The evaluative information ecology: On the frequency and diversity of “good” and “bad”. Christian Unkelbach, Alex Koch & Hans Alves. European Review of Social Psychology, Volume 30, 2019 - Issue 1, Pages 216-270. Nov 24 2019. https://doi.org/10.1080/10463283.2019.1688474
Abstract: We propose the Evaluative Information Ecology (EvIE) model as a model of the social environment. It makes two assumptions: Positive “good” information is more frequent compared to negative “bad” information and positive information is more similar and less diverse compared to negative information. We review support for these two properties based on psycho-lexical studies (e.g., negative trait words are used less frequently but they are more diverse), studies on affective reactions (e.g., people experience positive emotions more frequently but negative emotions are more diverse), and studies using direct similarity assessments (i.e., people rate positive information as more similar/less diverse compared to negative information). Next, we suggest explanations for the two properties building on potential adaptive advantages, reinforcement learning, hedonistic sampling processes, similarity from co-occurrence, and similarity from restricted ranges. Finally, we provide examples of how the EvIE model refines well-established effects (e.g., intergroup biases; preferences for groups without motivation or intent) and how it leads to the discovery of novel phenomena (e.g., the common good phenomenon; people share positive traits but negative traits make them distinct). We close by discussing the benefits relative to the drawbacks of ecological approaches in social psychology and how an ecological and cognitive level of analysis may complement each other.
Keywords: Evaluation, ecology, halo effects, person perception, intergroup biases
Excerpts. Check the full paper for figures, tables, references, etc.
Implications
So far, we provided evidence and explanations for the higher frequency of
positive information relative to negative information (i.e., traits, experiences,
behaviours), and the higher similarity of positive information to other positive
information. In the remainder, we aim to back up our claim that the interaction of these properties with well-established social-cognitive principles within
the organism may lead to the discovery of novel phenomena and alternative
explanations for classic social psychological findings. We will address halo
effects, the relation of similarity and liking, the relation of frequency and liking,
as well as the field of intergroup biases. In the following review of our empirical
findings, anything that is reported as a difference is significant (i.e., probability
of the test statistic under the H0 is p < . 05), unless indicated otherwise; all
reported experiments had proper power considerations and reported all conditions, all data exclusions, and all variables. In addition, we predicted the
empirical findings from the assumed properties and did not derive the EvIE’s
properties from these studies; thus, the following experimental work supports
the EvIE as a general model for people’s social reality.
Halo effects: being honest makes you industrious, but lying does not make you lazy
Halo effects are among the best-established findings in psychology. Thorndike
(1920) coined the term when he observed a “constant error in psychological
ratings”: When army officers were evaluated by their superiors, theoretically
independent dimensions constantly correlated more highly than they should.
Thus, raters either used information on one dimension to rate another dimension or made inferences from a global impression about the to-be-rated target
(Cooper, 1981). Probably the most famous halo effect is from ratings of
physical attractiveness to ratings of intelligence or morality, famous under
the “What is beautiful is good” label (Dion, Berscheid, & Walster, 1972).
Based on our assumptions about the EvIE, an intriguing prediction
follows from the similarity property, namely that halo effects should be
most apparent given positive traits and rating dimensions, but less pronounced given negative traits. This is a strong prediction insofar as there is
consensus in the literature that negative information has more impact than
positive information on social evaluations (e.g., Kanouse & Hanson, 1972;
Peeters & Czapinski, 1990; Skowronski & Carlston, 1989).
To test this idea, Gräf and Unkelbach (2016) presented participants with
targets’ positive or negative traits as well as behaviours from two dimensions
of social perception (Bakan, 1966; see also Abele & Wojciszke, 2018), namely
communion (e.g., being honest) and agency (e.g., being industrious), and
asked participants to rate the targets on other traits either from the same or
the other dimension. Across three experiments, Gräf and Unkelbach investigated halo effects on 30 traits and 48 different behaviours. Participants
observed a target showing either a trait label or a behavioural description and
were asked how likely it was that the target would possess another trait
(Experiments 1 and 2) or would show another behaviour (Experiment 3).
Importantly, they varied the valence and the social perception dimension.
For example, participants saw a lying target (i.e., a negative communion trait)
and answered how likely this person was to also be lazy (i.e., a negative
agency trait), or in another trial, how likely this person was also to be egoistic
(i.e., negative communion trait). Similarly, they would see an honest target
and answer how likely this person was to also be industrious (or, in another
trial: helpful). Thus, the trials tested whether halo effects, an inference from
one behaviour/trait to another behaviour/trait, vary as a function of trait/
behaviour valence and as a function of within/between dimension inferences
on the two fundamental dimensions of social perception.
Figure 5 shows the data from these three experiments. As predicted from
the EvIE’s similarity property, positive traits and behaviours lead to substantially stronger halo effects, both within and across the dimensions of communion and agency (Gräf & Unkelbach, 2016; Exp. 1 to 3; see also, 2018, for
a conceptual replication). These findings are difficult to reconcile with classic
assumptions about the unconditional higher impact of negative information
on social evaluations, but they follow from the EvIE’s similarity property. The
results may also explain apparent features in the literature, namely why there
are few published studies showing “negative” halo effects (i.e., “horn” effects),
simply because they usually do not exist (i.e., lying does not make you lazy).
The EvIE’s frequency property also suggests an intriguing point; namely,
that the observed halo effects might not be an error in ratings (Thorndike,
1920), but a true property of the ecology. Similar to our argument concerning
how higher similarity follows from a higher frequency, the higher frequency
of occurrence of positive traits and behaviours also implies that any positive
trait or behaviour is more likely to co-occur. People should, therefore, learn
that positive traits and positive behaviours appear together on a person-level.
If our assumption about the EvIE’s frequency property is correct, then the
personality profile of being both honest and industrious is factually more
likely than the profile of being dishonest and lazy. From an ecological view,
the constant error in ratings observed by Thorndike might not be entirely an
error after all, but a generalisation of observed ecological co-occurrence to
a task involving trait ratings in a psychology experiment. Investigating this
alternative source for halo effects provides a fascinating venue for future
research.
Similarity and liking: your friends are all alike
The EvIE model states that positive information is more similar and less
diverse compared to negative information; as Figure 4 illustrates, there is
only one way (or fewer ways) to be good compared to the many ways
someone might be bad. One implication of this ecological property is that
liked people (i.e., someone’s friends) should be more similar to one another
compared to disliked people.
This is an interesting prediction, because, based on the hedonic sampling
principle discussed above, people should spend more time with other people
they like compared to people they do not like (Denrell, 2005). This increase
in spent time should lead to more knowledge about liked people, and thereby
to a more differentiated representation of these liked others. Smallman and
Roese (2008) explicitly stated this as follows: “to cherish a loved one is to
relish the fine nuances of his or her personality” while “the rejected and
forsaken are construed on a relatively surface level” (p. 1228). However, if we
assume that people like each other because they possess positive traits,
attributes, or qualities which makes them likeable, the EvIE’s assumed
similarity property predicts that these people should be very similar, particularly in comparison to disliked people. Their mental representation might
be highly differentiated as proposed by Smallman and Roese, but this differentiation does not make them dissimilar, just because the properties (i.e.,
traits and behaviours) that lead to liking are factually highly alike.
Alves, Koch, and Unkelbach (2016) conducted seven experiments to test
whether people see other people they like as more similar to one another
compared to people they dislike. We discuss five of these experiments in the
following. The basic paradigm was straightforward. Participants generated
names of target persons they liked and of targets they disliked. Then, they
used the spatial arrangement method described above (see Figure 1‘s right
panel; Hout et al., 2013) or pairwise similarity ratings (see Figure 1‘s left
panel) to arrange these targets on the screen according to the similarities of
their personalities. They also provided ratings of the time spent together with
these people and of how much they knew about them. As expected, participants reported having spent more time with liked compared to disliked
targets, and they reported knowing more about the liked compared to the
disliked targets. Yet, in line with the prediction from the EvIE, participants
consistently reported higher similarity for liked and disliked targets.
Figure 6 provides a summary of the similarity judgements from
Experiments 1, 3 and 5. Experiment 1 used target persons participants knew
personally with spatial arrangement to assess similarity. Experiment 3 used
target persons participants knew personally with pairwise comparisons to
assess similarity. Experiment 5 used celebrity targets with pairwise comparisons. As Figure 6 shows, participants consistently reported liked targets to be
more similar than disliked targets, despite spending more time with them. We
omit Experiments 2 and 6 here; Experiment 2 replicated Experiment 1 with
target valence manipulated between participants and Experiment 6 replicated
Experiment 5 with a larger set of celebrity targets.
Experiment 4 tested the underlying EvIE structure directly. Participants
generated as many traits as they could for each of the four liked and disliked
targets they named. First, in line with the assumed greater knowledge for liked
targets, participants generated on average 6.9 traits for liked, but only 3.9 traits
for disliked targets. Second, we computed the probability that a trait was shared
among the targets. Figure 7‘s left panel shows the relevant data. The probability
that participants generated shared traits among liked targets was substantially
higher compared to disliked targets. This was true within participants’ eight
targets, but also across participants; that is, even across participants, liked targets
were more likely to share traits and therefore be more similar, providing support
for the assumption that there are ecologically fewer ways to be liked than to be
disliked. This difference in shared traits also held when controlling for the
number of generated traits in a regression analysis.
Experiment 7 then flipped the paradigm and asked participants to generate the names of two people they personally knew without specifying
whether they had to be liked or disliked. Instead, we asked them to generate
either positive traits or negative traits that described each of the two targets.
After providing as many traits as they could, we asked participants to rate the
similarity of the two targets. First, as expected, participants showed the
reversed effect as well – generating positive traits made the two targets appear
more similar compared to generating negative traits. As the targets were
selected in both conditions before we asked for positive or negative traits, any
alternative explanation in terms of differential target generation is taken care
of. In addition, participants generated more traits in the positive traits
condition, 6.4 on average, compared to the negative traits condition, where
they generated only 3.8 traits on average. Replicating Experiment 4, as shown
in Figure 7‘s right panel, the probability that participants generated shared
traits among positive traits was substantially higher compared to negative
traits. This was again true within and also across participants, and also when
controlling for the absolute number of traits generated.
Across seven experiments, of which we summarised five here, we found that
positive traits are more frequently generated and these generated traits also are
more likely to be found across targets, leading to the conclusion that liked
people tend to be seen as alike. In particular, the within-participant comparisons might partially follow from intra-psychic mechanisms (e.g., motivated
reasoning to see your friends as similar and good); however, the effects acrossparticipants are difficult to explain without the presented EvIE model (see
Figure 7).
Frequency and valence: the common good in person perception
In another series of experiments (Alves, Koch, & Unkelbach, 2017b), we
tested a prediction from the frequency property discussed above: If positive
information is more frequent, then it should more likely co-occur with other
positive information compared to negative information. Across people, this
implies that people have positive traits in common, but their negative traits
make them distinct: “Those attributes that connect different people and that
define their similarities are usually good attributes. Those attributes that
distinguish different people and make them unique are often bad attributes.”
(p. 512). This prediction follows solely from the frequency property and does
not depend on the similarity of the information.
For illustration, let us again consider the formal relation of shared and
unshared positive and negative attributes, as we did above for personality
traits. For example, positive attributes may have the probability of being
present in any person of p(pos) = 0.6, and negative attributes may have
a probability of being present of p(neg) = 0.2. The probability of a shared
attribute (i.e., being simultaneously present in two persons) being positive is
then p(positive|shared) = p(pos)*p(pos) = 0.36, while the probability for the
negative attribute is p(negative|shared) = p(neg)*p(neg) = 0.04. In other
words, if a positive trait is three times more likely in the ecology than
a negative trait, it is nine times more likely to be shared than a negative
trait. This leads to two hypotheses: positive traits should be more likely to be
shared amongst targets compared to negative traits, p(shared|positive) > p
(shared|negative), and shared traits should be more likely to be positive
compared to negative traits, p(positive|shared) > p(negative|shared).
To test these hypotheses, Alves et al. (2017b) asked participants to
sample traits of target persons. Experiments 1a and 1b tested the first
prediction, p(shared|positive) > p(shared|negative). In Experiment 1a
(n = 41), participants generated two people they knew personally and
then generated four positive traits and four negative traits for one of the
two. Then, we asked them which of the eight traits also described the other
person. In line with our first prediction, participants assigned on average
3.4 positive traits (i.e., almost all) two both targets. Out of the four negative
traits, they assigned only 1.1 to both targets. Figure 8‘s left panel reports the
respective conditional probabilities for positive and negative traits.
To generalise this result, Experiment 1b (n = 82) asked participants to
generate 10 target persons. Then, we randomly sampled a given set of four
positive and four negative traits from Experiment 1a and participants had to
indicate to which of the 10 targets each of the traits applied. Replicating 1a,
participants assigned on average 3.1 of the positive traits to a target from
their own sample, but only 1.2 of the negative traits. Figure 8 shows the
resulting conditional probabilities. As the left panel shows, positive traits
were much more likely to be shared across participants compared to negative
traits. And as the trait and target generation were separated in Experiment
1b, this replication provides support for our ecological argument.
Experiment 2 in this series of “common good” experiments (Alves et al.,
2017b) tested the second prediction: if a trait is shared as opposed to
unshared, it should be more likely positive, and thus, p(positive|shared) > p
(negative|shared). Participants again generated two target names; then, we
asked them for either shared or unshared traits. We asked for four shared
traits in the former, and two traits that belonged uniquely to the first target,
and two traits that belonged uniquely to the second target, in the latter
condition. Then, participants rated the valence of the generated traits.
Figure 8‘s right panel shows the probabilities: Overall, participants generated
more positive traits than negative traits in both conditions, reflecting the
general positivity prevalence. Yet, in the shared condition, 3.5 traits were
positive on average, and only 0.2 traits were negative. In the unshared
condition, 2.3 traits were positive and 1.3 traits were negative. Thus, the traits
people have in common are usually positive.
Experiment 4a (n = 176) in Alves et al. (2017b) aimed to show that
searching for similarities (i.e., shared traits) amplifies the ecological default,
and searching for differences (i.e., unique traits) attenuates it. Thus, the
experiment replicated Experiment 2 but included a “natural” condition, in
addition to the “shared” and “unshared” conditions. The “natural” condition
asked participants to generate traits for two target persons without specifying
whether these should be shared or unshared traits. Again, across conditions,
participants generated substantially more positive traits: about 4.8 traits out
of six were positive. However, the probability of generating a positive trait
varied as a function of the traits being generated as “shared”, “unshared”, or
“natural” (i.e., without specific instructions). Figure 9 shows these probabilities of a trait being positive. The probability of a trait being positive was
smaller in the natural condition compared to the “shared” condition, and
smaller in the “unshared” condition compared to the “natural” condition.
Thus, as predicted, looking for similarities amplifies the prevalence of positive traits, while looking for differences attenuates it.
A basic drawback in the reported “common good” studies so far is that
participants self-generated targets, which makes the observed “common good”
effect less surprising, as most people might generate people they know and also
like, and the phenomenon might follow from the “my friends are all alike”
effect described above. However, the present approach is different as it is solely
based on the proposed EvIE’s frequency property. The similarity property
implies that positive information should always be more similar to other
positive information (again; there is only one way to be good), and thus, as
long as people have friends they like, these should be alike.
The present “common good” effect, however, follows only if the available
information is predominantly positive. This leads to the reverse prediction if
the available information is predominantly negative. Thus, in Experiments 5
and 6 in Alves et al. (2017b) “common good” series, participants did not
generate targets, but we provided liked and disliked targets for which the
available trait information should be either predominantly positive or negative,
respectively. To do so, Experiment 5 took advantage of the US’s bipartisan
political structure of Democrats and Republicans and recruited 310 US participants online. Half of the participants generated either shared or unshared
traits for Mitt Romney and George W. Bush, two well-known republicans, and
the other half did the same for Bill Clinton and Barack Obama, two wellknown democrats. To divide the sample, we asked participants how much they
liked these political figures; 160 participants reported liking the politicians in
their respective conditions, and 143 participants reported disliking them.
Seven participants reported neither liking nor disliking them and were
excluded from the analysis.
In Experiment 6 (n = 307), we sampled the target persons from a list of the
10 most popular and most unpopular people other participants generated.
The 10 most popular people for US citizens were Abraham Lincoln,
John F. Kennedy, Elvis Presley, Martin Luther King, Oprah Winfrey, Taylor
Swift, George Washington, Michael Jordan, Beyoncé Knowles, and Jesus
Christ. The 10 most unpopular people were Adolf Hitler, Donald Trump,
George W. Bush, Osama Bin Laden, Saddam Hussein, Joseph Stalin, Kim
Jong Un, Justin Bieber, Fidel Castro, and Kanye West. For example, participants generated four traits that Abraham Lincoln and Elvis Presley shared or
two traits that were unique to Lincoln and Presley, respectively. Each pairing
was randomly created for each participant. In the negative targets condition,
for example, participants generated traits that Adolf Hitler and Justin Biber
shared, or two traits that were unique to each of these targets.
Figure 10 shows the results for these two studies, plotting the frequency of
traits being positive and negative as a function of being shared or unshared
among the target persons. For liked targets, the trait frequencies replicate the
previous studies. Both for liked political figures of that time as well as
consensually liked persons, looking for similarities yielded many positive
traits, and few negative traits. Looking for differences yielded fewer positive
traits and more negative traits. However, when participants disliked the
targets, that is, when operating in an ecology of predominantly negative
information, they generated more negative traits in the shared compared to
the unshared condition. Conversely, they provided fewer positive traits in the
shared compared to the unshared condition. This pattern of results provided
distinct evidence for the “common good” implication of the EvIE’s assumed
frequency property. Looking for similarities between targets amplifies, and
looking for differences between targets attenuates, the underlying base-rate;
and this base-rate is, in most cases, marked by a high frequency of positive
information, leading to a “Common Good” phenomenon.
Thus, based on the assumption that positive information is more frequent,
we predicted and found a novel phenomenon in person perception – the
common good effect. The attributes people have in common are usually good
attributes, and negative attributes are rather unique. In addition, searching
for similarities leads to the discovery of the common good, while searching
for differences subjectively attenuates the prevalence of positive information.
Intergroup biases: a cognitive-ecological explanation
Having shown implications of positive information’s higher similarity
(strong halo effects from positive traits; friends are more alike than enemies)
and positive information’s higher frequency (the common good phenomenon), our final example provides a genuinely new explanation for intergroup biases (Alves, Koch, & Unkelbach, 2018), by combining basic
cognitive processes with our assumptions about the EvIE.
One of the most prominent effects in social psychology is that people tend to
devalue minorities (e.g., refugees, immigrants) and out-groups (e.g., rival sport
teams, other states). There is a wealth of models and theories to explain these
biases (e.g., Tajfel and Turner’s Social Identity Theory, 1979; or Brewer’s
theory of optimal distinctiveness, 1991). However, taking the assumed EvIE
properties offers a novel explanation.
For this explanation, we only need the assumption that out-groups and
minorities are “novel” groups in comparison to ingroups and majorities. This
is highly plausible, as people usually come in contact first with their ingroups
(e.g., family, fellow citizens) and majorities (e.g., Whites, Christians); they
learn about outgroups and minorities later and these groups are then novel
in comparison to the former.
On the cognitive side, novel groups are defined in relation to existing groups
(i.e., ingroups, majorities) by the attributes that make them unique, rather than
by the attributes they have in common with existing groups (Hodges, 2005;
Sherman et al., 2009; Tversky & Gati, 1978). On the ecological side, as the
presented evidence suggests, positive attributes are less diverse or more similar
than negative information, and positive information is more frequent than
negative information. Consequently, unique attributes that differentiate
a novel group from already-known groups are likely to be negative.
Thus, the argument is as follows: Minorities and outgroups are most likely
novel groups to social perceivers, compared to majorities and ingroups.
Novel groups are defined by their unique attributes (i.e., the cognitive part)
and unique attributes are most likely negative (i.e., the ecological part),
leading to an association between outgroups and minorities with negative
attributes, which in turn may cause negative stereotypes and prejudice.
To test this explanation, we invited participants to take the role of space
explorers. On a novel planet, they would encounter members of two alien
tribes. We used the neutral aliens provided by Gupta et al. (2004) as stimuli.
Participants would encounter one member of the first tribe and receive
information about one of the alien’s trait; that is, they saw a picture of the
alien and the alien’s respective trait (e.g., helpful, intelligent, anxious, or
aggressive). After participants had encountered six members of the alien
tribe, we instructed participants to imagine that they would now continue
their travels and encounter another alien tribe. Then, they would learn about
the traits of six members of the second tribe. In the real world, people should
probabilistically learn first about members of their ingroup before learning
about members of outgroups. Similarly, they are more likely to meet majority
group members before meeting minority group members. Thus, the first
tribe is functionally similar to a majority or ingroup, and the second tribe is
functionally similar to minorities or out-groups. After these learning phases,
participants chose which group they preferred.
The central manipulation across three experiments was the trait pool from
which we assigned the two tribes’ traits. After learning, we asked participants
which tribe they prefer; that is, we elicited a binary preference choice
between the first and the second tribe as the central dependent variable.
Experiment 1 manipulated whether the positive or whether the negative
attributes were shared or unshared among the two groups. That is, in one
condition, the groups’ positive attributes were identical, while their negative
attributes differed, and this was reversed in the other condition. Table 4‘s left
section presents the resulting preference frequencies. As predicted from our
cognitive-ecological explanation, participants preferred the first group when
the positive attributes were shared and negative attributes were unique, but
preferred the second group when positive attributes were unique and negative attributes were shared. In other words, although the distribution of
positive and negative traits was identical, there was a bias against the novel
group in a standard ecology (i.e., where negative information is unique),
which reversed as a function of the trait ecology.
Experiment 2 then manipulated the similarity of evaluative information in
the ecology. We created two attribute ecologies. In the standard ecology,
positive attributes were less diverse compared to negative attributes. In the
reversed ecology, negative attributes were less diverse. We manipulated
diversity by the number of unique traits in a given ecology. In the standard
ecology condition, we randomly sampled each alien tribe’s three positive
traits from a set of four traits, while we sampled the three negative traits from
a set of 16 traits (i.e., there were more ways to be negative). In the reversed
ecology condition, we sampled the alien tribes’ three negative traits from a set
of four traits, and their positive traits from a set of 16 traits (i.e., there were
more ways to be positive). Consequently, in the standard ecology, the
positive traits were likely to be shared and the first tribe should be preferred.
In the reversed ecology, the negative traits were likely to be shared and
the second tribe should be preferred. As Table 4‘s middle panel shows, the
preference frequencies replicated Experiment 1. Participants preferred the
first group in the standard ecology (i.e., when negative attributes were likely
unique), but in the reversed ecology they preferred the second group (i.e.,
when positive attributes were likely unique).
Experiment 3 then manipulated the EvIE’s second property, the frequency
of evaluative information. In the standard ecology, both groups possessed
more positive than negative attributes, while in the reversed ecology, negative attributes were more frequent. Specifically, in the standard ecology, both
tribes displayed four positive traits and one negative trait. Both positive and
negative traits were randomly sampled from a set of six positive and six
negative traits. In the reversed ecology, both tribes displayed four positive
and one negative trait. Consequently, in the standard ecology (positive
frequent), unique attributes were likely to be negative, while in the reversed
ecology, unique attributes were likely to be negative.
Table 4‘s right section shows the respective preference frequencies.
Replicating Experiments 1 and 2, participants preferred the first group in
the standard ecology, but they preferred the second group in the reversed
ecology. One apparent feature of Table 4 is that the standard ecologies (i.e.,
when negative information is unique) yield stronger differences between the
tribes, while the preference differential is less strong when positive information is unique. This is actually in line with our overall assumptions about the
EvIE. We did not control for the connotative similarity of the positive and
negative traits, but research on the similarity of personality traits
(Bruckmüller & Abele, 2013; Gräf & Unkelbach, 2016; Leising et al., 2012)
shows that positive traits are more similar to each other compared to
negative traits. By implication, the positive unique traits were, less “unique”
compared to the negative unique traits. This differential valence asymmetry
explains at least part of the differential impact of the ordering.
Thus, across three experiments, participants associated a novel group
with its unique attributes, which differentiate the group from previously
encountered groups. Depending on the ecology’s properties, unique attributes were more likely to be positive or negative, and participants’ preferences followed accordingly. As the general structural properties of the EvIE
make unique attributes more likely negative, p(negative|unique) > p(positive|unique), an evaluative disadvantage for novel groups, and thereby for
minorities and outgroups, follows. In other words, people do not need
a real conflict (Sherif, Harvey, White, Hood, & Sherif, 1961), motivated
reasoning (Kunda, 1990), or a hostile personality structure to show differential preferences for minorities and outgroups (Altemeyer, 1998). Rather,
all they need is a cognitive system that tries to differentiate different groups
in an ecology that is marked by high similarity and a high frequency of
positive information
Summary of the implications
We have provided two examples of how our EvIE model refines our knowledge
about classic and important social psychological phenomena. First, halo effects;
we have delineated and shown that halo effects appear predominantly for
positive traits, but are largely absent for negative traits, despite the typically
assumed stronger impact of negative information (Baumeister et al., 2001; Ito,
Larsen, Smith, & Cacioppo, 1998). Second, intergroup biases; we have provided a cognitive-ecological explanation for intergroup biases that do not rely
on motivated reasoning (Kunda, 1990; Tajfel & Turner, 1979), but builds solely
on cognitive processes that interact with the EvIE’s properties.
We have also provided two examples that illustrate the discovery of genuinely new phenomena. First, people’s friends are all alike. Based on the proposed
similarity property, we have shown that people perceive others they know and
like as more similar to one another, just because there is not much room for
variety on the positive side. Second, the common good phenomenon; based on
the proposed frequency property, we have shown that what people have in
common are usually positive attributes, just because negative attributes are
infrequent, and their joint occurrence is therefore unlikely.
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