The Value of Behavioral and Interpersonal Information as an Addition to Self-Reports of Socio-Affective Dynamics

Abstract

This is a comment on the target article by Vize at al., who demonstrated how trait dimensions of psychopathy relate to different indicators of affective and interpersonal processes in a daily life study. We propose three paths on how to advance this research in the future. First, interpersonal information from dyads and groups can supplement self-reports of psychopathy-relevant behavior. Second, the role of the time frames in which psychopathy phenomena unfold needs further exploration and measurement groundwork. Third, passive mobile sensing allows for deriving more objective behavioral measures and sampling across longer time frames with higher temporal resolution.

In their ambitious article, Vize at al. (2025) provided a comprehensive test on how trait dimensions of psychopathy relate to different indicators of affective and interpersonal processes in daily life. The two studies used intensive ecological momentary assessments (EMAs) with impressive amounts of data per person: on average 116 observations per person across 21 days in Sample 1 and 59 observations per person across 4 days in Sample 2. We applaud the authors for the comprehensive approach on this important topic with two complementary samples and intensive daily life data. Here, we offer three directions on how to advance this research in the future.

Interpersonal Space of Psychopathy-Relevant Behavior

It is important to consider the interpersonal space where the behavior of people high in psychopathy occurs (Hopwood, 2018; Hopwood, Wright, & Bleidorn, 2022; Wright et al., 2023). Specifically, dyadic or group processes in response to such behavior are important. Dysfunctional social behavior from people high in psychopathy is often characterized by superficially adjusted and agreeable overtones, which mask manipulative and antagonist motives that are shamelessly pursued (Hopwood, 2018). Relying on only one person of the interpersonal system overlooks its dynamic social nature and the underlying motives. In addition, using self-report information from persons high in psychopathy might be especially problematic because they show deceitful behavior more often and thus unlikely provide honest reports of relevant interpersonal situations featuring themes of aggression or manipulation (Jones & Paulhus, 2017; Rassin et al., 2025).

Different ambulatory assessment techniques could address such interpersonal aspects related to trait-level psychopathy dimensions. These techniques include experience sampling with assessment schedules coordinated among dyads or groups (e.g., Timmons et al., 2017), which allows detecting discrepancies in self and other ratings of personality (e.g., agreeableness) and behavior (e.g., perceived manipulation). In addition, the coding of audio snippets, such as from the Electronically Activated Recorder (Danvers et al., 2023; Mehl, 2017), may be combined with EMA surveys among dyads to gain better insights into daily interpersonal functioning of people high in psychopathy.

Recent studies have demonstrated that such designs are feasible, have the potential to track interpersonal processes from multiple perspectives, and allow one to examine dyad processes over long time periods in daily life. For example, a recent measurement-burst study followed romantic couples over several weeks with five assessments per day and again after several months (Bühler et al., 2024).

Exploration of the Role of Time in Dynamic Processes

Intensive daily life studies on psychopathy such as presented by Vize et al. (2025) automatically lead to dense, repeated measures that capture temporal dynamics of the assessed interpersonal behavior and affect. As a result, a clear understanding and differentiation is needed of what “temporal dynamics” mean (for review of the term “dynamics” in different research traditions, see Kuper et al., 2021) and how they play out in people depending on their psychopathy dispositions. From a nomothetic research tradition, the goal is to find patterns in within-person processes that are characteristic of larger populations, whereas idiographic research aims to derive person-specific parameters of such processes independent of whether they generalize across other people.

In Vize et al. (2025), the temporal dynamics mostly relate to socio-affective processes characterized by specific emotional reactions to the experience of social interactions since the previous assessment (with assessments occurring roughly every 160 min in Sample 1 and every 45 min in Sample 2). For example, people felt more intense hostile affect after recent social interactions that were subjectively experienced as negative (i.e., insults, criticism, or rejection). In addition, mediational multilevel structural equation models confirmed hostile affect at the previous time point as a predictor of negative social interactions. Unexpectedly, different psychopathy dimensions did not moderate these within-person associations across the two studies. Still, it is possible that moderation effects are either smaller than expected (exacerbating power issues with detecting interaction effects; Sommet et al., 2023) or play out across different timescales than the ones examined here. As an example, perceived insults and criticism might lead to hostile feelings within the next minutes, especially among people high in impulsivity, but after half an hour and more, effects might already have dissipated (Vize et al., 2025). This is important because dysfunctional emotion regulation has been suggested as a mediator through which psychopathy fosters impulsive aggression (Long et al., 2014), although evidence from mediation with cross-sectional data is ultimately inconclusive regarding the underlying processes (Garofalo et al., 2021).

A better understanding of the role of time in (micro-)longitudinal processes has recently been called for (Hamaker & Wichers, 2017; Hopwood, Bleidorn, & Wright, 2022), accompanied by advancements in longitudinal modeling of temporally continuous processes (Hecht et al., 2023; Koch et al., 2023). We believe that advancing this understanding requires extensive piloting and construct-validation studies of a more descriptive nature to provide more reliable information about base rates of phenomena and the lags, durations, and latencies of processes such as emotional reactions (e.g., Scott et al., 2017; Wrzus et al., 2015). In addition, homeostatic regulation and feedback-loop processes within these systems should be considered (Wrzus, 2018). For example, it has been suggested that psychopathy relates to lower well-being through poor interpersonal relationships (Love & Holder, 2014). In addition, bidirectional pathways and affective dysfunction predicting lower relationship quality are equally likely (Baskin-Sommers & Newman, 2013; Patrick, 2022; Verschuere et al., 2018), although they occur potentially across different time spans (e.g., hours vs. days).

Therefore, choosing the appropriate EMA sampling schedule and type (pseudorandomized, fixed time, time slots, event-triggered EMA, reminder beeps or not) requires considerable descriptive groundwork (Wrzus & Neubauer, 2023). With EMA self-reports, attention should also be paid to participant burden because too-intensive schedules might already act as an intervention itself or lead to high dropout because of participants’ frustration or competing time demands. In addition, such high-intensity self-report studies limit the generalizability of the sample because specific groups, such as people employed full-time, are less likely to take part. Alternatively, limiting the study duration to just a few days might lead to missing low-base-rate phenomena of psychopathy, such as interpersonal aggression. Both options potentially restrict the generalizability of the findings. Vize et al. (2025) relied on two studies with different assessment schedules and found relatively similar results across both, with little evidence for differential socio-affective dynamics depending on psychopathy dimensions. Still, it is possible that other modes of data collection may help uncover such effects in the future by allowing for a higher temporal coverage and longer study durations.

Mobile-Sensing Measures

Thus, for our third and final point, we propose that novel measurement tools, such as mobile sensing on smartphones and wearables, have great potential to complement EMA measures (Danvers et al., 2023; Ebner-Priemer & Santangelo, 2024; Mehl, 2017). Continuous, passive mobile sensing allows for high temporal coverage and longer assessment schedules with low participant burden, which could be very beneficial for phenomena with a relatively low base rate, such as many of the relevant symptoms and expressions of high psychopathy (e.g., interpersonal aggression, manipulation). During this longer protocol duration, the use of the full ambulatory-assessment toolbox, for example, a combination of EMA and mobile sensing, might help improve on previous data-collection approaches limited by their temporal coverage and sole focus on self-reports.

Specifically, audio signals can be used to either code by hand measures of social interaction quantity and quality (Danvers et al., 2023; Sun et al., 2020) or algorithmically detect conversations and, in part, also qualitative aspects of these interactions (Hebbar et al., 2021, 2024; Rabbi et al., 2011). Collecting conversation data in that level of detail and with high temporal coverage allows one to study how psychopathy dimensions relate to everyday language, for example, regarding emotional detachment (de Almeida Brites, 2016; Hancock et al., 2013; Niemeijer & Kuppens, 2024), although important differences in nonverbal communication would still be missed (ten Brinke et al., 2017). Furthermore, lexical analyses of logged texts typed on the smartphone (especially in text messages) or of detected speech can provide valuable insight into daily life emotional experiences (Bemmann & Buschek, 2020; Bemmann et al., 2024; Schoedel et al., 2023). Aggressive and manipulative behavior of people high in psychopathy might nowadays also play out in online and social media contexts to a considerable extent (Castaño-Pulgarín et al., 2021; Walther, 2022). Finally, the combination of EMA and mobile sensing allows for many different ways to elicit situationally relevant EMA short questionnaires, thus potentially reducing participant burden by focusing on assessments directly after relevant phenomena (Ebner-Priemer & Santangelo, 2024). Research has used psychophysiological measures (e.g., heart rate) to elicit EMA assessments (de Geus & Gevonden, 2024; Hoemann et al., 2020; van Halem et al., 2020). This might be especially suited to assess emotionally arousing social interactions that are relatively rare in daily life (Hoemann et al., 2020). Although challenging to implement from a technical side, a strategy like this could help sample interpersonal conflicts and other situations that are relevant for socio-affective dynamics of psychopathy.

Taken together, more objective behavioral data, which is not distorted by self-report biases or common-method bias (when analyzed in relation to other self-reported constructs), might also alleviate some of the potential issues described above in our first point, that is, relying on self-reports alone. Thus, the potential promise of passively assessed mobile-sensing data stands out. At the same time, there is still enormous work to be done in validating these measures and integrating them into the research process, especially if parts of the target population might not have access to smartphones, as Vize et al. (2025) noted. Examples in the measurement of social interactions demonstrate that small to moderate agreement of mobile-sensing methods with EMA can be reached but that this is dependent on the exact measures that are used and the time frames of aggregation (Langener et al., 2023, 2024; Roos et al., 2023).

Conclusion

Understanding how psychopathy manifests in socio-affective dynamics in daily life is an important endeavor because the understanding might help offset its detrimental consequences for individuals, social relationships, and societies. We have argued that other information sources, such as from interaction partners and unobtrusive behavioral assessments, and a deeper knowledge of the timescales on which socio-affective dynamics unfold will advance this understanding. In conclusion, although much work still lies ahead in terms of validation and piloting studies, we believe that clinical psychology has much to gain from a more extensive use of ambulatory-assessment tools, which can enable the field to obtain a better database to test theories about temporal dynamics and their relation to trait measures (Brose et al., 2022).