talking nets: a multiagent connectionist approach to communication and trust between ... · ·...

Talking Nets: A Multiagent Connectionist Approach to Communicationand Trust Between Individuals

Frank Van Overwalle and Francis HeylighenVrije Universiteit Brussel

A multiagent connectionist model is proposed that consists of a collection of individual recurrentnetworks that communicate with each other and, as such, is a network of networks. The individualrecurrent networks simulate the process of information uptake, integration, and memorization withinindividual agents, and the communication of beliefs and opinions between agents is propagated alongconnections between the individual networks. A crucial aspect in belief updating based on informationfrom other agents is the trust in the information provided. In the model, trust is determined by theconsistency with the receiving agents’ existing beliefs and results in changes of the connections betweenindividual networks, called trust weights. These weights lead to a selective propagation and thus to thefiltering out of less reliable information, and they implement H. P. Grice’s (1975) maxims of quality andquantity in communication. The unique contribution of communicative mechanisms beyond intrapersonalprocessing of individual networks was explored in simulations of key phenomena involving persuasivecommunication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack ofsharing unique information in group decisions.

Keywords: social connectionism, multiagent models, communication, persuasion

Cognition is not limited to the mind of an individual agent butinvolves interactions with other minds. A full understanding ofhuman thinking thus requires insight into its social development.Sociologists and developmental psychologists have long noted thatmost of one’s knowledge of reality is the result of communicationand social construction rather than of individual observation.Moreover, important real-world problems are often solved by agroup of communicating individuals who pool their individualexpertise while forming a collective decision that is supposedlybetter than what they might have achieved individually. Thisphenomenon may be labeled collective intelligence (Levy, 1997;Heylighen, 1999) or distributed cognition (Hutchins, 1995). Tounderstand such group-level information processing, one mustconsider the distributed organization constituted by different indi-viduals with different forms of knowledge and experience togetherwith the social network that connects them and that determineswhich information is exchanged with whom. In addition to qual-itative observations and conceptualizations, the phenomenon ofdistributed cognition has been studied in a quantitative, operational

manner, although from two very different traditions: social psy-chology and multiagent simulations.

Social psychologists have typically studied group-level cogni-tion using laboratory experiments, and their research has docu-mented various fundamental shortcomings of collective intelli-gence. We often fall prey to biases and simplistic stereotypes aboutgroups, and many of these distortions are emergent properties ofthe cognitive dynamics in interacting minds. Examples are con-formity and polarization, which move a group as a whole towardmore extreme opinions (Ebbesen & Bowers, 1974; Isenberg, 1986;Mackie & Cooper, 1984); communication within groups that re-inforce stereotypes (e.g., Lyons & Kashima, 2003); and the lack ofsharing of unique information so that intellectual resources of agroup are underused (Larson, Christensen, Abbott, & Franz, 1996;Larson, Foster-Fishman, & Franz, 1998; Stasser, 1999; Witten-baum & Bowman, 2004). Although this research provides empir-ical evidence about real people to convincingly test particularhypotheses, it is often limited to small groups with minimal struc-tures performing tightly controlled tasks of limited duration.

In contrast, the approach of multiagent systems originates indistributed artificial intelligence (Weiss, 1999) and the modelingof complex, adaptive systems, such as animal swarms (Bonabeau,Dorigo, & Theraulaz, 1999) and human societies (Epstein &Axtell, 1996; Nowak, Szamrej, & Latane, 1990). In these systems,agents interact in order to reach their individual or group objec-tives, which may be conflicting. However, the combination of theirlocal and individual actions produces emergent behavior at thecollective level. In contrast to experimental psychology, this ap-proach has been used to investigate complex situations, such as theemergence of cooperation and culture as well as the self-organization of the different norms and institutions that govern theinteractions between individuals in an economy. It manages totackle such complex problems by means of computer simulations

Frank Van Overwalle, Department of Psychology, Vrije UniversiteitBrussel, Brussels, Belgium; Francis Heylighen, Department of Philosophy,Vrije Universiteit Brussel, Brussels, Belgium.

Portions of this work were presented at the 7th International Conferenceon Cognitive Modeling, Trieste, Italy, April 5–8, 2006. This research wassupported by Grant OZR941 of the Vrije Universiteit Brussel to Frank VanOverwalle. We are grateful to Margeret Heath for her suggestions on aversion of this article.

Correspondence concerning this article should be addressed to FrankVan Overwalle, Department of Psychology, Vrije Universiteit Brussel,Pleinlaan 2, B-1050, Brussel, Belgium. E-mail: [email protected]

Psychological Review Copyright 2006 by the American Psychological Association2006, Vol. 113, No. 3, 606–627 0033-295X/06/$12.00 DOI: 10.1037/0033-295X.113.3.606

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in which an unlimited number of software agents interact accord-ing to whatever simple or complex protocols the researcher hasprogrammed them to obey. Although the complexity of situationsthat can be studied is much greater than in an experimentalparadigm, there is no guarantee that the results produced by thesimulation say anything meaningful about real human interaction.

Moreover, many of the earlier simulations meant to representagent interaction are too rigid and simplistic to be psychologicallyplausible. The behavior of a typical software agent is strictly rulebased: If a particular condition appears in the agent’s environment,the agent will respond with a particular, preprogrammed action.More sophisticated cognitive architectures may allow agents tolearn—that is, adapt their responses to previous experience—butthe learning will typically remain within a symbolic, rule-basedlevel. Perhaps the most crucial limitation of many models is thatthe individual agents lack their own psychological interpretationand representation of the environment. As Sun (2001) deplored,multiagent systems need “better understanding and better modelsof individual cognition” (p. 6).

Another shortcoming of many multiagent models is their rigidrepresentation of relations between agents. In the most commonmodels, cellular automata, each agent (automaton) occupies a cellwithin a geometrical array of cells, typically in the form of acheckerboard. Agents will then interact with all the agents in theirgeometric neighborhood. This is clearly a very unrealistic repre-sentation of the social world, in which individuals interact differ-entially with other individuals depending on their previous expe-riences with those others. Other types of simulations, such associal network models, are more realistic with respect to humanrelationships but still tend to be rigid in the sense that the agentscannot change the strength of their relationship with a particularother agent (for a full discussion, see Comparisons With OtherModels).

The present article presents a first step toward overcoming thesevarious limitations by proposing an integration of social psychol-ogy and multiagent approaches. For this, we present a genericmultiagent simulation environment that is as much as possiblepsychologically plausible, while at the same time remaining assimple as possible. First, each individual agent is represented by arecurrent connectionist network (described shortly) that is capableof representing internal beliefs as well as external information.Such a network can learn new associations between its differentconcepts through experience. This type of model has been usedsuccessfully in the past to model several phenomena in socialcognition, including person impression formation (Smith & De-Coster, 1998; Van Overwalle & Labiouse, 2004), group impres-sion formation (Kashima, Woolcock, & Kashima, 2000; Queller &Smith, 2002; Van Rooy, Van Overwalle, Vanhoomissen, Labi-ouse, & French, 2003), attitude formation (Van Overwalle &Siebler, 2005), causal attribution (Read & Montoya, 1999; VanOverwalle, 1998), and many other social judgments (for a review,see Read & Miller, 1998).

Second, and this is the real innovation proposed in this article,we extend this connectionist model of individual cognition to thesocial relations between agents. Inspired by previous models de-veloped by Hutchins (1991) and Hutchins and Hazlehurst (1995),our model consists of a collection of individual networks that eachrepresent a single individual and that can communicate with eachother. This is depicted in Figure 1 for three agents. In our model,

unlike earlier multiagent models, any agent can in principle inter-act with any other agent, but the strength of the interaction willadapt to experience. We introduce the fundamental concept ofcognitive trust as a measure of the degree to which one agentinfluences another. Cognitive trust can be defined as the confi-dence that one agent has in the information communicated byanother one. It thus refers to the perceived validity or credibility ofreceived information given the receiver’s knowledge about thesender. Note that this is different from the more common meaningof trust, which—apart from other specific uses in law and busi-ness—can be broadly summarized as the expectation that anotheragent would perform some action (as part of an exchange, as aresult of one’s relationship with the other, or of the power one hasover the other). In the remainder of the article, however, we use theshorthand term trust to refer to cognitive trust. Communicationinvolves the transmission of information on the same conceptsfrom one agent’s network to another agent’s network, along con-nections whose adaptive weights reflect the perceived trust in theinformation transmitted. Hence, the group of agents functions asan adaptive, socially distributed network in which information andknowledge are distributed among and propagated between differ-ent individual networks.

To demonstrate that this model is realistic, we ran severalsimulations that can accurately reproduce several of the key ex-perimental results obtained by psychologists studying communi-cation and cognition in groups. Thus, our model was able todirectly integrate the two major approaches toward the study ofdistributed cognition: social psychology and multiagent models.We further situate our model within the broad multiagent land-scape by comparing it with related models, and we end with theimplications and limitations of the proposed model and identifyareas in which further theoretical developments are needed. Suf-

Figure 1. A multiagent network model of interpersonal communication.Each agent consists of an auto-associative recurrent network, and thecommunication between the agents is controlled by trust weights. Thestraight lines within each network represent intraindividual connectionweights linking all units within an individual net, and the arrows betweenthe networks represent interindividual trust weights (only some of them areshown).

607TRUST: A CONNECTIONIST MODEL OF COMMUNICATION

fice it to say here that at present our model is purely focused on theprocessing and exchange of information: Our agents do not try toreap any goal, reward, or utility, like in most game-theoreticsimulations of economic rationality or cooperation. The only “ac-tions” they perform are the transmission and interpretation ofinformation, on the basis of prior experience. Yet, these simpleassumptions are shown to accurately explain many of the concretebiases that characterize group-level cognition. First, however, wedescribe the proposed connectionist model in some detail.

A Connectionist Approach to a Collectionof Individual Nets

Given the plethora of alternative models that have been appliedin the simulation of collective cognition, one may wonder why aconnectionist approach was taken to model individual agents.There are several characteristics that make connectionist ap-proaches very attractive (for an introduction, see McLeod, Plun-kett, & Rolls, 1998). A first key characteristic is that the connec-tionist architecture and processing mechanisms are based onanalogies with properties of the human brain. Human thinking isseen as an adaptive learning mechanism that develops accuratemental representations of the world. Learning is modeled as aprocess of online adaptation of existing knowledge to novel infor-mation provided by the environment. For instance, in group judg-ments, the network changes the weights of the connections be-tween the target group and its attributes so as to better represent theaccumulated history of co-occurrences between the group and itsperceived attributes. In contrast, most traditional models in psy-chology are incapable of such learning. In many algebraic models,recently acquired beliefs or attitudes about target groups or personsare not stored somewhere in memory so that, in principle, theyneed to be reconstructed from their constituent components (i.e.,attributes) every time a judgment is requested or a belief is ex-pressed (e.g., Anderson, 1981; Fishbein & Ajzen, 1975). Similarly,activation spreading or constraint satisfaction models recently pro-posed in psychology can only spread activation along associationsbut provide no mechanism to update the weights of these associ-ations (Kunda & Thagard, 1996; Read & Miller, 1993; Shultz &Lepper, 1996; Spellman & Holyoak, 1992; Spellman, Ullman, &Holyoak, 1993; Thagard, 1989). This lack of a learning mechanismin earlier models is a significant restriction (see also Van Over-walle, 1998).

Second, connectionist models assume that the development ofinternal representations and the processing of these representationsoccur in parallel by simple and highly interconnected units, con-trary to traditional models in which the processing is inherentlysequential. The learning algorithms incorporated in connectionistsystems do not need a central executive, which eliminates therequirement of centralized and deliberative processing of informa-tion. This suggests that much of the information processing withinagents is often implicit and automatic. Most often, only the endresult of these preconscious processes enters the individual’sawareness. Likewise, in human communication, much of the in-formation exchange is outside the agents’ awareness, as individu-als may not only intentionally express their opinions and beliefsverbally but may also unknowingly leak other nonverbal informa-tion through the tone of voice, facial expressions, and so on(although we do not study these latter aspects).

Finally, connectionist networks have a degree of neurologicalplausibility that is generally absent in previous algebraic ap-proaches to information integration and storage (e.g., Anderson,1981; Fishbein & Ajzen, 1975). They provide an insight in lowerlevels of human mental processes beyond what is immediatelyperceptible or intuitively plausible, although they go not so deep asto describe real neural functioning. Unlike traditional models, theyreveal a number of emergent properties that real human brains alsoexhibit, such as the lack of a clear separation between memory andprocessing. Connectionist models naturally integrate long-termmemory (i.e., connection weights) and short-term memory (i.e.,internal activation) with outside information (i.e., external activa-tion). In addition, on the basis of the principle that activation in anetwork spreads automatically to interconnected units and con-cepts and so influences their processing, connectionist modelsexhibit emergent properties such as pattern completion and gen-eralization, which are potentially useful mechanisms for an ac-count of the confirmation bias of stereotype information dissemi-nation within and between agents in a group.

An Individual’s Net: The Recurrent Model

An individual agent’s processing capacities are modeled by therecurrent auto-associator network developed by McClelland andRumelhart (1985). We apply this network for two reasons. First,we want to emphasize the theoretical similarities that underlie thepresent simulations on multiagent communication with earliersimulation work on social cognition mentioned earlier (e.g., Smith& DeCoster, 1998; Van Overwalle & Labiouse, 2004; Van Over-walle & Siebler, 2005; Van Rooy et al., 2003). Second, this modelis capable of reproducing a wider range of social–cognitive phe-nomena and is computationally more powerful than other connec-tionist models that represent an individual agent’s mental process-ing, like feedforward networks (Van Overwalle & Jordens, 2002;see Read & Montoya, 1999) or constraint satisfaction models(Kunda & Thagard, 1996; Shultz & Lepper, 1996; for a critiquesee Van Overwalle, 1998).

A recurrent network can be distinguished from other connec-tionist models on the basis of its architecture (how information isrepresented in the model), the manner in which information isprocessed, and its learning algorithm (how information is consol-idated in the model). It is important to have some grasp of theseproperties, because the extension to a collection of individualnetworks described shortly is based on very similar principles.

Architecture

The architecture of an auto-associative network is illustrated inFigure 2 for a talking and a listening agent (the trust weightsbetween agents should be ignored for now and are discussed later).Its most salient property is that all units are interconnected with allthe other units (unlike, for instance, feedforward networks inwhich connections exist in only one direction). Thus, all units sendout and receive activation. The units in the network represent atarget issue (e.g., objects, persons, or groups such as the Jamayans;see Lyons & Kashima, 2003) as well as various attributes associ-ated with the issue (e.g., behaviors, abilities, or traits such as smartand honest). The connections linking the issue’s object with itsattributes represent the individual’s beliefs and knowledge about

608 VAN OVERWALLE AND HEYLIGHEN

the issue. For instance, an individual may believe that waitressesare talkative, and the strength of this belief is represented by theweight of the waitress 3 talkative connection.

Information Processing

In a recurrent network, processing information takes place intwo phases. During the first activation phase, each unit in thenetwork receives activation from external sources. Because theunits are interconnected, this activation is automatically spreadthroughout the network in proportion to the weights of the con-nections to the other units. This spreading mechanism reflects theidea that encountering a person, a group, or any other objectautomatically activates its essential characteristics from memory.The activation coming from the other units is called the internalactivation (for each unit, it is calculated by summing all activationsarriving at that unit). Together with the external activation, thisinternal activation determines the final pattern of activation of theunits (termed the net activation), which reflects the short-termmemory of the network. Typically, activations and weights havelower and upper bounds of approximately �1 and �1.

In nonlinear versions of the auto-associator used by severalresearchers (Read & Montoya, 1999; Smith & DeCoster, 1998),the net activation is determined by a nonlinear combination ofexternal and internal inputs updated during a number of internal

updating cycles. In the linear version that we use here, the netactivation is the sum of the external and internal activations afterone updating cycle through the network. Previous simulations byVan Overwalle and colleagues (Van Overwalle & Labiouse, 2004;Van Overwalle & Siebler, 2005; Van Rooy et al., 2003) revealedthat the linear version with a single internal cycle reproduced theobserved data at least as well.

Because we introduce a similar activation updating algorithmfor our extension later on, we now present the automatic activationupdating in more specific mathematical terms. Every unit i in thenetwork receives external activation, termed exti, in proportion toan excitation parameter E which reflects how much the activationis excited, or

ai � E � exti. (1)

This activation is spread around in the auto-associative network, sothat every unit i receives internal activation inti which is the sumof the activation from the other units j (denoted by aj) in proportionto the weight of their connection to unit i, or

inti � �j

(wj3i � aj) (2)

for all j � i. The external activation and internal activation are thensummed to the net activation, or

neti � E � (exti � inti). (3)

According to the linear activation algorithm (McClelland &Rumelhart, 1988, p. 167), the updating of activation at each cycleis governed by the following equation:

�ai � neti � D � ai, (4a)

where D reflects a memory decay term. In the present simulations,we used the parameter values D � E � 1 as done previously byVan Overwalle and colleagues (Van Overwalle & Labiouse, 2004;Van Overwalle & Siebler, 2005; Van Rooy et al., 2003). Giventhese simplifying parameters, the final activation of unit i reducesto the sum of the external and internal activation, or

ai � neti � exti � inti. (4b)

Memory Storage

After the first activation phase, the recurrent model enters thesecond learning phase in which the short-term activations arestored in long-term weight changes of the connections. Basically,these weight changes are driven by the difference between theinternal activation received from other units in the network and theexternal activation received from outside sources. This difference,also called the error, is reduced in proportion to the learning ratethat determines how fast the network changes its weights andlearns. This error-reducing mechanism is known as the deltaalgorithm (McClelland & Rumelhart, 1988; McLeod, Plunkett, &Rolls, 1998).

For instance, if the external activation (e.g., observing a talk-ative waitress) is underestimated because a weak waitress 3talkative connection (e.g., the belief that waitresses are not verytalkative) generates a weak internal activation, then the waitress3talkative weight is increased to reduce this discrepancy. Con-

Figure 2. The functional role and adjustment of trust weights in commu-nication between agents, illustrated when a talking agent expresses his orher opinion on the honesty (attribute) of the people of the Jamayans (issueobject); if the talker’s expressed activation is close to versus different fromthe listener’s internal activation on the same attribute, this may lead to anincrease versus decrease of the trust weight involving that attribute.


versely, if the external activation is overestimated because a strongwaitress 3 talkative connection generates an internal activationthat is too high (e.g., the belief that waitresses do not stop talking),the weight is decreased. These weight changes allow the networkto develop internal representations that accurately approximate theexternal environment.

In mathematical terms, the delta algorithm strives to match theinternal predictions of the network inti as closely as possible to theactual state of the external environment exti and stores this infor-mation in the connection weights. This error-reducing process isformally expressed as

�wj3i � � � (exti � inti) � aj (5)

(see McClelland & Rumelhart, 1988, p. 166), where �wj3i is thechange in the weight of the connection from unit j to i, and � is alearning rate that determines how fast the network learns. Animplication of this learning algorithm is that when an object and itsfeature co-occur frequently, then their connection weight graduallyincreases to eventually reach an asymptotic value of �1. Whenthis co-occurrence is not perfect, then the learning algorithmgradually converges to a weight (e.g., �.50) that reflects theproportion of supporting and conflicting co-occurrences (e.g.,50%).

Communication Between Individual Nets:The Trust Extension

The standard recurrent model was augmented with a number offeatures, which enabled it to realistically reproduce communica-tion between individual agents. This extension assumes that beliefsabout objects and their attributes are represented in broadly thesame manner among different agents. Communication is thenbasically seen as transferring the activation on the object and itsattributes from talking to listening agents. This is accomplished byactivation spreading between agents in much the same way asactivation spreading within the mind of a single individual, withthe restriction that activation spreading between individuals is (a)limited to identical attributes and (b) in proportion to the trustconnection weights linking the attributes between agents. A crucialaspect of these trust connections is that they reflect how much theinformation on a given object or attribute expressed by a talkingagent is deemed trustworthy, reliable, and valid. Thus, the con-nections through which individuals exchange information are notsimply carriers of information but, more crucially, also reflect thedegree of trust in this information. This is the cornerstone of ourextension to a collection of recurrent networks, and therefore, wetermed our extended model trust.

Because agents can play the role of speaker or listener, the trustconnections in the model go in two directions: sending connectionsfor talking and receiving connections for listening (see Figure 2),and each agent has both. Note that different trust weights exist forevery meaningful object or attribute in the network. Thus, we maytrust someone’s knowledge on a psychological topic but distrusthis or her sports expertise; or we may trust someone’s ideas onhonesty but disagree that they apply to a particular person or groupor even doubt that we are talking about the same persons after all.Because connectionist systems do not make a principled differen-tiation between units, all units—be it an individual, social group,

or their attributes—play the same role in the network. In develop-ing this extension toward multiagent information exchange, wewere strongly inspired by a number of communicative principlesput forward by philosophers and psychologists. Specifically, thetwo trust connections implement to a great deal Grice’s (1975)communicative maxims of quality and quantity.

Maxim of Quality: Sending Trust Weights

The maxim of quality suggests that to communicate efficiently,communicators generally try to transmit truthful information (“Tryto make your contribution one that is true”; Grice, 1975, p. 46). Inthe model, the maxim of quality is implemented on the side of thereceiving agent. Listeners filter information in proportion to howmuch they trust that information based on their extant beliefs. Thisis implemented in sending trust connections from the talking to thelistening agent for each issue (see Figure 2). When trust concern-ing a specific issue is maximal (�1), the information expressed bythe talking agent is unfiltered by the listening agent. To the degreethat trust is lower, information intake by the listener is attenuatedin proportion to the trust weight. When trust is minimal (0), noinformation is processed by the listening agent.

For convenience, we refer to all units involving a communicatedissue and its associated attributes as issue i. The listener l receivesinformation on each issue i from all other talking agents t inproportion to the trust weights and then sums it; or in mathematicalterms, for each issue i,

extli � �t

(trustti3li � ati), (6)

where extli represents the external activation received by the lis-tening agent l on issue i, trustti3li is the trust weight from thetalking agent t to the listening agent l, and ati denotes the activationgenerated and expressed by talking agent t. Through comparisonwith Equation 2, it can be seen that this mechanism of trustspreading between agents is a straightforward copy of activationspreading of standard connectionist models within a single agent.

Maxim of Quantity: Receiving Trust Weights

Grice’s (1975) maxim of quantity suggests that communicatorstransmit only information that is informative and adds to theaudience’s knowledge (“Make your contribution as informative asis required for the current purpose of the exchange,” p. 45).Similarly, research on group minority suggests that communicatorstend to increase their interaction with an audience that does notagree with their position. This is implemented in the model by thereceiving trust weights from the listening agents to the talkingagent (see Figure 2). These weights indicate how much the talkingagent trusts the listening agent on a particular issue and hencecontrol how much the taking agent talks about it. To the extent thatthese trust weights are high (above trust starting weights, trustO),knowledge and agreement on an issue is assumed, and the talkingagent will reduce expression of these ideas (attenuation). In con-trast, when these weights are low (below trust starting weights,trustO), the talking agent increases expression of these ideas morestrongly (boosting). Because the talking agent may receive differ-ent receiving trust weights from different listening agents l, we


take the maximum1 receiving trust weight to talking agent t onissue i, or trustTI � max(trustti4li) for all agents l. Hence, for eachtarget issue i expressed by talking agent t,

if trustTI �� trustO, then ati � ati � (1 � trustTI),

else ati � ati � (1 � trustTI), (7a)

where ati is the activation expressed by the talking agent t on issuei (limited between �1 and �1), and �� means that the term on theleft is at least a small threshold (of .05) smaller than the term onthe right. In contrast, for all other issues j agent t is talking about,the reverse change of activation occurs (limited between �1 and �1):

If trustTI �� trustO, then atj � atj � (1 � trustTI),

else atj � atj � (1 � trustTI). (7b)

Note that when an agent talks about several issues, then aninteraction ensues between attenuation and boosting. For instance,if listeners agree strongly on two issues i and j, then issue i tendsto boost talking about j, but conversely, issue j tends to attenuatetalking about j (and similarly for talking about i), resulting in littlechange in the activation of i and j overall. However, there will bemore talking about further issues k.

Adjustment of Trust Weights

Given that perceived trust plays a crucial role in the transmis-sion of information, it is important to describe how the trustweights are developed and changed in the model. This adjustmentprocess is alike for receiving and sending trust weights, becausethey depend on the same underlying process within the listeningagent. Like the standard delta learning algorithm in which learningis determined by the error between internal and external signalswithin the individual agents, trust depends on the error betweenexternal beliefs (expressed by another talking agent) and theagent’s own internal beliefs. For a given issue, if the error withinthe listening agent is below some trust tolerance, the trust weightfor that issue is increased toward 1; otherwise, the trust weight isdecreased toward 0. In mathematical terms, the change in trustweight by the listening agent l on issue i expressed by agent t, or�trustti3li, is implemented as follows:

If |extli � intli| � trust tolerance,

then �trustti3li � � � (1 � trustti3li) � |ati|,

else �trustti3li � � � (0 � trustti3li) � |ati|, (8)

where extli represents the external activation received by the lis-tening agent l, intli is the internal activation generated indepen-dently by the listening agent l, � is the rate by which trust isadjusted, and |ati| is the absolute value of the activation on issue iby the talking agent t. In contrast to Equation 5 of the deltaalgorithm, here the absolute value of the error and the talkingactivation i is taken, because trust is assumed to vary from low (0)to high (�1) only and hence depends on the absolute error betweenexternal and internal activation (in proportion to the absolute strengthof the activation by which the talking agents express their ideas).

To give an example, imagine that the talking and listening agentagree on the honesty of the Jamayans and that both have a

connection of �.80 between these two units (see Figure 2). Howdoes the system, and more in particular the listener, recognize thecommunication on the attribute honesty as truthful? For the sake ofsimplicity, we ignore attenuation and boosting (see Equation 8).When the talker activates the Jamayans unit (with default activa-tion �1), this activation spreads internally to honesty in proportionto the connection weight (�.80; see Figure 2, dotted arrow on theleft) and thus becomes �.80. This activation is then communicatedor transmitted along the trust weight (�.50; see Figure 2, dottedarrow on bottom), arriving at the listener with an external activa-tion extli of �.40 for honesty. Now, something similar happenswithin the listener. When the talker expresses the Jamayans issue,this activation of �1 is transmitted through the trust weight (�.50)arriving at the listener with an activation of �.50, which is theninternally spread within the listener through the connection withhonesty (�.80; dotted arrow on the right) resulting in an internalactivation intli of �.40. Thus, the external activation (what thelistener hears) and the internal activation (what the listener be-lieves) on the attribute honesty are identical, and the talker 3listener trust weight involving this attribute will increase. Con-versely, as soon as the external and internal activations begin todiffer, and this difference exceeds the trust threshold, the trustweight will begin to decrease.

In summary, the larger the sending trustti3li becomes, the morethe listening agent l will trust the talking agent t on issue icommunicated and the more influential the talking agent willbecome (maxim of quality). In addition, the larger the receivingtrustti4li becomes, the more the talking agent t will restrain theexpression of his or her ideas on this issue (maxim of quantity). Asummary of the steps in the simulation of a single trial is given inTable 1.

General Methodology of the Simulations

Having spelled out the assumptions and functioning of the trustmodel, we apply it to a number of classic findings in the literatureon persuasion, social influence, interpersonal communication, andgroup decision. For explanatory purposes, most often, we repli-cated a well-known representative experiment that illustrates aparticular phenomenon, although we occasionally also simulated atheoretical prediction. Table 2 lists the topics of the simulations wereport shortly, the relevant empirical study or prediction that weattempted to replicate, as well as the major underlying processing

1 To control attenuation and boosting on an issue, we not only appliedthe maximum but also tested the average of all receiving trust weights ofall listeners on an issue. The maximum reflects the notion that if at leastsomeone (or a minority) agrees with the talker, then he or she will not talkabout the issue; the average reflects the same if the majority agrees with theagent. We did not test the minimum, or the notion that a talking agentwould want to reach unanimity from the audience, as this seemed not veryplausible. As the number of listeners in the present simulations wastypically limited to only one or a few, the difference between maximumand average was negligible. However, we are aware that the desire toconvince only a minority or a majority is very much under the strategiccontrol of the speaker (see also Limitations), so that it could be entered inthe model as an additional parameter, especially when simulating a largeraudience. We did not do so at present to keep the model conceptuallycoherent and as simple as possible.


principle responsible for reproducing the data. Although not allrelevant data in the vast attitude literature can be addressed in asingle article, we believe that we have included some of the mostrelevant phenomena in the current literature. However, beforediscussing these simulations in more detail, we first provide anintroductory overview of the different processing phases in thesimulations, and we end with the general parameters of the model.

Coding of Learning and Communicating

In all simulations, we assumed that participants brought withthem learning experiences taking place before the experiment. This

was simulated by inserting a prior learning phase, during which webriefly exposed the individual networks to information associatinga particular issue object with some attributes. Although these priorlearning phases were kept simple and short for explanatory reasons(involving only 10 trials), it is evident that real-life exposure ismore complex, involving direct experiences or observations ofsimilar situations, or indirect experiences through communicationor observation of others’ experiences. However, our intention wasto establish connection weights that are moderate so that laterlearning still has sufficient impact.

We then simulated specific experiments. This mostly involved atalking and listening phase during which agents communicated. Asdemonstrated shortly, given that the issue topic was typicallyimposed externally (by the experimenter), this was designed asexternal activation, and the internal activation spread automaticallyfrom this external input reflects the participant’s own thoughts onthe issue. We then simulated talking by spreading these twosources of activation in the talking agent’s units—through the trustweights—to the corresponding listening agents’ units representingthe same concepts (see Table 1 for the exact order of the compu-tations). The particular conditions and trial orders of the focusedexperiments were reproduced as faithfully as possible, althoughminor changes were introduced to simplify the presentation (e.g.,fewer trials or arguments than in the actual experiments).

Measuring Beliefs and Communicated Content

At the end of each simulated experimental condition, we ran testtrials to assess the dependent variables of the experiments. Thespecific testing procedures are explained in more detail for eachsimulation. The obtained test activations of the simulation werethen compared with the observed experimental data. We report the

Table 1Summary of the Main Simulation Steps for Each Trial in theTrust Model

Steps Equation no.

A. Set external activation for each issueobject (or attributes) present (withinagents) 1

B. Spread that activation within allnonlistening agents 2, 4b if agent � l

C. Attenuate and boost the internal activationof talking agents before transmitting it 7a, 7b

D. Spread (i.e., transmit) activation fromtalking agents to listening agents 6

E. Spread that activation within all listeningagents 2, 4b if agent � l

F. Update trust weights (between agents) 8G. Update connection weights (within agents) 5

Note. Steps are for all issues and their attributes (i.e. all units). “Within”refers to all units within an agent. During learning without communication,only Steps A, B, E, and G are functional.

Table 2Overview of the Simulations

Simulation and no. TopicEmpirical evidence/theoretical

prediction Major processing principle

Persuasion and social influence1 No. of arguments The more arguments heard,

the more opinion shiftInformation transmission leads to

changes in listener’s neta

i Polarization More opinion shift after groupdiscussion

More information transmission by amajority

Communication and stereotyping2 Referencing Less talking is needed to

identify objectsOveractivation of talker’s and listener’s

netii Word use Acquiring word terms,

synonyms, and ambiguouswords

Information transmission on wordmeaning and competition betweenword meaningsa

3 Stereotypes in rumorparadigm

More stereotype-consistentinformation is transmittedfurther up a communicationchain

Prior stereotypical knowledge of eachtalker and novel informationcombine to generate morestereotypical thoughtsa

4 Perceived sharedness Less talking about issues thatthe listener knows andmore talking about otherissues

Attenuation vs. boosting of informationtransmission if receiving trust is highvs. low

5 Sharing uniqueinformation

Unique information iscommunicated only aftersome time in a freediscussion

Same as Simulation 4

a The maxim of quantity (attenuation and boosting) did not play a critical role in these simulations.


correlation coefficient between simulated and empirical data andalso project the simulated data onto the observed data using linearregression (with intercept and a positive slope) to visually dem-onstrate the fit of the simulations. The reason is that only thepattern of test activations is of interest, not the exact values.

General Model Parameters

In spite of the fact that the major experiments to be simulatedused very different stimulus materials, measures, and procedures,all parameters of the auto-associator are set to the same values,unless noted otherwise. As noted earlier, the parameters of theindividual nets were the same as in earlier simulations by VanOverwalle and colleagues (Van Overwalle & Labiouse, 2004; VanOverwalle & Siebler, 2005; Van Rooy et al., 2003; E � D �number of internal cycles � 1, and a linear summation of internaland external activation; see Equation 4). The last parameter im-plies that activation is propagated to neighboring units and cycledone time through the system. The other parameters are listed inTable 3. To ensure that there was sufficient variation in the data toconduct meaningful statistical analyses, we initialized all weightsat the specified values plus additional random noise between �.05and �.05.

Persuasion and Social Influence

Once people are in a collective setting, it appears that they areonly too ready to conform to the majority in the group and toabandon their own personal beliefs and opinions. Although dis-senting minorities may possibly also have some impact, the greaterinfluence of the majority is a remarkably robust and universalphenomenon. Two major explanations have been put forward toexplain the influence of other people in a group: pressure toconform to the norm and informative influence. This sectionfocuses on this latter informative explanation of social influence:Group members assess the correctness of their beliefs by searchingfor adequate information or persuasive arguments in favor of oneor the other attitude position. Perhaps one of the most surprisingupshots of this informative influence or conversion is group po-larization—the phenomenon that after a group discussion, mem-bers of a group on average shift their opinion toward a more

extreme position. The next simulation demonstrates that the num-ber of arguments provided to listeners is crucially important inchanging their beliefs. In addition, we illustrate that the trustmultiagent model predicts decreasing group polarization as com-munication between group factions declines.

Simulation 1: Number of Arguments

Key experiment. To demonstrate that the sheer number ofarguments communicated to other people strongly increases theirwillingness to adopt the talker’s point of view, we now turn to anillustrative experiment by Ebbesen and Bowers (1974, Experiment3). This experiment demonstrates that shifts in beliefs and opinionsare to a great extent due to the greater number of argumentsreceived. Participants listened to a tape recording of a groupdiscussion, which contained a range from few (10%) to many(90%) arguments in favor of a more risky choice. As can be seenin Figure 3, irrespective of the arguments heard, the participantsshifted their opinion in proportion of the risky arguments heard inthe discussion.

Simulation. Table 4 represents a simplified learning history ofthis experiment. The first five cells reflect the network of thetalking agent (i.e., the discussion group), and the next five cellsreflect the network of the listener. Each agent has one unit reflect-ing the issue object (i.e., topic of discussion) and four unitsreflecting its attributes (i.e., the features of the arguments). Eachrow reflects a trial or act of learning or communicating, anddepending on the specific issues and features present or absent ata trial, the respective units are turned on (activation level � 0) orturned off (activation level � 0). Because this is the first simula-tion, we describe its progress in somewhat more detail.

First the discussion group from which the recording was taken(talking agent) learns the features or characteristics involving thediscussion topic. This learning was implemented in the first priorlearning phase of the simulation (see Table 4).

Next, each participant listens to the recorded talks by the dis-cussion group (i.e., talking agent). As can be seen, talking isimplemented during the talking and listening phase by activatingthe issue object (i.e., topic of discussion) in the talking agent andthen allowing the agent’s network to spread this around andgenerate internal activation (i.e., own beliefs). Both sources of

Table 3Principal Parameters of the Trust Model and How These Represent Features of Individual andGroup Processing

Parameter Human feature

Parameters of individual netsLearning rate � .30 How quickly new information is incorporated in prior

knowledgeStarting weights � .00 .05 Initial weights for new connections

Parameters of communication amongindividual nets

Trust learning rate � .40 How fast the trust in receiving information changesTrust tolerance � .50 How much difference between incoming information

and own beliefs is tolerated to be considered astrustworthy

Trust starting weights � .40 .05 Initial trust for new information

Note. .05 denotes a random number between �.05 and �.05 that was added to introduce some noise in thestarting weights.


activation then initiate the talking about one’s own opinions; thatis, they spread to the listening agent in proportion to the trustweights where it is received (as indicated by a “?”—denoting littleears—in the cells). The varying number of arguments is imple-mented in the simulation by having one, three, five, seven, or ninetalking and listening trials, which corresponds exactly to the num-ber of risky arguments used by Ebbesen and Bowers (1974). In thesimulation, we employed the same set of four feature units todenote that these units represent essential aspects in all arguments(i.e., that the behavior involves risk), and a repetition of theseaspects is what increases the changes in the listener’s opinion.

Finally, after the arguments have been listened to, the listener’sopinion is measured in the test of belief phase by activating (orpriming) the topic in the listening agent and allowing the neuralnetwork of the listener to generate its internal activation (i.e., ownbeliefs). This internal activation is then recorded (as indicated by“?”) and averaged. These simulated data are then projected ontothe observed data for visual comparison.

Results. The statements listed in Table 4 were processed bythe network for 50 participants with different random orders. In

Figure 3, the simulated values (dashed lines) are compared withthe attitude shifts (bars) observed by Ebbesen and Bowers (1974).Because the starting weights of the listener are always 0 (andadditional random noise), the simulated data reflect a final attitudeas well as an attitude shift. As can be seen, the simulation fits verywell, and the correlation between simulated and observed data issignificant, r � .98, p � .01. An analysis of variance (ANOVA) onthe simulated attitude further reveals a significant main effect ofthe proportion of arguments heard, F(4, 245) � 4.63, p � .001.Note that these results do not change appreciably when attenuationand boosting (maxim of quantity) is turned off in the simulation.

Extensions and discussion. The simulation demonstrates thatsome minimal level of trust is needed to pass information from oneagent to another. Trust may have several determinants, such asfamiliarity, friendliness, and expertise of the source. Another verypowerful determinant of trust is membership of a group. Typically,people trust members from their own group more than members ofanother group, as dramatically demonstrated by Mackie and Coo-per (1984). Using the same paradigm as Ebbesen and Bowers(1974), they found that listeners’ attitudes were strongly alteredafter hearing arguments from an ingroup but were much lessaffected when the same arguments came from an outgroup or acollection of unrelated individuals. We successfully simulated thiseffect by repeating the previous simulation, with the crucial mod-ification that in the ingroup condition, all starting talking 3listener trust weights were set to �1 (to reflect that the listenerstrust the talking agents completely), and in the outgroup/individualcondition, all starting talking 3 listener trust weights were set to0 (to reflect complete lack of trust).

Interlude Simulation: Polarization

The trust model predicts that persuasive communication aloneleads to group polarization because the continuing influence of themajority’s opinions gradually shifts the minority’s dissident posi-tion in majority direction (except, of course, when trust betweengroup members is very low). This prediction is consistent with ameta-analysis by Isenberg (1986) demonstrating that persuasiveargumentation has stronger effects on polarization than grouppressure or social comparison tendencies to conform to the norm.This prediction is briefly demonstrated in the next simulation. Wesimulated 11 agents, each having three units denoting the topic ofdiscussion, as well as a positive and a negative valence unitdenoting the position on the topic (for a similar approach, see VanOverwalle & Siebler, 2005). By providing more or fewer learning

Table 4Persuasive Arguments in Simulation 1

Trial

Talking agent Listening agent

Topic Feat1 Feat2 Feat3 Feat4 Topic Feat1 Feat2 Feat3 Feat4

Prior learning of arguments (#10) 1 1 1 1 1Talking and listening (#1-3-5-7-9) 1 i i i i ? ? ? ? ?Test of beliefs of listener 1 ? ? ? ?

Note. Schematic version of learning experiences based on Ebbesen and Bowers (1974, Experiment 3). Each row represents a trial (i.e., act) of learningor communicating, where cell entries denote external activation, and empty cells denote no activation. Feat � features of the arguments; # � number oftimes the trial is repeated; i � internal activation generated by the talking agent after activating the discussion topic; ? � (little ear) external activationreceived from the talking agent; in the test phase, ? � denotes the activation read off for measuring activation.

Figure 3. Simulation 1: Attitude shifts as a function of the number ofarguments heard. Human data are denoted by bars, and simulated valuesare denoted by dashed lines. The human data are from Figure 1 in“Proportion of Risky to Conservative Arguments in a Group Discussionand Choice Shift” by E. B. Ebbesen and R. J. Bowers, 1974, Journal ofPersonality and Social Psychology, 29, p. 323. Reprinted by permission ofthe author.


trials with the positive or negative valence units turned on, wemanipulated the agents’ position on an activation scale rangingbetween �1 and �1. We then allowed all agents to exchange theirideas with all other agents (on each discussion round, each agenttalked two times to everybody else). Afterward, we measured theagents’ attitude by priming the topic and reading off the differencebetween the positive and negative valence units.

As can be seen in the left panel of Figure 4, polarization wasalready obtained after one discussion round, as all agents movedtheir position in the direction of the majority, and this pattern wasfurther strengthened the more the participants exchanged theirideas. It is important to note that to obtain these results, we set thetolerance parameter to a stricter value of .10 instead of .50. Iftolerance was left at the default value of .50, all agents in the groupconverged to the middle position (average activation 0). Thissuggests that to shift the minority to a majority position (to obtainpolarization), deviance must be tolerated less. This is probably dueto the nature of the task. It seems plausible that when people talkabout important beliefs and values, they are less likely to changetheir ideas than when they are informed about an unknown ornovel topic on which they have no a priori opinion, as in Simula-tion 1.

Previous simulation work with cellular automata (see Compar-isons With Other Models) has suggested that deviant minorities areprotected against majority influence by the spatial distance be-tween the two groups. We extended this idea by considering socialdistance as a more crucial and realistic parameter, because peoplemay be less influenced by others because they are farther apart notonly in space but also in social status, role, group, and the like. Inline with the dominant effect of persuasion in polarization men-tioned earlier (Isenberg, 1986), we simulated social distance byrestricting the exchange of information between the minority andmajority groups (a) to half the amount of the previous simulationor (b) to zero (while keeping the overall communication alike byincreasing the communication within groups). As the middle andright panels of Figure 4 demonstrate, under these circumstances

the influence of the majority was strongly reduced and even totallyabolished.

Communication

Communication is a primary means by which people attempt toinfluence and convert each other (Kraus & Fussell, 1991, 1996;Ruscher, 1998). Our primary focus is on research exploring infor-mation exchange and how this affects participants’ beliefs andopinions. These studies go one step further than those from theprevious section by studying actual and spontaneous conversation,and by recoding the content of these conversations, the researcherscollected data on the natural course of information exchange.

Simulation 2: Referencing

Key experiment. Several studies explored how people use lan-guage to identify abstract or concrete objects. This paradigm istermed referencing (e.g., Kingsbury, 1968; Krauss & Weinheimer,1964, 1966; Schober & Clark, 1989; Steels, 1999; Wilkes-Gibbs &Clark, 1992). Typically, one person is designated as the “director”and is given the task to describe a number of unfamiliar pictures toa “matcher” who cannot see these pictures and has to identifythem. To provide a satisfactory solution to the task, both partici-pants have to coordinate their actions and linguistic symbols torefer to the pictures and have to establish a joint perspective duringthe conversation (Schober & Clark, 1989). This collaborativeprocess is also termed grounding. The aim of the research is toassess how this perspective taking and coordination influences theparticipants’ messages and the adequacy of these messages. Figure5 (top panel) depicts the typical outcome of such studies (Kraus &Fussell, 1991). On their first reference to one of the pictures, mostdirectors use a long description, consisting of pictorial attributes.Next, matchers often ask clarifying questions or provide confir-mation that they understood the directions (e.g., Schober & Clark,1989, p. 216). Over the course of successive references, the de-scription is typically shortened to one or two words. Often thereferring expression that the conversants settle on is not one that byitself would evoke the picture. This is taken as evidence thatpeople do not simply decode and encode messages but collaboratewith each other moment by moment to try to ensure that what issaid is also understood. Schober and Clark (1989) conducted sucha referencing experiment, in which a director described a numberof unfamiliar pictures, and the matcher had to identify the intendedone from a collection of pictures. Their experiment is interesting,because unlike previous studies, they recorded not only the verbaldescriptions of the director but also the reactions of the matcher(Experiment 1). An analysis of the messages (see Figure 5, bottompanel) shows that both directors and matchers used progressivelyfewer words to describe the pictures, although directors—becauseof their explanatory role in the conversation—used more wordsthan did matchers.

Simulation. Table 5 represents a simplified simulation of thisexperiment. The architecture and learning history are very similarto the previous simulations, as it contains two agents, each havingone unit to refer to the image and four units to describe its features.For illustrative purposes, we used the features from the martiniexample in Figure 5 (top panel). First, the director studied a figureduring a prior observation phase. Next, during the talking and

Figure 4. Interlude Simulation i: Polarization as a function of progress inthe discussion and amount of communication. Decreasing polarization asthe discussion unfolds is illustrated from the left panel to the right panelwhen communication between majority and minority groups is equal aswithin the groups (left), is reduced to half that amount (middle), or istotally cut off (right) while keeping the overall communication alike.


listening phase, both agents talked and listened to each another ina randomized sequence, with the sole limitation that the directortalked more often than the matcher. Although this is clearly farfrom the content of a natural conversation in which a matcher asksfor specific clarification and elaboration and the director providestargeted answers and descriptions, as we will see, these minimalassumptions with respect to the amount of talking are sufficient toreplicate the basic effect. Of most importance here is that not allfeatures are equally strongly connected with the object. However,rather than allowing the network to randomly generate strongerand weaker connections for some features (which would be morenatural, as the connection strength differs between the partici-pants), we set the activation values of the two last features to alower value to make this assumption explicit. To test the content ofthe conversation, we simply measured the average activation dur-ing the talking and listening phase.

Results. The statements listed in Table 5 were processed bythe network for 50 participants with different random orders. In

Figure 5 (bottom panel), the simulated values (dashed lines) arecompared with the observed number of words (bars). As can beseen, the simulated and observed number of words matched verywell, and the correlation between them was significant (r � .99,p � .001). An ANOVA on the simulated data further revealed asignificant main effect of the number of words for the director,F(5, 294) � 111.01, p � .001, as well as for the matcher, F(5,294) � 56.26, p � .001.

Extensions and discussion. The same simulation approach wasapplied with success to related work on referencing (Kingsbury,1968; Krauss & Weinheimer, 1964, 1966) with the same param-eters, except for a lower initial trust weight in Kingsbury (1968).Schober and Clark (1989) also tested the accuracy of matchers andother people who overheard the communication between directorand matchers, and we were also able to replicate these accuracyresults.

How does the simulation produce the decreasing pattern ofwords over time? In the introduction of the trust model, weexplained that the maxim of quantity is implemented by the trustweights from the listening agent to the talking agent. A hightalker 4 listener weight indicates that the listener is to be trustedand reduces the expression of topics that the listener is alreadyfamiliar with, while boosting the expression of other information.Consistent with the maxim of quantity, when attenuation andboosting was turned off in the present simulation, the pattern ofresults changed (i.e., the number of words did not decline com-pletely and started to go up again after some time). However, therewas also a second, more important reason for the reduced numberof words.

Although our network cannot make a distinction between, forinstance, descriptions, questions, or affirmations, it ensures thatonly the strongest connections with a feature of the picture arereinforced and are ultimately singled out as “pet” words to de-scribe the whole picture. The weaker connections die out becauserepeating the same information over again overactivates the sys-tem. Because stronger connections already sufficiently describethe object, the weaker connections are overshadowed by them andbecome obsolete. This property of the delta algorithm is alsoknown as competition or discounting (see Van Overwalle, 1998;Van Overwalle & Labiouse, 2004; Van Rooy et al., 2003). In thesimulation, this overshadowing by stronger connections is clearlydemonstrated when all the features are given an equal and maxi-mum connection strength of �1 (by providing all an activation of�1) in the prior learning phase. Under this specification, thesimulated pattern differs from the observed data (i.e., it showed aless robust and smooth decrease in number of words so that, forinstance, the number of word was not always the highest on thefirst trial).

Thus, whenever people repeat information, our cognitive systemmakes its memory representations more efficient by making ref-erence to only the strongest features, and the other features aregradually suppressed. For instance, people typically simplify re-curring complexities in the outside environment by transformingthem into stereotypical and schematic representations. This pointsto a cognitive mechanism of efficiency in memory as an additionalreason for the decreasing number of words, in addition to coordi-nation and establishment of a joint perspective during the conver-sation (Schober & Clark, 1989). One might even argue that jointcoordination through simplification in the reference paradigm is so

Figure 5. Simulation 2: Referencing (top). From “Constructing SharedCommunicative Environments” (p. 186), by R. M. Krauss and S. R.Fussell, 1991. In L. Resnick, J. Levine, and S. Teasley (Eds.), Perspectiveson Socially Shared Cognition. Reprinted by permission of the author.Words per reference by the director and matcher (bottom). Human data aredenoted by bars, and simulated values are denoted by dashed lines. Thehuman data are from Figure 2 in “Understanding by Addressees andOverhearers,” by M. F. Schober and H. H. Clark, 1989, Cognitive Psy-chology, 21, p. 217. Copyright 1989 by Academic Press. Adapted withpermission.


easy and natural precisely because it relies on a basic principle ofcognitive economy that our brain uses constantly.

Interlude Simulation: Talking Heads

In his talking heads experiment, Steels (1999, 2003, 2005; Steels& Belpaeme, 2005) describes an open population of cognitiverobotic agents who could detect and categorize colored patterns ona board and could express random consonances to depict them.With this naming game about real-world scenes in front of theagents, Steels (1999) wanted to explore how humans createdmeaningful symbols and developed languages. Although thepresent trust model obviously does not have the full capacities ofSteels’s robots, it is able to replicate the naming game that therobots also played. Four different sorts of meaning extraction arepotentially problematic in this process: the creation of a new word,the adoption of a word used by another agent, the use of synonymsby two agents, and ambiguity when two agents use the same wordfor referring to different objects (i.e., homonyms).

To simulate these four types of meaning creation, a talking andlistening agent first learned their respective word for two objects,and then after mutual talking and listening (to an equal degree), wetested how the listening agent generated the meaning of a wordthat (a) is new for the listener and was used by the talking agent(e.g., for the listening agent a new circle 3 xu connection iscreated), (b) matches the word of the talking agent (for both agentsthe same circle3 xu connection is used), (c) is a synonym for thesame object (for the talking agent circle 3 xu is used, and forlistening agent circle3 fepi is used), (d) is ambiguous in that thesame word is used for different objects (for the talking agentcircle3 xu is used, and for the listening agent square3 xu is used).

Figure 6 displays how the meanings of the words are created andchanged under these four circumstances. Note that we turned offthe criterion of novelty (attenuation and boosting) so that we couldconcentrate on the acquisition of meaning rather than the expres-sion and communication of it. As can be seen, the simulatedprocess for the creation of a new word and matching an existingword are obvious. The new word gradually acquires strength, andthe matched word keeps the high strength it has from the begin-ning. For synonyms, the simulation reveals a competition betweenwords. To denote a circle, the listening agent gradually loses its

preference for fepi in favor of the word xu that is then used moreoften (although the synonym fepi is still used). For ambiguouswords, the simulation predicts no competition so that the ambigu-ity is not really solved. Instead, xu tends toward a meaning at ahigher categorical level referring to both objects alike (like the

Figure 6. Interlude Simulation ii: Lexical acquisition for new, matched,synonymous, and ambiguous words.

Table 5Referencing in Simulation 2

Trial


Picture Martini Glass Legs Each side Picture Martini Glass Legs Each side

Prior observation of figure by director#10 1 1 1 .8 .4

Talking and listening#12 1 i i i i ? ? ? ? ?#8 ? ? ? ? ? 1 i i i i

Test of talkingDirector ? ? ? ?Matcher ? ? ? ?

Note. Schematic version of learning experiences based on Schober & Clark (1989, Experiment 1). Each row represents a trial of learning orcommunicating, where cell entries denote external activation, and empty cells denote no activation. Trial order was randomized in each phase and condition.# � number of times the trial is repeated; i � internal activation generated by the talking agent after activating the picture; ? � (little ear) external activationreceived from the talking agent; in the test phase, ? � activation of talking agents during the previous talking phase.


term geometric figures refers to circles and squares), although thelistening agent keeps its initial preference for square3 xu becauseit is the more prototypical member of the category.

Simulation 3: Stereotypes and the Rumor Paradigm

A surprising finding in recent research on stereotyping is thatour stereotypic opinions about others are not only generated bycognitive processes inside people’s head (see Van Rooy et al.,2003, for a connectionist view) but are further amplified by themere communication of these beliefs. Several investigations havedemonstrated that information that is consistent with the receiver’sbeliefs is more readily exchanged, whereas stereotype disconfirm-ing information tends to die out (Brauer, Judd, & Jacquelin, 2001;Kashima, 2000; Klein, Jacobs, Gemoets, Licata, & Lambert, 2003;Lyons & Kashima, 2003; Ruscher & Duval, 1998; Schultz-Hardt,Frey, Luthgens, & Moscovici, 2000; Thompson, Judd, & Park,2000). This process reinforces extant stereotypes even further,attesting to the crucial role of social communication such asrumors, gossip, and so on in building impressions about others.

Key experiment. The maxim of quality suggests that commu-nication is more effective when the information is consideredtrustworthy. Hence, we might expect that people with similarstereotypical background (and who thus are mutually experiencedas trustworthy), exchange their stereotypical beliefs more often. Incontrast, people with a different background evaluate each other asless trustworthy. In addition, because they exchange conflictinginformation (from their opposing background), their messages tendto cancel each other out. To illustrate these predictions of the trustmodel, we simulate a study undertaken by Lyons and Kashima(2003, Experiment 1). This study stands out because the exchangeof information was tightly controlled and recorded for each par-ticipant. Specifically, information was communicated through aserial chain of 4 people. One person began to read a set ofinformation before reproducing it from memory to another person.This second person then read this reproduction before then report-ing it verbally to a third person and so on, much the same way asrumors are spread in a community. The information in the studyinvolved a story depicting a member of a fictional group, theJamayans. Before disseminating the story along the chain, generalstereotypes were induced about this group (the background infor-mation). In one of the conditions, all 4 participants in the chainwere given the same stereotypes about the Jamayans as beingsmart and honest (actual shared condition). In another condition, 2participants were given stereotypes about the Jamayans that wereopposite to that given to the other 2 participants, so that eachsubsequent participant in the chain held opposing group stereo-types (actual unshared condition). The target story given afterwardalways contained mixed information that both confirmed anddisconfirmed the stereotype.

As can be seen in Figure 7 (left panel), when the stereotypeswere shared, the reproduction became more stereotypical furtheralong the communication chain. The story was almost stripped ofstereotype-inconsistent (SI) information, whereas most of thestereotype-consistent (SC) information had been retained. In con-trast, when the stereotypes were not shared (right panel), thedifferences between SC and SI story elements were minimal.

Simulation. We simulated a learning history that was basicallysimilar to the original experimental procedures used by Lyons and

Kashima (2003, Experiment 1). The architecture involved fiveagents, each having five units consisting of the topic of theinformation exchange (i.e., Jamayans), two SC traits (smart andhonest), and two SI traits (stupid and dishonest). As can be seen inTable 6, for the actual shared condition, we provided during theprior SC information phase 10 stereotypical trials indicating thatthe Jamayans were smart (by activating the Jamayans and thesmart unit) and 10 stereotypical trials indicating that they werehonest (by activating the Jamayans and the honest unit) for each ofthe agents. For the actual unshared condition, Agents 2 and 4received contradictory information indicating that the Jamayanswere stupid and dishonest (by activating the stupid and liar units)during the prior SI information phase. Next, during the story phase,the first agent received ambiguous information about a member ofthe Jamayans: five SC trials reflecting story elements indicatingthat he was smart and five SI story elements indicating that he wasa liar. This story was then reproduced by this agent and receivedby the next agent. That is, the Jamayans unit in Agent 1 wasactivated and, together with the internal activation (i.e., expressionof beliefs) of the other smart/stupid and honest/liar units in Agent1, was then transmitted to Agent 2. After listening, Agent 2expressed his or her opinion about the Jamayans to Agent 3, thenAgent 3 to Agent 4, and finally Agent 4 to Agent 5 (the partici-pants in the fourth position of the experimental chain were led tobelieve that there actually was a fifth participant). After eachtalking and listening phase, we measured how much the talkingagent had expressed or communicated the notion that the Jamayanswere smart, stupid, honest, or liars.

Results. We ran the statements from Table 6 for 50 partici-pants in the network with different random orders. As can be seenin Figure 7, the simulation closely matched the observed data (r �.93, p � .001). Separate ANOVAs with sharing (shared vs. un-shared) and position (1, 2, 3, or 4) as factors, reveal a significant

Figure 7. Simulation 3: Proportion of stereotype-consistent (SC) andstereotype inconsistent (SI) story elements as a function of the actualsharedness. Human data are denoted by bars, and simulated values aredenoted by dashed lines. The human data are from Figure 2 (averagedacross central and peripheral story elements) in “How Are StereotypesMaintained Through Communication? The Influence of Stereotype Shared-ness,” by A. Lyons and Y. Kashima, 2003, Journal of Personality andSocial Psychology, 85, p. 995. Reprinted by permission of the author.


interaction for both the SC and SI story element, F(3, 392) �19.29–19.93, ps � .001. Separate t tests show that all adjacentpositions differed reliably, except for SC information in the sharedcondition. This suggests that in the shared condition, the amount ofSC information transmitted from one agent to the other was almostcompletely maintained, in contrast to the SI information, whichdecreased. In the unshared condition, the expression of both typesof information decreased. Note that boosting or attenuation ofbelief expression (maxim of quantity) should not play a role here,as the agents had no opportunity to hear their communicationpartners before telling their own story and so were unable to testwhether they agreed on the Jamayans’ attributes. To verify this, weran the simulation with boosting and attenuation turned off andfound identical results. This strongly suggests that for a rumorparadigm involving novel interlocutors, the initial trust in a talkingagent’s statements (maxim of quality) was sufficient for creating astereotype confirmation bias during group communication.

Extensions and discussion. We also successfully simulatedrelated work on stereotyping and impression formation using therumor paradigm and free discussions (but without recording andanalyzing the conversation itself), such as the studies by Thomp-son, Judd, and Park (2000, Experiments 1 and 2); Brauer, Judd,and Jacquelin (2001, Experiment 1); Schultz-Hardt et al. (2000,Experiment 1); and Ruscher and Duval (1998, Experiment 1).Although we had to change our parameters somewhat in some ofthe simulations (e.g., changing the initial trust or tolerance levels),overall, this attests to the wide applicability of our approach.

Simulation 4: Maxim of Quantity and the Expressionof Stereotypes

The maxim of quantity suggests that when the audience isknowledgeable and agrees with the communicator’s position, lessinformation is transmitted. Recall that in the model, the maxim ofquantity (implemented by a strong talker 4 listener trust weight)indicates that the listener is to be trusted and that expression of thesame issue or attribute can be attenuated, but the expression ofanother issue or attribute should be boosted.

Key experiment. To illustrate the working of the maxim ofquantity, we now apply the trust model to another data set from thesame empirical study by Lyons and Kashima (2003), describedabove. In this study, Lyons and Kashima provided half of theirparticipants with the false information that the other participants inthe chain had received completely similar background informationon the Jamayans (perceived complete knowledge), whereas theother half were given the false information that the other partici-pants were completely ignorant (perceived complete ignorance).As shown in Figure 8, the results indicated that given the belief ofcomplete knowledge, both SC and SI story elements were repro-duced, and no substantial stereotype bias emerged. In contrast, inthe complete ignorance condition, a stereotype bias became appar-ent in that SI story elements were strongly suppressed.

Simulation. We ran the same simulation as before, with thefollowing modifications. To obtain high trust weights from thelistening agents to the talking agents, (a) we included only the

Table 6Rumor Paradigm in Simulation 3

Trial


Jamayans Smart Stupid Honest Liar Jamayans Smart Stupid Honest Liar

Prior SC information onJamayans: Per agent

Smart (#10) 1 1Honest (#10) 1 1

Prior SI information onJamayans: Per agent

Stupid (#10) 1 1Liar (#10) 1 1

Mixed (SC � SI) story toAgent 1

Smart (#5) 1 1Liar (#5) 1 1

Talking and listening by Agents1 3 2, 2 3 3, 3 3 4, and4 3 5

Intelligence (#5) 1 i i ? ? ?Honesty (#5) 1 i i ? ? ?

Test of talking by each agentSmart ?Stupid ?Honest ?Liar ?

Note. Schematic version of learning experiences based on Lyons and Kashima (2003, Experiment 1). Each row represents a trial of learning orcommunicating, where cell entries denote external activation and empty cells denote no activation. The shared condition is always preceded by the SCinformation for each agent, and the unshared condition is preceded by the SC or SI information alternately for each agent, both followed by the mixed story,and talking, and listening phases. Trial order was randomized in each phase and condition. SC � stereotype consistent; SI � stereotype inconsistent; # �number of times the trial is repeated; i � internal activation generated by the talking agent after activating the Jamayans; ? � (little ear) external activationreceived from the talking agent; in the test phase, ? � activation of talking agents during the previous talking phase.


actual shared condition, and (b) in the perceived complete knowl-edge condition, we set the initial trust weights from listening totalking agents .20 above the trust starting weight for the unitsinvolved in the transmission of SC information (Jamayans, smart,honest). These high trust weights directly simulate the notion thatthe listening agents were to be trusted more than usual becausethey share the same background with the speaker.

Results. As can be seen in Figure 8, the simulation matchedthe observed data, although not above conventional levels ofsignificance (r � .81, p � .19), partially because of a lack of datapoints (only four) and partly because of the implementation of themaxim of quantity. If only boosting on other issues were imple-mented (without attenuation of familiar issues), then the simulationwould match almost perfectly the observed data. However, be-cause attenuation of known information is the core idea of themaxim of quantity (see Grice, 1975), we left this crucial aspectintact. An ANOVA revealed the predicted significant interactionbetween perceived sharing (knowledge vs. ignorance) and type ofinformation (SC vs. SI), F(1, 796) � 139.92, p � .001. Further ttests revealed that although the difference between SC and SI wasstill significant under complete knowledge, t(398) � 4.45, p �.001, it was much less so than under complete ignorance, t(398) �23.25, p � .001. The implementation of the maxim of quantity wascrucial in the simulation of higher SI information in the completeknowledge condition. This strongly suggests that when peoplebelieve they share a similar background, the maxim of quan-tity helps to neutralize the stereotype confirmation bias incommunication.

Simulation 5: Group Problem Solving and SharingInformation

It is often believed that a group as a whole has more intellectualresources to help solve a problem than do individuals, because

some members may have crucial information that others do nothave and that may lead to a solution. By pooling all such uniqueinformation, the group as a whole should make considerably betterdecisions. However, contrary to this ideal of group problem solv-ing, research has revealed that unique information is discussed lessoften than shared information, and if it is, it is often brought in thediscussion much later (Stasser, 1999; Stasser & Titus, 1985). This,of course, reduces the efficiency and speed of group problemsolving (Larson, Christensen, Abbott, & Franz, 1996; Larson,Foster-Fishman, & Franz, 1998).

This result is unexpected. Would one not expect, on the basis ofGrice’s (1975) maxim of quantity, that group members discussknown or shared information less in favor of novel information?Why, then, are they doing the opposite? One explanation, putforward by Stasser (1999) is that shared information has a sam-pling advantage. That is, because shared information has a higherprobability of being mentioned than does unique information (be-cause many members hold shared information), groups tend todiscuss more of their shared than their unshared information.However, each time an item of shared information is brought forth,because most group members hold this just-mentioned item, theprobability to sample additional shared information is reducedmore than unique information. This sampling explanation predictsmore discussion of shared information at the start of a discussion,and unique information is brought in the discussion later. Anotherexplanation, based on the idea of grounding, was put forward byWittenbaum and Bowman (2004). They argued that group mem-bers attempt to validate one’s thoughts and ideas by assessingtheir accuracy and appropriateness through comparison with oth-ers. Shared information can be socially validated and hence pro-vides opportunities to evaluate each other’s task contributions,whereas unshared information cannot. Therefore, it is exchangedmore often.

The idea that people validate their thoughts and ideas throughcomparison with others is also at the core of our approach. Indeed,social validation and trust depend on the information being con-sistent with one’s beliefs. As put forward by Stasser (1999),because of uneven sampling probabilities, shared information iscommunicated more often at the beginning of a group discussion.However, after the information has been validated, trust is high,and the maxim of quantity kicks in. This implies that after a while,discussion of shared information is attenuated while discussion onother issues is boosted, and so increases the discussion of uniqueideas.

Key experiment. To illustrate the working of the maxim ofquantity in group problem solving, we conducted a simulation ofan empirical study by Larson et al. (1996). Participants in thisstudy watched a short videotaped interview of a patient by aphysician in an examination room. Three different videotapes werecreated. Roughly half of the information in each tape was alsopresent in the other tapes (shared condition), and the remaininginformation was present in one tape alone (unique condition). Eachvideotape was shown to different teams consisting of a mixture ofmedical students and established experts. Afterward, each teamdiscussed the case and produced a differential diagnosis. Thisdiscussion typically lasted less than 20–25 min. Figure 9 showsthe percentage of shared as opposed to unique information as thediscussion unfolded, where each discussion position reflects thepoint in the discussion at which a new item was introduced (i.e.,

Figure 8. Simulation 4: Proportion of stereotype-consistent (SC) andstereotype-inconsistent (SI) story elements as a function of perceivedsharedness. Human data are denoted by bars, and simulated values aredenoted by dashed lines. The human data are from Figure 1 in “How AreStereotypes Maintained Through Communication? The Influence of Ste-reotype Sharedness,” by A. Lyons and Y. Kashima, 2003, Journal ofPersonality and Social Psychology, 85, p. 995. Reprinted by permission ofthe author.


omitting the repetitions). As can be seen, there is a negative lineartrend in that initially a lot of shared items are discussed, whereasat the end more unique items are mentioned.

Simulation. We simulated a discussion by three agents (al-though only two are shown in Table 7). Each agent had seven units(although only five are shown) consisting of the object of discus-sion (i.e., the patient), three shared items, and three unique items.To simulate the viewing of the videotape, during the learningphase, we ran five trials for each agent, in which all of the shareditems and a single unique item were learned. Next, in the talkingand listening phase, we let all agents freely talk about all of theshared and unique items. Because agents knew only about a single(unique) item at the beginning of the discussion, this actuallyprovided a sampling advantage for the shared information at thestart. To avoid an a priori sampling advantage in the simulation,we used the minimal assumption that each agent expressed a singleshared and a single unique item during each discussion round.Given the interplay between attenuation and boosting, this lefteach agent in the simulation completely free to talk as much as heor she wanted about the issue. After each round, we measured howmuch each agent had communicated the shared and unique items.

Results. We ran the statements from Table 7 for 50 partici-pants with different random orders. As can be seen in Figure 9, thesimulation closely matched the observed data (r � .85, p � .001).A one-way ANOVA on the percentage of shared informationreveals the predicted main effect of the discussion position, F(34,2665) � 335.98, p � .001. This confirms that according to thesimulation, as the discussion progresses, more unique informationis transmitted.

Extensions and discussion. Research by Stewart and Stasser(1995) indicated that when members were explicitly told abouttheir relative expertise (i.e., their knowledge of their unique infor-mation), then the communication of unique information was facil-itated. To simulate this effect, we set all receiving trust weights to

�1 for the unique information on which the agent was the soleexpert and then ran the simulation again. As can be seen inFigure 9, more unique information was communicated under theseconditions, consistent with the empirical findings. Note that pro-viding high trust weights to all information, including sharedinformation, did not lead to this effect because that again gives asampling advantage to shared information.

We also simulated a well-known study by Stasser and Titus(1985) that illustrated the lack of sharing unique information. Thisstudy served as input for the DISCUSS computer simulationdeveloped by Stasser (1988) to explore and illustrate his theoret-ical ideas about the integration of information in group discussionand decision making. We did not select this study for the presentarticle, in part because the data input and simulation are somewhatmore complex and because it provided data not on actual infor-mation exchange but only on the end result in participants’ beliefs.Nevertheless, a simulation with our model and the same parame-ters (except the learning rate which was reduced to .25 for morerobust results) replicated the major significant results and yieldeda mean correlation of r � .79 with observed postdiscussion pref-erences (Stasser & Titus, 1985, Table 5).

Comparisons With Other Models

It is not the first time that computer modeling has been used toaid in the understanding of social relationships and influence andto verify the assumed theoretical processes underlying these phe-nomena. Several categories of computer models can be distin-guished: cellular automata, social networks, and different types ofneural networks (see also Nowak, Vallacher, & Burnstein, 1998).We describe each of these approaches, discuss their shortcomingsand strengths, and compare them with the present model.

Cellular Automata and Social Networks

In cellular automata and social networks, the units of the modelrepresent single individuals, and the connections represent therelationships between individuals. This type of model deals notwith processes within an individual, but rather with those betweenindividuals.

Cellular automata. Cellular automata consist of a number ofagents (automata) arranged in a regular spatial pattern such as acheckerboard. Each automaton possesses a limited number ofattributes (typically one or two) that can be in a limited (oftenbinary) number of states, such as cooperate or defect, or positive ornegative opinion. Typically, individuals are linked to their neigh-bors, and the nature of the linkage is changed by an updatingalgorithm. Depending on the specific models, these links mayrepresent different characteristics such as persuasiveness, propen-sity for cooperation, decision strategies, and so on (Nowak et al.,1998). Cellular automata allow for capturing of regularities andpatterns of social norms or opinions in a group, whereas individualagents update their behavior solely on the basis of local informa-tion between neighboring agents. For instance, the pioneeringwork by Nowak et al. (1990) on the social impact model gave riseto important insights such as the role of geographical clustering inprotecting deviant minority opinions from being overthrown by avast majority (see also Interlude Simulation: Talking Heads).Likewise, Axelrod’s (1997) culture model (see also Axelrod,

Figure 9. Simulation 5: Percentage of shared versus unique informationas a function of discussion position. Human data are denoted by bars, andsimulated values are denoted by dashed lines. The human data are fromFigure 1 in “Diagnosing Groups: Charting the Flow of Information inMedical Decision-Making Teams,” by J. R. Larson, Jr., C. Christensen,A. S. Abbott, and T. M. Franz, 1996, Journal of Personality and SocialPsychology, 71, p. 323. Reprinted by permission of the author.


Riolo, & Cohen, 2002; Riolo, Cohen, & Axelrod, 2000) showedthat a whole population can converge to a local common culturewhen agents adopt their attributes on the basis of their similaritywith neighboring agents (producing roughly an effect like trustweights). In more recent work, Barr (2004) demonstrated conver-gence to spatially organized symbolic systems (such as a commonlanguage or several dialects), and Couzin, Krause, Franks, andLevin (2005) illustrated that a small proportion of informed indi-viduals can guide a group of naive individuals toward a goal.Kennedy (1999) proposed a more sophisticated particle swarmalgorithm that implements error-driven learning from an agent’sown experiences or other neighboring agents and in which agentsconform to their neighbors, not on the basis of a single attributelike similarity as in previous models, but when their (learned)performance is better than their own. Although cellular automataare flexible with respect to the types of connections they support,they are very rigid with respect to the structure of the socialrelationships, in that individuals are influenced only by their closeneighbors and less (or negligibly) so when they are farther apart inspace. Hence, social relations are strongly determined by thegeometry of the social space, rather than being based on individualchoices (Nowak et al., 1998).

Social networks. This geometrical limitation is relaxed in so-cial networks, in which social relations between individuals aredepicted as connections among units in a graph. This makes itpossible to describe social properties of individual agents (e.g.,popularity vs. isolation) as well as properties of the groups ofindividuals (e.g., clustering of opinions in cliques). For instance,Janssen and Jager (2001) developed a consumat approach in whichagents decide to consume products that have the highest personalpreference or social preference (e.g., have the largest marketshares). However, a limitation of social networks is that the linksare often specified in binary positive–negative (or present–absent)terms and do not allow for graded strength. Perhaps more impor-tant, these models do not provide a general theoretical frameworkto update the connections in the network. Rodriguez and Steinbock(2004) recently developed a social network model that includedgraded and adjustable trust relationships between individuals,which appear to better capture the representativeness of decision

outcomes. However, given the lack of a general framework, theproposed solution in this as well as in other social networks isoften idiosyncratic.

Attractor or Constraint Satisfaction Networks

Like cellular automata and social network, the units of attractoror constraint satisfaction networks represent single individuals,and the connections represent the relationships between individu-als. However, unlike these previous models, the connections havegraded levels of strength, and their adjustments are driven bygeneral algorithms of weight updating. Specifically, the computa-tions for spreading of information and updating relationships areadopted directly from neural networks. Typically, the architectureis a recurrent structure (like the present model), so that all indi-viduals have unidirectional connections with all other individuals.

Many attractor models represent opinion change as the spread-ing of information or beliefs across individuals. This social spread-ing is formalized in a manner similar to that of the spreading ofactivation in neural models. Eventually, after going through mul-tiple cycles, the model reaches an equilibrium in which the state ofthe units does not change anymore; that is, the model settles in astable attractor that satisfies multiple simultaneous constraintsrepresented by the supporting and conflicting states and connec-tions of the other units. Hence, an attractor reflects a stable socialstructure in which balanced social relationships or attitudes areattained. Nevertheless, attractor networks have a fundamental lim-itation as models of social relations. Because they are based on ananalogy with the brain, Nowak et al. (1998) warned that

it is important to remember that neurons are not people and that brainsare not groups or societies. One should thus be mindful of the possiblecrucial differences between neural and social networks . . . [some]differences . . . may reflect human psychology and are difficult tomodel within existing neural network models. (pp. 117–118)

Perhaps the most crucial limitation in this respect is that theindividuals in the networks do not have a psychological represen-tation of their environment so that individual beliefs are reduced toa single state of a single unit. This shortcoming was overcome by

Table 7Shared vs. Unique Information in Simulation 5

Trial


Patient Shared1 Shared2 Uni1 Uni2 Patient Shared1 Shared2 Uni1 Uni2

Learning the medical case from videotape#5 1 1 1 1 0#5 1 1 1 0 1

Talking and listeningShared 1 i i ? ? ?

? ? ? 1 i iUnique 1 i i ? ? ?

? ? ? 1 i iTest of talking ? ? ? ?

? ? ? ?

Note. Schematic version of learning experiences based on Larson, Christensen, Abbort, and Franz (1996). Each row represents a trial of learning orcommunicating, where cell entries denote external activation, and empty cells denote no activation. Trial order was randomized in each phase and condition.Uni � unique; # � number of times the trial is repeated; i � internal activation generated by the talking agent after activating the patient; ? � (little ear)external activation received from the talking agent; in the test phase, ? � activation of talking agents during the previous talking phase.


Hutchins (1991). He used constraint satisfaction networks to cap-ture an individual’s psychology and memory. Thus, an individual’sbelief was viewed as the formation of a coherent interpretation ofelements that support or exclude each other, in line with earlierconstraint satisfaction models of social cognition (Kunda & Tha-gard, 1996; Read & Miller, 1993; Shultz & Lepper, 1996; Spell-man & Holyoak, 1992; Spellman, Ullman, & Holyoak, 1993).Crucially, he combined these individual constraint satisfactionnetworks into a community of networks so that they could ex-change their individual information with each other. One of theparameters in his simulations is the persuasiveness of the commu-nication among individuals’ nets, which is related to our trustweights, although it was only incorporated as a general parameter.A similar approach for two individual nets was developed byShoda, LeeTiernan, and Mischel (2002) to describe the emergenceof stable personality attractors after a dyadic interaction. However,in this model, information exchange was accomplished by inter-locking the two nets directly (as if two brains were interconnectedwithout any check or control on the veracity or credibility of theinformation coming from another person), which is implausible asa model of human communication.

Assemblies of Artificial Neural Networks

The constraint satisfaction model of Hutchins (1991) discussedin the previous section was a first attempt to give individuals theirown psychological representation and memory. However, onemajor shortcoming was that the connections in the individualconstraint satisfaction networks were not adaptive; that is, theycould not change on the basis of previous experiences (see alsoVan Overwalle, 1998). This limitation was addressed by Hutchinsand Hazlehurst (1995) in a model that consisted of a collection ofrecurrent nets. Each individual was represented by a single recur-rent net with adaptive weights (and with hidden layers), and all thenets could exchange information with each other. The modelillustrated how a common lexicon is created by sharing informa-tion between individuals. However, as we understand it, the outputof one individual’s network served as direct input for anotherindividual’s network as if two brains were directly interlockedwith each other, without moderation of the perceived persuasive-ness or validity of the received information. This limitation wasovercome in the present trust model. The Clarion model developedby Sun and Peterson (1999) combined information encoding andlearning at the symbolic level as well as the lower connectionistlevel and permitted that agents influence each other at both levels.Possibly, to date, this is the most sophisticated model in thisapproach, apart from the robots developed by Steels (1999, 2003;Steels & Belpaeme, 2005), which give the individual networks abody with primitive perception and movement.

What Has Been Accomplished? Limitationsand Implications

The trust model does more justice to the complexity of thehuman mind, in comparison with earlier multiagent models, byzooming in on the individual level. This allowed simulation ofvarious aspects of human influence and communication in smallergroups as uncovered in empirical research. This constitutes impor-tant postdictions of our trust model. Other simulations also dem-

onstrate that scaling up the trust model to larger group phenome-non such as polarization was successful and suggests that the trustmodel may be applied to societal phenomena at large (althoughthis is beyond the scope of this article).

Robustness of the Simulations

To what extent are the trust simulations dependent on thespecifics of the current architecture or parameters? Earlier mul-tiagent simulation work was often limited in the range of phenom-ena simulated or used a slightly different model for each. Admit-tedly, for some of the extensions and interlude simulations, weoccasionally set the parameters to values other than the default toreplicate a particular study or domain of investigation. Perhaps,this is not surprising given that we explored such different con-texts. However, it is important to establish the robustness for themajor Simulations 1–5 reported earlier, and to do this we ran thesesimulations again with different parameter ranges and a differentarchitecture.

First, we performed an exhaustive search of the novel trustparameters (i.e., trust learning rate, trust tolerance, and trust start-ing weights; see also Table 3) in a range between .30 and .60 (or.10 all default trust parameters) and identified the lowest corre-lation between simulated and observed data. For all major simu-lations except one, the lowest correlation was generally only .02lower than the correlation with default parameters reported earlier,and the simulation showed the same substantive pattern as thehuman data. The only exception was Simulation 4 on perceivedsharedness (Lyons & Kashima, 2003; lowest r � .60), whichsometimes underestimated the proportion of stereotypical utter-ances expressed in the shared condition. Although this result mayreflect plausible dependencies on trust parameters that also exist inthe real world (such as the degree of knowledge by others), as faras we know, Lyons and Kashima’s (2003) manipulation has not yetbeen replicated, so the implications are unclear.

Second, for ease of presentation of the major simulations, upuntil now we used a localist representation, in which each unitrepresented a single symbolic concept. However, a more realisticcode is a distributed representation, in which each concept isrepresented by a pattern of activation across a set of units that eachrepresents some subsymbolic microfeature of the concept (Thorpe,1994). We reran the major simulations with such a distributedarchitecture,2 and the results showed on average the same corre-lation with the human data as the major simulations reported

2 Each unit was replaced by a set of five units. Their activation wasrepresented by a pattern drawn from a normal distribution with mean asindicated in the learning histories (i.e., Tables 4–7) and SD � 0.10, and thisrandom pattern was redrawn after each 10th simulation run. In addition, toreflect the imperfect conditions of perception and inference, random noisedrawn from a normal distribution with M � 1.0 and SD � 0.10 was addedto these activations (hence, the variation in the starting weights wasdropped). We kept the same parameters as for the localist representation,except that the standard learning rate was lower (as more units are in-volved), and the best fitting learning rate we found was either 0.03 or 0.06.


earlier. In sum, then, the major simulation results hold across awide range of parameter settings and stimulus distributions.3

Limitations

However, it is also evident that we have charted only the firstmodest steps in modeling human collective intelligence throughcommunication. Several aspects have been left out so that wecould focus on the most important social aspects of communica-tion in a small group of individuals.

First, our model leaves unspecified by what linguistic meansinformation is encoded and decoded during transmission fromspeaker to listener. However, at least at the moment, the omissionof language as a tool of communication seems a sensible simpli-fication that already leads to interesting simulation results thatreplicate existing research data. Some theorists attempted to de-velop connectionist models in which language was seen as anessential means of human communication that emerges from thedemands of the communication rather than from anything insidethe individual agents (Hazlehurst & Hutchins, 1998; Hutchins &Hazlehurst, 1995; Steels, 1999; Steels & Belpaeme, 2005).

Second, our model also largely ignores intentional acts by thecommunicators. To name a few, it does not incorporate commu-nicative acts such as demands, promises, or other requests, or otherstrategic decisions to fit the information better to the audience’sknowledge and background. It also leaves out more subtle strategicmaneuvering to please the audience, such as when speakers believethat they can make a better impression by speaking with praiseabout a person that is liked by the audience (Echterhoff, Higgins,& Groll, 2005; McCann & Higgins, 1992; McCann, Higgins, &Fondacaro, 1991; Schaller & Conway, 1999). Because such stra-tegic acts are often made deliberatively, they are beyond thecurrent connectionist approach.

Third, a question left unanswered is how trust about agentsthemselves, such as experts, can be built in the model. Mostprevious multiagent systems are incapable of developing and ap-plying such knowledge about trustworthy agents. Although spacelimitations prevented us from elaborating on this in detail, let usbriefly note that the present model can incorporate the metanotionabout an expert agent as an additional unit in an individual net-work, so that trust on a given topic carries over through internalconnections to a potential expert source. In their single-agentrecurrent connectionist model, Van Overwalle and Siebler (2005)further demonstrated how knowledge about source expertise influ-ences one’s opinion on an issue.

What to Do Next? Novel Hypotheses

Although incorporating earlier theories and data in a singlemodel is an accomplishment in its own right, theory building isoften judged by its ability to inspire novel and testable predictions.In this section, we focus on a few novel predictions involvingcognitive trust in information, because this is a novel contributionof the trust model that we see as theoretically most crucial in acommunicative context. Although there is increasing awarenessthat trust is an important core motive in human interaction (Fiske,2005), there have been few empirical studies on cognitive trust insocial cognition, let alone on its specific role in human commu-nication and information exchange.

What are the determinants and consequences of cognitive trust?Our model proposes that if no a priori expectations on agents exist,people’s trust of information is determined by the fit with theirown beliefs. Although some degree of divergence is tolerated, ifthe discrepancy is too high, the information is not trusted. Thus,rather than some internal inconsistency or some internal ambigu-ities in the story told, it is the inconsistency with one’s own beliefsthat compels the listener to distrust the information. With respectto the consequences of cognitive trust, our model predicts thatmore cognitive trust results in more and easier adoption of theexpressed views and solutions. In contrast, it takes more time toread and judge untrustworthy messages because as they conflictwith one’s own beliefs, they require scrutiny of multiple interpre-tations or more elaboration to be accepted or rejected.

Is cognitive trust applied automatically, outside of conscious-ness? Because acceptance of information on the basis of trust isbased on similar processes in the trust model as activation spread-ing in standard connectionist models, we suggest that this is a quiteautomatic process. Likewise, because the change of trust weightsin the model is a straightforward extension of error-driven learningalgorithms in connectionist models, we suggest that cognitive trustis changed automatically. Consequently, we predict that inferenceson cognitive trust are developed spontaneously on the face ofinformation given and that social inferences and decisions aremade only when the information is (automatically) seen as trust-worthy. There is some initial support for these predictions, asSchul, Mayo, and Burstein (2004) recently found that trustedmessages spontaneously elicit congruent associations, whereasdistrust leads to the spontaneous activation of associations that areincongruent with a suspected message. In contrast, although auto-matic to some degree, we expect that other criteria such as noveltyand attenuation of talking about known information can be moreeasily overruled by controlled processes, such as task instructionsand goals, as the act of speaking itself is largely within the controlof the individual.

On a more societal level, another hypothesis worth furtherinvestigation is suggested by our simulation on the diminishedadoption of a majority position when the communication betweenminority and majority groups is decreased, leading to more diver-gent opinions (see Figure 4). The interesting question now is, whatis the exact amount and type of communication needed for two ormore groups to either merge their opinions or remain separate?This problem has immense potential applications, as it describesthe all too common situation in society through which two culturesor subcultures live in close contact. In some cases, the minority isassimilated in the majority (e.g., most initial European communi-ties in the United States), in other cases there is strong andapparently growing polarization between groups (e.g., Islamiccommunities in Europe), leading to potentially violent outbursts.Our model suggests at least two factors that influence the outcome:(a) the amount of communication outside the community, because

3 The reader can also assess the robustness of the simulations by explor-ing other parameters. The files of the simulations are available uponrequest, and the program for running the simulations is available at www.vub.ac.be/PESP/VanOverwalle.html#FIT. Within the program, parameterscan be changed and explored by choosing the menu Simulation, thenchoosing Parameters.


the more people communicate outside their group, the smaller theprobability of divergence between groups and (b) the degree ofinitial inconsistency between the opinions in the two communities(e.g., different beliefs on women wearing head scarves, which isobligatory according to Islamic extremists and unwanted by aEuropean majority). Further simulations with more agents, moredifferentiated beliefs, and a more fine-grained manipulation of theamount of communication between the groups should throw morelight on the precise conditions in which two cultures will eithermerge or divide.

Conclusion

The proposed multiagent trust connectionist model combines allelements of a standard recurrent model of impression formationthat incorporates processes of information uptake, integration, andmemorization, with additional elements reflecting communicationbetween individuals. Although one of many possible implementa-tions, our model reflects the theoretical notion that informationprocessing within and between individuals can be characterized byanalogous yet unique processing principles.

One of the unique features of the model is the assumption thatacquired cognitive trust in the information provided by communi-cators is an essential social and psychological requirement ofcommunication. This was implemented through the inclusion oftrust weights, which change depending on the consistency of theincoming information with the receiving agents’ existing beliefsand past experiences. Trust weights lead to a selective filtering outof less reliable data and selective propagation of novel informa-tion, and therefore bias information transmission. From this im-plementation of cognitive trust emerged Grice’s (1975) maxims ofquality and quantity in human communication. In particular, themaxim of quality was implemented by outgoing trust weights,which led to an increased acceptance of stereotypical ideas whencommunicators shared similar backgrounds, whereas the maxim ofquantity was simulated by attenuation in the expression of familiarbeliefs (as determined by receiving trust weights), which led to agradual decreased transmission of stereotypical utterances. Byspanning a diversity of findings, the present simulations demon-strate the broad applicability and integrative character of ourapproach. This may lead to novel insights for well-known social-psychological phenomena and may point to potential theoreticalsimilarities in less familiar domains. It may help in the understand-ing of why communicating information among the members of agroup sometimes makes their collective cognition and judgmentsless reliable.

One element that distinguishes the present model from earlierapproaches, but that is common to most connectionist modeling, isthe dynamic nature of our system. It conceives of communicationas a coordinated process that transforms the beliefs of the agents asthey communicate. Through these belief changes, it has a memoryof the social history of the interacting agents. Thus, communica-tion is at the same time a simple transmission of information aboutthe internal state of the talking agent, as well as a coordination ofexisting opinions and emergence of novel beliefs on which theconversants converge, and therefore leads to a common ground.By combining all those elements in a social-distributed network ofindividual networks, the unique contribution of our model is that itextends distributed processing, which is sometimes seen as a

defining characteristic of connectionism, into the social dimension.This is how social psychology can contribute to the further devel-opment of connectionism as tool of theory construction (cf. Smith,1996).

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Received September 19, 2005Revision received February 23, 2006

Accepted February 23, 2006 �


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