Positive–Negative Asymmetry in the Evaluations of Political Candidates. The Role of Features of Similarity and Affect in Voter Behavior

Abstract

In this study we followed the extension of Tversky’s research about features of similarity with its application to open sets. Unlike the original closed-set model in which a feature was shifted between a common and a distinctive set, we investigated how addition of new features and deletion of existing features affected similarity judgments. The model was tested empirically in a political context and we analyzed how positive and negative changes in a candidate’s profile affect the similarity of the politician to his or her ideal and opposite counterpart. The results showed a positive–negative asymmetry in comparison judgments where enhancing negative features (distinctive for an ideal political candidate) had a greater effect on judgments than operations on positive (common) features. However, the effect was not observed for comparisons to a bad politician. Further analyses showed that in the case of a negative reference point, the relationship between similarity judgments and voting intention was mediated by the affective evaluation of the candidate.

Informant Discrepancies in Judgments About Change During Mental Health Treatments

“…Why do our participants trust the insightful pessimists and not the insightful optimists? Judgments that weight negative evidence more heavily relative to positive evidence were seen in a range of other domains, such as processing negative information more and weighting negative information more in the formation of impressions about other people (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001; Ito, Larsen, Smith, & Cacioppo, 1998; Peeters & Czapinski, 1990; Skowronski & Carlston, 1989). The negativity bias is often explained from an evolutionary perspective: It is often more costly to miss negative information than to miss positive information (Baumeister et al, 2001).…”

Is good more alike than bad? Positive-negative asymmetry in the differentiation between options. A study on the evaluation of fictitious political profiles

“…According to normative predictions, two positive features added to an object represented by two positive and four negative features should lead to comparable changes as those produced by two negative features added to an object characterized by two negative and 10.3389/fpsyg.2022.923027 four positive features. However, as shown by Falkowski and Jabłońska (2018), the empirical results tend to diverge from these normative predictions.…”

“…The profiles mainly enumerated certain positive and negative characteristics a given candidate was supposed to have. The features were based on previous research ( Falkowski and Jabłońska, 2018 ) and were additionally tested in a pilot study to make sure that positive and negative features used to describe candidates differed in their valence but not overall strength. Each participant evaluated two randomly selected candidate profiles and the order of presentation was randomized.…”

“…According to the model, a political candidate can be regarded as a set of positive and negative features that describe him or her. Depending on the favorability of candidate image (i.e., the ratio of positive and negative features), additional favorable and unfavorable pieces of information lead to divergent changes in evaluations ( Falkowski and Jabłońska, 2018 ; Falkowski et al, 2021 ). For instance, although positive features will make all candidates more attractive, the improvement will be more visible in the case of bad candidates than the already good ones.…”

Similarity judgements: the comparison of normative predictions and subjective evaluations – A study of the ratio model of similarity in social context

“…1.3 The comparison of objective and subjective similarity estimates and the rationale for the study Falkowski and Jabłońska (2018) and Falkowski et al (2021) were among a few researchers who adopted Tversky's feature-based model of similarity in the context of positive-negative asymmetry. Their investigations delved into how adding or removing positive and negative information influenced the evaluation of fictitious political candidates (Falkowski and Jabłońska, 2018) and cities (Falkowski et al, 2021), depending on the initial favourability of objects under analysis. The results demonstrated that, for objects initially perceived as favourable, negative information had a more substantial impact, whereas for those initially regarded as unfavourable, positive features induced more pronounced changes in evaluation.…”