Comparing Trait-Wise and Profile-Wise Approaches to Assess the Links Between Metaperceptions and Romantic Interest During Speed-Dating

Abstract

Our perceptions of how we come across to others—metaperceptions—potentially shape whether romantic connections flourish or fade. The current study advanced the investigation of metaperceptions in relationship initiation in two ways: (a) by comparing two leading approaches to assessing meta-accuracy and meta-bias (the profile-wise social accuracy model and the trait-wise truth and bias model) and (b) by examining how metaperceptions of evaluative mate attributes (attractiveness, trustworthiness, status) relate to romantic interest. Using data from a naturalistic speed-dating study (N = 214, 1,730 dyads), we found that perceivers who believed they were seen by others in line with what is normative (meta-normativity) expressed more romantic interest in others, whereas perceivers who underestimated how positively they were seen expressed lower romantic interest. In contrast, more accurate metaperceptions predicted greater romantic interest from others. These findings demonstrate that different analytic approaches offer unique insights into the role of metaperceptions in relationship initiation.

Keywords

metaperceptions truth and bias social accuracy model speed-dating

Introduction

In dating, where even brief initial encounters can direct the course of a romantic trajectory (Baxter et al., 2022), first impressions are critical. People are motivated to understand how they are perceived (e.g., whether they are seen as attractive or trustworthy) to guide who to approach as potential partners. These metaperceptions—how we think others perceive us—can be strongly influenced by subjective experiences, heuristics, and expectations, reflecting meta-biases, and/or can align with other’s impressions, reflecting meta-accuracy (Kenny & DePaulo, 1993). Meta-bias and meta-accuracy can be independent (Fletcher & Kerr, 2010; Gagné & Lydon, 2004; West & Kenny, 2011), and have social implications, including how much people are liked by others, especially in first-impression contexts (Carlson, 2016; Mastroianni et al., 2021; Tissera et al., 2021, 2023, 2025).

Yet, different analytic approaches may yield different insights into meta-accuracy and meta-bias. Two common approaches offer distinct lenses: The profile-wise approach evaluates the overall pattern of metaperceptions across multiple attributes (e.g., Does Mia believe Pavel sees her as more trustworthy than attractive?), whereas the trait-wise approach assesses accuracy and bias for each attribute separately (e.g., Does Mia underestimate how attractive Pavel finds her?). Divergent perspectives suggest that these approaches assess conceptually different processes (Hall et al., 2017) or may be statistically equivalent (Biesanz, 2021). In the current study, we extend the metaperception literature by applying a signature profile-wise approach (the social accuracy model; Biesanz, 2010) and trait-wise approach (the truth and bias model; West & Kenny, 2011) to (a) illustrate the different types of meta-bias and meta-accuracy that can be assessed (see Table 1) and (b) examine how these different types of meta-bias and meta-accuracy relate to initial liking. This comparison should advance understanding of the similar and different insights yielded by each approach.

Table 1.

Overview of Key Terms.

Term	Definition	Illustrative example
Profile-wise approach (social accuracy model)
Meta-normativity	The degree to which metaperceivers perceive that others view them in line with the average, normative profile.	Mia perceives that Pavel sees her as being more trustworthy than attractive, consistent with the average profile (i.e., how most people tend to be).
Distinctive meta-accuracy	The degree to which metaperceivers accurately perceive how their unique pattern of various attributes is viewed by others, beyond the pattern that is generally viewed as desirable and normative and how each perceiver generally views others.	Mia accurately perceives that Pavel uniquely sees her as being more trustworthy than attractive, beyond what is considered normative and how Pavel generally rates others on those traits.
Trait-wise approach (truth and bias model)
Directional meta-bias	The degree to which metaperceivers overestimate or underestimate how they are viewed on a specific attribute compared to how others actually view them on that attribute.	Mia perceives that she is viewed as less attractive than others actually rate her to be.
Tracking meta-accuracy	The degree to which metaperceivers accurately track relative differences in how multiple others view them on a specific attribute.	Mia accurately perceives that Paul views her as more attractive than Pavel does, who, in turn, views her as more attractive than Pradeep does.

We also extended metaperception research beyond personality traits by focusing on evaluative attributes particularly relevant in romantic attraction: attractiveness, trustworthiness, and status (Fletcher et al., 1999). Using a naturalistic speed-dating paradigm, we explored how meta-biases and meta-accuracy in these mate-relevant attributes relate to romantic interest. Speed-dating offers an ideal context for studying these dynamics: Participants form rapid impressions of others and assess how they are being perceived. Hence, meta-biases and meta-accuracy may be especially consequential. For example, overestimating others’ perceptions of one’s attractiveness or trustworthiness (meta-bias) may reduce interest from others, whereas accurately assessing others’ evaluations (meta-accuracy) may enable smoother interactions, promoting liking.

Two Approaches to Meta-Bias and Meta-Accuracy

The Profile-Wise Approach

Profile-wise (or person-centered) approaches (see Borkenau & Leising, 2016) assess metaperceptions across various attributes simultaneously. For example, Mia might accurately recognize that Pavel perceives her as relatively high in attractiveness, somewhat lower in trustworthiness, and even lower in success. Profile meta-accuracy captures whether Mia’s metaperception profile across attributes corresponds to Pavel’s impression profile across attributes. By examining patterns of traits, this approach enables examination of person-level metaperceptual tendencies (e.g., do more accurate metaperceivers elicit more romantic interest?) rather than trait-level metaperceptions (e.g., are accurate metaperceptions of “attractive” linked to greater romantic interest?).

Profile agreement can be influenced by multiple sources, but two key sources are typically examined: normative and distinctive agreement (e.g., Biesanz, 2010, 2021; Borkenau & Leising, 2016; Cronbach, 1955; Wood & Furr, 2016). Meta-normativity reflects whether metaperceptions correspond to the normative profile, typically assessed as the average self-rating (sample means) for each attribute (see Table 1). Table 2 presents a speed-dating example. In this example, the normative profile reflects the averages of how people (metaperceivers) in the sample see themselves on each attribute: on average, people see themselves as moderately attractive (6), relatively high in trustworthiness (8.5), and high in success (8). Mia’s metaperceptions of Pavel reveal that she believes he sees her as somewhat attractive (5), highly trustworthy (8), and relatively successful (7), which aligns with the relative pattern of the normative profile (see Table 2). Thus, Mia displays meta-normativity as her metaperceptions align with the normative profile. Meta-normativity also indexes general positivity of metaperceptions because normative profiles tend to be strongly correlated with socially desirability (Wood & Furr, 2016).

Table 2.

Example of Meta-Normativity and Distinctive Meta-Accuracy.

Metaperceiver	Attribute	Normative profile	Speed-dating partner	Metaperceiver ratings of each attribute	Partner’s ratings of metaperceiver on each attribute	Distinctive partner ratings
Mia	Attractiveness	6	Pavel	5	6.5	0.5
Mia	Trustworthiness	8.5	Pavel	8	9.5	1
Mia	Successful	8	Pavel	7	9	1

Note. This table is based on hypothetical data for illustration purposes. The normative profile reflects the sample averages of how people (metaperceivers) rated themselves on each attribute. Distinctive partner ratings are the partner’s impressions of the metaperceiver for each attribute after subtracting the normative mean for that item. Mia displays meta-normativity: how the relative levels of her metaperceptions across the different traits align with the normative profile. Mia also displays distinctive meta-accuracy: her metaperceptions align with the pattern of Pavel’s distinctive impressions of her. Specifically, she believes she’s seen as less attractive than successful or trustworthy, aligning with Pavel’s distinctive impressions of her.

Distinctive meta-accuracy captures the overlap between metaperceptions and that partner’s unique impressions after adjusting for normativity (see Table 1). Conceptually, distinctive meta-accuracy reflects whether people realize others’ unique impressions of them, beyond how people are normally viewed (i.e., the average profile of attributes in the sample). For example, Mia’s metaperceptions of Pavel show that she correctly realizes Pavel sees her as less attractive (0.5) than trustworthy (1) and successful (1) independent of the normative profile (see distinctive partner ratings column, Table 2). Controlling for normativity is essential because the agreement between the profiles of metaperceptions and partner’s ratings of metaperceivers can be artificially inflated given both are similar to the normative profile (see Table 2, also see Borkenau & Leising, 2016; Wood & Furr, 2016).

The social accuracy model (SAM; Biesanz, 2010) is a commonly used profile-based analytic framework that decomposes variance in accuracy into normative and distinctive components within a single model. Developed to assess how accurately people perceive others, SAM has been adapted to examine metaperceptions (e.g., Carlson, 2016; Tissera et al., 2021; 2023). By decomposing metaperceptions into meta-normativity and distinctive meta-accuracy, researchers can determine whether a metaperceiver’s insight stems from assuming one is seen as typical or desirable (meta-normativity) or from accurately detecting what a particular partner uniquely thinks of them (distinctive meta-accuracy).

Research employing SAM finds that people generally display both meta-normativity and distinctive meta-accuracy of personality traits in platonic and dating contexts (e.g., Carlson, 2016; Elsaadawy & Carlson, 2022; Elsaadawy et al., 2021; Tissera & Lydon, 2022; Tissera et al., 2023), including at speed-dating (Tissera et al., 2021). However, levels of meta-normativity tend to be high whereas levels of distinctive meta-accuracy tend to be moderate-to-low. This pattern indicates people’s judgments about others’ impressions are heavily influenced, implicitly or explicitly, by norms and social desirability (Elsaadawy & Carlson, 2021), highlighting the importance of partialing out meta-normativity when assessing meta-accuracy.

The profile-wise SAM has several statistical advantages: using items as the unit of analysis enhances reliability and power (minimizing Type II error) and reduces the number of analyses (minimizing Type I error) compared to analyzing each trait separately. However, profile-wise approaches are best suited for questions about person-level patterns. When the primary interest is metaperceptions of specific traits, such as whether accurate metaperceptions of attractiveness predict romantic interest, a trait-wise approach is more appropriate.

The Trait-Wise Approach

Trait-wise (or variable-centered) approaches assess metaperceptions of specific attributes. For instance, Mia might accurately perceive how attractive she is seen by Pavel relative to how she is seen by Paul and Pradeep, but not be accurate in her metaperceptions of trustworthiness. Trait-wise approaches may be important if some attribute categories carry distinct implications. For instance, attractiveness may be attended to more consistently and be more important role in initial attraction (Eastwick et al., 2014), whereas trustworthiness may be more important in evaluating long-term compatibility (Fletcher et al., 1999; Wincenciak et al., 2015). Thus, levels and consequences of meta-bias and meta-accuracy may vary across these attribute categories.

The Truth and Bias Model of Judgment (T&B; West & Kenny, 2011) is a trait-wise method that assesses two distinct indices of bias and accuracy: directional bias and tracking accuracy (Table 1). Applied to metaperceptions, directional meta-bias reflects whether metaperceivers overestimate or underestimate how they are rated by partners on an attribute. With multiple partners, directional bias reflects the tendency to underestimate or overestimate across interaction partners. Table 3 presents a speed-dating example. Mia’s metaperceptions show an underestimation or negative directional bias because, compared to how partners rated Mia’s attractiveness, she underrates how attractive she is viewed by her dates by one point.

Table 3.

Example of Directional Meta-Bias and Tracking Meta-Accuracy for Attractiveness.

Metaperceiver	Speed-dating partner	Metaperception rating of attractiveness	Partners’ ratings of metaperceiver’s attractiveness
Mia	Pavel	4	5
Mia	Paul	5	6
Mia	Pradeep	3	4

Note. This table is based on hypothetical data for illustration purposes. Here, Mia displays a negative directional bias in her metaperceptions by consistently underestimating how much speed-dating partners’ view her attractiveness (i.e., metaperceptions of ratings of attractiveness are consistently 1 point lower than partner’s ratings of metaperceiver’s attractiveness across partners), but has high tracking meta-accuracy by differentiating which partners view her as higher or lower on attractiveness (i.e., metaperceptions of attractiveness correlate with partners’ ratings of metaperceivers’ attractiveness).

The T&B simultaneously assesses tracking accuracy, which reflects whether people track relative differences in others’ impressions of them on a given attribute (see Table 1). In a speed-dating context, tracking meta-accuracy involves accurately perceiving relative differences in the impression of different dates. Directional meta-bias and tracking meta-accuracy are distinct (West & Kenny, 2011). Returning to the example in Table 3, despite underestimating how partners are viewing her attractiveness, Mia still discerns that Paul views her as more attractive than Pavel, and Pavel views her as more attractive than Pradeep. The correlation between Mia’s metaperceptions and her dates’ impressions are 1.0, indicating perfect tracking meta-accuracy.

Research on directional meta-bias indicates that people tend to underestimate how much they are liked in early platonic and romantic interactions (Boothby et al., 2018; Vorauer & Cameron, 2002), a pattern that also extends to ongoing relationships (Elsaadawy & Carlson, 2022). These underestimation biases may emerge to protect the self from rejection (Fletcher & Kerr, 2010). However, this tendency may not apply across all attributes; in first impression contexts, people tended to underestimate how positively they are perceived on warmth-related traits (e.g., agreeableness, likability) but not on physical attractiveness (Elsaadawy & Carlson, 2022). These potential differences emphasize the value of applying trait-wise approaches to metaperceptions because meta-bias may vary across attribute categories.

Despite the potential for meta-bias, previous research suggests that people still demonstrate meta-accuracy (e.g., Mastroianni et al., 2021; Tissera & Lydon, 2021). However, meta-accuracy tends to be stronger for attributes that are more observable and more consistently judged by others (e.g., physical attractiveness) because there is more shared agreement in how these traits are perceived (Fletcher et al., 2014; Kenny & DePaulo, 1993). In contrast, less observable attributes (e.g., trustworthiness) may show lower accuracy because concrete behavioral cues may not emerge in initial interactions and thus judgments may be more subjective (Fletcher et al., 2014; Letzring et al., 2006). Perceived modifiability could also play a role: people monitor and evaluate attributes differently when they believe those attributes can be adjusted (Dauenbeimer et al., 2002), which may heighten accuracy for attributes that could be malleable (like attractiveness) relative to qualities viewed as more stable.

How Do Profile-Wise and Trait-Wise Approaches Compare?

Meta-Normativity Versus Directional Bias

Meta-normativity and directional meta-bias both provide insight into how people perceive the positivity of others’ impressions of them, but in different ways. Meta-normativity uses the association between a person’s metaperception profile and the normative profile to index how much that person perceives others’ views of them align with a generally desirable profile across multiple attributes. However, meta-normativity indexes a relative association and thus does not reveal the underlying reasons for a low score. Low meta-normativity could mean that the person believes they are viewed more negatively, or conversely, that they think their partner sees them even more positively than the normative profile. Directional meta-bias, in contrast, indexes a general tendency for metaperceivers to overestimate or underestimate how they are seen on specific attributes, such as whether people tend to think they are seen as more (overestimation) or less (underestimation) attractive compared to how others actually view their attractiveness.

Distinctive Meta-Accuracy Versus Tracking Meta-Accuracy

The profile-wise and trait-wise approaches have been argued to reflect different ways of forming metaperceptions. According to Hall et al. (2017), the profile approach involves a more holistic process capturing whether metaperceptions align with the overall pattern of how people are viewed across multiple attributes. For instance, Mia may not consciously track how each of her dates sees her on separate attributes like attractiveness, trustworthiness, and status. However, if Mia’s ratings reflect how others perceive her general profile, this could suggest a broader ability to detect a pattern of social feedback beyond isolated judgments. A potential limitation is that the profile approach does not indicate which attributes contribute most to accuracy. Mia’s high distinctive meta-accuracy could be driven by just one or two attributes rather than an awareness of her full social profile. In contrast, Hall et al. (2017) suggest that the trait-wise approach assesses whether people can accurately track specific attribute-by-attribute impressions, providing more granular insight within each trait domain (e.g., whether they are seen as more or less attractive or more or less trustworthy).

Despite these proposed conceptual differences, Biesanz (2010, 2021) shows that distinctive meta-accuracy is mathematically equivalent to average tracking accuracy across traits, meaning they may capture the same underlying ability rather than distinct cognitive processes. In other words, if Mia scores high in distinctive meta-accuracy, it suggests that her ratings align with both how others uniquely rank her attributes (e.g., she is seen as more attractive than trustworthy) and her overall standing across attributes compared to others (e.g., she is seen as more attractive and less trustworthy than average). However, even if these models represent the same ability, the mean trait-wise accuracy provided by SAM could still yield different results than each trait-wise accuracy score because the average does not necessarily reflect what happens for each attribute. Moreover, the SAM is higher in statistical power, so it is possible to see significant distinctive accuracy and correlates with outcomes, while some trait-wise accuracy scores might not be significant or significantly correlated with outcomes, simply because of less power. Thus, by applying both approaches, the current study assesses whether these different measures of meta-accuracy yield distinct insights or represent different ways of quantifying the same ability.

Implications of Meta-Bias and Meta-Accuracy for Initial Romantic Interest

Our second aim was to compare how these different indices of meta-bias and meta-accuracy relate to important social outcomes, such as both the metaperceiver’s and the partner’s romantic interest.

Links with Metaperceiver’s Romantic Interest

Meta-Bias

Metaperceivers who believe they are seen positively by their speed-dates may report greater romantic interest; Mia might report greater romantic interest in Pavel if she believes Pavel sees her positively. People enjoy holding positive self-views (Dufner et al., 2019; Taylor & Brown, 1988), which they could reaffirm by believing that others see them positively. People also tend to like those who like them (e.g., Backman & Secord, 1959; Eastwick et al., 2007), potentially making positive metaperceptions a reinforcing factor in romantic attraction. Indeed, meta-normativity in personality impressions is associated with metapeceivers’ liking of others in first-impression contexts (Carlson, 2016; Tissera et al., 2021, 2023). Thus, meta-normativity may relate to metaperceiver romantic interest in speed-dating, perhaps even more strongly since the mate attributes investigated here are more evaluative than personality traits.

Yet, the link between positive metaperceptions and romantic interest might depend on the type of attribute being assessed. Given attractiveness is a primary driver of romantic selection in speed-dating contexts (Eastwick et al., 2014), under- or over-estimating attractiveness may be particularly influential. A person who believes they are seen as more versus less attractive may feel more versus less confident (Bale & Archer, 2013), leading them to express more versus less interest in potential partners. Employing a trait-wise approach may provide more insight into which attributes are most relevant to positive metaperceptions and liking of others at speed-dating.

Meta-Accuracy

Tracking meta-accuracy may be especially beneficial in initial interaction contexts. In outcome-dependent situations involving high uncertainty, such as speed-dating, accurately knowing how one is perceived may reduce uncertainty about interpersonal outcomes, thereby easing self-regulatory demands. As a result, interactions may feel more fluent and familiar, enhancing enjoyment (Reber et al., 2004; Reis et al., 2011). This potential association may be bidirectional: Greater romantic interest in a partner might elicit metaperceivers’ attention to that partner, promoting meta-accuracy. Therefore, tracking meta-accuracy, particularly of key attributes, may relate to metaperceivers’ romantic interest.

Distinctive meta-accuracy for personality has been largely unrelated to liking in first impression contexts (Carlson, 2016; Tissera et al., 2021). One possible explanation is that distinctive meta-accuracy, using the profile approach, averages accuracy across multiple attributes, some of which may have opposing or neutral effects on romantic interest. Given that traits like attractiveness and warmth play a central role in driving romantic interest (Eastwick et al., 2014; Fletcher et al., 1999), meta-accuracy on these attributes may be more relevant to romantic interest but could be positive or negative depending on the content of the meta-accuracy (i.e., realizing that a partner views you as especially attractive may foster confidence and romantic interest; whereas realizing that a partner views you as especially unattractive may reduce confidence and romantic interest). Conversely, being highly accurate in perceiving that one is seen as ambitious may not relate to romantic attraction in the same way, given status-related attributes have a more variable role in romantic interest (Durante et al., 2014; Kavanagh et al., 2010). Thus, distinctive meta-accuracy as a broad measure may not predict romantic interest, but accuracy on specific mate-relevant attributes may.

Links With Romantic Interest From Partners

Meta-Bias

Do metaperceivers who believe they are seen more positively elicit greater interest from potential partners? Interestingly, personality meta-normativity is only weakly or unrelated to being liked by new acquaintances (Carlson, 2016; Tissera et al., 2021, 2023). However, a positive association between meta-normativity and romantic interest may emerge when considering attributes especially relevant in early attraction. Believing that a speed-dating partner views them positively on attributes like attractiveness and warmth could foster confidence and self-assuredness, potentially enhancing social and romantic appeal (Back et al., 2011; Eastwick et al., 2014). Thus, a trait-wise approach may reveal significant associations for specific attributes.

Meta-Accuracy

Previous studies have shown a consistent link between personality distinctive meta-accuracy and positive social correlates: People who are better at understanding how others see them tend to be better liked by others (Carlson, 2016; Tissera et al., 2021, 2023), possibly because distinctive meta-accuracy aids self-presentation and signals self-knowledge. If Mia realizes that Pavel sees her as someone who is less warm than outgoing, she might further display her warm side during their conversation. Partner’s interest in metaperceivers also could boost meta-accuracy. People tend to disclose more to those they like (Collins & Miller, 1994). Thus, if Pavel is romantically interested in Mia, Pavel might be more transparent with Mia, which in turn enhances Mia’s understanding of how Pavel perceives her. In the current studies, we assess whether the link between distinctive meta-accuracy of personality and liking emerges for mate attributes during speed-dating and/or is specific to particular attributes assessed by the trait-wise approach.

The Present Research

This study makes two important contributions examining meta-bias and meta-accuracy in a naturalistic speed-dating context. First, we contrast two approaches—the SAM (profile-wise) and the T&B (trait-wise)—to assessing meta-bias and meta-accuracy. Comparing different indices of meta-bias (meta-normativity and directional meta-bias) and meta-accuracy (distinctive meta-accuracy and tracking meta-accuracy) across approaches will advance understanding of the similar and different insights each approach yields. Second, we extended prior research on metaperceptions that has focused on personality traits to assess how meta-bias and meta-accuracy of evaluative mate attributes (e.g., warmth, attractiveness, status) relate to romantic interest. Our primary goal was to apply and compare the two approaches within an important evaluative context, and thus we did not specify a priori predictions regarding differences between how (a) meta-normativity and meta-bias and (b) distinctive meta-accuracy and tracking meta-accuracy relate to romantic interest. Finally, we examined the role of gender across both analytical approaches in the Supplemental Online Materials.

Method

The data were originally collected for other research purposes. Thus, we did not pre-register the methods. We pre-registered all analyses (https://osf.io/6kgc7/overview), and clearly report any deviations from the preregistration. All analyses were conducted using R (R Core Team, 2020).

Sample and Power

Data were collected across speed-dating events held on a large city-based university campus in New Zealand. The sample size (N = 214) was determined based on the existing dataset (see Table 4 for sample characteristics). Using the effect size (r = .24) and sample size (N = 376) reported by Tissera et al. (2021), and accounting for the uncertainty of the effect size estimate, power analysis using the fabs package in R (github\jbiesanz\fabs; also see Biesanz & Schrager 2017; McShane & Bockenholt, 2016) indicated a power of 0.88 for the current study with 214 participants. Thus, the sample size is adequate for the planned analyses.

Table 4.

Sample Characteristics.

Characteristic	Value
Sample size (N)	214
Age	M_age = 20.10 SD_age = 1.83
Gender	102 Men, 112 women
Speed-dating events	12
Participants per event	M = 16 (range = 11–23)
Ethnicity
NZ European/Pakeha	64.9%
Maori 4.2%	4.2%
Asian	14.0%
Non-NZ European	8.4%
Indian	0.5%
Another ethnicity not listed	7.9%
Total number of dyads	1,889
Excluded dyads	159
Final dyads analyzed	1,730

Note. Familiarity was assessed using the item “To what extent did you know this person before this speed-dating event?” (1 = not at all; 10 = a lot).

Procedure

Participants enrolled in speed-dating events that were advertised campus-wide via noticeboard flyers, electronic notices, and event “pitches” delivered in lectures and other student body meetings. Upon enrolment and before each event, participants completed a pre-event questionnaire assessing demographics, partner ideals, and well-being measures (e.g., self-esteem). At the event, participants engaged in a series of 4-min timed dates with potential romantic partners. After each date, participants independently rated the partner on mate attributes (impressions), how they thought the partner viewed them on these attributes (metaperceptions), and their level of romantic interest in the partner.

Materials

Mate Attributes

Participants were asked to rate their impression of each date for eight mate attributes (e.g., This person is . . . “Attractive/Sexy,” “Kind/Understanding,” “Ambitious/Motivated”; 1 = well-below average; 10 = well-above average). Using the same Likert scale, participants also rated their metaperceptions on the same items (e.g., This person thinks you are . . . “Attractive/Sexy,” “Kind/Understanding,” “Ambitious/Motivated”). In the pre-event questionnaire, participants also provided self-ratings of these same items.

For the trait-wise analyses, we computed three composite scores. Six of the eight items mapped onto three main mate standard categories (Fletcher et al., 1999): attractiveness-vitality (“Attractive/Sexy,” “Outgoing/Adventurous”), warmth-trustworthiness (“Kind/Understanding,” “Honest/Trustworthy”), and status-resources (“Ambitious/Motivated,” “Successful”). Preregistered psychometric analyses revealed that the item “Intelligent” closely correlated with the status-resources category (r = .77) and the item “Well-spoken/Witty” strongly correlated with the attractiveness-vitality category (r = .64). Accordingly, we included these additional items in the respective composite scores. See Tables 4 and 5 for descriptives and correlations among attributes (also see Supplemental Online Materials for item-level descriptive statistics).

Table 5.

Descriptive Statistics of Key Measures.

Measure	Mean	SD	Reliability (α)
Attribute partner ratings
Attractiveness-vitality	6.20	1.72	.81
Warmth-trustworthiness	6.85	1.52	.88
Status-resources	7.03	1.58	.91
Attribute metaperceptions
Attractiveness-vitality	6.32	1.38	.83
Warmth-trustworthiness	6.50	1.37	.89
Status-resources	6.67	1.46	.91
Outcome variable
Romantic interest	4.68	2.40	.93

Note. All items were rated on a 10-point Likert scale.

To verify that the positively valenced items provided sufficient profile differentiation, we examined within-profile variability. At the dyadic level, standard deviations across the eight items showed good spread (impressions: M = 1.19, Median = 1.07; metaperceptions: M = 0.88, Median = 0.83), indicating that participants discriminated meaningfully among attributes. At the participant level, average profile variability also was good (impressions: M = 1.20, Median = 1.13; metaperceptions: M = 0.88, Median = 0.86). These results suggest that participants did not rely on uniform response tendencies, and the profiles contained sufficient variability for estimating meta-accuracy.

Romantic Interest

Participants also indicated their romantic interest toward each date by rating 3 items: “I am romantically interested in this person,” “I would like to get to know this person better,” “I am attracted to this person” (1 = not at all—10 = very much). All three items were averaged to index romantic interest (see Table 5 for descriptive statistics and Table 6 for correlations with attribute scores).

Table 6.

Overview of Correlations Between Key Variables.

Attribute	1	2	3	4	5	6	7	8
Perception variables
1. Attractiveness-vitality	—
2. Warmth-trustworthiness	.58	—
3. Status-resources	.56	.57	—
Metaperception variables
4. Attractiveness-vitality	.19	.03	.11	—
5. Warmth-trustworthiness	.06	.01	.03	.74	—
6. Status-resources	.13	.05	.16	.75	.67	—
Romantic interest variables
7. Metaperceiver interest	−.13	−.01	−.03	.25	.27	.21	—
8. Perceiver interest	.75	.45	.40	.14	.06	.08	−.13	—

Note. These are zero-order correlations. We did not account for the dependency in the data when computing these correlations so we cannot interpret the significance of these correlations. Nevertheless, these provide an idea of the strength of the associations between the constructs studied here.

Results

The Profile-Wise Approach

We applied the SAM to metaperceptions (see Tissera et al., 2021, 2023 for further details). First, we computed the normative profile, obtained by taking the sample average self-reports for each attribute.¹ To calculate the distinctive impressions profile, we subtracted the normative profile from the raw impression ratings. To parse out within- and between-person effects, we also computed a perceiver-effect profile for each partner by averaging their impressions across speed-dates for each item, which represents a partner’s general tendency to perceive others on each item. Perceiver-effects capture stable, person-specific tendencies with which someone perceives others across interactions (Kenny, 1994; also see Elsaadawy et al., 2021; Rau et al., 2021). To obtain distinct impressions of each metaperceiver, we centered partners’ raw impression ratings on their general impressions profile (see Supplemental Online Materials for annotated syntax and preregistration for detailed model equations).

To assess meta-normativity and meta-accuracy, we regressed the grand-mean-centered metaperception profile on three predictors:

(a) the grand-mean centered normative profile (i.e., regression coefficient reflects the correspondence between metaperceptions and the normative profile and thus indexes meta-normativity),

(b) the grand-mean centered perceiver-effect profile (i.e., regression coefficient reflects perceiver-effect meta-accuracy, which is modelled to obtain a cleaner within-person indicator of dyadic distinctive meta-accuracy, and

(c) the unique impressions profile (i.e., regression coefficient reflects the correspondence between metaperceptions and the partner’s distinctive impressions and thus indexes within-person distinctive meta-accuracy, which we refer to as distinctive meta-accuracy).

For completeness, we report the results for the general impressions profile in the Supplemental Online Materials. To address data dependence, we modelled random intercepts and slopes to vary by metaperceiver, partner, and dyad when possible.

Meta-Normativity and Distinctive Meta-Accuracy

The baseline levels from the profile-wise approach are shown in Table 7. The fixed effects reflect the average meta-normativity and distinctive meta-accuracy. A significant coefficient for meta-normativity illustrated that people’s perceptions of how their dates saw them aligned with a normative profile; people, on average, tended to believe they were seen in line with what is normative. A significant coefficient for distinctive meta-accuracy indicated that, above and beyond normativity and the partner’s general impressions, metaperceptions aligned with partner’s distinct impressions of metaperceivers; people, on average, appeared to accurately recognize their dates’ unique impressions of them.

Table 7.

Baseline Levels of Distinctive Meta-Accuracy and Meta-Normativity.

Model parameter	Fixed effects			Random effects (SD)
Model parameter	Estimate (SE) [95% CI]	r	T	Metaperceiver	Perceiver	Dyadic
Meta-normativity	0.28 (0.03)** [0.22, 0.34]	0.55	9.24	0.39	—	—
Distinctive meta-accuracy	0.06 (0.01)** [0.03, 0.08]	0.32	4.39	0.12	0.07	0.16

Note. Random effects (SDs) are reported for parameters where variance components could be reliably estimated. “—” indicate random effects were not estimated because they were too small and produced convergence issues.

p < .001.

The random effects reveal meaningful individual and dyadic differences underlie the average effects. We applied benchmarks from prior work (e.g., Biesanz, 2021; Elsaadawy et al., 2021) to interpret SDs of ~.05 as small, ~.10 moderate, and ~.24 or higher large. The SD for distinctive meta-accuracy showed moderate variability at the metaperceiver level (SD = 0.12) and dyadic level (SD = 0.16), indicating that meta-accuracy differed between individuals and also across specific pairs of speed-daters. Partner level variability was small-to-moderate (SD = 0.07), suggesting that speed-dates had less influence on people’s meta-accuracy. For meta-normativity, the metaperceiver-level variability (SD = 0.39) was large, highlighting substantial between-person differences, which aligns with other work examining meta-accuracy in platonic contexts (e.g., Elsaadawy et al., 2021; Hater et al., 2023). The perceiver and dyadic level random effects for meta-normativity could not be estimated due to convergence issues.

Links With Romantic Interest

To examine the links with romantic interest, we first added metaperceivers’ romantic interest as a moderator in the model (Table 8). Because the SAM estimates meta-normativity and meta-accuracy as slope coefficients, variables such as romantic interest are incorporated as moderators rather than outcomes (also see Human et al., 2020). This approach allows tests of whether meta-bias and -accuracy are stronger or weaker for partners whom metaperceivers find more or less romantically appealing. Metaperceivers displaying higher meta-normativity reported lower romantic interest in their speed-dates,² whereas distinctive meta-accuracy was not significantly related to metaperceivers’ romantic interest.

Table 8.

Profile-Wise Results: Associations Between Meta-Normativity, Distinctive Meta-Accuracy, and Romantic Interest.

Association with	Meta-normativity				Distinctive meta-accuracy
Association with	b [95% CI]	r	t	p	b [95% CI]	r	t	p
Metaperceiver interest	−0.02 [−0.03, −0.01]	.19	−2.39	.017	−0.01 [−0.01, 0.004]	.07	−1.57	.117
Partner interest	−0.0003 [−0.01, 0.01]	.01	−0.04	.966	0.01 [0.002, 0.02]	.17	3.03	.002

Note. Effect sizes (r) were calculated using the formula from Rosenthal and Rosnow (2008): r = $\sqrt{\frac{t^{2}}{t^{2} + d f}}$ .

Bolded values indicate that p < .05. To aid interpretability, please note that each row reflects results from a separate model. Specifically, the baseline estimates of meta-normativity and distinctive meta-accuracy are re-estimated in each model while controlling for the relevant predictors. For example, in models testing “Links with Partner Interest,” the model includes both the baseline effects and the main effect of partner interest, though only the moderation effects are reported here.

Next, we added partners’ romantic interest as a moderator in the model (Table 8) to test the links between distinctive meta-accuracy, meta-normativity, and romantic interest. Here, meta-normativity was not associated with greater romantic interest from speed-dates, whereas higher distinctive meta-accuracy was significantly associated with greater romantic interest from speed-dates.

In sum, metaperceivers believing they are seen in a normative way across ideal mate attributes hinders metaperceivers’ interest in potential speed-dates. In contrast, accurately recognizing speed-dates’ distinctive impressions elicits greater romantic interest from speed-dates. To illustrate, if Mia believed Pavel viewed her in line with the average person’s profile of attributes (e.g., more trustworthy than attractive), she might be less interested in Pavel. However, if she accurately tracked Pavel’s distinct impression of her (e.g., even more trustworthy than attractive than the norm), Pavel might be more interested in her.

Controlling for Metaperceiver Self-Ratings

To evaluate whether the profile-wise results reflected genuine meta-accuracy rather than metaperceivers’ own self-views (Kenny & DePaulo, 1993), we conducted an additional model that incorporated metaperceivers’ self-ratings as a predictor of their metaperceptions. We centered each metaperceiver’s self-rating profile on the average self-rating normative profile and included this distinct self-report profile as an additional fixed effect (termed meta-transparency, reflecting the extent to which a metaperceiver believes their partner viewed them in line with their unique self-report profile). This isolates meta-insight, which is the extent to which metaperceivers recognize partners’ unique impressions that differ from their own self-views, above and beyond the normative profile and the perceiver-effect profile. This additional model produced virtually identical results as those reported above. Meta-normativity continued to be related to metaperceivers’ romantic interest and distinctive meta-insight continued to be related to partners’ romantic interest. Meta-transparency was not related to metaperceivers’ or partners’ romantic interest. See Supplemental Online Materials for full results.

The Trait-Wise Approach

Using the T&B (West & Kenny, 2011), we examined directional meta-bias and tracking meta-accuracy for three key attribute categories: attractiveness-vitality, warmth-trustworthiness, and status-resources. Each category was analyzed separately. We centered both metaperceptions (people’s beliefs about how their partners perceive them; the judgments) and partner impressions (partners’ actual ratings; the truth criterion) on the grand mean of partner impressions. Then, we regressed metaperceptions onto partner impressions (see preregistration for model details). This modeling and centering strategy means that the intercept represents the average directional meta-bias. A negative intercept indicates underestimation meta-bias (i.e., metaperceivers underestimate how positively their speed-dates perceive them) and a positive intercept indicates overestimation meta-bias (i.e., metaperceivers overestimate how positively their speed-dates perceive them). The regression coefficient of partner impressions indexes the correspondence between metaperceptions and partner impressions, reflecting tracking meta-accuracy. To account for the non-independence of the data, we included random effects for metaperceiver, partner, and dyad, simplifying the model when necessary to address convergence issues.

Directional Bias

Significant negative intercepts for warmth-trustworthiness and status-resources revealed that people, on average, significantly underestimated how much their partners perceived them to be warm-trustworthy and high in status-resources. There was no significant directional meta-bias for attractiveness-vitality, suggesting that people, on average, did not under- or over-estimate how attractive their speed-dates found them (see Table 9). Nevertheless, as shown in Figure 1, there appears to be variability in the differences between metaperceptions and partner impressions for each attribute.

Table 9.

Trait-Wise Results: Associations Between Meta-Bias, Meta-Accuracy, and Romantic Interest.

Attribute	Directional meta-bias				Tracking meta-accuracy
Attribute	b [95% CI]	r	t	p	b [95% CI]	r	t	p
Baseline levels
Attractiveness-vitality	0.12 [−0.03, 0.27]	.11	1.58	.115	0.08 [0.03, 0.12]	.24	3.54	<.001
Warmth-trustworthiness	−0.35 [−0.50, −0.20]	−.30	−9.72	<.001	−0.04 [−0.02, −0.05]	.06	−1.41	.162
Status-resources	−0.36 [−0.51, −0.20]	−.29	−4.55	<.001	0.05 [0.01, 0.09]	.20	2.68	.009
Links with metaperceiver interest
Attractiveness-vitality	0.16 [0.14, 0.18]	.72	15.25	<.001	−0.004 [−0.02, 0.01]	−.05	−0.82	.414
Warmth-trustworthiness	0.14 [0.12, 0.17]	.66	12.68	<.001	0.0005 [−0.01, 0.01]	.02	0.07	.942
Status-resources	0.14 [0.12, 0.17]	.63	11.75	<.001	−0.004 [−0.02, 0.01]	−.04	−0.54	.589
Links with partner interest
Attractiveness-vitality	0.03 [0.003, 0.06]	.15	2.18	.030	−0.002 [−0.02, 0.01]	−.02	−0.24	.812
Warmth-trustworthiness	0.02 [−0.01, 0.05]	.10	1.39	.164	0.003 [−0.01, 0.02]	.04	0.51	.609
Status-resources	0.02 [−0.01, 0.05]	.10	1.43	.153	0.02 [0.002, 0.03]	.16	2.27	.024

Note. Effect sizes (r) were calculated using the formula from Rosenthal and Rosnow (2008): r = $\sqrt{\frac{t^{2}}{t^{2} + d f}}$ . Bolded values indicate that p < .05.

Figure 1.

Distribution of Difference Scores Between Metaperceptions and Partner Ratings for Each Attribute.

Tracking Meta-Accuracy

The regression coefficient for partner impressions indexing tracking meta-accuracy was significant for attractiveness-vitality and status-resources attributes indicating that metaperceivers, on average, accurately tracked the degree to which different speed-dates evaluated these attributes. However, metaperceivers did not show significant tracking meta-accuracy of warmth-trustworthiness attributes (see Table 9).

Links With Romantic Interest

To examine the links with romantic interest, we included (1) metaperceivers’ romantic interest as a predictor of the intercept (testing the links between directional bias and romantic interest) and moderator of the effect of partners’ impressions (testing the links between tracking meta-accuracy and romantic interest, see Table 9). Significant associations between directional meta-bias and metaperceivers’ romantic interest emerged for all attributes, indicating the more metaperceivers underestimated their partner’s impressions of their warm, attractiveness, and status, the less interested they were in their dates. Accurately tracking speed-dates’ impressions of attractiveness-vitality, status-resources, and warmth-trustworthiness was not associated with metaperceivers’ romantic interest in their speed-dates. Thus, the results indicate that the more Mia underestimates Pavel’s impression of her on warmth, attractiveness, and status attributes, the less interested she will likely be in Pavel.

Analogous analyses with partners’ romantic interest revealed a significant association between directional meta-bias and speed-dates’ romantic interest for attractiveness-vitality only; the more metaperceivers underestimated how attractive their partners perceived them the less likely their dates were romantically interested them (see Table 9). Additionally, a significant association between tracking meta-accuracy and speed-dates’ romantic interest suggested that people who were able to better track the impressions of their status-resources across different dates received more romantic interest from their dates. Put differently, if Mia is better able to recognize how her different dates judge her status, Pavel will likely be more interested in her.

Controlling for Metaperceiver Self-Ratings

To ensure the trait-wise findings were not due to reliance on self-perceptions when forming metaperceptions, we estimated an additional model including metaperceivers’ self-ratings as an additional predictor, centered on the grand-mean of the truth criterion (i.e., partner impressions) following the T&B (West & Kenny, 2011). The overall pattern of results when controlling for self-perceptions (termed projection in T&B terminology; meta-transparency in SAM) was highly similar to those reported above. Directional meta-bias continued to be related to metaperceiver interest across all attributes, and tracking meta-accuracy for status- resources was related to partner interest. There was one exception: the association between directional meta-bias for attractiveness and partner interest observed in the primary model was no longer significant, suggesting that this small effect may be attributable to projection of self-perceptions. See Supplemental Online Materials for full results.

General Discussion

The present research examined whether knowing speed-dating partners’ impressions of attributes such as attractiveness and trustworthiness related to romantic interest using two popular analytical approaches. Comparing a trait-wise approach and profile-wise approach provided new insight into the different types of meta-bias and meta-accuracy that can be assessed, and how each relates to initial liking. Below, we discuss how comparison of the two models identifies connections and distinctions between assessments of meta-bias and meta-accuracy. We then consider links between meta-bias and meta-accuracy with romantic interest from metaperceivers and their partners.

Do People Display Meta-Bias?

Analyses from the profile approach that examined the overall pattern across multiple attributes indicated significant meta-normativity—metaperceivers believed their pattern of attributes were viewed in line with the ‘average’ profile (see Table 1). Additional analyses (see Supplemental Online Materials) revealed that people displayed meta-normativity for all attribute categories, albeit at smaller levels for warmth-trustworthiness attributes. By comparison, analyses from the trait-wise approach revealed that metaperceivers tended to display a negative directional meta-bias for warmth-trustworthiness and status-resources (but not attractiveness-vitality) attributes indicating that people generally underestimated how positively their dates saw them on these dimensions. This pattern aligns with prior work showing that people underestimate how positively they are viewed on key attributes (e.g., Boothby et al., 2018; Elsaadawy & Carlson, 2022; Tissera et al., 2025), but also prior findings of no directional bias for attractiveness-vitality in getting-acquainted interactions (Elsaadawy et al., 2023). The differences across attribute categories may suggest that people are more attuned to how they are seen on observable traits like attractiveness, perhaps because of clearer social cues and receiving more consistent feedback compared to more subjective dimensions like trustworthiness or status.

The results from the profile-wise and trait-wise approaches might seem contradictory, but offer unique insights into meta-bias. Often treated as an index of positivity (Wood & Furr, 2016), meta-normativity reflects people perceiving that they are seen as similar to the average profile of attributes across people. By contrast, directional meta-bias reflects people perceiving they are seen as more or less negative than how they are perceived on average by others. Thus, people might be overly critical of how they are perceived in key mate attributes, while still believing they align with an average or normative profile. For example, it is normative to perceive oneself as more intelligent (M_self-ratings = 7.92) than ambitious (M_self-ratings = 7.77), and metaperceivers believe they are seen as more intelligent (M_{metaperceptions} = 6.76) than ambitious (M_{metaperceptions} = 6.63). Yet, overall, people still demonstrate directional bias across these items by underestimating how much their speed-dates perceived them as intelligent (M_{partner-ratings} = 7.14) and ambitious (M_{partner-ratings} = 6.98).

This perceptual pattern could be the result of two distinct psychological processes. One explanation for average levels of negative meta-bias relies on error management (Haselton & Buss, 2000). Overestimating the positivity of a potential romantic partner’s impressions may increase the risk of rejection by failing to motivate appropriate impression management and attention toward the partner. The potential cost of this error may motivate people to be more cautious on average.³ Despite these more pessimistic judgments, meta-normativity—believing one is viewed in line with what is normative—may help maintain a balanced sense of self-worth, since positive self-views protect well-being (Dufner et al., 2019; Taylor & Brown, 1988).

In sum, examining meta-bias employing two approaches illustrates that considering people’s overall impression across different attributes as well as bias in specific attributes enhances understanding of daters’ metaperceptions. Applying only one approach would lead to potentially conflicting conclusions, including that people tend to generate positive metaperceptions (meta-normativity) or are negatively biased (directional meta-bias). Instead, these results reveal that metaperceptions contain different biases that might involve a positive overall impression that one’s relative ordering of traits is viewed normatively, but that levels of some traits are seen more negatively. For instance, Mia may correctly infer that Pavel sees her as more trustworthy than attractive, matching the normative profile, but she may nonetheless underestimate how trustworthy he actually found her. This combined pattern might ensure that Mia attends to conveying key attributes in the impressions she is making to protect from rejection while also protecting her self-worth by feeling viewed as normative.

Do People Display Meta-Accuracy?

Results from the profile-wise approach illustrated that people displayed significant levels of distinctive meta-accuracy when considering metaperceptions across all attributes. Results from the trait-wise approach illustrated that metaperceivers accurately tracked relative differences in partners’ impression of their attractiveness-vitality and status-resources (but not warmth-trustworthiness), and additional analyses examining distinctive meta-accuracy for specific attributes mirrored this pattern (see Supplemental Online Materials). So, Mia might accurately gauge that Pavel sees her as higher on attractiveness than status, and simultaneously realize that Pavel sees her as being more attractive than Paul sees her.

Accurately perceiving how others view one’s attractiveness-vitality and status-resources aligns with past findings that people can generally gauge how they are seen in getting-acquainted contexts (see Tsankova & Tair, 2021 for review). However, our results suggest this might not be the case for less observable attributes, like warmth-trustworthiness, that might require more information to assess beyond brief initial interactions. Other empirical evidence suggests that people are more meta-accurate about more observable attributes, such as talkativeness versus honesty (Carlson & Kenny, 2012; Elsaadawy & Carlson, 2022). Additionally, people tend to self-evaluate differently depending on whether a trait is seen as malleable or stable (Dauenbeimer et al., 2002), which may help explain why meta- accuracy was higher for attractiveness, as people can adjust cues through clothing or grooming, but lower for trustworthiness, which may be considered more stable and less amenable to change. Overall, the present findings suggest a meta-accuracy blind spot in perceptions of trustworthiness in speed-dating contexts. Future research could examine the potential reasons underlying this blind spot, such as observability and perceived malleability.

Do Meta-Bias and Meta-Accuracy Relate to Metaperceivers’ Romantic Interest in Speed-Dates?

Analyses from the profile-wise approach revealed that metaperceivers reported lower romantic interest in speed-dates when they believed they were seen in line with the normative profile, with moderate effect size (r = −.19; Funder & Ozer, 2019). Past work examining personality meta-normativity in speed-dating has shown the opposite pattern: Meta-normativity of personality traits was positively associated with interest in speed-dates (Tissera et al., 2021). Importantly, additional analyses disentangling the normativity from the socially desirable profile (see Footnote 2) confirmed the negative association between meta-normativity and romantic interest was driven by metaperceivers believing they were seen normatively and not simply positively.

Notably, normative and socially desirable profiles are typically highly correlated for personality traits (r = .98; Tissera et al., 2021), but this correlation was weaker (though still high) in our examination of mate attributes (r = .80), possibly because normativity in such attributes is not viewed as highly positive or ideal. Being normative in personality traits may be more desirable than being normative in attractiveness or trustworthiness, which may have lower “average” ratings. Another factor may be the format of the scales used in the current study. Participants rated their impressions and metaperceptions using a scale ranging from well-below (1) to well-above (10) average, explicitly inviting comparison to a perceived population norm. Prior studies have primarily used scales ranging from strongly disagree to strongly agree, which may encourage less comparative and more favorable responding. This difference in framing could reduce the normativity–desirability confound (Wood & Furr, 2016), leading to greater divergence between normative and desirable profiles.

Although diverging from prior work on personality metaperceptions, our results align with Wessels et al. (2020), who found that liking was negatively associated with normative accuracy after accounting for positivity. This pattern suggests that standing out may be more appealing than blending in. In speed-dating contexts, where people often seek reasons to quickly eliminate potential partners (Rosenbaum, 1986), being seen—or believing one is seen—as “average” may signal a lack of distinctiveness, reducing romantic appeal. These findings underscore the importance of evaluative domain in shaping whether meta-normativity fosters or dampens interpersonal interest, and call for further examination of when normativity helps versus hinders social connection.

More consistent with past work (Carlson, 2016; Tissera et al., 2021, 2023), analyses from the trait-wise approach indicated that metaperceivers reported greater romantic interest in their speed-date partners when they believed they were seen more positively on all attributes (with large effect sizes; rs = .63–.72; Funder & Ozer, 2019). People are naturally drawn to others who seem to like them (Lowe & Goldstein, 1970), and believing that one is seen positively by a date likely signals greater liking from that date. Metaperceivers might be more interested in people they believe view them positively as this likely boosts self-esteem (Blain & Crocker, 1993) and signals greater acceptance and safety in approaching potential partners. Examining these potential mechanisms is a key direction for future research.

Do Meta-Bias and Meta-Accuracy Relate to Romantic Interest From Speed-Dates?

Results from the profile-wise approach aligned with previous research on personality meta-accuracy (Carlson, 2016; Tissera et al., 2021, 2023). Distinctive meta-accuracy—accurately perceiving how speed-dates viewed one’s overall attribute profile—was associated with greater interest from speed-dating partners. Similarly, analyses from the trait-wise approach indicated that metaperceivers who more accurately tracked how they were seen in terms of status-resources (but not attractiveness-vitality or warmth-trustworthiness) elicited greater romantic interest from other speed-daters. The mixed tracking meta-accuracy effects may be due to lower statistical power when assessing specific attribute categories versus assessing all attributes in profile-wise approach. Indeed, a key strength of the profile-wise approach is greater statistical power as capturing accuracy across all attributes enhances sensitivity to detect small-to-moderate effects (rs = .16–.17; Funder & Ozer, 2019). Supporting that statistical power may have contributed to the differences across the profile-wise and trait-wise approach, follow-up analyses applying the profile-wise approach to assess distinctive meta-accuracy across attributes in each category revealed that effects of distinctive meta-accuracy on partners’ romantic interest did not differ across warmth-trustworthiness, attractiveness-vitality, and status-resources.

Accurately perceiving how one is viewed by speed-dates may elicit greater romantic interest for several reasons. First, speed-dating involves making quick judgments about potential partners. Knowing the impressions one is making allows tailored self-presentation to emphasize strengths and address potential weaknesses or misunderstandings. Second, higher distinctive meta-accuracy might signal to dates that people understand their perspective, are paying attention to them, and are in tune with them during the interaction. Third, a lack of self-awareness or knowledge about a dating partner’s impression could be off-putting, even in brief encounters. Indeed, self-knowledge in itself is an appealing quality (Tenney et al., 2013).

Beyond meta-accuracy, trait-wise analyses showed that more positive metaperceptions of attractiveness-vitality were linked to greater romantic interest from partners. Thus, Pavel may be more attracted to Mia if she does not underestimate Pavel’s impression of her attractiveness. However, this effect was small-to-moderate (r = .15), not observed for other attributes, and meta-bias may be confounded by overall levels of perceived attractiveness (Humberg et al., 2018; Krueger & Wright, 2011; see further discussion below). Indeed, metaperceivers did not show average meta-bias in attractiveness-vitality and were high in tracking accuracy; hence the link with partners’ romantic interest may simply reflect metaperceivers who are more (vs. less) attractive eliciting more (vs. less) romantic interest. Gathering objective ratings of speed-daters’ attractiveness-vitality would clarify this possibility.

SAM & T&B Among Other Analytic Approaches

By directly comparing two models, the present research highlights that the analytic approach meaningfully shapes conclusions drawn about meta-accuracy, meta-bias, and romantic interest. Other frameworks used in the accuracy and bias literature offer different inferential advantages. We briefly discuss these differences with the aim to highlight the value of future research comparing the insights gained by applying different models.

An additional profile-wise approach, the social metaperception accuracy model (SMAM; Hater et al., 2023), extends the logic of the SAM by additionally estimating generalized meta-accuracy, capturing insight into people’s stable reputation across perceivers. When the primary goal is to estimate the overall degree of meta-accuracy or meta-bias, or to examine how these relate to downstream outcomes, the SAM provides a direct and parsimonious framework. By contrast, the SMAM is appropriate for additional questions that target the processes underlying meta-accuracy, such as whether a predictor (e.g., personality) relates to reputation-level, dyadic, or perceiver-effect meta-accuracy, each reflecting distinct psychological mechanisms.

Shifting to trait-wise approaches, the social relations model (SRM; Kenny, 1994) is applied with the goal to partition variance in metaperceptions into actor (metaperceiver), partner, and dyadic components. The SRM estimates components for, and so can be used to derive meta-accuracy at, each level (e.g., dyadic vs. reputation-level meta-accuracy) for impressions and metaperceptions. However, the SRM does not allow decomposition into normative and distinctive components or flexible moderator tests because it quantifies meta-accuracy by correlating variance components rather than by estimating slopes linking metaperceptions to partner impressions. Accordingly, the SRM is most appropriate to answer structural questions, such as where does meta-accuracy reside? rather than to test moderators of accuracy or bias regression parameters as in the current study.

Other trait-wise models are more useful for assessing the correlates of meta-bias/accuracy. Drawing on polynomial regressions, response surface analysis (RSA; Edwards, 2002) models how agreement and discrepancy between two judgments predict outcomes. Instead of estimating accuracy and bias as parameters of the judgment process (as we demonstrated using the SAM and T&B), RSA treats congruence and incongruence between metaperceptions and partner impressions as predictors of an outcome. However, it does not decompose judgments into normative, perceiver-effect, and distinctive components, nor does it provide person-level accuracy estimates. Thus, RSA is less suited for quantifying meta-bias or accuracy itself, but offers complementary insight into the correlates of meta-bias and accuracy.

Condition-based regression analysis (CRA; Humberg et al., 2018) is another trait-wise approach that examines the correlates of bias and accuracy, but unlike RSA is restricted to linear relations. Yet, one key benefit of both RSA and CRA is the ability to separate the effects of bias from mean-level judgment tendencies, which can be confounded in difference-score-based approaches like the T&B. Difference scores are correlated with their components, which may mean that the correlation between directional meta-bias and an outcome could be primarily due to one component (Humberg et al., 2018). For example, we found that a lower negative directional bias was associated with greater romantic interest, but this could be primarily due to being less humble (meta-bias effect) or believing one makes more positive impressions (main effect of metaperceptions) promoting greater romantic interest. RSA and CRA more effectively isolates the role of discrepancies between metaperceptions and partners’ impression from main effects of metaperceptions and partners’ impression, and thus—although do not assess normative, perceiver-effect, and distinctive components—are valuable additions to assess the outcomes of meta-bias. However, these approaches require large sample sizes and well-distributed data across the predictor space to reliably estimate linear and higher-order effects, particularly when modeling interactions or polynomial terms.

Finally, other methods such as quasi-signal detection models have been used to separate sensitivity (ability to discriminate true partner states) from bias/criterion (general tendency to infer positive or negative partner responses). This decomposition is conceptually similar to the T&B but grounded in detection theory (e.g., Gable et al., 2003) and to our knowledge has not yet been adapted in the metaperception literature.

In sum, all of these methods can be used to study bias and accuracy in metaperceptions and each emphasize different components. Situating the two approaches we applied and compared among these alternatives further highlights how different analytic tools foreground different psychological processes. Building on the SAM/T&B comparison we provide here, future investigations comparing the results and insights across these different methods will contribute to a more complete understanding of how people believe they are seen by others.

Caveats and Future Directions

The current analysis sheds light on the complex interplay between different types of meta-bias, meta-accuracy, and romantic interest in speed-dating contexts, but some limitations and unanswered questions invite further exploration. First, cross-sectional data limit causal inferences. Meta-accuracy might elicit romantic interest from others, but partners who are more romantically interested in their date may also elicit greater meta-accuracy by metaperceivers (e.g., disclose more personal information, Collins & Miller, 1994). Employing longitudinal designs or experimental manipulations would clarify how metaperceptions and romantic interest influence each other.

Regarding generalizability, our sample involved young adults from a university student population. Older adults might have different characteristic profiles of attributes (Brown & Shinohara, 2013), and knowing how others view these attributes could elicit unique responses. Moreover, the rise of online dating and dating applications raises questions about the relevance of speed-dating as a model for modern dating given key differences across these dating contexts. Although both contexts involve initial encounters with potential partners, online dating involves more asynchronous communication and curated self-presentation compared to face-to-face speed-dating interactions. One useful avenue for future research is to examine whether the asynchronous nature of online dating apps, which can promote overthinking and self-doubt, distorts meta-accuracy and weakens its association with romantic interest.

Furthermore, people vary in levels of meta-accuracy and meta-bias. In the current study, we focused on average effects to compare two analytic approaches. Nonetheless, for both approaches, meta-bias and meta-accuracy varied across individuals (see Table 5 and Figure 1). Prior work has identified key predictors of this between-person variability, and different approaches may provide distinct insight into between-person differences. Profile-wise approaches have shown that people who are better adjusted display greater distinctive meta-accuracy (Hater et al., 2023; Tissera et al., 2021), which our results indicate will elicit more romantic interest from others. By contrast, trait-wise approaches have shown that high versus low self-esteem perceivers tend to over- versus under-estimate acceptance (Cameron et al., 2011), which our results indicate will promote versus hinder romantic interest in others. Moreover, the implications of meta-accuracy and meta-bias for liking may differ as a function of metaperceiver characteristics, such as self-esteem. That is recognizing how others see you, or under- or over-estimation of others’ impressions, could be more beneficial for some people than others. Future studies testing the role of between-person differences as both predictors of metaperceptions and moderators of their implications, using both approaches, will provide further insight into the psychological processes and outcomes associated with distinct meta-accuracy and meta-bias assessments, including whether one approach is more theoretically relevant to test the processes of interest (e.g., adjustment and distinctive meta-accuracy vs. self-esteem and directional meta-bias).

Finally, our assessment of accuracy and bias relied on self-reports, which may be subject to social desirability or limited self-awareness. Future research could incorporate video-recording methods to capture real-time behaviors and self-presentation strategies during interactions. This would allow researchers to examine biases in impressions and metaperceptions compared to more objective measures, and whether people actively modify their behaviors based on their metaperceptions.

Conclusion

We provided a novel comparison of the levels and social correlates of meta-bias and meta-accuracy by applying two approaches used to assess metaperceptions. Comparisons across trait-wise and profile-wise analyses revealed nuanced ways in which metaperceptions can simultaneously be negatively biased, positively biased, and accurate, and demonstrated that biases and inaccurate metaperceptions may be more likely to occur for specific attributes (e.g., warmth-trustworthiness vs attractiveness-vitality attributes). Overall, we found consistent evidence supporting that positive metaperceptions might promote metaperceivers’ romantic interest while accurate metaperceptions might promote partners’ romantic interest. This unique comparison lays the groundwork for understanding how metaperceptions and accuracy shape attraction in initial social interactions, paving the way for future comparisons of the insights gained by different analytic approaches, the between-person differences and psychological processes underpinning accuracy and bias in metaperceptions, and exploration of these processes in more diverse dating contexts.

Supplemental Material

sj-docx-1-psp-10.1177_01461672261436564 – Supplemental material for Comparing Trait-Wise and Profile-Wise Approaches to Assess the Links Between Metaperceptions and Romantic Interest During Speed-Dating

Supplemental material, sj-docx-1-psp-10.1177_01461672261436564 for Comparing Trait-Wise and Profile-Wise Approaches to Assess the Links Between Metaperceptions and Romantic Interest During Speed-Dating by Hasagani Tissera, Nickola C. Overall, Emily J. Cross, Lauren J. Human and John E. Lydon in Personality and Social Psychology Bulletin

Footnotes

ORCID iDs

Hasagani Tissera

Nickola C. Overall

Lauren J. Human

John E. Lydon

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The preparation of this manuscript was supported by SSHRC’s Postdoctoral Fellowship to Hasagani Tissera, University of British Columbia’s Principal’s Research Chair program to Lauren J. Human, and Social Sciences and Humanities Research Council (SSHRC) of Canada Grants to Lauren J. Human (435-2016-0499) and John E. Lydon (435-2017-0689).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material is available online with this article.

Notes

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