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A meta-analytic investigation of the processes underlying the similarity-attraction effect


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This research investigated two competing explanations of the similarity effect: Byrne’s (1971) reinforcement model and the information processing perspective. A meta-analysis of 240 laboratory-based similarity studies explored moderators important to the similarity effect, including set size, proportion of similarity, centrality of attitudes, and information salience. Results indicated effects for proportion of similarity, centrality of attitudes, and information salience, and were largely consistent with predictions of the information processing perspective. We discuss the implications of these findings for the two perspectives, for other models for the similarity effect, and for the role of affect and cognition in the experience of interpersonal attraction.
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A meta-analytic
investigation of the
processes underlying
the similarity-attraction
R. Matthew Montoya
and Robert S. Horton
This research investigated two competing explanations of the similarity effect: Byrne’s
(1971) reinforcement model and the information processing perspective. A
meta-analysis of 240 laboratory-based similarity studies explored moderators important
to the similarity effect, including set size, proportion of similarity, centrality of attitudes,
and information salience. Results indicated effects for proportion of similarity, centrality
of attitudes, and information salience, and were largely consistent with predictions of the
information processing perspective. We discuss the implications of these findings for the
two perspectives, for other models for the similarity effect, and for the role of affect and
cognition in the experience of interpersonal attraction.
similarity effect, interpersonal attraction, liking, reinforcement, cognition, affect
One of the most robust phenomena in attraction literature is the similarity effect (Byrne,
1997): Increased similarity with a target—with respect to attitudes, personality traits, or
a number of other attributes—is associated with increased attraction to the target. The
similarity effect has been observed in a multitude of different populations (e.g., Gaynor,
University of Dayton, USA
Wabash College, USA
Corresponding author:
R. Matthew Montoya, University of Dayton, 300 College Park, Dayton, OH 45469, USA
Journal of Social and
Personal Relationships
30(1) 64–94
ªThe Author(s) 2012
Reprints and permissions:
DOI: 10.1177/0265407512452989
1971; Murstein & Beck, 1972; Tan & Singh, 1995) and has been observed in relation to
personality traits (e.g., Carli, Ganley, & Pierce-Otay, 1991; Steele & McGlynn, 1979),
attitudes (e.g., Bond, Byrne, & Diamond, 1968; Byrne & Blaylock, 1963), hobbies
(Jamieson, Lydon, & Zanna, 1987), and values (Davis, 1979; Lewis & Walsh, 1979),
among other attributes (e.g., Gillis & Avis, 1980; Hill, Rubin, & Peplau, 1976; Peterson
& Miller, 1980; Spuhler, 1968; Stevens, Owens, & Schaefer, 1990; Susanne & Lepage,
1988). A meta-analysis of over 300 similarity studies observed that similarity produces a
positive, moderately sized effect on attraction (Montoya, Horton, & Kirchner, 2008).
Despite the overwhelming evidence that individuals are attracted to others who are
similar to them, the explanation for this effect has been the subject of much debate. The
two models that have garnered the most empirical attention—the reinforcement model
popularized by Byrne and colleagues (e.g., Byrne, 1971; Byrne, Clore, Griffitt, Lam-
berth, & Mitchell, 1973; Byrne & Rhamey, 1965) and the information processing per-
spective (e.g., Ajzen, 1974; Fishbein & Ajzen, 1972; Kaplan & Anderson, 1973)—have
both received substantial support as well as criticism for their (in)ability to account for
the empirical evidence. The reinforcement model, for example, cannot readily explain
why attraction does not occur as often in field studies compared with laboratory studies
(Montoya et al., 2008; Sunnafrank, 1992) or the lack of attraction from similarity of
negative traits (e.g., Ajzen, 1974; Novak & Lerner, 1968). The information processing
perspective has been questioned as to why similarity on less important attitudes (i.e.,
peripheral attitudes) does not lead to less attraction than similarity on important attitudes
(i.e., central attitudes; Clore & Baldridge, 1968) and why the similarity effect is not
affected by the number of attitudes one is regarded as similar to a target (i.e., set size;
Byrne et al., 1973).
Although several other models have been posited to explain the similarity effect (e.g.,
the repulsion hypothesis; Rosenbaum, 1986), the two models outlined above have
generated the vast majority of empirical work and have been elaborated on in enough
detail to generate specific predictions for a diversity of findings associated with the
effect. In the current research, we conducted a meta-analysis of laboratory investigations
of the similarity effect, in order to (a) test the validity of the reinforcement and informa-
tion processing perspectives, and (b) investigate variables that moderate the similarity
effect, in the hope of understanding more clearly what is responsible for interpersonal
Models of the similarity effect
Byrne’s reinforcement model
The early dominant model of the similarity effect was posited by Byrne (1971), who
borrowed concepts from cognitive dissonance theory (Festinger, 1957) and classical
conditioning to argue that similar attitudes serve as reinforcers. According to this per-
spective, individuals have a fundamental need for a logical and consistent view of the
world, a need that Byrne called the effectance motive. Individuals favor stimuli that
reinforce the logic and consistency of their world. People who agree with us validate our
ideas and attitudes and, in so doing, reinforce the logic and consistency of our world (i.e.,
Montoya and Horton 65
satisfy our effectance motive). Similar people are reinforcing and thus are associated
with positive feelings, which in turn lead to attraction. People who disagree with us
create inconsistency in our world (i.e., do not satisfy the effectance motive) and are
associated with anxiety and confusion—feelings that lead to repulsion or, at the very
least, lack of attraction. Importantly, such reinforcements, like other classically
conditioned associations, occur automatically and in the absence of conscious awareness
(Byrne & Clore, 1970; Byrne, Rasche, & Kelley, 1974; Clore & Gormly, 1974). Byrne
and colleagues labeled their theoretical account the reinforcement model (Byrne et al.,
Information processing
A second explanation of the similarity effect posits that it is a function of the valence and
weight of information that one infers about an individual based on similarity or dis-
similarity. According to this information processing perspective (e.g., Ajzen, 1974;
Kaplan & Anderson, 1973; Tesser, 1971), one person’s attraction to another is deter-
mined by the information one has about the other. The available information acts as a
direct and immediate influence on attraction. If the information is favorable, then
attraction results. The effect of similarity on attraction can be understood as a product of
the information implied by a target’s similar or dissimilar attitudes, personality traits, or
other attributes. In the words of Kaplan and Anderson (1973, p. 304), ‘‘when we are told
that X has similar attitudes, we like him not because that information acts as an
unconditioned stimulus, but because it leads us to expect that he has various positive
aspects to his personality.’’
First, the information inferred from an attribute (e.g., attitude or personality trait) is
assigned a valence. Information that is regarded as positive will lead to attraction.
Importantly, individuals use their own attributes as an anchor by which to assess the
information they infer about another (Ajzen, 1974; Insko et al., 1973). Because indi-
viduals evaluate their own attributes positively, attributes similar to their own are also
evaluated positively. Dissimilar attributes are judged less positively and result in dislike
(e.g., Stalling, 1970). In turn, we infer positive information about similar others and less
positive information (or even negative information) about dissimilar others, information
that translates into differential attraction to similar and dissimilar others.
Second, attributes and the information they imply are assigned a weight, or impor-
tance. That weight is at least partially a function of the amount of information one infers
about a target. The more information one infers about a target from a particular attribute,
the more important that attribute will be to determining one’s attraction to the target. As
such, more informative attributes should produce more polarized judgments than less
informative attributes. In this way, whereas informative positive and negative stimuli
should lead to relatively extreme evaluations and to attraction and repulsion, respec-
tively, non-informative stimuli, either of positive or negative valence, should have less of
an impact on interpersonal judgments.
Third, the salience of information is an important determinant of interpersonal
judgments. That is, the more attention one allocates to information, the more that
information will affect one’s judgments. This presumption is consistent with previous
66 Journal of Social and Personal Relationships 30(1)
work emphasizing the role of salience on judgments (e.g., Hastie & Kumar, 1979; Jones
& Davis, 1965), and several theorists have gone farther to specifically posit that it is the
reduced salience of the similarity/dissimilarity information that reduces similarity’s
impact outside of the laboratory setting (Montoya et al., 2008; Sunnafrank, 1992). In all,
it suggests that the impact of similarity on attraction should be particularly potent when
the information implied by such similarity is salient.
Different predictions?
The reinforcement model and the information processing perspective make different
predictions regarding factors affecting the magnitude of the similarity effect. In fact, the
empirical literature has identified multiple variables that affect the magnitude of the
similarity effect and about which these two perspectives make different predictions. As
is discussed in the following sections, these moderators provide the means by which to
compare the two explanations.
Moderators of the similarity effect
Type of stimuli
One frequently explored moderator of the similarity effect is the centrality, or the
importance, of the stimuli used in the description of the target. Newcomb (1956, p. 578)
argued that ‘‘the discovery of agreement between oneself and a new acquaintance
regarding some matter of only casual interest will probably be less rewarding than the
discovery of agreement concerning one’s own pet prejudices.’’ However, past research
related to attitude importance has been equivocal: Some studies have found an effect for
attitude importance (Davis, 1981; Cheney, 1975); others have not (Byrne & Nelson,
1964; 1965).
With respect to the reinforcement model, Byrne and colleagues (e.g., Byrne, London,
& Griffitt 1968; Clore & Baldridge, 1968, 1970) concluded, based on the evidence
available at the time, that there was no effect for attitude importance on the similarity
effect. However, it is important to note that Byrne and Rhamey (1965, p. 887, emphasis
added) acknowledge the possibility of such an effect by suggesting that ‘‘if such weights
can be empirically established, the [Byrne–Rhamey] attraction law should be rewritten’’
and claim that such an effect is not fundamentally inconsistent with the reinforcement
model. In such a case, Byrne and colleagues (1973) posited, central attitudes would be
associated with a greater weight than peripheral attitudes, and such additional weight
would then result in greater attraction (or repulsion).
With respect to the information processing perspective, attitude centrality should
influence the weight of the stimuli, such that central stimuli are likely to imply more
information about a target than peripheral stimuli. As such, the information processing
perspective posits that central attitudes should produce more liking from similarity (and
more disliking from dissimilarity) than do peripheral attitudes, because there is more
information implied about the target (Kaplan & Anderson, 1973; Tesser, 1971).
Montoya and Horton 67
Personality traits versus attitudes. A related question is whether the similarity effect
operates to a similar degree for personality traits as it does for attitudes. Previous
research found mixed results when investigating whether personality trait similarity
leads to attraction: Some researchers detected a relation (e.g., McLaughlin, 1970; 1971),
whereas others failed to (Hoffman & Maier, 1966; Katz, Cohen, & Castiglione, 1963;
Reilly, Commins, & Stefic, 1960).
For the reinforcement model, Byrne, Griffitt, and Stefaniak (1967, p. 83) stated that
‘similarity to self, whether involving attitudes or values or abilities. provides evidence
that one is functioning in a logical and meaningful manner’’ and, as a result, should cause
increased attraction to the target. In this way, the reinforcement model predicts that
personality trait similarity should result in increased attraction, as does attitude simi-
larity, but did not hypothesize that it should lead to more or less attraction than attitude
similarity (see also Byrne et al., 1973).
For the information processing perspective, attitude similarity should have a different
effect on attraction than personality trait similarity, to the extent that the amount of
information implied by attitudes is greater (or less) than it is for personality traits.
Though the question has rarely been explored empirically, direct and indirect evidence
indicates that attitudes are, in fact, more informative than personality traits: Individuals
are better able to evaluate others when the other is described in terms of the attitudes they
hold versus the personality traits they possess (e.g., Higgins & Winter, 1993; Reeder,
2009; Smith & Collins, 2009), and observers infer more information about targets from
their attitudes than from their personality traits (Horton & Montoya, 2012). As such, the
information processing perspective would posit that attitudes should be associated with a
larger effect for similarity.
Set size
Set size refers to the number of stimuli used to manipulate and assess similarity. Previous
research has been inconsistent regarding the impact of set size on attraction (Byrne,
1971). Whereas Byrne and Rhamey’s (1965) law of attraction stated that attraction is a
function of the proportion of similar attitudes, regardless of whether 10 or 100 attitudes
were used in the similarity manipulation (see also Byrne & Nelson, 1965; Rosenblood,
1970), other research has emphasized the influence of set size (e.g., Kaplan & Anderson,
As with the case of the Type of Stimuli moderator, the prediction of no effect for set
size is taken from the empirically derived Byrne–Rhamey (1965) law of attraction rather
than the reinforcement model. Had Byrne and colleagues identified an effect for set size
in past research, it is likely that the reinforcement model—and the law of attraction—
would have been modified to match the empirical findings. In this way, the reinforce-
ment model does not ‘‘rule out’’ an effect for set size per se, but nor does it predict one
(Byrne et al., 1973).
Alternatively, the information processing perspective posits that set size will have an
effect on the magnitude of the similarity effect because the amount of information
increases as set size increases (Kaplan & Anderson, 1973). Moreover, this perspective
posits that the relation between attraction and set size will increase linearly for small set
68 Journal of Social and Personal Relationships 30(1)
sizes, but will then asymptote for larger set size values (Kaplan & Anderson, 1973), as
each additional stimulus is relatively less informative of the other’s attributes.
Information salience
Information salience refers to the degree to which the information regarding the target is
consciously available immediately before the attraction assessment. Montoya and
Horton (2004), for instance, manipulated whether participants completed four ‘‘cogni-
tive evaluation’’ items prior to assessing attraction to a similar or dissimilar target.
Responding to the four evaluation items made salient the information implied by the
attitude similarity (or not) and resulted in increased attraction; not doing so left that
information relatively non-salient and eliminated the influence of similarity on attrac-
tion. Similarly, Simons (2008) manipulated the order in which interpersonal attraction
and cognitive evaluation items were assessed. He found that when the cognitive evalua-
tion items were assessed before the attraction assessment (high salience), the cognitive
evaluation items were significantly better mediators of the similarity effect than when
they were presented after the attraction assessment (low salience).
The reinforcement model—grounded in the principles of classical conditioning—
maintains that one’s assessment of attraction results from the amount of reinforcement
that is associated with the target—a process that is independent of one’s cognitive
processes. The reinforcement model suggests that such cognitive processes occur either
simultaneously with (Byrne et al., 1974) or after (e.g., Byrne & Clore, 1970; Clore &
Gormly, 1974) the experience of interpersonal attraction. By this reasoning, then, sal-
ience of information should not affect attraction.
The information processing perspective posits that salient information will have a
greater influence on attraction (Fishbein & Ajzen, 1975). As noted previously, this
perspective proposes that similarity drives a positive evaluation of the target other, which
then produces attraction. The more salient that evaluation is, the more powerful its
influence should be (Shaffer & Tabor, 1980; Snyder & Ebbesen, 1972).
Proportion of similarity
Proportion of similarity refers to the ratio of similar to dissimilar stimuli. This moderator
is the foundation of the similarity effect and is the defining characteristic of similarity
studies. The literature has consistently found an effect for proportion of similarity: Byrne
(1962), for example, manipulated seven progressive levels of proportion of similarity
and found that attraction increased linearly. In additional studies designed to investigate
different proportions of similarity, both Byrne and Nelson (1965) and Byrne and Rhamey
(1965) found similar results.
Proportion of similarity is a moderator about which the reinforcement model and
information processing perspective agree: Both predict an effect. The reinforcement
model regards a higher proportion of similarity as providing more reinforcement for
one’s own views, and thus more positive affect associated with a target. The information
processing perspective regards the higher proportion of similarity as providing a higher
proportion of positive, as opposed to negative, information about a target. Such relative
Montoya and Horton 69
prevalence of positive information should produce more attraction. Although the two
perspectives make similar predictions, we included this in our analysis to confirm the
basic predictions of these models.
Purpose of this research
We conducted a meta-analysis of 240 laboratory studies of similarity effect to compare
the merit of the reinforcement and the information processing perspectives. A meta-
analysis provided the opportunity to test the different predictions of the two perspectives.
As summarized in Table 1, these predictions involved moderators of the similarity effect:
Type of stimuli, set size, and information salience. We also tested the moderating effect
of proportion of similarity to test a basic assumption of both perspectives.
Meta-analysis sample
The studies included in this meta-analysis are a subset of those reported in Montoya et al.
(2008). Studies for the original sample were gathered in three ways. First, an electronic
literature search was conducted using the PsycINFO (1887 – July 2004) and Dissertation
Abstracts International (1861 – July 2004) databases. Keywords were ‘‘assumed,’’ ‘‘atti-
tude,’’ ‘‘attraction,’’ ‘‘complimentary,’’ ‘‘congruence,’’ ‘‘dissimilarity,’’ ‘‘homogamy,’
‘ideal self,’’ ‘‘liking,’’ ‘‘perceived,’’ ‘‘personality,’’ ‘‘reinforcement-affect,’’ ‘‘repulsion,’’
and ‘‘similarity,’’ Second, the sent a request for relevant studies to an Internet discussion
forum; third, we contacted investigators who had frequently published research on the
similarity effect. We selected only laboratory-based studies that compared similar and
dissimilar attitudes or similar and dissimilar personality traits, resulting in 240 laboratory
studies. From these studies, we extracted 337 similarity–dissimilarity effect sizes with a
total sample size of 28,674 participants. Sample sizes ranged from 13 to 509
(M¼83.84, SD ¼71.24). Inspection of the funnel plot of laboratory studies by Montoya
et al. (2008) provided no support for publication bias for this set of studies (see Montoya
et al., 2008, p. 889 for the funnel plot).
Data coding
Type of stimuli. Laboratory studies of the similarity effect most frequently manipulate
similarity using either attitudes or personality traits (Montoya et al., 2008). In order to
test predictions of the reinforcement and information processing perspectives using as
many studies as possible, we first coded this variable as a categorical variable with two
levels: Attitude study and personality study. A study was coded as an attitude study if
participants were asked to evaluate specific objects or issues (e.g., death penalty,
abortion, discotheques). We coded studies as a personality trait study if participants
completed either a personality trait assessment questionnaire (e.g., California Personality
Inventory, Minnesota Multiphasic Personality Inventory) or a specific personality trait
assessment (e.g., extraversion, agreeableness, hypertraditionality).
70 Journal of Social and Personal Relationships 30(1)
As a more sensitive test of this moderator, we further classified attitude studies into
one of three categories: Peripheral, central, and unclassified. Studies that used attitudes
to manipulate similarity in which the attitudes were defined by the authors as ‘‘central,’
‘critical,’’ or ‘‘important’’ attitudes were coded as ‘‘central’’ attitude studies. Studies
that included attitudes that were described by the authors as ‘‘unimportant,’’ ‘‘irrele-
vant,’’ or ‘‘peripheral’’ were coded as ‘‘peripheral’’ attitude studies. A vast majority of
studies did not report their attitudes and therefore were labeled ‘‘unclassified.’’ Such a
process resulted in a four-level variable: personality trait studies, central attitude studies,
peripheral attitude studies, and unclassified attitude studies.
Set size. We coded set size as a continuous variable that was equivalent to the number of
items used to manipulate the degree of similarity. In any study in which the participant
received information from the target other, set size was defined as the number of stimuli
about which the participant received information.
Information salience. Information salience was coded as a categorical variable with two
levels: Salient evaluation present and salient evaluation absent. We coded each study by
noting the questions used to assess the target prior to the assessment of interpersonal
attraction. For example, the Interpersonal Judgment Scale (IJS, Byrne, 1971) includes
four ‘‘filler’’ questions that ask the participants about the attributes of the target other and
that precede the two attraction items. Montoya and Horton (2004) argued that the
‘filler’’ questions that ask participants to rate the target other’s intelligence, adjustment,
morality, and competence on a given task make salient their evaluation of the target
other. We coded as ‘‘information salient’’ those studies in which participants assessed
the overall quality of the target before indicating their attraction to the target. We coded
as ‘‘information not salient’’ those studies that did not meet this criterion.
Proportion of similarity. We coded proportion of similarity as a continuous variable with the
value assigned equal to the percentage of similar attributes. The proportion of similarity
was the percent of the partner’s attributes that were similar to those of the participant. For
example, in studies that were characterized by ‘‘75 vs. 25,’’ participants shared 75%of
attitudes in the similar condition and 25%of attitudes in the dissimilar condition, and
thus these studies were coded with a value of ‘‘75.’’ We excluded from this analysis any
Table 1. Predictions for the reinforcement and information integration models
Reinforcement model Information processing perspective
Set size No Yes, but curvilinear
Type of stimuli No
Attitude vs. personality trait No Yes
Informational salience No Yes
Proportion of similarity Yes Yes
Later theorizing indicated that this prediction would change to ‘‘Yes’’ if empirical data were to become
Montoya and Horton 71
study that failed to report the degree of similarity or was continuous in nature (i.e., the
percentage of similarity between individuals was derived from a post hoc comparison of
the participant’s attributes compared with another’s attributes).
Other variables. For each similarity–dissimilarity comparison we coded basic descriptive
information and additional variables for exploratory and sensitivity analyses. These
variables included: Author and full citation; source (journal, edited volume, thesis or
dissertation, or unpublished manuscript); sample (college students, adults, or school
children); year of publication; type of personality traits measured (specific personality
trait, complete scale); type of relationship (stranger, friend, boyfriend/girlfriend, mar-
riage partner); recruitment method (participant pool, monetary incentive, or volunteer);
sample size; and sex composition of the sample (all men, all women, men and women in
interactions that were homogenous with respect to sex, or men and women in interactions
that were heterogeneous with respect to sex).
Statistical methods
Effect sizes used
The effect size index was Fisher’s z(Fisher, 1928), calculated such that greater positive
values indicated greater attraction for similar others and negative values indicated
greater attraction for dissimilar others. An effect size of zero indicates no relation
between similarity and attraction. Following the recommendations of Rosenthal (1994),
we used the effect size zbecause of its conceptual superiority over effect size dfor
studies involving continuous data.
Random-effects model
We selected a random-effects model in order to make unconditional inferences that gen-
eralized to the hypothetical population of all studies that could exist, rather than simply
to the studies included in the present sample (Hedges & Vevea, 1998). We used the
method of unconditional maximum likelihood to estimate model parameters. Effect sizes
were computed by weighting each individual effect size by the inverse of its variance.
We computed a Q-statistic to test the assumption of homogeneity and an I
to test the
amount of heterogeneity.
For our analyses, we first tested an empty model to determine the average size of the
similarity effect. We then tested a model with the four proposed moderators of interest
entered simultaneously.
Sensitivity analyses. We made a number of assumptions in this meta-analysis. For example,
we identified several moderators for theoretical reasons (e.g., set size, proportion of simi-
larity) and, as such, implicitly assumed that others were less important. To investigate
the possible consequences of these assumptions, we performed a series of sensitivity
analyses. Our sensitivity analyses involved two processes. First, we tested the six two-
way interactions between the four moderators of interest to determine whether important
72 Journal of Social and Personal Relationships 30(1)
interactions would emerge. Second, to determine if a potentially important moderator
was excluded from our a priori analyses, we conducted additional analyses in which
other potential moderators (e.g., gender, sample, source) were included one at a time into
the a priori model.
Overall model
AQ-test of the null hypothesis that it is plausible that the true variance component is zero
was significant (variance component ¼0.055), Q(337) ¼2886.41, p< .05. A test of
heterogeneity indicated substantial heterogeneity across studies, I
dence interval [CI] ¼79%to 96%). Using a random-effects estimate, the effect size was
strong (r¼.59; 95%CI: .55, .63) and different from zero, z¼29.60, p< .05.
Type of stimuli. As noted in Table 2, the type of stimuli moderator reached significance.
The means for type of stimuli are presented in Table 3. We contrast coded this four-level
variable using three orthogonal contrasts. Contrasts were calculated to test two key
hypotheses: (a) whether central attitudes were associated with a larger effect than periph-
eral attitude studies, and (b) whether attitude studies, compared with personality trait
studies, were associated with a larger effect. The first contrast, which compared periph-
eral attitude studies to central attitudes, was significant, w
(1) ¼4.39, p< .05, and indi-
cated that central attitudes were associated with a larger effect than peripheral attitudes.
The second contrast detected a difference between all attitude studies and personality
trait studies, w
(1) ¼6.25, p< .05, revealing that attitude studies are associated with a
larger effect size than personality trait studies. The final contrast, which was included
without theoretical prediction, compared central and peripheral attitudes to personality
trait studies, and was not significant, w
(1) ¼0.41, p¼.52. The contrast indicated that
the averaged similarity effect for central and peripheral attitudes was not different from
that for the personality trait studies.
Set size. As presented in Table 2, the main effect for set size was not significant. Given
the predictions of the information integration perspective, we also tested set size as a
quadratic function. The effect was also not significant, w
(1) ¼1.51, p¼.21.
Information salience. The main effect for information salience was significant. The main
effect indicated that the similarity effect was more potent when the information was
made salient than when it was not.
Proportion of similarity. We treated proportion of similarity as a continuous variable. The
similarity effect was stronger as the proportion of similarity increased. To illustrate the
effects of proportion on the similarity effect, Table 3 presents proportion of similarity as
Montoya and Horton 73
a categorical variable using the commonly used operationalizations of proportion. There
was a clear trend for the similarity effect to increase as proportion of similarity increased.
Sensitivity analyses
Sensitivity analysis discovered that one additional factor, gender, was also associated
with the size of the similarity effect, w
(3) ¼12.39, p< .05. To account for gender’s
association with the similarity effect, we created a factor that accounted for not only the
gender of the participant, but also the gender of the target other. The factor included four
participant gender and target gender combinations: female participant–female target,
male participant–male target, unspecified participant (defined as groups of participants
Table 3. Mean effect sizes (in r) for each level of the similarity effect moderators
k Effect size (r)
Overall effect 337 .59
Information salience
Salient 239 .60
Not salient 98 .54
Type of stimulus
Personality trait 26 .48
Unclassified attitude 244 .62
Central attitude 50 .52
Peripheral attitude 16 .41
Proportion of similarity
75% similar–25% dissimilar 54 .47
80% similar–20% dissimilar 42 .54
83% similar–17% dissimilar 48 .61
90% similar–10% dissimilar 19 .62
100% similar–0% dissimilar 142 .62
Gender (Gender of participant–gender of target)
Female–female 41 .69
Male–male 54 .47
Unspecified gender–same gender partner 197 .61
Unspecified gender–opposite gender partner 45 .49
Note. Positive values indicate stronger relation between similarity and attraction. Numbers may not add up to
337 due to missing values for some studies.
Table 2. Parameter estimates for similarity effect moderators
d.f. P
Set size 0.21 1 .64
Type of stimuli 9.05 3 .00
Informational salience 4.11 1 .00
Proportion of similarity 7.50 1 .00
74 Journal of Social and Personal Relationships 30(1)
whose gender was not specified, nor accounted for, in the original study)–matched gender
target, and unspecified participant–opposite gender target. To explore this moderator, we
created three orthogonal contrasts. The first contrast compared female–female interactions
to male–male interactions. This contrast revealed that female–female interactions were
associated with a stronger similarity effect than were male–male interactions, w
(1) ¼4.87,
p< .05. The secondcontrast compared unspecified participant–matched gendertarget to the
male–male and female–female conditions, and was not significant, w
(1) ¼0.19, p¼.63.
The final contrast, which compared the unspecified participant–opposite gender target
condition to the three other levels (i.e., contexts in which the gender of the target matched
that of the participant), was significant, w
(1) ¼5.10, p< .05. This contrast indicated that
unspecified–matched interactions produced a weaker similarity effect than did the combi-
nation of all gender-specified interactions.
Information salience by proportion of similarity. Additional tests identified an Information
Salience Proportion of Similarity interaction, w
(1) ¼6.42, p< .05, such that increased
proportion of similarity was associated with a stronger similarity effect when informa-
tion was salient than when information was not salient. In other words, the slope for
proportion of similarity was greater when the available information was salient than
when it was not.
Information salience by type of stimuli. There was also an Information Salience Type of
Stimuli interaction, w
(3) ¼9.11, p< .05, such that salient information led to a stronger
similarity effect in studies that used central attitudes or that were ‘‘unclassified,’’ but not
when the studies used peripheral attitudes or personality traits as stimuli.
Our meta-analysis of 240 laboratory studies resulted in support for three of the four
effects proposed by the information processing perspective, and for two of the four
proposed by Byrne’s reinforcement model. More specifically, we observed effects for
two moderators which information processing predicted and the reinforcement model
did not: Type of stimuli and information salience. First, more informative stimuli
(e.g., central attitudes) were associated with a larger similarity effect than were less
informative stimuli (e.g., peripheral attitudes). Second, the similarity effect was greater
when similarity information was salient before the attraction assessment. Proportion of
similarity was also influential, but this was an effect that both the reinforcement model
and information processing perspective predicted.
In addition, two interactions identified during the sensitivity analyses were consistent
with the information processing perspective. Specifically, the type of stimulus and
proportion of similarity mattered more when the information about the target was made
salient compared to when it was not. Given the emphasis on cognitive processing in the
information processing perspective, having more information available and salient
should have a greater impact on the later experienced attraction. Alternatively, given the
emphasis and importance of affective processing of the reinforcement model (and the
Montoya and Horton 75
related suggestion that any cognitive processes occur simultaneously or after the
affective processes), such findings would not necessarily be predicted by this approach.
Importantly, not all of the meta-analytic findings contradict Byrne’s reinforcement
model. First, Byrne et al. (1973) stated that if set size was found to be important to the
similarity effect, the model would require revision. However, we found no evidence that
the similarity effect is affected by the number of stimuli. Despite a positive slope within a
meta-analysis of a large sample (k¼337), the main effect for set size was not significant.
As such, it appears that Byrne et al. (1973) were accurate in suggesting that any influence
of similarity information is as important for 10 stimuli as it is for 100 stimuli. Second,
Byrne and colleagues treated centrality as a hypothetical possibility that may need to
be included in the model if data indicated such need. In several papers (e.g., Byrne &
Clore, 1970), the researchers presented an alternative equation for the law of attraction
had there been sufficient empirical evidence to support ‘‘differential weighting in the
similarity effect:’
such that AR is the affective response (e.g., attraction), Mis the Magnitude (i.e., the
weight associated with the reinforcements), and PR and NR represent the positive and
negative reinforcements, respectively. The current findings at the very least provide
empirical evidence to support the adoption of such a weighted model.
It should be noted that given the large sample size of the meta-analysis, it was likely
that significant effects were more ‘‘statistically’’ significant than they were ‘‘practically’
important. For example, whereas we identified a significant effect for Information Sal-
ience, the mean difference between ‘‘salience’’ and ‘‘no salience’’ studies was .06, with
‘no salience’’ studies still producing a notable effect size. When comparing the predic-
tive ability of different theoretical models, however, the critical question is whether there
is a difference between models in their ability to explain the extant data; in that light,
such (perhaps small) effects remain meaningful.
Implications for other models of the similarity effect
It is important to acknowledge that the current work focused on only two explanations
for the similarity effect and excluded others, such as the rewards of interaction
(Berscheid & Walster, 1969), anticipation of liking (Condon & Crano, 1988), and
repulsion (Rosenbaum, 1986) perspectives. We focused on the reinforcement and
information processing perspectives because they specifically theorized about mod-
erators important to the similarity effect and have been explicitly tested in previous
work. But it is important to note that aspects of other models can be informed by the
findings of this meta-analysis.
Rewards of interaction. A rewards of interaction perspective (Berscheid & Walster, 1969)
posits that the degree of similarity between two persons predicts the number of rewards
76 Journal of Social and Personal Relationships 30(1)
an individual expects to experience in an interaction with another. One testable aspect of
this approach is that items that are more informative of the rewards (e.g., central
attitudes) should be more affected by similarity than less informative items (e.g.,
peripheral attitudes; Santee, 1976; see also Davis, 1981). From the rewards of interaction
perspective, similarity of central attitudes should have a greater influence on attraction
because they are more informative about the rewards that might be expected in the
future. The results of this meta-analysis are consistent with this prediction.
Second, the rewards of interaction approach has its roots in equity theory (Walster,
Walster, & Berscheid, 1978), and as such, includes an emphasis on the cognitive pro-
cesses that determine benefits and costs. In this light, salience of the anticipated
rewards—particularly those inferred from central attitudes—should be more influential
on attraction. As described earlier, we found a main effect for salience, in addition to two
interactions involving the salience variable that indicate that the effects for similarity are
stronger for salient similarity information. In this way, many of the predictions of the
rewards of interaction approach would be supported by the results of this meta-analysis.
Repulsion hypothesis. Rosenbaum (1986) proposed that the positive relation between simi-
larity and attraction may result from the repulsion caused by the dissimilar attitudes—the
greater the number of dissimilar attitudes, the greater the repulsion. Although this is a tes-
table proposition for a meta-analysis, to our knowledgeonly four studies have independently
varied the number of similar and dissimilar attitudes, making a meta-analysis impractical.
Additionally, results of those studies are mixed: Rosenbaum found that the number of dis-
similar attitudes had aneffect on attraction but the number of similar attitudes did not, while
Smeaton, Byrne, and Murnen (1989, Study 1) held the number of dissimilar attitudes con-
stant but changed the number of similar attitudes. Contrary to Rosenbaum’s (1986) findings,
attraction increased with an increasing number of similar attitudes.
However, what is really at the heart of this debate is whether dissimilar attitudes have
a disproportionate influence on attraction when compared with similar attitudes. Indeed,
research has repeatedly demonstrated that dissimilar attitudes affect attraction more than
similar attitudes do (e.g., Singh & Teoh, 1999). Whereas the notion that dissimilar
attitudes contributes more to the attraction process than do similar attitudes was dis-
missed out of hand by Byrne and colleagues (Smeaton et al., 1989), it has become clear
that dissimilar stimuli—due to a multitude of different affective and cognitive pro-
cesses—contribute more to the experience of attraction than do similar stimuli (e.g., Jia
& Singh, 2009; Singh & Ho, 2000; Singh, Lin, Tan & Ho, 2008; Singh & Teoh, 1999).
Such an explanation could be incorporated into both the information processing and
reinforcement perspectives: (a) for the reinforcement perspective, it would necessitate a
different weight for positive versus negative reinforcements (called ‘‘magnitude’’ in
Equation 1 above); and (b) for the information processing perspective, it would simply
involve more information being inferred from negative stimuli than from positive
stimuli. Such an interpretation indicates that both models—and thus the present
meta-analytic findings—can incorporate the expectations of the repulsion hypothesis.
Anticipation of liking. Condon and Crano (1988) argued that a person’s attraction toward
another is a function of the extent to which the other is assumed to like that person. From
Montoya and Horton 77
this perspective, people anticipate that those with similar attitudes will like them, and
those with dissimilar attitudes will dislike them. In effect, people like similar others
because they expect those others to like them. This explanation mirrors another classic
finding in the attraction literature: The reciprocity of liking effect (Gouldner, 1960). It is
interesting to note that past research has found that the reciprocity of liking effect is
approximately three times stronger than that of the similarity effect (e.g., Bell & Baron,
1974; Byrne & Ervin, 1969; Clore & Baldridge, 1970).
There is likely one prediction of this approach that is inconsistent with the meta-
analytic results: Specifically, set size. It may be hypothesized that this approach would
argue that increased set size should be associated with a smaller similarity effect. With
respect to the influence of a specific stimulus, similarity researchers have concluded that
the effects of similarity information are additive (Byrne, 1971), whereas the same addi-
tive rules may not apply to liking information. In other words, inconsistent liking infor-
mation (i.e., ‘‘I like you. I don’t like you. I like you.’’) may influence the evaluations of
others differently from similarity information’s influence on the evaluations of others.
There are two reasons for this difference: (a) As noted within the gain–loss literature
(Aronson, 1969; Aronson & Linder, 1965), not all expressions of (dis)liking are
weighted equally, such that expressions of dislike after liking have a disproportionate
influence on evaluations; and (b) expressions of liking are informative of another’s
‘benevolent’’ orientation toward the other (Montoya & Insko, 2008) and situations in
which a benevolent orientation is repeatedly violated may lead to reduced attraction.
Such a difference would be most prominent with greater set sizes, such that whereas
similarity models would posit that such reinforcements/punishments would accumulate
additively, the reciprocity of liking literature would posit that there is a greater opportu-
nity for benevolent expectations to be repeatedly violated. In this way, it is plausible to
hypothesize that a reciprocity of liking approach would posit that increased set size
would be associated with a smaller similarity effect—a result inconsistent with the find-
ings of this meta-analysis.
The bigger picture
The reinforcement model and the information processing perspective represent two
distinct perspectives on the experience of attraction: At their ideological cores, the
information processing perspective posits a principally cognitive approach, whereas the
reinforcement model posits a fundamentally affective approach. Our findings not only
provide evidence regarding the efficacy of these models for explaining the similarity
effect, but also provide additional evidence for the processes that underlie the broader
experience of attraction. As noted earlier, the findings were largely consistent with the
information processing perspective: We found a main effect for salience, and discovered
that the salience variable interacted with two variables important for the operation of the
similarity effect.
Indeed, the current findings are consistent with recent explanations of other attraction
phenomena that focus on underlying cognitive processes. For example, whereas initial
explanations of the pratfall effect focused on affective forces (e.g., Aronson, Willerman,
& Floyd, 1966), more recent explanations have focused on a combination of cognitive
78 Journal of Social and Personal Relationships 30(1)
and affective processes (e.g., Herbst, Insko, & Gaertner, 2003). Similarly, initial
explanations of the reciprocity of liking effect focused considerably on affective pro-
cesses (e.g., Adams, 1963); but subsequent research posited that cognitive processes
played a dominant role in the effect (e.g., Montoya & Insko, 2008). Further, initial
explanations of increased attraction to others who are present during an anxiety-
producing event focused on affect (Dutton & Aron, 1974), but later interpretations
focused on the influential role of cognitive processes (e.g., Foster, Witcher, Campbell,
& Green, 1998). Specific to the similarity effect, research has provided evidence that
cognitive processes, compared to affective processes, do a better job at mediating the
effect. In a series of studies that specifically tested the different mediators of the similar-
ity effect, Singh and colleagues (Singh et al., 2007; Singh, Ng, Ong, & Lin, 2008) found
that affect did not mediate the influence of similarity on attraction when cognitive med-
iators were included in the model, whereas cognitive processes consistently mediated the
effect. Overall, then, cognitive processes effectively explain a variety of attraction phe-
nomena, including the similarity effect, many of which were originally presumed to be a
result of affective processes.
It is noteworthy that this cognitive perspective can account effectively for not only the
current meta-analytic findings, but also some interesting inconsistencies in the similarity
effect. As an example, take the finding that similarity on negative attributes does not lead
to attraction (Novak & Lerner, 1968). Though other models struggle to explain this find-
ing (e.g., Ajzen, 1974), the information processing perspective argues that similarity of
negative attributes conveys negative information about the target. This negative informa-
tion would not lead to attraction, but to avoidance. The fact that the information process-
ing perspective explains diverse, and sometimes disparate, findings through the same
cognitive model speaks to its value as a more general theory of attraction.
This meta-analysis explored four critical moderators of the similarity effect. The key
findings included significant main effects for type of stimulus, proportion of similarity,
and information salience. The main effect for information salience, in addition to two
interactions involving this variable, provided evidence for the role of cognitive processes
in the experience of attraction. In this way, these results provided more support for the
information processing perspective than for the reinforcement model and, more gener-
ally, provided more support for a cognitive approach than for an affective-based
We are grateful to all the authors who made additional information available on their studies. We
also thank the SCAR Group for their comments and assistance with this research.
This research received no specific grant from any agency in the public, commercial, or not-for-
profit sectors.
Montoya and Horton 79
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... These managers, teachers, and the like create opportunities for some institutional members to develop psychological capital and deprive others thereof, and their behaviors may potentially differ when targeting in-group and out-group members. Social-psychological research needs to investigate the various relational processes in institutions through which psychological capital is maintained and reproduced within dominant social groups, for example, through offering opportunities for mastery and success to in-group members; recognizing, appreciating and supporting in-group members; offering in-group members more time, attention, information, visibility, and credit and evaluating their performance more positively than they would do in the case of others; and offering them high status assignments and responsibilities and thus setting in-group members up for success (Dreher & Cox, 1996;Goldberg & McKay, 2015;Harrison et al., 2002;Montoya & Horton, 2013;Philipp-Muller et al., 2020;Varma & Stroh, 2001;Williams, 2001). ...
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During the past decade, a rich literature emerged focusing on “psychological capital,” a multidimensional concept encompassing self-efficacy, hope, optimism, and resilience. So far psychological capital has been predominantly studied in the areas of work and organizational psychology, management, and organizational behavior. This paper argues that (1) the relevance of psychological capital is much broader than assumed so far and (2) that not only the outcomes but also the (social) origins and sources of psychological capital need to be studied. More specifically, the key questions that we address in this paper concern (1) how the notion of psychological capital can be integrated into a broader capital framework that allows studying (the reproduction of) social inequalities, (2) what such integration adds to disciplines such as psychology and sociology, (3) and which avenues for further research can be derived from such framework? Informed by the work of Pierre Bourdieu, we argue that psychological capital is the missing link to develop a comprehensive framework for studying (the reproduction of) social inequalities. Based on our theory building, we develop an interdisciplinary research agenda.
... Nass et al.'s Computers are Social Actors paradigm affirms that people subconsciously apply social heuristics to technologies, despite their conscious awareness that these technologies are not sentient [68]. This paradigm has been exhibited across multiple contexts: People form first impressions of a voice's "personality" [53] much like how they form first impressions of people [4], and are attracted to computer voices that demonstrate similar personality characteristics as themselves [44,66] just as people are attracted to those who are similar to them [59]. People also apply social codes of politeness towards voice assistants [11], much like how we employ politeness among other people [14]. ...
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Language technologies have a racial bias, committing greater errors for Black users than for white users. However, little work has evaluated what effect these disparate error rates have on users themselves. The present study aims to understand if speech recognition errors in human-computer interactions may mirror the same effects as misunderstandings in interpersonal cross-race communication. In a controlled experiment (N=108), we randomly assigned Black and white participants to interact with a voice assistant pre-programmed to exhibit a high versus low error rate. Results revealed that Black participants in the high error rate condition, compared to Black participants in the low error rate condition, exhibited significantly higher levels of self-consciousness, lower levels of self-esteem and positive affect, and less favorable ratings of the technology. White participants did not exhibit this disparate pattern. We discuss design implications and the diverse research directions to which this initial study aims to contribute.
... Our participants expressed this as they often felt additional comfort when interacting with other transgender individuals, especially when negotiating sexual activities, as there was a mutual understanding of transgender identity. Additionally, similarity to oneself is a significant determinant in attraction (Montoya & Horton, 2013;Wetzel & Insko, 1982). For example, implicit egotism is the unconscious preference for those who are like oneself (Pelham et al., 2005), as similar individuals are more likely to reinforce our beliefs (Mayer & Puller, 2008) and reduce the likelihood of conflict in a relationship (Burleson et al., 1994). ...
Dating app use is common and has become particularly relevant for transgender and non-binary people seeking platonic, romantic, and sexual connections with others. In this qualitative study, 15 transgender and non-binary individuals (M = 22.67 years, SD = 3.09 years) were interviewed to explore their experiences using dating apps. Thematic analysis was used to generate themes and subthemes. Six themes were identified: 1) connection to queer community; 2) expression of gender identity on dating apps; 3) fetishization on dating apps; 4) impacts of dating apps on sexual experiences; 5) safety on dating apps; and 6) recommendations for dating app developers. Results show that dating apps are an important tool used by trans/non-binary individuals to connect with others in the queer community and find platonic, romantic, and sexual partners. However, there are concerns about their use such as fears for safety and experiences of fetishization. More research, education, and implementations of app development, including the involvement of trans and non-binary people, are needed to address these concerns.
... Similarity attraction attributes compatibility to people with similar attitudes or personality profiles (Byrne and Griffitt, 1969;Wetzel and Insko, 1982;Yeong Tan and Singh, 1995;Montoya and Horton, 2013). According to this theory, an extravert prefers extravert communication partners, and an introvert prefers introvert interlocutors. ...
Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.
... Individuals are positively inclined towards people who are similar to themselves. This simple but striking assertion underpins the similarity-attraction hypothesis (SAH), which frames much relationship and interpersonal attraction research (e.g., Byrne 1971;Montoya and Horton 2013). According to Byrne (1971), when people perceive themselves to be similar to other people, they experience positive feelings of attraction towards them. ...
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Although the similarity-attraction hypothesis (SAH) is one of the main theoretical foundations of management and industrial/organizational (I/O) psychology research, systematic reviews of the hypothesis have not been published. An overall review of the existing body of knowledge is therefore warranted as a means of identifying what is known about the hypothesis and also identifying what future studies should investigate. The current study focuses on empirical workplace SAH studies. This systematic review surfaced and analyzed 49 studies located in 45 papers. The results demonstrate that SAH is valid in organizational settings and it is a fundamental force driving employees’ behavior. However, the force is not so strong that it cannot be overridden or moderated by other forces, which includes forces from psychological, organizational, and legal domains. This systematic review highlights a number of methodological issues in tests of SAH relating to the low number of longitudinal studies, which is important given the predictive nature of the hypotheses, and the varying conceptualizations of attraction measurement.
Purpose: The increasing diversity among workforces - as well as the increasing diversity among patient populations served - offers a variety of opportunities and potential pitfalls for healthcare organizations and leaders. To unravel this complexity, the authors aim to holistically understand how to maximize provider and patient experiences regardless of (1) the degree to which diversity is present or lacking, and (2) the type(s) of diversity under consideration. Design/methodology/approach: This conceptual paper develops a framework that combines three organizational behavior theories - emotional labor theory, similarity-attraction theory and climate theory - with evidence from the broader healthcare literature. Findings: Authentic interactions yield positive outcomes for providers (i.e. improved job attitudes and work-related well-being) and patients (i.e. patient satisfaction) and acts as a mediator between demographic diversity and positive outcomes. Demographic similarity facilitates authentic interactions, whereas demographic diversity creates an initial barrier to engaging authentically with others. However, the presence of a positive diversity climate eliminates this barrier. Originality/value: The authors offer a conceptual model to unlock positive outcomes - including reduced absenteeism, better morale and improved patient satisfaction - regardless of the level and types of diversity present within the workforce. In addition to deriving an agenda for future research, the authors offer practical applications regarding how diversity can be more effectively managed and promoted within healthcare organizations.
The present study investigates the effects of a chatbot’s motivation support style on the learner’s experience and intention to continue the study in the context of online English lectures. Seventy-nine undergraduate students were recruited from a large private university in Seoul, South Korea, and assigned to one of three learning plan development groups: develop a plan alone, autonomy support (i.e., a chatbot stimulating intrinsic motivation), or control support (i.e., a chatbot promoting extrinsic motivation) groups. The learners were classified into two groups based on their learning motivation types (i.e., intrinsic and extrinsic), and by doing so, the present study created a chatbot’s matched and non-matched motivation support conditions in learning plan development. The two support strategies were compared with a control condition (i.e., learners’ own plan making), and the results suggest that a chatbot with a non-matched motivation strategy increases learner self-efficacy, enjoyment, and intention to continue using the lecture. Furthermore, the study also explores the moderation effect of learning motivation types, and reveals that a chatbot’s control support significantly improves the learning experience. The present study provides new insight into improving user evaluation by strategically differentiating a chatbot’s conversational style and a user’s characteristics.
-To look at the usefulness of a neutral task in studying 2-person interactions, to ascertain some behavioral correlates of experimentally manipulated attraction, to investigate the generality of the similarity-acttaction hypothesis, and to examine the behavioral relevance of scores on a commonly used paperand-pencil attraction scale, Ss interacted wirh confederates posing as Ss. Each S was given the impression that C had similar or dissimilar attitudes and twice S performed an incomplete sentences task administered by C. On one set of trials, S was instructed to act as if he liked C, and on the other set as if he did not. Measured behaviors were eye contact, interview length, number of words used, smiles, and both S's and C's response latencies. In the high-attraction roleplaying condition, more smiles and a higher percentage of eye contact occurred than in the low-attraction role-playing condition. But no differences in the other behaviors were noted, only very mild support was obtained for the similarity-attraction hypothesis, and none of the measures were related to scores on the paperand-pencil index of interpersonal attraction.
It was hypothesized that a college instructor who is attitudinally similar to oneself will be evaluated more positively than an attitudinally dissimilar one for open-mindedness, promoting feelings of at ease, being stimulating and interesting, over-all teaching competence, personal attractiveness, and desirability as an instructor. Each undergraduate S filled out a 14-item Survey of Attitudes, and, during a later class session, received an attitude survey representing the attitudes and opinions of a hypothetical college instructor who showed either 14% or 86% agreement with S's own views. S then filled out an Instructor Evaluation Scale for his evaluations of the stimulus person. The hypothesized effect of attitude similarity was confirmed for all of the evaluation variables.
The hypothesized interactive effect of need for vindication in the relationship between attitude similarity and attraction was confirmed ( p < .05) for 133 male but not for 129 female undergraduates tested.