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Towards a Deeper Understanding of Impression Formation – Title page with All Author Information Towards a Deeper Understanding of Impression Formation – New Insights Gained from a Cognitive-Ecological Perspective Johannes Prager1, Joachim I. Krueger2, and Klaus Fiedler 1 1University of Heidelberg 2 Brown University Running head: Cognitive-ecological analysis of impression formation Author Note: The work underlying the present article was supported by a grant provided by the Deutsche Forschungsgemeinschaft to the last author (FI 294/23-1). Email correspondence can be addressed either to [email protected] or to [email protected] Masked Manuscript without Author Information Towards a Deeper Understanding of Impression Formation – New Insights Gained from a Cognitive-Ecological Perspective XXXXX Running head: Cognitive-ecological analysis of impression formation Author Note: XXXXX 2 Cognitive-ecological analysis of impression formation Abstract Impression formation is a basic module of fundamental research in social cognition, with broad implications for applied research on interpersonal relations, social attitudes, employee selection, and person judgments in legal and political context. Drawing on a pool of 28 predominantly positive traits used in Solomon Asch’s (1946) seminal impression studies, two research teams have investigated the impact of the number of person traits sampled randomly from the pool on the evaluative impression of the target person. Whereas Norton, Frost, and Ariely (2007) found a “less-is-more” effect, reflecting less positive impressions with increasing sample size n, Ullrich, Krueger, Brod, and Groschupf (2013) concluded that an n-independent averaging rule can account for the data patterns obtained in both labs. We address this issue by disentangling different influences of n on resulting impressions, namely varying baserates of positive and negative traits, different sampling procedures, and trait diagnosticity. Depending on specific task conditions, which can be derived on theoretical grounds, the strength of resulting impressions (in the direction of the more prevalent valence) (a) increases with increasing n for diagnostic traits, (b) is independent of n for non-diagnostic traits, or (c) decreases with n when self-truncated sampling produces a distinct primacy effect. This refined pattern, which holds for the great majority of individual participants, illustrates the importance of strong theorizing in cumulative science (Fiedler, 2017) built on established empirical laws and logically sound theorizing. Key words: less-is-more, Brunswikian sampling, Thurstonian sampling, primacy advantage, diagnosticity 3 Cognitive-ecological analysis of impression formation Introduction Drawing on the seminal impression-formation paradigm originally developed by Asch (1946), Norton, Frost, and Ariely (2007, 2011), and then Ullrich, Krueger, Brod, and Groschupf (2013) investigated evaluative impressions as a function of the number of traits sampled from a basic set. Participants were asked to evaluate a target person described by a sample of traits that were randomly drawn from a universe of 28 traits used in Asch’s (1946) seminal work. The majority of traits was positive, reflecting the preponderance of positive (i.e., normative) behavior in reality. The central finding reported by Norton et al. (2007, 2011) was called a less-is-more effect: Smaller samples led to more positive impressions. However, two follow-up studies by Ullrich et al. (2013), one of which was coordinated with the Norton team to be an exact replication, did not replicate the less-is-more effect. Instead, judgments closely resembled the average valence of all sampled traits, regardless of sample size. A synthesis of all experiments led Ullrich et al. (2013) to conclude that an unbiased averaging algorithm – in line with Anderson’s (1981) information-integration model – provides a satisfactory account of the entire evidence, including the seemingly divergent findings reported by Norton, Frost, and Ariely (2013). Ullrich et al. (2013) only relied on aggregation of all available data, not on substantial theorizing about underlying mechanisms. Although they discussed several reasons why evaluations might decrease, remain constant, or even increase with sample size (as in a set-size effect, Anderson, 1967), their experiments were not designed to critically test these ideas. We contend that strict theorizing is worthwhile (Fiedler, 2014, 2017), arguing that an averaging rule – though flexible in explaining different kinds of data (Dawes, 1979) – oversimplifies sample-based impression formation. Theoretical progress and a deeper understanding of how impressions depend on sample size can only be obtained when the trait sampling process is set apart from the cognitive trait-integration process. Our cognitive- 4 Cognitive-ecological analysis of impression formation ecological perspective on impression formation leads to several new insights, highlighting that intrapsychic processes can only be understood when the environmental sampling process is analyzed in the first place (Fiedler & Kutzner, 2015). Our aim is not to study naturally occurring impression formation in a dynamic social context, as a function of perceivers’ deliberate search strategies (Waggoner, Smith & Collins, 2009) or impressions in different stages of close relationships (Finkel, Norton, Reis, Ariely, Caprariello, Eastwick, Frost & Maniaci, 2015). Rather, to answer the questions raised by Norton et al. (2007) and Ullrich et al. (2013), it is necessary to rule out the complexities of dynamic social interactions in an experimental design that relies on random sampling of traits. Within such an idealized experimental set-up, statistical sampling theory can be used to derive hypotheses about distinct causal influences on sample-based impression formation. Functional Analysis of the Impression Formation Task With this goal in mind, we extend the paradigm in several ways. First, we ask participants to provide impression judgments of multiple targets across trials, rather than only one judgment as in the preceding experiments. A sequential design does not only increase the overall data base and the reliability of empirical results. It also allows us to relate sample size to strength of impressions within participants, across targets described by varying numbers of traits. Second, we manipulate the base-rates of positive and negative traits in different reference sets, from which the target traits are sampled. Across trials within participants, the positivity rate p+ in the reference traits can take on four levels (.20 vs. .33 vs. .67 vs. .80), yielding repeated measures for positive (p+ > .5) versus negative targets (p+ < .5) and for moderate (p+ =.33 or .67) versus extreme (p+ =.2 or .8) targets. Such an overall flat distribution of impressions at all valence levels should prevent participants from anchoring their judgments in prior expectancies of moderately positive behavior, which is most common in the real world. 5 Cognitive-ecological analysis of impression formation Third, the traits sampled from the four reference sets vary in diagnosticity. Negative and extreme traits are more diagnostic than positive and moderate traits (Koch, Alves, Krüger & Unkelbach, 2016; Peeters & Czapinski, 1990; Unkelbach, Fiedler, Bayer, Stegmüller Danner, 2008). Regarding the “big two”, negative traits related to morality (e.g., dishonesty) and positive traits related to ability (high expertise) are more diagnostic than positive morality traits (honesty) and negative ability traits (low expertise; cf. Fiske, Cuddy & Glick, 2007; Reeder & Brewer, 1979; Skowronski & Carlston, 1987). So, to analyze the impact of diagnosticity, we classify stimulus traits not only by valence and extremity but also by the big two, as referring to ability or morality. Fourth, we introduce a crucial distinction between experimenter-determined and self- truncated sampling. Sample size is either set to n = 2, 4, or 8 in the fixed-n condition, or, in a self- truncated sampling condition, respondents can stop sampling at will. Finally, each participant in a yoked-control condition receives exactly the same stimulus samples as one yoked participant in the self-truncated condition. As we shall see in the next section, the impact of the number of traits on resulting impressions will be radically different in these three sampling conditions. Last but not least, we include two different measures of the resulting impressions. In addition to participants’ final subjective impression judgments, we include a measure of actuarial judgments (Dawes, Faust & Meehl, 1989), defined as the average valence scale value of all traits in a sample. Actuarial judgments capture the stimulus samples that provide the input to the cognitive integration of sampled traits in the final impression judgments. Implications Derived from Pertinent Theories Within this enriched paradigm, a number of distinct theoretical implications can be tested. Let us first outline these basic implications, which may not all be common sense in social cognition research, before we lay out specific predictions and experimental methods. 6 Cognitive-ecological analysis of impression formation Sampling theory. To the extent that either positive or negative valence prevails the distribution of samples drawn from this universe will be skewed. The more common outcome will be overrepresented in the majority of samples, especially when sample size is small (Hadar & Fox, 2009; Hertwig & Pleskac, 2010).
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