Copyright ( 2001 by
Lawrence Erlbaum Associates, Inc.
Personality and Social Psychology Review
2001, Vol. 5, No. 3, 201-215
Computer Simulation as a Method of Further Developing a Theory:
Simulating the Elaboration Likelihood Model
Hans-Joachim Mosler, Karsten Schwarz, Florin Ammann, and Heinz Gutscher
Department ofPsychology, Division of Social Psychology
University ofZurich
Implemented specifically as a method of theory development, computer simulation allows
clarification of a theory and investigation of its implications. Using Petty and Cacioppo 's
(1986a, 1986b) Elaboration Likelihood Model (ELM), the use of simulation in formalizing a
theory, testing the simulation model, and conducting simulation experiments is demonstrated.
With formalization of the theory in the form of a block diagram, the entire pattern of
causal effects in the ELM core statements becomes visible at a glance. The simulation
model was tested through comparing simulated individuals' reactions to stimuli with
the experimental and statistical observed reactions of real participants in experiments. The simulation experiments revealed a dynamic attitude shift in dependency on
the development ofprocessing intensity.
Computer simulation is a method that has been known
for some time (Abelson, 1968; Abelson & Bernstein,
1963; Cohen, 1963; Coleman, 1965; Hagerstrand, 1965).
In 1988, an entire issue (Volume 24, Issue 5) of the Journal ofExperimental Social Psychology (Messick, 1988)
was devoted to computer simulation, but it has yet to be
applied more than sporadically within social psychology.
This is the case in spite of the usefulness of the method as
recognized by many authors (Abelson, 1968; Ostrom,
1988; Stasser, 1988; Whicker & Sigelman, 1991) who
have shown that
* Empirical or theoretical partial findings can
be synthesized by means of computer simulation.
* Computer simulation is an excellent tool for the
transmission of knowledge for educational and
training purposes.
We refer here only to computer simulations that
perform experiments via a computer with no intervention by or interaction with human participants. The
contribution of this article focuses on computer simulation as a method of theory development, and we discuss the possibilities opened up by implementation of
simulation for this purpose.
* Computer simulation allows rigorous description of the subject of study.
* Insight is gained into the structure and behavior
of a system, which allows explanation or conditional prediction of system states and processes.
* Computer simulation is a useful tool in redesigning existing systems and designing new systems.
* Simulation may be implemented for the purpose of clarifying theories and investigating
their implications.
* Through simulation, the consequences of decision options may be examined and optimization
achieved.
Three Areas of Knowledge Gain
Through Simulation
Using the Elaboration Likelihood Model (ELM) of
Petty and Cacioppo (1986a, 1986b) as an example, in
the following we show the types of knowledge that can
be gained by means of simulating a familiar theory. We
take a detailed look at three areas: (a) formalizing a
theory, (b) testing a simulation model, and (c) conducting simulation experiments.
The advantage of theory formalization within a simulation model lies in its demand for complete specification of the assumptions and postulates, number and
kind of variables involved, the type of relations among
them, and so on. However, when this advantage is
cited, it is frequently overlooked that resulting computer programs-due to the many program specifications-are very nontransparent and hard to grasp.
This investigation was carried out in the framework of the project
"Conception and analysis of an integrated, process-oriented theory of
the group phenomena convergence, polarization, and minority influence" (1114-046804.96/1) forthe Swiss National Science Foundation.
We thank Juerg Artho for the simulation framework.
Requests for reprints should be sent to Hans-Joachim Mosler, Department of Psychology, Division of Social Psychology, University
of Zurich, Plattenstrasse 14, CH-8032 Zurich, Switzerland. E-mail:
mosler@ sozpsy.unizh.ch
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MOSLER, SCHWARZ, AMMANN, & GUTSCHER
Discussion of theory by experts becomes more difficult. We believe that this is the reason why computer
simulation has made so little headway in social psychology. In our own computer simulation approach,
therefore, we do not move directly from the theory formulated in words to the computer program, but rather
insert an intermediary step. Here the theoretical quintessence is first reproduced in the form of core statements, and then the theory is formalized in a
systems-theoretical model (a kind of a block diagram).
In this way, the basic assumptions of any simulation of
a theory can be understood and criticized without special computer knowledge. It becomes possible for anybody, using the systems-theoretical model with its
functions as well as the initial values of the independent variables, to calculate resulting values of dependent variables "by hand." ELM researchers, for
example, using the systems-theoretical model, may
compare the fit between their findings and our model.
In testing the simulation model, we examine whether
the pattern of findings of available studies can be replicated
with the model. The better that the most diverse findings
are reproduced, the more permissible the conclusion that
the computer simulation shows basic structural validity. Of
primary importance, therefore, is not a simple representation of input-output relations (event validity) but rather a
sufficient degree of similarity between the real system and
the modeled system (structural validity; Shannon, 1975;
Stanislaw, 1986; Whicker & Sigelman, 1991). The success
of this type of computer simulation thus depends on the adequacy of hypotheses regarding the underlying psychological structures and processes.
In conducting simulation experiments, we made use
of the advantages offered by the simulation method
and investigated dynamic processes of attitude change.
We examined repeated influence on a simulated individual, whereby the focus was on attitudes changing
continually through the course of time. Simulation also
allows analysis of dynamic change of internal factors,
however, such as elaboration likelihood. On the basis
of the simulation experiments, we have reached a new
possible interpretation of the effect of repeated influencing that is based solely on the processes proposed
by the ELM. This allows new working hypotheses to
be developed for empirical investigation.
Method
Our simulation approach stands out in that simulation
is implemented to model a theory that has already been
otherwise empirically tested and not, as in many other
simulation projects (Abelson & Bernstein, 1963; Gilbert
& Doran, 1994; Nowak & Latane, 1994; Nowak,
Szamrej, & Latane, 1990), to produce a new theory and
new findings. The starting point is an established and empirically sound theory, which is transposed to a formal
systems-theoretical model. Then implementation takes
place using computer language, followed by findings-oriented testing of the model and, finally, the conducting of
simulation experiments. This manner of proceeding aids
the assimilation of the findings into current discussion
within an existing research tradition so that researchers in
a particular field may more readily evaluate the validity
and heuristic value of the simulation. Theory specifications or additions derived from simulations may then, for
example, be tested experimentally in a traditional manner. In this sense, our procedure should contribute to the
integration of the simulation method as an important
method-with great explanatory potential-into the
ranks of established methods of theory formation and
theory development. For our simulation of the ELM, the
following steps were required.
The first step of simulation was precise formulation of the core statements of the ELM. Theoretical
content had to be reduced to few, central statements.
The question of which statements form the core of a
theoretical area will-as long as theories have yet no
formalized form-always remain the subject of scientific discussion. Thus, our choice of aspects as core
statements should be seen as a contribution to such
discussion. It should be emphasized that our model is
not a general model of attitude change but rather a
model oriented strictly according to the original statements of the ELM. Other theories of attitude change,
such as the Heuristic-Systematic Model (HSM) by
Chaiken (1980, 1987) and Eagly and Chaiken (1993,
pp. 326-346), are not considered in our model here.
Thus, in our simulation model, central and peripheral
factors within the dimension of processing intensity
stand in a reciprocal relation and interfere with each
other in an additive fashion (compare modeling in
Block 7 later). In the HSM, in contrast, the assumption is that on one hand, central and peripheral processing are linked to separate preconditions that are
independent of one another. On the other hand, central and peripheral factors are assumed to interact (in
a multiplicative relation). In a second step, the ELM
was modeled in the form of a systems-theoretical
model. Here the variables described in the ELM were
placed in relation to each other, using the core statements, according to systems theory rules (see Shannon, 1975). The systems-theoretical model of the
ELM will be presented following in as much detail as
the framework of this contribution allows.
Implementation, the third step, involved the transposition of the systems-theoretical model of relations
into a computer program. To do this, the program
THINK PASCAL 4.0 was used on a Macintosh computer operating system. The details of implementation
will not be examined in this article.
The fourth step consisted of testing of the simulation model with the replication of empirical findings.
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SIMULATING THE ELM
Again, within the framework of this article we must
limit ourselves to the most important aspects.
As a final and fifth step there followed diverse simulation experiments with the modeled ELM.
The steps in the procedure (with the exception of
implementation) will be set out in the following. This
section on Method covers the formulation of core
statements, modeling, and testing, whereas simulation
experiments will be discussed in the Results section.
Core Statement 4: Peripheral
Processing
When processing is superficial, argument quality
will have little effect, and attitude change will be determined mostly by peripheral cues (Postulate 5). A positive peripheral cue leads to attitude change in the
desired direction, whereas negative peripheral cues
lead to attitude change in the opposite direction (Postulate 3b).
Core Statements
The following core statements sum up the central
points of the theory of the ELM. In their publications, Petty
and Cacioppo (1986a, 1986b) presented the ELM in the
form of several postulates. Reference will be made to their
postulates (Petty & Cacioppo, 1986b) in parentheses.
Core Statement 1: Likelihood of
Thoughtful Scrutiny (Elaboration
Likelihood)
The way in which persons form attitudes is dependent on the extent to which issue-relevant persuasive arguments are processed cognitively. The likelihood of
cognitive elaboration increases with a person's increasing motivation and ability to evaluate the communication presented (Postulate 2).
Core Statement 2: Elaboration
Continuum
The continuum of the likelihood of elaboration ranges
from central processing with high elaboration to peripheral processing with low elaboration (Postulates 2 and 5).
When elaboration is high, people engage in thoughtful
analyses of issue-relevant arguments, but when elaboration is low, people engage in less thoughtful analyses of
arguments or take their orientation from peripheral cues.
Core Statement 3: Central Processing
When engaging in intensive elaboration, people recognize and differentiate convincing and threadbare arguments (objective processing, Postulate 4). Here,
strong arguments lead to attitude change in the persuasive direction, whereas weak arguments affect attitude
change away from the intended direction (Postulate 3a).
If processing is intensive, but biased, people will increase the value of arguments conforming to their own
initial attitudes and devalue incongruent arguments (biased processing, Postulate 6). Through this, arguments
that are congruent to initial attitudes will effect more attitude change in the intended direction, whereas discrepant arguments can more strongly effect the opposite
(Postulate 3c).
Core Statement 5: Integration of New
Knowledge
Through intensive cognitive processing of new information, people integrate new contents into their
guiding attitude schemas (Postulate 2).
Core Statement 6: Multiple Effects
Elaboration likelihood can be enhanced by positive
peripheral cues and reduced by negative ones. Howver, this applies only to the condition of moderate
elaboration likelihood (complex factors; Petty &
Cacioppo, 1986a, 1986b).
Core Statement 7: Consequences of
Elaboration Type
Attitude changes that result mainly from central
processing will show greater temporal persistence,
greater prediction of behavior, and stronger resistance
to counterargument than attitude changes that result
mainly from peripheral cues (Postulate 7).
This set of core statements was chosen as our starting point to keep the model as simple as possible. Modifications of these statements within the framework of
the ELM are certainly conceivable. For example, core
Statement 4 could take into consideration that there are
also high peripheral cues that lower motivation and
low cues that raise motivation (see Wegener, Petty, &
Smith, 1995). To take this finding into account, a further moderator variable would have to be introduced.
Possible core statements that can be derived from theories other than the ELM (such as from the HSM by
Chaiken, 1980, 1987, and Eagly & Chaiken, 1993, pp.
326-346) were deliberately not included in this study.
Modeling
In constructing a model (see Figure 1), theoretical
and empirical connections are reduced to a comprehensible measure. The most important and, where possible, most thoroughly tested variables are used to
generate the different types of elaboration (e.g., the
variable personal relevance stands as representative of
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MOSLER, SCHWARZ, AMMANN, & GUTSCHER
Peripheral
Cue
Source
Attitude
Figure 1. Systems-theoretical model of the Elaboration Likelihood Model. Variables are labeled on the arrows, and relating
functions stand in the numbered blocks. The transition functions
are explained in the text.
diverse variables that influence elaboration motivation), or variables are summarized in a general variable
(e.g., the variable peripheral cue stands for source expertise, credibility, and so on). Following the conventions of systems theory, variables are represented as
arrows, whereby their direction indicates the direction
of their effects.
Input variables do not have a direct influence on the
output variable. This means that input variables do not
automatically cause higher output variables. Instead,
transition functions are found within the blocks of the
model, and calculation takes the place of the usually
many input variables leading to one output variable.
Thus, the input variables that enter a block are calculated in the block to result in the output variable.
The five variables entering from outside the individual represent external influences on the processing individual. The three variables at the top of Figure 1 are
influences that come from the source. Through these
variables a message is transmitted to the individual from
the source of persuasion, who advocates an attitude position with a certain quality of argument coupled with a
peripheral cue. As the fourth external variable, distraction stands for all variables that affect elaboration ability, and as the fifth external variable, personal relevance
stands for all variables that affect elaboration motivation. From the left, one inner input variable (value orientation) affects information processing. The processes
represented in the model do not change this variable.
Personal relevance, distraction, and value orientation
have been named for illustrative purposes only. These
variables should be described in a broad sense as motivation variables (personal relevance), ability variables
(distraction), and bias variables (value orientation). The
many-sided role of peripheral cues is not explicit in the
model in full detail. In the model, peripheral cues can influence attitude change and elaboration likelihood. Although not carried out in the model, it is indeed possible
for these cues to also function as bias variables, and, if
scrutinized, they can serve as arguments.
The dimensions of all variables lie between 0 and 10.
Two-poled variables such as attitude, value orientation,
peripheral cue, argument quality, and bias position thus
have a point of indifference at 5, whereby 10 and 0 represent an extremely positive and extremely negative
value, respectively. The values of the one-poled variables such as personal relevance, distraction, elaboration motivation, elaboration ability, and elaboration
likelihood are minimal at 0 and maximal at 10.
In the following we describe the transition functions
in qualitative fashion. The complete algorithms can be
found in the Appendix. We used Petty and Cacioppo' s
figure (1986b, p. 135), with some of our own additions,
to fine-tune the functions.
Block 1. Elaboration motivation is determined
by personal relevance. With increasing personal relevance, the individual's motivation to process increases.
Personal relevance stands for all possible variables that
can influence elaboration motivation.
Block 2. Elaboration ability is determined by (attitude-consistent) knowledge and distraction. By
knowledge we mean the amount of objective knowledge as an ability-promoting variable (Petty, Priester,
& Wegener, 1994; Wood, Rhodes, & Biek, 1995). Distraction reduces, as its value increases, the ability-promoting effect of knowledge in an increasing manner.
Distraction stands for all possible variables that can affect elaboration ability.
Block 3. Elaboration likelihood is calculated by
multiplying the variables elaboration motivation and
elaboration ability (core Statement 1). This multiplicative relation guarantees that no elaboration arises
when motivation or ability, or both equal zero. For example, arguments presented in a foreign language can
not be processed even when there is motivation to do
so. Elaboration likelihood is influenced further by the
peripheral cue of the source. A highly positive peripheral cue has an enhancing effect, whereas a lowly negative peripheral cue has a reducing effect on elaboration likelihood. This influence is at its maximum with
moderate values of elaboration likelihood and decreases continuously at both higher and lower values
(core Statement 6).
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SIMULATING THE ELM
Block 4. A bias position is calculated from the attitude and value orientation of the individual (core Statement 3). The formula considers the direction of the bias
as well as the amount of bias. Stahlberg and Frey (1993,
p. 346; see also Johnson & Eagly, 1989) pointed out that
bias does not refer only to an individual's own attitude
position, but rather also to the underlying value orientation. Value orientation stands for all variables that can
produce bias. These may of course also include peripheral cues (such as mood, expertise of the source).
Block 5. The central factor is made up of both objective and biased processing (core Statement 3). This
additive relation, not made explicit in the ELM, is our
own assumption. Strong arguments bring about attitude
change in the intended direction, whereas weak arguments have a counterproductive effect ("boomerang effect" in Petty & Cacioppo, 1984, p. 70). Biased processing is manifested in such a way that a bias factor
overlays objective processing. When the bias position
of the individual and the attitude advocated by the
source are both favorable or both unfavorable, the central factor is increased by the bias factor in accordance
with the argument quality of the source. When the positions of the individual and the source diverge, the bias
factor has a reducing effect on the central factor.
How strongly the central factor will be overlaid by
the bias factor depends on the bias position of the individual. If this is moderate, the bias strength is minimal,
but it increases continuously both up and down from the
middle point. Thus, the more extreme the bias position
is, the more strongly the individual will counter-argue
communications opposing his or her positions and
cognitively bolster congruent messages (Petty &
Cacioppo, 1986a, 1986b; Stahlberg & Frey, 1993).
Block 6. The peripheral factor gains, through a
highly positive peripheral cue of the source, a positive
value, whereas a lowly negative peripheral cue results
in a negative value (core Statement 4).
Block 7. An individual's attitude after persuasion
results from the addition of the previously held attitude
(attitude, -; t - 1 = prior to persuasion) and the attitude
change generated by persuasion. For attitude change,
first the difference between the attitudes of the source
and the individual is calculated. Although the ELM offers no clear basis for assuming that the difference of
the attitudes represented affects attitude change, other
conceptions of attitude change most certainly do
(Festinger, 1950; Nemeth & Endicott, 1976; see also
Sherif & Hovland, 1961).
Thus far, attitude difference expresses the extent
and direction of attitude change. The final change in attitude is then calculated from the factor that is composed of the central and peripheral factor. If an
individual's elaboration likelihood reaches a degree of
8, for example, attitude change will be determined to
80% by the central factor and to 20% by the peripheral
factor (core Statement 2). That is, peripheral and central factors show additive interference.
Block 8. This block introduces a change of argument quality in the recipient in relation to his or her processing of the arguments of the source. This introduction is an extension of the ELM, although it can be
derived from comments made by Petty and Cacioppo
(1986b). The arguments of the source become integrated into one's own argumentation schema in dependency on the degree of elaboration likelihood (Petty
& Cacioppo, 1986b, p. 128). The calculation of argument quality changed by persuasion is performed in
analogy to that of attitude change. To prior argument
quality (argument quality, 1), in proportion to elaboration likelihood, a central portion and a peripheral portion are added. The central portion increases argument
quality of the individual only when the argument quality of the source is higher than that of the individual
(core Statement 5). Even if peripheral processing dominates, we assume that there can also be a change in the
argument quality of the individual, because central processing is of course never totally absent. Through the
peripheral part, the argument quality of the individual
can not only be enhanced, but also-where argument
quality of the source is low-even reduced. This is an
addition to the ELM (and not substantiated on that basis) that expresses our own assumption that relatively
uncontemplated acceptance of weak arguments can impair one's own argument quality. The fact that poor arguments can still be processed is due to the fact that
there usually exists a certain portion of elaboration.
Block 9. In our model, we assume that there is a relation between argument quality and an individual's
knowledge. This assumption represents an extension of
the ELM that is based on a statement by Petty and
Cacioppo (1986b, pp. 166 & 169), whereby prior knowledge serves cognitive argumentation against persuasive
messages. This means that individuals develop their arguments from the knowledge they possess. This allows us to
conclude that the individual's argumentation schema is
integrated into his or her knowledge (similar considerations are found in Petty et al., 1994). Through the conception of a relation between argument quality and an individual's knowledge, knowledge-as an ability factor in
elaboration likelihood-becomes dynamic. Knowledge,
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MOSLER, SCHWARZ, AMMANN, & GUTSCHER
therefore, and thus also elaboration likelihood, can
change in the course of social influencing. This is a new
extension of the ELM that, although inherent in the theory, is revealed only as a result of the modeling.
The consequences of the type of elaboration (core
Statement 7) were not modeled separately. Nonetheless, as we show in the simulation experiments, resistance arises through iterative influencing.
The model as presented here does not represent the
full, multifaceted ELM. Instead, it focuses mainly on
central elements of the ELM and adds some extensions. It is thus a model that is based on the ELM and,
in parts, goes beyond it.
The algorithms for the computer progi m were designed in accordance with the previous formulations of
the relating functions.
Testing the Simulation Model
Testing of a computer simulation model is principally concerned with comparison of the simulation
model to reality. Here the experimental settings of published studies are "translated" and entered into the simulation. Then simulation results are compared to the
original study findings (Whicker & Sigelman, 1991). In
the following, the various elaboration processes of the
ELM will be compared to empirical findings. To do this,
no other changes may be made to the simulation model.
The model, in the form presented, must replicate study
findings. Complete quantitative replication is not possible, however, because in any study there will be missing
information on some variables. There are possible measurement errors to consider as well. Replication of a
study by the simulation model is then considered successful if the pattern of original findings can be replicated. First the basic types of elaboration, peripheral
processing and central objective processing, and then
central biased processing, will undergo testing. Peripheral processing versus central objective processing will
be tested with a study by Petty, Cacioppo, and Goldman
(1981), and peripheral processing versus central biased
processing will be tested with a study by Wood,
Kallgren, and Preisler (1985). The studies were selected
according to two criteria. First, they had to be sensitive
to one type of elaboration or a double combination of
elaboration types (see previously). Second, not only the
values of dependent variables but also values of independent variables must be stated or at least described
with sufficient precision to allow for realistic estimates.
Peripheral Processing Versus Central
Objective Processing
The study by Petty et al. (1981; also Petty &
Cacioppo, 1986b, p. 154) serves as the first for repli-
cation. The study investigated the effect of personal
relevance on the likelihood of information processing. To persuade students of the advantage of midterm examinations, strong (statistically founded) and
weak (quotations, personal opinions) arguments were
presented, which in turn were made by a source of either high (Carnegie Commission on Higher Education) or low (students') expertise (see Petty et al.,
1981). In addition, either high (examinations will be
instituted the following year) or low (examinations
will be instituted in 10 years) personal relevance was
triggered. A 2 x 2 x 2 analysis of variance (Personal
Relevance x Expertise x Argument Quality) revealed
attitude changes with low personal relevance due to
expertise (peripheral cue) and with high personal relevance due to argument quality.
Petty and Cacioppo (1986b, p. 154) presented
graphs of the results of the four different experimental
situations. To be able to compare the findings with
simulation results, they must be transformed into a
representation of temporal course, or progression. In
a representation of the temporal course of influencing, the manipulation in the studies can be seen as the
state prior to and following the persuasion. In the simulation, this corresponds to a calculation cycle or one
run, respectively. A temporal course representation
requires, however, that we know the participants' initial attitudes. Because the study is presented by Petty
and Cacioppo (1986b) as an example of objective
processing, it can be assumed that the initial attitude
of the participants was indifferent, which in Figure
2A is the value 0. The individual takes an objective
and thus indifferent attitude position having the value
5. For argument quality, a value of 5 is also given, as
we can assume average knowledge. As there was no
distraction in the study by Petty et al. (1981), it is assigned the value 1. The source of persuasion advocates total support of a position so that the attitude
takes on the value of 10. We had to estimate values for
attitude position and distraction of the individual as
well as attitude position of the source, because they
were not provided by the study. Otherwise, we followed the principle of using all values given in the
study for the simulation, except for personal relevance. Although values are available for personal relevance (high-involvement M = 5.5; low-involvement
M = 2.7; scale range 1-11), both values lie on the
same side of the scale. This means, with reference to
the scale, that they do not differentiate between high
and low personal relevance. The simulation produces
differing results only if the input values do not lie on
the same side of the scale. Personal relevance thus
takes on the values 9 or 1 for the simulated individual
in the different conditions. This is the one and only
time that we do not adhere to the principle of using the
values as measured in the study.
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SIMULATING THE ELM
A: Temporal Course Representation of Study Findings
1: Low Personal Relevance
a:
E
11: High Personal Relevance
0.60.40.2o
a)
0
-0.6
Before
After
Before
Experiment
After
B: Computer simulation results
1. Low Personal Relevance
+
11. High Personal Relevance
6.5 -
6-.
a5.5
-
3.50
1
0
Runs
-
Strong Arguments
--A-- Weak Arguments
Positive Periph. Cue
Positive Periph. Cue
- ---- Strong Arguments
- -,A- - Weak Arguments
Negative Periph. Cue
Negative Periph. Cue
Now the model is tested using the previous values.
The results of the simulation reproduce in principle the
findings of the Petty et al. (1981) study (cf. Figure 2B
with Figure 2A).
The criterion for successful replication of experimental findings is that all relations of the curves to one
another must correspond. This means that a curve that
in the experiment runs lower than another should not,
in the simulation, run higher than the other. The pattern
of the findings must be replicated in principle. It would
be unrealistic to require more exact correspondence,
because in the real-life experiment participants are influenced by a number of additional factors that, in the
simulation, are eliminated. For this reason, asymmetries in the original Petty et al. (1981) findings cannot
be seen in this simulation. Qualitatively, however, the
simulated attitude changes with peripheral processing
(Figure 2B, Panel I) show the same wide spread as
found in the study (Figure 2A, Panel I). Attitude
changes through central, objective processing (Figure
2B, Panel II) are quite a good replication of the split of
the results in the study (Figure 2A, Panel II).
Peripheral Processing Versus Central
Biased Processing
Figure 2. A: Temporal course representation of the findings by
Petty, Cacioppo, and Goldman (1981). When personal relevance
is low, participants change their attitudes in analogy to the peripheral cue. When personal relevance is high, attitude change is
determined by argument quality. B: Replication of findings of
the study by Petty et al. (1981) with computer simulation of the
ELM. Here the values are shown of an individual who is influenced by a source.
For the following variables, exact values can be
taken from the study. The values for argument quality
and peripheral cue were determined on a scale ranging
from 1 (not very strong arguments) to 11 (very strong
arguments). By means of a simple transformation (-1)
along the dimensions (0-10) of the simulation model,
the following values of the variables result. For argument quality, strong arguments have a mean value in the
study of 8.9 transformed to 7.9, and weak arguments
have a mean value of 4.5 transformed to 3.5. For peripheral cue, the mean value of a high peripheral cue is 6.8
transformed to 5.8, and a low peripheral cue has a mean
value of 5.7 transformed to 4.7. Finally, for the simulation, the values of 8 or 3 are taken for argument quality,
whereas for peripheral cue the values are 6 or 4.1
Linear scale transformation is not unproblematic, as the number
of categories can influence the results measured (Wedell & Parducci,
1988). Henss (1989), however, was able to demonstrate that results
from different rating scales can very well be linearly transformed into
each other. Kantowitz (1992) even found that linear transformation
for interval scales is the only permissible one.
The basis for replication with regard to biased processing is a study by Wood et al. (1985; see also Petty
& Cacioppo, 1986b, p. 169). Wood et al. (1985) investigated the effect of strong or weak arguments for a not
environmentally friendly attitude on individuals having high or low prior knowledge and, on the average,
very environmentally oriented attitudes. In this study it
was found that individuals with much prior knowledge
reacted more strongly to strong and weak arguments
than individuals having little prior knowledge (Figure
3, Panel I). After persuasion, both groups with low
prior knowledge moved their average attitude closer to
the propagated not environmentally friendly position
than the two groups with higher prior knowledge.
This led Petty and Cacioppo (1986b) to conclude
that the groups having high prior knowledge were able
to cognitively counterargue the persuasive communications, thus engaging in biased processing. This was
revealed particularly by the fact that weak arguments
were less effective, as they were easier to discount than
strong arguments.
It is important to distinguish between what biased
elaboration allows and what bias causes. Thus, in our
simulation model-and in agreement with Biek,
Wood, and Chaiken (1996)-knowledge as such does
not lead irrevocably to biased processing. Only where
there is a sufficient discrepancy between the bias position of the recipient and the source attitude does
knowledge enable the recipient to engage in biased
processing. If the recipient and the source hold similar
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MOSLER, SCHWARZ, AMMANN, & GUTSCHER
1. Study Findings
9.
8.5 8 7.5 7 6.5 6-
~
~
---o--- Weak Arguments
Strong Arguments
~
I~~~~~~~~~~~~~~~~~~
Low
High
Knowledge
.4J
4-J
4-J
11. Temporal Course
Representations
Ill.
Computer Simulation
Results
98.5 8 7.5 7 6.5 6 -
In addition, we tested the simulation using a study
by Lord, Ross, and Lepper (1979) and a study by
Chaiken and Maheswaran (1994). Due to space considerations, however, the tests are not presented here.
We refer the reader to Ammann (1997). Simulation
replicated the patterns of the findings in all these studies without a single change to the parameters of the
model. We thus ascertain that studies on attitude
change on the basis of the ELM can be replicated using
the simulation model. This justifies the assumption
that the simulation model adequately represents the
processes described in the ELM.
Simulation Experiments
After
Before
Experiment
a
High Knowledge
Strong Arguments
High Knowledge
Weak Arguments
0
1
Runs
--
-
Low Knowledge
Strong Arguments
Low Knowledge
Weak Arguments
Figure 3. Effect of argument quality with low or high issue-relevant knowledge (Wood, Kallgren, & Preisler, 1985). Panel I
shows the recalculated values of the illustration from Petty and
Cacioppo (1986b). Persons having much knowledge are influenced by argument quality in contrast to those with little knowledge. Panel II is study findings presented as a temporal course.
Wood et al. (1985) tapped the average attitude of participants before persuasion. (The data were transformed to the scale used in
the simulation.) Panel III is computer simulation results.
attitudes, knowledge allows the recipient to engage in
objective central processing.
Here again, the comparative presentation of end
values from Petty and Cacioppo (1986b, p. 169) has
to be transformed into a representation of the temporal course (Figure 3, Panel II). The required initial position of the participants is derived from the average
measured in the original study by Wood et al. (1985).
Note that the direction of persuasion, in contrast to the
study previously mentioned, descends (in the direction of not environmentally friendly; value of 0). The
initial values of the variables are derived in analogy to
the previous example in that values measured in the
study by Wood et al. (1985), on a scale from 1 to 15,
are recalculated to the simulation scale of 0 to 10. The
derived values of the variables are now entered into
the simulation already tested with regard to peripheral
and objective processing. The distribution of the results of the simulation (Figure 3, Panel III) accords almost exactly with the basis for replication (Figure 3,
Panel II) in Petty and Cacioppo (1986b; and Wood et
al., 1985).
On the basis of the tested simulation model we can
now conduct experiments that have reliable
meaningfulness. Here we make use of the advantages
of simulation by investigating dynamic processes of
attitude change. This means that we examine the
changes "in the individual" under the condition of repeated influence, starting from a just-produced state.
This is quite a divergence from the frequently applied
type of experimenting in which a unique or single instance of iterative influence occurs and then attitude
change in the influenced person is measured. In the following experiments, influence occurs and then the
changes in the model variables of the simulated individual are established after one run-through of the
model. The changed variables are now the new starting
values of the simulated individual who will be subject
to another instance of influencing, and so on. These dynamic changes more adequately represent reality than
presentation of multiple influences all at once and then
measurement of change. This is because we can assume that in reality there must be changes in the individual after each single persuasive influence. The
simulation experiments, moreover, have the advantage
that they are investigations within participants and are
not, as in so many studies, investigations between participants. With simulation we can, as a particularly
special feature, follow inner changes in the individual
(such as elaboration likelihood), whereby the external,
measurable attitude changes can be interpreted more
clearly. Here is a source of interesting research
heuristics. It must be understood that in the case of
simulation, we are dealing with hypotheses that remain
to be verified empirically. The ultimate proof is
whether primary data can produce the simulated pattern under the appropriate conditions.
In the following experiments, simulated individuals
are subjected to the same influence 10 to 35 times, in
the sense that they, for instance, see or hear the same
commercial everyday for 2 to 5 weeks. In our simulation experiments, we seek those conditions under
208
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SIMULATING THE ELM
which U-shaped attitude changes arise as the consequence of repeated influence. Simulation Experiments
1 through 6 analyze the reasons for such U-shaped
courses of attitude change. The analyses provide the
grounds for a concept of two-phase processing. Again,
we emphasize that in the following experiments the
only processes in effect are those described in the
ELM. Factors such as boredom or tedium are not taken
into account, even though in reality they can certainly
play a role. We will show, however, that (inverted)
U-shaped attitude changes can be explained solely on
the basis of the processes described in the ELM. The
last simulation experiment is based on the experiments
of Petty and Cacioppo (1984) and Petty et al. (1981).
We show that there may indeed be conditions under
which, despite iterative influence, no U-shaped attitude changes occur in the individual.
A: Simulation 1
Agreement/
High Elaboration 7
Likelihood
Elaboration
a0
Likelihood
in
2c
a0
Disagreement/
Low Elaboration 2
0
Likelihood
Attitude
5
10
15
20
Runs
.a
25
30
35
B: Simulation 2
4
Agreement/
High Elaboration 7
Likelihood
3
'~---
Elaboration
-------- Likelihood
c
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Attitude
4@
Disagreement/
Low Elaboration 2Likelihood
0
5
Simulation Experiments 1 to 4
Our assumption, that in reality there must be
changes in the individual after each single influence,
can be tested for plausibility using simulation experiments. Our simulations with multiple runs show that
processing of information may be explained by the fact
that recipients process repeated messages peripherally
and centrally, one type of processing after the other,
whereby the order of the processing types may vary.
Simulations 1 and 2 (depicted in Figure 4, Panel A and
B) show clearly that increasing rejection of the advocated position can be the result of either a negative peripheral cue or weak argument quality of the source. If
elaboration likelihood decreases, negative peripheral
cues will determine rejection in the second phase (see
Figure 4, Panel B). However, if elaboration likelihood
increases, rejecting attitudes will be determined in the
second phase by the weak arguments (see Figure 4,
Panel A). Simulations 1 and 2 thus illustrate that the
course of attitudes in an inverted U-shaped curve, with
definite rejection of the source attitude, can be explained exclusively in terms of a decrease or increase
in central processing by the recipient. This certainly
appears plausible if we consider some everyday examples. In the one case, the recipient begins to centrally
process the content of the repeated communication
more intensively and then establishes that the argument is weak. In the other case, through time the recipient is no longer able to elaborate the content of the
message and bases rejection of the advocated position
simply on the negative cues.
Simulations 3 and 4 (depicted in Figure 5, Panel A
and B) show further that message repetition must not
inevitably lead to an increase in acceptance in a first
phase, with a decrease in acceptance in a second. The
reverse order is also observed within the simulation;
15
10
20
25
30
35
Runs
Simulation 1 (Panel A):
Source: Attitude = 10, Positive Cue = 6, Low Argument Quality = 3
Recipient: Low Prior Knowledge = 1
Simulation 2 (Panel B):
Source: Attitude = 10, Negative Cue
Recipient: High Prior Knowledge = 9
=
3, High Argument Quality = 7
Figure 4. Simulation 1 (Panel A) shows that adapting to the superior argument quality of the source, the recipients increase
their elaboration likelihood. In the first phase the positive peripheral cue elicits agreement with the advocated position,
whereas in the second phase the elaboration likelihood has grown
enough to elicit disagreement due to weak message arguments.
Simulation 2 (Panel B) shows that adapting to the inferior argument quality of the source, the recipients decrease their elaboration likelihood. In the first phase the strong message arguments
cause agreement with the advocated position, whereas in the second phase, due to the decreased elaboration likelihood, the negative peripheral cue can cause recipients' disagreement. The steep
drop of elaboration likelihood in the first run in Simulation 2 is
caused by the negative peripheral cue.
namely, in a first phase the recipients increasingly distance themselves from the advocated position only to
accept it in a second. With increasing elaboration likelihood, the strong arguments of the source will be decisive for the recipient's acceptance, although at the
start-with low elaboration likelihood-the recipient
held a rejecting attitude due to the negative cues (cf.
the simulation depicted in Figure 5, Panel A). With decreasing elaboration likelihood in the recipient, the recipient will agree with the message if there are positive
cues. This occurs despite the fact that the recipient
originally rejected the position of the source due to
weak arguments (cf. Figure 5, Panel B). A comparison
of Simulations 1 and 3 (depicted in Figures 4 and 5,
Panel A) reveals, by the way, that superior arguments
of the source do not-in spite of the triggered increase
in central processing-guarantee acceptance in all
cases. If the superior source arguments are weak (value
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MOSLER, SCHWARZ, AMMANN, & GUTSCHER
A: Simulation 3
Agreement/
High Elaboration
Likelihood
c
10-
Attitude
9
8-
<D-C
~--
7-
~~;/ Elaboration
Likelihood
6-
54-
.L
O
Rn
m
2-
1
0
Disagreement/
Low Elaboration
Likelihood
x
(
10
15
20
Runs
25
30
35
B: Simulation 4
Agreement/
High Elaboration 10 Likelihood
9
0
8, i
6-
ic'1: 2c
S-
a0-
3
Attitude
-4X
Disagreement/
Low Elaboration
Likelihood
Elaboration
Likelihood
-
2-
17
0
C0
5
10
20
15
25
30
to the transition functions in Blocks 2 and 9. On this
new basis, the dynamics of attitude change could then
be investigated using our simulation model.
It is important to note that it is not the fact of multiple
runs in and of itself that produces attitude change. It is
rather the variation of elaboration ability, or elaboration
likelihood, in dependency on the quality of the information received that is responsible. On the basis of this systems-theoretical formalization, according to which the
recipient's knowledge adapts to the argument quality of
the source over time, particular dynamics are found that
may represent the possible patterns of two-phase attitude change. The effects shown in the simulation experiments were not built into the simulation model
explicitly but rather are the result of an ELM now made
dynamic. The effects of the dynamization are revealed
when we, with the aid of the computer, allow the simulation model to run along a discrete time axis.
35
Runs
Simulation 3 (Panel A):
Source: Attitude = 10, Negative Cue = 2, High Argument Quality = 1Recipient: Rather Low Prior Knowledge = 4
Simulation 4 (Panel B):
Source: Attitude = 10, Positive Cue = 7, Low Argument Quality = 2
Recipient: Medium Prior Knowledge = 5
Figure 5. Simulation 3 (Panel A) shows that adapting to the superior argument quality of the source, the recipients increase their
elaboration likelihood. In the first phase the negative peripheral
cue causes disagreement with the advocated position, whereas in
the second phase the elaboration likelihood has grown enough for
eliciting agreement due to strong message arguments. Simulation
4 (Panel B) shows that adapting to the inferior argument quality of
the source, the recipients decrease their elaboration likelihood. In
the first phase the weak message arguments cause disagreement
with the advocated position. In the second phase, however, the
elaboration likelihood is low enough that the positive peripheral
cue can cause recipients' agreement. The steep drop and climb of
elaboration likelihood in the first run in Simulation 3 and 4 are
caused by the negative, or positive, peripheral cue.
< 5), recipients reject them all the more firmly the longer that they process them centrally.
In sum, Simulations 3 and 4 reveal that not only the
more familiar inverted U-shaped curve but also the U
pattern can be the result of decreasing or increasing
central processing. Yet why does the central processing in recipients change in our simulations? According
to our simulation model, the recipient profits from the
source if arguments are superior. If this is the case, then
the elaboration ability rises and central processing increases. If the source arguments are inferior, the recipient's ability to evaluate the message falls, and with it
central processing. We grant that currently these are
just hypotheses. Should experimental data become
available that explain the relation between elaboration
ability in the recipient and the quality of the information received, the simulation model can be adapted relatively easily. The only modifications required will be
Simulation Experiments 5 and 6
The dynamics of attitude change depend not only on
the quality of information received but also on the recipient' s prior knowledge and initial attitude. In the experiment by Wood et al. (1985), participants showed
biased central processing because the not environmentally friendly position advocated was contrary to the
recipients' pro-ecological attitudes. In Simulations 5
and 6, recipients' prior knowledge was varied from the
values 1 to 10 with increments of 1. In Simulation 5
(see Figure 6, Panel A), the recipients' initial attitudes
have a value of 7, and for this reason, processing is biased toward the advocated position.
If the prior knowledge of the recipients is greater than
or equal to 4, processing of the message is one phase, central, and biased. However, if the recipients have little
prior knowledge (less than or equal to 3), processing
takes place in two phases. The message is first processed
peripherally and then centrally. Due to the low level of
knowledge, processing is at first peripheral, and the negative cue causes rejection of the advocated position. Only
when central processing increases does the recipient
agree with the advocated position thanks to strong message arguments. In analogy to the variations of prior
knowledge in the Wood et al. (1985) experiment mentioned previously, in Simulation 6 recipients' prior
knowledge is also varied; moreover, recipients' initial attitudes as in the Wood et al. (1985) experiment-are
contrary to the advocated position (see Figure 6, Panel
B). The initial values used in Simulation 6, however, are
not identical with those in the Wood et al. (1985) experiment. This means that Simulation 6, in terms of the result
space, is located in the neighborhood of the experiment
carried out by Wood et al. (1985), because recipients are
subjected to the same conditions in terms of content but
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SIMULATING THE ELM
A: Simulation 5
Recipients' Prior Knowledge
Petty and Cacioppo (1984) and Petty et al. (1981),
Simulation 7 shows a 2 (strong vs. weak argument) x 2
(positive vs. negative peripheral cue) factorial design.
Here the subject matter of the persuasion has strong
personal relevance for the recipient, and the influence
10
Agreement 10
827
Recipients' Prior
Knowledge= 1
' 4
3
2
1
Disagreement
0
0
2
6
4
8
10 12 14 16 18 20
Runs
B: Simulation 6
Agreement 10
-Recipients'
Prior
Knowledge
10
27
t65-
14
Recipients'
Knowledge
2
Recipients'
1
Knowledge
Prior
5
Prior
1
Disagreement 00
2
8 10 12 14 16 18 20
Runs
Simulation 5 (Panel A) and 6 (Panel B):
Source:
Attitude = 10, Negative Cue = 3, High Argument Quality 8
Recipient:
High Personal Relevance = 9
4
of the source remains constant throughout all 10 runs
(see Figure 7).
Recipients' Prior
Knowledge 4
Z
6
Figure 6. Simulations 5 (Panel A) and 6 (Panel B) demonstrate
that for the question of whether the recipient will show one-phase
or two-phase processing and accept or reject the advocated position, both prior knowledge and initial attitude position are significant. Recipients who have a supporting initial position process
the message in two phases only if their prior knowledge is low
enough. Recipients who initially disagree with the advocated position elicit a two-phase processing only if their amount of prior
knowledge is medium.
to different conditions in terms of numerical values. In
Simulation 6 (depicted in Figure 6, Panel B), recipients
show two-phase processing (i.e., first peripheral and then
central) only when their prior knowledge is medium and
their own arguments have a value of 5, respectively. If
prior knowledge of the recipients is varied by value of 1,
we observe that when prior knowledge of the recipients is
less than or equal to 4 or greater than or equal to 6, processing is one phase. If recipients have little prior knowledge (less than or equal to 4), the message is processed
only peripherally, whereby the recipients definitively
take on a position contrary to the source attitude because
of the negative cue. If recipients have comparatively high
knowledge, and their argument quality is greater than or
equal to 6, processing is exclusively central and biased.
Here the recipients take on the source attitude because of
the strong arguments the source presents.
Simulation Experiment 7
The final simulation experiment is an adaptation of
two classical experiments in the literature. Following
As a consequence of this high personal relevance, the
primary influence lies in the arguments brought forward
for accepting or rejecting the message position. However,
the value of the peripheral cue also has an influence on
the course of the attitude curve. Where arguments are
strong, if a negative instead of a positive peripheral cue is
presented recipients show a comparable degree of agreement only at a later time (compare Curves 1 and 2 in Figure 7). If arguments are weak, a positive peripheral cue
can postpone complete rejection by two runs (compare
Curves 3 and 4 in Figure 7). Simulation 7 reveals that the
influences of a persuasive argument and a peripheral cue
become stronger the longer that recipients are subjected
to them. Yet after nine runs, the recipient's attitude
changes hardly at all. That is, message repetition does not
necessarily lead to a course of attitudes that is an inverted
U-shaped curve. Apparently, in addition to message repetition, other conditions must be fulfilled for such a phenomenon to occur (see simulation Experiments 1 to 4
depicted in Figures 4 and 5).
The curves representing elaboration likelihood in
Figure 7 show the extent to which elaboration ability
and elaboration likelihood are determined by the recipients' prior knowledge and the argument quality of the
source. Elaboration likelihood increases over time
only when strong arguments are presented. Only then
are the persuasive arguments superior to the recipients'
knowledge. Increases and decreases of elaboration
likelihood over time are not affected by the peripheral
cue. Its valence is only responsible for whether or not
at the very start of the simulation the recipient increases or decreases his or her elaboration likelihood.
Discussion
We now examine the gains in findings but also some
problems with our simulation method. The discussion
traces our steps in procedure and concludes with an outlook toward opportunities for further development.
Core Statements
Each social psychological theory has components
and further developments too numerous to be examined
by simulation completely and in a differentiated manner. For this reason it is necessary to seek out, in a conservative manner, the most essential statements and
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211
MOSLER, SCHWARZ, AMMANN, & GUTSCHER
Agreement 1 0-
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Simulation 7
Source: Attitude = 1 0
- 1: Strong Arguments = 8, - -A-- 3: Weak Arguments = 3,
Positive Source Cue = 6
Positive Source Cue = 6
-a--- 2: Strong Arguments = 8, - h-- 4: Weak Arguments = 3,
Negative Source Cue = 4
Negative Source Cue = 4
Recipient:
Medium Prior Knowledge = 5, High Personal Relevance = 9
Figure 7. Simulation 7 shows attitude change over time (runs) in
dependency on argument quality of the source and peripheral
cue. The personal relevance to the recipient is high, and his or her
prior knowledge is medium. Participants who receive strong (vs.
weak) arguments agree with the message and increase the
amount of their elaboration likelihood.
classes of findings of a theory. There are no general
rules or guidelines for doing this. Just how it is that each
researcher reaches a certain body of core statements will
always be somewhat unclear. However, the advantage
of setting up core statements is that the theory base of a
simulation model becomes transparent. The ELM is relatively easy to express in the form of core statements, as
the theory has already been presented in the form of postulates. Even so, we cannot assume that the entire research community will agree with our formulation of
the core statements of the theory. This is the reason why
we have clearly stated the limits of our theory statements. The exact contents of a theoretical area that form
its true core will always remain the subject of scientific
discussion. It is possible that the very process of agreeing on core statements will give new impetus to theory
development in social psychology.
Modeling
When formalizing the core statements of a theory,
the variables, the relationships of effect among them,
and the type of those effects-the transition functions-are defined completely within a model. A sym-
bol system (see Ostrom, 1988, for a discussion of symbol systems) was used in our model that, although as yet
rather new to the field of social psychology, allows the
causalities postulated by a theory to be represented
clearly and in overview. Representation uses a combination of graphic, linguistic, and mathematical means.
With a complex theory such as the ELM, systems-theoretical modeling has a clear advantage over descriptive
formulation in words. The interaction among variables
is defined unambiguously and may be grasped at a
glance. For example, the dual role of peripheral cues as
complex factors becomes clearly evident. These factors
have a reinforcing effect on elaboration likelihood on
one hand and on attitude change on the other. Modeling,
however, also reveals theoretical implications of the
ELM that were not previously obvious. Much more evident than when formulated verbally is the change in persuasive influence dependent on elaboration likelihood
and resistance, which results from the feedback of persuasive strength, as a consequence of elaboration: With
high elaboration likelihood, the arguments of the source
become integrated into the recipient's store of knowledge. Thus, with improving elaboration ability, elaboration likelihood increases. Then the recipient's bias
position, based on his or her value orientation, plays a
stronger role, which leads to increased resistance to
noncongruent arguments. On the background of our
model we offer the following possible alternative explanation, which is also strongly supported by Wood et al.
(1995): Prior knowledge may increase elaboration likelihood, so that participants engage in more central processing. Yet if participants' initial positions oppose the
persuasive communications, they process centrally, although with a bias.
The model reproduces the entire causal net of what
were previously individual ELM concepts standing side
by side. This makes it easier to discover the points at
which extensions to the theory are needed. In addition,
theory comparisons can now be carried out, such as with
the HSM (Chaiken, 1980, 1987; Eagly & Chaiken,
1993, pp. 326-346), in that for these theories too, block
diagrams can be set up and direct comparisons made.
Testing
Testing serves to determine the extent to which
simulation reflects real processes. To do so meaningfully, our simulation must be capable of making reliable predictions of the expected behavior of
individuals under specified conditions. Our method
of testing consists in comparing the reactions of our
simulated individuals to stimuli with the experimental and statistical observed reactions of real participants in experiments. It can be argued that the true test
of the simulation model would consist in predicting
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SIMULATING THE ELM
the findings of real experiments. This is indeed so, because only in a first step does it make sense to investigate whether the findings of well-known studies can
be replicated. The next steps in testing should not be
taken by our team, for we could be accused of knowing the results beforehand. We invite other researchers to take data unfamiliar to us and to calculate their
results using our model. This can be done easily by
hand. Deviations of real results from simulation
model results might require that we make changes to
the simulation model. However, this is the normal
route in the building of a theory: New findings inconsistent with a theory must be integrated by means of
expansion of the theory. The more findings that can
be replicated by our model, the greater its explanatory
power. The greatest problem in testing is the insufficient database. Many experiments fail to give important information on variable values, so that they may
only be estimated after the fact. The research community is urged to tap and document all relevant variables in a theoretical paradigm. In addition to the
input variables in a model, it would be very important
to measure changes in output variables over time
(e.g., attitude position), as well as changes in hypothetical inner variables, such as elaboration likelihood and bias position. Experiments using repeated
persuasion must take measurement of the variables
after each presentation. We are aware that our demands would entail considerable methodological difficulties, such as the problem of the reactivity of
measurement. Still, we believe that efforts in this direction would yield advances in more valid formulations of social psychological theories.
Simulation Experiments
As can be seen in our simulations, the influence exercised by the argument quality of the source and the
source cue is dependent on the prior knowledge and the
elaboration likelihood of the recipient. Our simulations
also showed that apparently, in addition to message
repetition, other conditions must be fulfilled for
two-phase attitude generation to occur. Petty and
Cacioppo (1986b, p. 143) explained the U-shaped
curve of attitude generation as arising from the opportunity given by message repetition in a first phase for
the recipient to elaborate the validity of the message
until its implications are known. Once the recipient has
processed the validity of the arguments sufficiently,
and further message presentations are given, a second
phase of processing begins. Petty and Cacioppo
(1986b) described this phase as characterized by phenomena such as tedium, reactance, and a lessening of
agreement by the recipient. Our simulations demonstrated that the U-shaped course of attitude changes
with message repetition can be explained on a purely
cognitive basis-that is, within the theoretical framework of the ELM. On the basis of the simulations discussed (see Figures 4 and 5), we know that initial
agreement can change to rejection with the repeated
presentation of a message. This can be explained
through either a decrease in central processing in
which there is a negative source cue, or an increase in
central processing in which arguments are weak. On
the other hand, initial rejection of the advocated position can change to agreement through either a decrease
in central processing in which there is a positive source
cue, or an increase in central processing in which arguments are strong. The explanatory approach to
two-phase processing gained through the simulations
does not, however, preclude the possibility-mentioned by Petty and Cacioppo (1986b, p. 143)-that
phenomena such as tedium or reactance may also result in two-phase processing. Cacioppo and Petty
(1979, p. 106) found it probable that their experiments
with message repetition would have shown one-phase
polarization effects if message positions had not been
supported by persuasive arguments. Our simulation
experiments make it clear that with message repetition,
whether a one-phase polarization effect or a two-phase
attitude generation with increasing and decreasing
agreement will occur, can depend on the interaction of
initial attitude and prior knowledge on the part of the
recipient (as shown in Figure 6). Thus, in the case of
message repetition, the addition of persuasive arguments alone does not always hinder the rise of
one-phase polarization effects, particularly if the recipient's prior knowledge is low (see Figure 6, Panel B).
The examples presented demonstrate that heuristics
for experiments with repeated influence can be designed on the basis of computer simulations and that
new interpretations of elaboration processes in the recipient can be proposed. Simulation-supported interpretations can be investigated in real experiments in a
between-participants design.
Going beyond the experiments presented here,
there is another way that U-shaped and inverted
U-shaped curves of attitude change might develop. If
there is a change in elaboration motivation or elaboration ability during the influencing process (due to
changing personal relevance or changes in distraction),
elaboration likelihood also changes. Through this, peripheral cues and argument quality take on varying influences on attitude change. We might even venture
the hypothesis that tedium is a motivational variable:
The more tedium the recipient experiences, the lower
the elaboration motivation. This would lead to decreased elaboration likelihood, and the process could
result in U-shaped and inverted U-shaped curves of attitude change. This argumentation explains the effect
of tedium on the basis of the effects of ELM.
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MOSLER, SCHWARZ, AMMANN, & GUTSCHER
Opportunities for Further
Development
Once a valid computer simulation has been constructed, it becomes possible to apply it to other phenomenon and research areas. One such area, for
example, is mutual social influence. Using a simple
dyad, we might examine how, and with what results,
two individuals influence each other according to the
postulates of the ELM. We might ask how the attitudes
of two individuals showing differing initial values
might change with multiple mutual influencing. If we
enlarge the group to include 3 or 10 individuals, we can
investigate group processes such as conformity, minority influence, and polarization. In a research project
currently in progress (see author' s notes), we have succeeded in generating these group processes using only
an ELM-based simulation, which has brought forth
completely new possibilities for explaining the emergence of the processes. With simulation, it is also possible to allow two groups of people to interact with
each other, which presents an opportunity to gain new
and simple insight into processes of social identity. We
have even proceeded to the further step of examining
processes of social diffusion at the level of populations, in a way similar to the simulations of Latane's
social impact theory (Latane, Nowak, & Liu, 1994;
Nowak et al., 1990). Here, fruitful hypotheses have
been generated with regard to the optimum design of
campaigns to change ecologically relevant behavior in
the public (Mosler, Ammann, & Gutscher, 1998).
In sum, we believe that a well-presented, comprehensible, and valid computer simulation provides a useful
impetus and tool for theory development and application
in psychology in a manner that is exploratory, yet always
well founded in model and theory. We hope with our report to fan the fires of discussion on computer simulation
so that simulation may mature to an established, equally
valued social psychological method.
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Appendix
Block
1:
Elaboration motivation
=
personal rele-
vance.
Block 2: Elaboration ability
(distraction / 10)).
=
knowledge
(1
-
Block 3: Intrinsic elaboration likelihood = (elaboration motivation * elaboration ability)1/2.
Elaboration likelihood = intrinsic elaboration likelihood + (((peripheral cue - 5) / 2) * (1 intrinsic elaboration likelihood - 51/ 5)).
Block_4: Bias position
tion) /2.
(attitude
=
+
value orienta-
Block 5: Central factor = [(source argument quality
5) / 2.5] + { [(5 - source argument quality) / 5 + C]
(Ibias position 51 / 5)2}.
if bias position > 5 and source attitude > 5 then C = +2
if bias position < 5 and source attitude < 5 then C = +2
if bias position > 5 and source attitude < 5 then C = -2
if bias position > 5 and source attitude < 5 then C = -2
-
-
Block 6: Peripheral factor = (peripheral cue) / 1.65.
Block 7: Attitude = attitudet
1
+
((source attitude
attitudet-1) / 3) (((elaboration likelihood / 10)
*
tral factor) + ((1
eral factor)).
-
*
-
cen-
elaboration likelihood / 1O) periph*
Block 8: Argument quality argument qualityt-I +
((elaboration likelihood / 10) * central portion) + ((1
elaboration likelihood / 10) peripheral portion).
Central portion = (source argument quality argument qualityt 1) / 10.
If source argument quality - argument qualityt 1 < 0,
then source argument quality - argument qualityt_ = 0.
Peripheral portion = (source argument quality argument qualityt_1) / 1O.
=
-
*
-
-
Block 9: Knowledge
quality knowledget 1).
=
knowledget-1
+
(argument
-
The algorithms for the computer program were designed in accordance with the preceding formulations
of the relating functions.
215
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