingly similar within interacting pairs re"ecting increased convergence on the signs. However, across the iterated learning chain and across the pseudo chain there is only a slight increase in similarity and this never reaches the neutral point (4.5). Furthermore, mean similarity of drawings within pseudo chains is substantially less than with chains. !ese observations are con#rmed with ANOVA.

0

1

2

3

4

5

6

7

1&2 2&3 3&4 4&5 5&6Game

Dra

win

g Si

mila

rity

InteractiveChainPseudo Chain

Figure 6. Rated similarity (0 = very dissimilar, 9 = very similar) for drawings produced by interacting pairs (Interactive), within iterated learning chains (Chain) and between iterated learning chains (Pseudo chain) across games 1&2, 2&3, 3&4, 4&5 and 5&6

Drawing similarity scores were entered into a within items design Linear Trends ANOVA with Condition (interactive, chain, pseudo chain) and Game (1&2, 2&3, 3&4, 4&5, 5&6) as factors. !is returned a main e%ect of Condition [F1,15 = 136.31, "p

2 = 0.90, p < .05] and Game [F1,15 = 81.66, "p2 = 0.85, p < .05]

that was mediated by a reliable Condition by Game interaction [F1,15 = 36.91, "p

2 = 0.71, p < .05]. !e interaction is explained by the strong increase in draw-ing similarity scores across games in the interactive condition [F1,15 = 122.46, "p

2 = 0.89, p < .05], a smaller increase in drawing similarity scores in the chain con-dition [F1,15 = 26.55, "p

2 = 0.64, p < .05] and a smaller increase in the pseudo chain condition [F1,15 = 12.11, "p

2 = 0.45, p < .05]. In addition, drawing similarity scores were higher for drawings produced in the interactive condition when compared to



the chain condition (t15 = 9.22, d = 2.02, p < .05) and higher in the chain condition when compared to the pseudo chain condition (t15 = 7.88, d = 1.01, p < .05).

Notice that the judged similarity of drawings in the interactive condition increased from dissimilar (3.70; game 1&2) to similar (5.84; game 5&6) as partners interacted across games. In contrast, in the transmission chain condition drawing similarity scores increased (2.93; game 1&2), but drawings remained dissimilar as they were transmitted from person to person (3.99; game 5&6). A similar pattern is seen in the pseudo chain condition (2.13 at game 1&2 increasing to 2.69 at game 5&6).

Discussion

!e main #nding from this study con#rms that the iterated learning version of the pictionary task leads to the development of signs that are similar to those observed when a single person repeatedly draws items for an imaginary audience. Rather than the signs becoming increasingly simple and symbolic they retain their complexity and iconicity when passed down the chain. !e other results on semantic alignment (as indicated by identi#cation accuracy) and structural alignment (as measured by similarity) of drawings also indicate that the iterated learning situation does not produce expressive graphical ‘languages’ in the way that the interactive pictionary situation does. !e drawings are not well identi#ed by members of the transmission chain and there is no indication of strong convergence on particular drawings.

It might be argued that di%erences in semantic and structural alignment for the chains re"ect the fact that there are more participants in the chain than in the interactive condition. However, results from the community version of the pictionary task reported earlier indicate that under interactive conditions large numbers of participants show both semantic and structural alignment at least as strong as that shown by interacting pairs.

As can be seen from Figure 6 the drawings within di%usion chains are also judged consistently more similar to each other than drawings within pseudo chains. !is indicates that there is a reasonable amount of transmission occur-ring within the chains. Another interesting observation is that the drawings within pseudo chains become consistently more similar over time ( F(1.15) = 12.11, p < 0.05; 'p2 = 0.45). !is re"ects the other characteristic of iterated learning that over time chains converge on a prior. So both in terms of transmission and across chain con-vergence the data are consistent with previous iterated learning situations.

General discussion and conclusions

We began this paper by contrasting two cultural accounts of language change – Iterated Learning and Social Coordination. Computer simulations indicate that both approaches lead to the emergence of structured ‘languages’ under the right



conditions. !ey also indicate that communities under each regime will converge on a shared language. By contrast, our results indicate that Iterated Learning in the context of the ‘pictionary’ task does not lead to the systematic simpli#cation and increasing symbolicity of graphical signs observed in the interactive situation. We suspect that this is because Iterated Learning does not naturally promote expres-sive languages, rather it promotes languages that are easy to learn.

!is raises questions about the extent to which the two regimes may tap into di%erent aspects of language evolution. It might be thought that whereas social coordination processes promote e$cient and expressive ‘languages’ they do not promote languages with structure or languages that are easy to learn. However, recent experimental #ndings suggest otherwise. !eisen, Oberlander and Kirby (this issue) show how under the right circumstances (e.g., by having many con-ceptually related items) ‘pictionary’ players produce systematic graphical signs. For example, they might include a fork icon in all drawings referring to agricultural concepts (e.g., farmer, barn, tractor). !is suggests the emergence of a taxonomic systematicity similar to derivational morphology (See Garrod et al., 2007). With respect to ease of learning, there is evidence that communities of ‘pictionary’ players tend to converge on more learnable graphical signs than isolated pairs of players. Fay et al. (2008) selected graphical signs generated by community and isolated pair players of pictionary from the #rst and #nal rounds of the community experiment described earlier (See Figure 2 for examples). !ey then presented them to other participants who had to learn which sign went with which concept or object as paired associates. In a subsequent speeded recognition task the late community signs proved far superior. !ey were faster to recognise and with greater accuracy than any of the other signs. !is suggests that communities exert selection pressure on the signs to promote ease of uptake from other members of the community.

!is last observation suggests that there may be a component of the Social Coordination mechanism operating in connected communities that is analogous to what is seen in Iterated Learning. In this way, group wide social coordination may optimize both for communicative e$ciency (e.g., producing simple signs) and ease of learning by other members of the community.

Acknowledgements

!is research was supported by an ARC Discovery Grant (Grant DP0556991) awarded to the second author. We thank two anonymous reviewers for their help-ful comments and Rachel Aldridge, Alan Bailey, Lara Bertolino, Saarah Bone, Sophie Brookman, Alexander Brownbill, Alison Chang, Jessica Cole, Grace Kovesi, Edmen Leong, Zhao Li Lo, Tam Nguyen, Jackie Paynter, Joseph Shaw, Julie Steiner, Libby Stone and Si Wei Tan for their assistance during data collection.



Notes

Refers to Bradley Walker whereas NF refers to Nicolas Fay

16 items ( 15 pairs/chains ( 2 conditions ( 5 pairs

References

Atkinson, Q.D., Meade, A., Venditti, C., Greenhill, S. & Pagel, M. (2008). Languages evolve in punctuational bursts. Science: 319(5863): 588.

Barr, D.J. (2004). Establishing conventional communication systems: Is common knowledge necessary? Cognitive Science, 28(6), 937–962.

Berwick, R.C. (2009). What genes can’t learn about language. PNAS, 106, 1685–1686.Chater, N., Reali, F. & Christiansen, M.C. (2009). Restrictions on bilogical evolution in language

evolution. PNAS, 106, 1015–1020.Christiansen, M.C. & Kirby, S. (2003). Language evolution: consensus and controversies. Trends

in Cognitive Sciences. 7, 300–307.Cornish, H. (2010). [this issue].Dediu, D.D., & Ladd, D.R.D.R. (2007). Linguistic tone is related to the population frequency

of the adaptive haplogroups of two brain size genes, ASPM and Microcephalin. PNAS, 104(26), 10944–10949.

Fay, N., Garrod, S., & Roberts, L. (2008). Fitness and functionality of culturally evolved com-munication systems. Trans. R. Soc. B. 363, 3553–3561.

Fay, N., Garrod, S., Roberts, L., & Swoboda, N. (in press). !e interactive evolution of commu-nication systems. Cognitive Science.

Galantucci, B. (2005). An experimental study of the emergence of human communication systems. Cognitive Science, 29(5), 737–767.

Garrod, S., & Doherty, G. (1994). Conversation, Coordination and Convention – an Empirical-Investigation of How Groups Establish Linguistic Conventions. Cognition, 53(3), 181–215.

Garrod, S., Fay, N., Lee, J., Oberlander, J., & MacLeod, T. (2007). Foundations of Representation: Where Might Graphical Symbol Systems Come From? Cognitive Science, 31(6), 961–987.

Harnad, S. (1990). !e symbol grounding problem. Physica D. 42, 333–346.Healey, P., Swoboda, N., & King, J. (2002). A tool for performing and analysing experi-

ments on graphical communication. In X. Faulkner, J. Finlay, & F. D’etienne (Eds.), People and computers XVI: Proceedings of HCI2002: The 16th British HCI Group Annual Conference (pp. 55–68).

Healy, P.G.T., Swoboda, N., Umata, I., & King, J. (2007). Graphical language games: Interactional constraints on representational form. Cognitive Science, 31, 285–309.

Kalish, M.L., Griffiths, T.L. & Lewandowsky, S. (2007). Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin and Review. 14, 288–294.

Kirby, S. (2002). Natural Language From Arti#cial Life. Arti!cial Life, 8(2), 185–215.Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in the laboratory: An exper-

imental approach to the origins of structure in human language. PNAS, 105, 10681–10686.Kirby, S., Dowman, M., & Gri$ths, T.L. (2007). Innateness and culture in the evolution of lan-

guage. PNAS, 104(12), 5241–5245.



Kirby, S., & Hurford, J. (1997). Learning, culture and evolution in the origin of linguistic con-straints. In P. Husbands & I. Harvey (Eds.), Proceedings of the Fourth European Conference on Arti!cial Life (pp. 493–502): Cambridge, MA: MIT Press.

Kirby, S., & Hurford, J. (2002). !e emergence of linguistic structure: An overview of the iterated learning model. In A. Cangelosi & D. Parisi (Eds.), Simulating the evolution of language (pp. 121–148). London: Springer.

Krauss, R.M. & Weinheimer, S. (1964). Changes in reference phrases as a function of frequency of usage in social interaction: A preliminary study. Psychonomic Science, 1, 113–114.

Krauss, R.M., & Weinheimer, S. (1967). E%ects of referent similarity and communication mode on verbal encoding. Journal of Verbal Learning and Verbal Behavior, 6, 359–363.

Lieberman, E., Michel, J.B., Jackson, J., Tang, T., & Nowak, M.A. (2007). Quantifying the evolutionary dynamics of language. Nature, 449(7163), 713–716.

Pelli, D.G., Burns, C.W., Farell, B., & Moore-Page, D.C. (2006). Feature detection and letter iden-ti#cation. Vision Research, 46(28), 4646–4674.

Pinker, S. (1994). "e language instinct. New York: HarperCollins.Steels, L. (1999). "e talking heads experiment. Vol 1.Antwerp, Laboratorium.Steels, L., Kaplan, F., McIntyre, A., & Van Looveren, J. (2002). Crucial factors in the origins

of word-meaning. In A. Wray (Ed.), "e Transition to Language (pp. 252–271). Oxford: Oxford University Press.

!eisen, C.A., Oberlander, J., & Kirby, S. (2010). [this issue].

Corresponding Author

Simon GarrodDepartment of PsychologyUniversity of Glasgow56 Hillhead StreetGlasgow G12 8QBScotland

Email: [email protected]: +44 (0)41 330 5033

Can iterated learning explain the emergence of graphical symbols?

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