Adolescent peer victimization and subsequent cognitive-affective biases in threat attention, reactivity, and interpretation

Adolescent peer victimization and subsequent cognitive-affective biases in threat attention, reactivity, and interpretation | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Adolescent peer victimization and subsequent cognitive-affective biases in threat attention, reactivity, and interpretation View ORCID Profile Jens Heumann , View ORCID Profile Manuel Eisner , View ORCID Profile Denis Ribeaud , View ORCID Profile Michael J. Shanahan doi: https://doi.org/10.1101/2025.07.12.664449 Jens Heumann 1 Jacobs Center for Productive Youth Development, University of Zurich , 8050 Zurich, Switzerland 2 Department of Sociology, University of Zurich , 8050 Zurich, Switzerland 3 Center for Human Immunology, University of Zurich , 8006 Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jens Heumann For correspondence: jens.heumann{at}jacobscenter.uzh.ch Manuel Eisner 1 Jacobs Center for Productive Youth Development, University of Zurich , 8050 Zurich, Switzerland 4 Institute of Criminology, University of Cambridge , Cambridge CB3 9DA, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manuel Eisner Denis Ribeaud 1 Jacobs Center for Productive Youth Development, University of Zurich , 8050 Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Denis Ribeaud Michael J. Shanahan 1 Jacobs Center for Productive Youth Development, University of Zurich , 8050 Zurich, Switzerland 2 Department of Sociology, University of Zurich , 8050 Zurich, Switzerland 3 Center for Human Immunology, University of Zurich , 8006 Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael J. Shanahan Abstract Full Text Info/History Metrics Preview PDF Abstract Background Peer victimization (PV) in adolescence—verbal, social, and physical aggression—may disrupt normative developmental processes and is associated with psychosocial difficulties. Although previous research has established associations between early adversity and altered attention and cognitive-affective processing, the specific mechanisms of information-processing through which adolescent PV may affect later social functioning remain unclear. Methods We conducted a counterfactual multimethod study in a sample of young adults (age 22; n = 191; 47.1% female), comparing individuals with and without a history of PV between ages 11–20. Specifically we employed: (1) an eye tracking task using a free-viewing paradigm to assess spontaneous threat-related attention; (2) a longitudinal analysis of changes in hostile attribution and reactive aggression using baseline data collected prior to PV exposure and follow-up data in young adulthood; and (3) a morph-based task assessing facial emotion discrimination capabilities between happy and angry expressions. Results Relative to controls, victims exhibited a vigilance–avoidance attentional pattern and blunted affective responses; they showed reduced initial gaze allocation, repeated returns to angry faces, and a tendency to rate more angry faces as neutral. Over adolescence, a flatter decline in hostile attribution and a steeper drop in reactive aggression led to elevated hostile attribution and reduced reactive aggression. Conclusions These results suggest a victim profile characterized by impaired attentional processing with biased social threat perception, a more hostile view of the social world, and blunted affect. Our study offers preliminary evidence that adolescent PV may induce enduring changes in social information processing, with implications for maladaptive social cognition and increased risk for psychopathology. Introduction One in three youths worldwide is targeted by peer victimization (PV) ( Biswas et al., 2020 ), an adversity involving verbal abuse, sabotage, exclusion, and violence, which can interfere with their fundamental need for social safety and optimal development ( Crick & Dodge, 1994 ; Slavich, 2020 ). Persistent psychosocial obstacles—including peer rejection, friendship difficulties, and romantic barriers—are common consequences for adolescents victimized by peers and can ultimately lead to loneliness and deprivation ( Coelho et al., 2022 ; D’Urso et al., 2023 ; Kochel et al., 2012 ; K. H. Rubin et al., 2009 ), with associated effects on stress physiology due to the resulting loss of social status and absence of prestige ( Sapolsky, 2004 ; Slavich, 2020 ). Unsurprisingly, social anxiety and social withdrawal are about three times more common among victims later in young adulthood ( Siegel et al., 2009 ; Silberg et al., 2016 ; Stapinski et al., 2014 ). While childhood adversity is known to affect later social adjustment by altering key domains of biopsychosocial development ( Crick & Dodge, 1994 ; Norman et al., 2012 ; Samson et al., 2024 )—such as attention to threat ( Briggs-Gowan et al., 2015 ; O’Mahen et al., 2015 ), attribution of intent ( Perren et al., 2013 ), reactive aggression ( Buss & Shackelford, 1997 ; Volk et al., 2022 ), and facial emotion processing ( Jaffee, 2017 )—the mechanisms of social information-processing through which adolescent PV may influence these domains have been scarcely examined to date ( Kellij et al., 2022 ). Attention to threat is often conceptualized as an evolutionary adaptation ( Ceccarini et al., 2024 ; Liu et al., 2021 ; Schimmack, 2005 ): besides promoting survival by enabling rapid threat detection, it also heightens sensitivity to status-relevant cues ( Gilbert, 2001 ; Schimmack, 2005 ). Indeed, healthy prepubertal children focus their attention toward threat, while the ability to suppress excessive threat-related attention typically emerges in adolescence as part of normative cognitive maturation ( Ceccarini et al., 2024 ; Dodge, 2006 ; Kindt & Van Den Hout, 2001 ; Rapee et al., 2023 ). However, adolescents and young adults with elevated anxiety symptoms or trauma—as observed in PV ( Pontillo et al., 2019 )—may retain exaggerated residual attention to threat ( Bishop, 2007 ; MacLeod et al., 1986 ; Rapee et al., 2023 ). Social adversity during this crucial learning phase—such as PV—can disrupt the development of healthy attribution of intent, which may later manifest in ambiguous situations as hostile attribution bias ( Craske et al., 2009 ; Dodge & Coie, 1987 ). Together, attribution of intent and threat-related attention are thought to be interconnected cognitive-affective domains of social threat processing ( Van Bockstaele et al., 2014 ). Both attention to threat and hostile attribution bias have been closely linked to reactive aggression ( Card & Little, 2006 ; Crick & Dodge, 1994 ; Dodge & Coie, 1987 ; Verhoef et al., 2019 ; Wilkowski & Robinson, 2008 ), and hostile attribution bias is a key driver of reactive aggression ( Crick & Dodge, 1994 ). Also, PV has been associated with reactive aggression ( Heilbron & Prinstein, 2008 ); victims may exert forms of retaliation ( Camodeca & Goossens, 2005 ; Herd & Kim-Spoon, 2021 ) or engage in displacement aggression to compensate for loss of social status ( Card & Little, 2006 ; Sapolsky, 2004 ). Lastly, processing of facial expressions—particularly of angry emotions—also continues to develop into adolescence ( Ceccarini et al., 2024 ; Durand et al., 2007 ; Pfaltz et al., 2019 ; Pollak & Kistler, 2002 ). Most of the evidence stems from studies on childhood maltreatment, who report heightened sensitivity to anger cues, which is thought to reflect an adaptive threat-avoidance mechanism ( Gibb et al., 2009 ; Pollak & Kistler, 2002 ). However, there are other studies reporting reduced sensitivity to negative stimuli, particularly following acute stress (F. S. Chen et al., 2014 ) or trauma exposure in adulthood ( Wormwood et al., 2016 ). Yet socially anxious individuals may still interpret ambiguous expressions as angry—not due to heightened sensitivity, but to avoid the cost of missing dominant threat and to adopt submissive behavior for safety ( Maoz et al., 2016 ). Whether such social stress—especially from PV—leads to biased interpretation of facial expressions with its psychosocial consequences, however, remains unclear. Threat-related attention is typically assessed using the dot-probe paradigm, during which emotional and neutral stimuli are briefly presented side by side and then replaced by a probe in one location, which participants localize as quickly as possible while their reaction times are recorded ( Lisk et al., 2020 ; MacLeod et al., 1986 ; Roy et al., 2008 ). Reaction times methods, however, have yielded mixed results; some studies report attention toward threat (e.g., MacLeod et al., 1986 ), while others report attention away from it ( Iffland et al., 2019 ; Lisk et al., 2020 ). One dot-probe study on children exposed to family violence used eye tracking and found faster first fixations but shorter attentional maintenance on emotional faces, suggesting a vigilance–avoidance pattern ( Hoepfel et al., 2022 ). Unlike reaction-time measures, eye tracking offers a direct, time-resolved measure of visual attention across the full trial with fewer confounds ( Armstrong & Olatunji, 2012 ; N. T. M. Chen & Clarke, 2017 ; Günther et al., 2021 ), especially in paradigms with longer viewing times than dot-probe, such as free-viewing tasks ( Günther et al., 2021 ). Hostile attribution bias and reactive aggression are often measured concurrently using questionnaires in which participants read about provocative but ambiguous social situations and indicate the extent to which they attribute hostile intent and how aggressively they would be likely to respond ( Camodeca & Goossens, 2005 ; Ziv et al., 2013 ). While most existing work is cross-sectional ( Klein Tuente et al., 2019 ), longitudinal designs may be better suited to gain insights into whether and how adversity contributes to shifts in these patterns over time. Facial emotion perception is commonly examined using paradigms in which participants rate representations of faces displaying emotional expressions—often artificially blended from two emotions ( Ito et al., 2017 ). Such static images, however, are limited in capturing perceptual biases, as they typically assess sensitivity to a single emotion and do not allow direct comparison between two emotional signals ( Claudino et al., 2019 ; Fu & Pérez-Edgar, 2019 ; Ito et al., 2017 ; Zupan & Eskritt, 2024 ). To remedy this, Ito et al. (2017) had participants morph facial expressions between emotions in real time. Using a counterfactual approach, this study: (1) assesses threat-related attention bias through eye tracking during a free-viewing paradigm—conceptually akin to the dot-probe task but allowing extended exposure; (2) leverages a rare longitudinal design to evaluate changes in reactive aggression, drawing on pre- and post-PV assessments within individuals to capture intra-individual change; and (3) examines facial emotion discrimination bias using an interactive morphing task, during which participants adjust facial expressions to their perceived neutral point. Methods Participants Participants were drawn from the Zurich Brain and Immune Gene Study (z-GIG), a subsample ( n = 200) of the longitudinal z-proso panel ( n ≈ 1,500; Ribeaud et al., 2022 ; z-proso Project Team, 2024 ), enriched for victimization. z-proso began in 2004 with Zurich primary school children. In 2019, following the eighth wave, the z-GIG study was conducted, incorporating physiological, biological, neuroimaging, and psychological outcomes. The present study focuses on cognitive-affective experiments and the Ambiguous Intentions Hostility Questionnaire (AIHQ; Combs et al., 2007 ). The analytic sample comprised n = 191 participants (mean age = 21.80; SD = 0.497; female: 47.1%). PV was assessed from the fourth wave (mean age = 10.90; SD = 0.40) to the eighth wave (mean age = 20.31; SD = 0.345) from retrospective self-reports across experienced and exerted social peer adversity across the domains verbal abuse, sabotage, exclusion, violence, among others, resulting in prospectively structured data ( Murray et al., 2021 ). A binary PV variable (23 victims; 12.0%) was derived, as detailed in the Supporting Information. Morphing and questionnaire data were available for all 191 participants, whereas eye-tracking data were available for 179 participants, as data from nine individuals could not be analyzed due to technical issues. Additionally, DNA was extracted from blood samples to account for genetic influences in the analysis. The z-GIG study was approved by the Zurich Cantonal Ethics Committee (KEK, BASEC No. 2017-02083), and all participants provided written informed consent. Design and Bias Adjustment The study draws on several methodological strategies: (1) longitudinal PV measures were used, with newly collected experimental and questionnaire data minimizing common method variances ( Gini & Pozzoli, 2013 ; Rutter et al., 2001 ); (2) genetically informed inverse probability weighting (IPW), incorporating psychosocial baseline confounders, polygenic risk scores, and genetic ancestry was applied to mitigate bias from both selection into treatment (i.e., PV) and selection into the enriched sample ( Hernan, 2006 ; D. B. Rubin, 1997 ) and to enable covariate adjustment without reducing degrees of freedom in the outcome model ( Rosenbaum & Rubin, 1984 ); (3) missing data were imputed using a random forest method, minimizing bias from list-wise deletion (D. B. Rubin, 1976 ); (4) confounding social adversities such as PV perpetration, sexual victimization, and childhood maltreatment (corporal punishment), were accounted for ( Baldwin et al., 2023 ; Catone et al., 2015 ; Geoffroy et al., 2018 ; Stapinski et al., 2014 ); and (5) longitudinal data were leveraged by adjusting for key time-varying confounders during the victimization period ( Zuber et al., 2023 ), including substance use, psychological help-seeking, and exercise, with principal components (explaining 75% of variance) used to maximize statistical power; and (6) since depression is also associated with biased attention ( Craske et al., 2009 ) cumulative depression was included as covariate, averaged across five waves using the SBQ internalizing scale ( Tremblay, 1994 ; z-proso Project Team, 2024 ), as well as subjective stress at age 20. References for additional scales are provided in Supporting Table S1 . Experimental Paradigms and Surveys Participants first completed a free-viewing task ( Lisk et al., 2020 ; Shechner et al., 2012 ), followed by the AIHQ questionnaire, and finally a morphing task ( Ito et al., 2017 ). Facial expressions for the tasks were selected from the Chicago Face Database ( Ma et al., 2015 ), comprising 42 angry–happy image pairs (21 male, 21 female) with closed-mouth expressions, colored on a white background, and matched to the participants’ age group, with predominantly European-like features for consistency with the sample. Stimulus luminance (mean = 0.814; SD = 0.030) was comparable across expressions ( t ( 82 ) = –1.185; p = 0.240), with any residual differences accounted for in analyses. The viewing distance was about 1 m, with a screen resolution of 1280 × 1024 px. Experiments were conducted in a room with constant artificial lighting, without daylight. Free-viewing? Each of the 42 faces was presented with a happy and an angry expression, randomly positioned on the left or right (537 × 378 px each) against a 50% gray background. Visual stimuli were displayed on a 19-inch Philips 109S2 CRT monitor (1280 × 1024 px resolution, 36.5 cm screen width, 4:3 aspect ratio, and 85 Hz refresh rate). The order of face pairs was randomized once and presented uniformly to all participants for consistency. Eye gaze ( x, y coordinates) was recorded using an EyeLink 1000 (v4.51) at 500 Hz (monocular desktop mounted, chin-stabilized setup; 1280 × 1024 px resolution). Each trial began with a 2 s fixation on a black dot centered on the background. To maintain their attention, participants were informed they would perform a dot-probe task ( MacLeod et al., 1986 ); after 5,000 ms, the faces were replaced by a randomly positioned pair of 20 px black dots (left or right, arranged either horizontally or vertically, with a center-to-center distance of 30 px). Participants responded as quickly as possible by pressing the left arrow key for horizontally arranged dots and the up arrow key for vertical dots. After two test trials, gaze coordinates and reaction times were recorded. Ten participants, including two victims, were excluded from the eye-tracking analysis due to poor calibration, excessive eye movement, or gaze instability (e.g., caused by glasses or poor concentration). Attention in the free-viewing task was assessed using adapted measures previously described ( Stilling et al., 2018 ): (1) attentional maintenance, defined as the ratio of fixations on angry versus happy faces; (2) initial orienting, defined as probability whether the first fixation was directed toward the angry face; (3) disengagement, operationalized as the time elapsed before the initial fixation was broken; and (4) hyperscanning, indexed through comparisons of scan path lengths (in px). Square regions of interest (247 × 247 px) were defined to encompass the entire facial area of each stimulus. Morphing For each of the 42 faces, 101 morphed frames were generated ( Ito et al., 2017 ), ranging from happy to angry expressions. Participants used an interactive morphing interface to adjust a static face (896 × 630 px, 50% gray background) along this continuum, selecting their perceived neutral point between happy and angry as quickly as possible by moving the computer mouse and confirming their choice with the space bar. Starting frames were randomly selected. A black fixation dot was presented for 2 s at the center of the screen between trials. After two test trials, mouse endpoint coordinates and reaction times were recorded. AIHQ The AIHQ ( Combs et al., 2007 ) measured hostile social-cognitive biases in response to ambiguous negative situations varying in intentionality. It includes the key indices hostility bias (hostile attribution) and aggression bias (reactive aggression) among others. In the z-proso panel at age 9, participants completed a similar instrument ( Dodge & Coie, 1987 ; Dodge et al., 1990 ), which closely parallels the AIHQ, except for the absence of a blame score (therefore not included in this analysis), and could be considered its child version. To ensure compatibility, two raters scored responses at ages 9 and 21 with the AIHQ scale (Krippendorff’s α : mean = 0.768; SD = 0.096). All scores were normalized. Genetic Data DNA of 153 (84.1%) participants was isolated from EDTA blood at LIFE & BRAIN GmbH (Bonn, Germany) for high-throughput bulk analysis and genotyped according to the protocol previously described ( Jagannath et al., 2020 ) using the Illumina Infinium GSA array, mapped to GRCh38. Polygenic risk scores, used in IPW, were calculated from European GWAS summary statistics for relevant psychiatric traits [attention-deficit/hyperactivity disorder ( Demontis et al., 2023 ), generalized anxiety disorder ( Otowa et al., 2016 ), major depressive disorder ( Wray et al., 2018 ), and panic disorder ( Forstner et al., 2021 )] converted to GRCh38 using the UCSC LiftOver Tool; quality control followed established guidelines ( Choi et al., 2020 ). The first four principal components of genetic ancestry (Supporting Fig. S3 ) were calculated to account for population structure ( Price et al., 2006 ). PLINK 1.90b7 ( Purcell et al., 2007 ) and GNU awk 5.1.0 was used for DNA bioinformatics. Statistical Analysis Analyses were performed in R 4.4.3 using custom scripts. IPW was applied, incorporating baseline biopsychosocial covariates, psychiatric polygenic risk scores, and genetic ancestry. Missing data were imputed using missRanger v2.6.0 ( Stekhoven & Buhlmann, 2012 ). The larger control group improved covariate balance and comparability with the PV group. Propensity score diagnostics indicated successful covariate balance (for diagnostics see the Supporting Information). For the free-viewing and morphing tasks, IPW-weighted mixed models with a random intercept structure were estimated to account for subject- and trial-specific effects. For the binary outcome (initial orienting toward angry faces), a linear probability mixed model was used due to its straight-forward interpretability; effect estimates and significance patterns showed no meaningful differences compared to binomial models. PV effects on AIHQ scores were estimated using IPW-weighted mixed-effects models with two time-points. All standard errors were estimated using 100,000 bootstrap replications to account for pseudo-population variance introduced by IPW. Results Sample characteristics in the weighted dataset were well balanced between the PV and non-PV control group ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1 Descriptive statistics of the weighted dataset Free-viewing Victims spent less time fixating on angry faces than controls when initially orienting to the happy stimulus; this difference was attenuated when first orienting to angry faces ( Table 2A ). No significant group differences were found in the probability of initially orienting toward either stimulus ( Table 2B ). When initially orienting to angry faces, victims disengaged faster than controls ( Table 2C ), with no significant group difference observed for disengagement from happy faces. Scan paths were about 20% longer in victims than controls ( Table 2D ). As expected in a free-viewing design with prolonged exposure, reaction times did not differ by group ( β = 0.009; 95% CI = [–0.005, 0.023]; p = 0.231). View this table: View inline View popup Download powerpoint Table 2 Results for the free-viewing task (n = 179). AIHQ In both groups, hostile attribution bias and reactive aggression decreased over time. However, victims followed a distinct trajectory. Compared to controls, they showed a weaker decline in hostile attribution bias and a stronger decline in reactive aggression ( Table 3 ; Fig. 1 ). View this table: View inline View popup Download powerpoint Table 3 Results from the AIHQ ( n = 191). Download figure Open in new tab Figure 1 Estimated marginal means of social-cognitive biases. T0 = 9y, T1 = 22y. Biases decreased over time in both groups, but with diverging trajectories. (A) Hostile attribution was initially lower in victims but surpassed that of controls at follow-up. (B) Reactive aggression started slightly higher in victims but declined more steeply, resulting in lower levels than controls. Morphing In the morphing task, victims placed their neutral point further toward the angry end of the morph continuum compared to controls ( Table 4 ). Reaction times for victims did not differ significantly between groups ( β = −0.032; 95% CI = [−0.099, 0.034]; p = 0.347). View this table: View inline View popup Download powerpoint Table 4 Results from the morphing task ( n = 191). Discussion Evidence from this study links adolescent peer victimization (PV) to disruptions in normative psychosocial development and impaired social functioning in young adulthood. Using a counterfactual approach, analyses showed that, compared to a non-PV control group, young adults previously exposed to severe adolescent PV (victims) showed vigilance-avoidance-driven attentional shifts, an interpretational bias toward perceiving angrier faces as neutral, and—over the course of adolescence—kept showing more hostile attribution and less reactive aggression than controls. Reduced attentional maintenance on angry faces among victims compared to controls aligns with findings in physically abused children, who tend to divert their attention away from threat—a behavior linked to social phobia and PTSD (Y. Chen et al., 2002 ; Jaffee, 2017 ; Pine et al., 2005 ). It may also reflect a broader tendency to avoid eye contact with angry—and thus likely more dominant—others, potentially indicating a subordinate social stance ( Sapolsky, 2004 ). Victims’ initial orienting to angry faces appeared to enhance attentional engagement among victims; disengagement from the first fixation was attenuated—i.e., modestly slower—followed by greater cumulative attentional maintenance. Alongside the observed one-fifth longer scan paths, this pattern suggests a sequence of early disengagement followed by repeated returns to threat-related stimuli. Such a profile may reflect a strategic attentional response to social threat (Gü nther et al., 2021; Kircanski et al., 2015 ; Rapee et al., 2023 ), in which initial avoidance is followed by controlled re-engagement aimed at mitigating emotional impact—a pattern consistent with vigilance–avoidance dynamics ( Hoepfel et al., 2022 ). While such avoidance strategies may buffer stress in the short term, this selective attention profile observed among victims—resembling patterns described in social anxiety disorder and PTSD (N. T. M. Chen & Clarke, 2017 ; Wald et al., 2011 ; Williams et al., 2024 )—may reflect a seemingly resilient yet dissociative adaptation during adolescence ( Herzog et al., 2018 ). Over time, this profile may impose sustained cognitive strain (Gü nther et al., 2021; Ke et al., 2022 ; McLaughlin et al., 2015 ), potentially contributing to impaired inhibitory control and cognitive decline in adulthood ( Avramescu et al., 2024 ; Pantoja-Urbán et al., 2024 ), underscoring the relevance of integrative neurobiological and psychological approaches to understanding developmental stress responses. Interestingly, first fixation–related patterns emerged, even though groups did not differ in the likelihood of initially fixating on a particular emotion. Effects specific to angry faces may mirror those reported in a recent meta-analysis (Gü nther et al., 2021), yet the modulation by first fixation raises open questions regarding the role of first fixations in emotion processing, which should be explored in more targeted paradigms. Longitudinal estimates of social-cognitive biases suggest that PV is associated with diverging developmental trajectories. Victims’ flatter decrease in hostile attribution from childhood to adolescence suggests that PV may interfere with the typical normative decline in perceived hostility. This finding complements previous studies showing that victimized adolescents perceive ambiguous social situations as more threatening ( Schacter et al., 2024 ; Ziv et al., 2013 ) with the finding that PV may play a role in the developmental progression of such cognitive biases. In contrast, the steeper decline in reactive aggression among victims suggests a more pronounced reduction in their tendency to endorse aggressive responses over time. Interestingly, animal models have similarly shown that repeated social stress in adolescence is associated with reduced aggression as well as increased avoidance, mediated by reduced HPA axis activity, highlighting adolescence as a sensitive period ( Ver Hoeve et al., 2013 ). While previous research has linked PV to elevated reactive aggression—primarily based on cross-sectional designs ( Card & Little, 2006 )—our findings may complement this literature by capturing a blunting of affective reactivity within victims over time. Consequently, bullies may not only select victims who are less likely to retaliate effectively ( Volk et al., 2022 ), but may also contribute to a maladaptive socialization process that increasingly disempowers victims, diminishing their capacity to respond assertively or redirect frustration. This aligns with findings that individuals of lower social status exhibit reduced displacement aggression and possess fewer means to buffer or cope with frustration, such as social affiliation or support networks, e.g., with fellow subordinate individuals ( Aupperle et al., 2012 ; Chen Zeng et al., 2022 ; Glowacki et al., 2020 ; Kruglanski et al., 2023 ; Sapolsky, 2004 ). This challenges prevailing theories emphasizing retaliation ( Camodeca & Goossens, 2005 ; Herd & KimSpoon, 2021) and highlights the importance of the present longitudinal findings. In a broader sense, such patterns may contribute to the friendship and romantic barriers frequently reported by victims ( Arseneault, 2018 ; D’Urso et al., 2023 ; Kochel et al., 2012 ), as general social approach behavior may also be adversely affected. Indeed, victims often report fewer sexual partners ( Gallup et al., 2009 ; Volk et al., 2022 ), a pattern that may reflect reproductive suppression as commonly observed in subordinate individuals in humans and other animals ( Sapolsky, 2004 ). Anger suppression was also observed in depressive individuals in line with evidence suggesting a negative association between depression and externalized aggression ( Riley et al., 1989 ). One potential mechanism may involve elevated cortisol levels among victims, consistent with evidence linking higher cortisol to reduced reactive aggression ( Pfattheicher & Keller, 2014 ). Notably, victims exhibited lower hostile attribution in childhood compared to controls, suggesting a more trusting or socially naïve interpretive style. This reduced sensitivity to potential interpersonal threat may have increased their vulnerability to PV. Victims exhibited a more anger-shifted neutral point on the happy–angry continuum relative to controls, suggesting a cognitive bias in facial emotion processing. One possible explanation is reduced perceptual sensitivity to angry expressions—potentially reflecting habituation—consistent with prior findings that acute stress can dampen anger perception in healthy children (F. S. Chen et al., 2014 ), a pattern similarly reported in victims of terrorist attacks ( Wormwood et al., 2016 ). Emotional numbing of this kind is commonly observed in PTSD ( Plana et al., 2014 ), suggesting that PV-induced trauma effects (e.g., Ossa et al., 2019 ) might outweigh those associated with social anxiety. Thus, while research on child maltreatment has often highlighted hypersensitivity to threat (e.g., Gibb et al., 2009 ; Pollak and Kistler, 2002 ), our findings in youth instead point to-ward blunted threat sensitivity as a possible downstream effect—consistent with desensitization hypotheses ( Kellij et al., 2022 ). The underlying mechanism could be a reduced neuronal responsivity to negative emotional stimuli. Lesion studies suggest that the ventromedial part of the prefrontal cortex (PFC) and orbitofrontal regions play a crucial role in recognizing facial emotions, especially subtle emotional cues and signs of anger ( Davidson et al., 2000 ; Heberlein et al., 2008 ; Hiser & Koenigs, 2018 ; Tsuchida & Fellows, 2012 ). Supporting this, fNIRS studies have shown reduced oxy-Hb responses in temporal regions during negative face processing in ADHD, a disorder similarly characterized by difficulties in facial emotion recognition ( Ichikawa et al., 2014 ). A similar bias toward perceiving happiness in ambiguous or negative facial expressions has been observed alongside reduced aggression ( Penton-Voak et al., 2013 ) and in individuals with amygdala distortions ( Monk et al., 2006 ; Sato et al., 2002 ), suggesting a broader neural basis involving vmPFC, OFC, amygdala, and temporal circuits. Alternatively, the pattern may reflect increased top-down modulation during emotion interpretation ( McLaughlin et al., 2015 ). Socially anxious individuals interpret happy faces more slowly than angry ones ( Maoz et al., 2016 ), suggesting greater cognitive load when processing positive expressions. Similarly, in children exposed to family violence, the typically negative correlation between amygdala and PFC activity was reversed, indicating heightened self-regulatory engagement in response to emotional stimuli ( Taylor et al., 2006 ). In fact, victims in our study may have selected a facial expression with higher anger intensity as neutral in order to reduce ambiguity and cognitive demand. This interpretation is consistent with the time pressure imposed by the task and the absence of significant group differences in reaction times. Yet another explanation is that victims, being arguably more experienced with anger cues, may be more accurate in identifying anger—suggesting that the actual bias lies with the control group, who tend to flag anger too readily. Limitations Although based on longitudinal data, the study analyzed PV as a binary outcome; youth who were always in the highest decile with victimization but never with perpetration between the ages of 11 and 20 were classified victims. This measurement strategy has arguably identified a distinctive group and provided valuable insights. It could, however, be seen as a limitation in that more detailed statistical analysis of assumed PV patterns (e.g. early vs. late, acute vs. chronic) was not possible; future research may analyze larger samples in which the resulting PV clusters reach a sufficient size for statistical analysis. Moreover, the lack of information about self-perception as a victim made it necessary to ascribe the victim role using observer-defined criteria which may cause underestimation of PV effects. In the free-viewing task, the anxiety level was not modified so it remains unclear whether victims have a different threshold depending on increasing emotional levels ( Cisler & Koster, 2010 ; Grossheinrich et al., 2022 ). Also, because socially anxious individuals may perceive emotional expressions—including happy faces—as threatening ( Liu et al., 2021 ), the happy–angry contrast may limit the interpretability of underlying attentional mechanisms. Conclusion Together, findings suggest that adolescent PV may initiate developmental cascades affecting attentional and social-cognitive functioning, as reflected in disrupted attention to threat, biased attribution of intent, blunted affective re-activity, and impaired facial emotion processing—all of which were observed in young adulthood. The observed patterns among PV victims resemble those seen in social anxiety disorder and post-traumatic stress disorder, which may shed light on the mechanisms behind social withdrawal and loneliness frequently reported by victims. These insights may help inform therapeutic approaches aimed at mitigating victims’ social difficulties. Finally, the enduring psychosocial ramifications of adolescent PV highlight its relevance as a public health concern and call for more targeted, youth-focused research. SUPPORTING INFORMATION Peer victimization variable definition PV classification was based on observer-defined criteria. This was necessary because, in the z-proso questionnaires, victims were only asked about experienced and exerted peer adversity but not about self-perception as a victim. Classification required consideration of subordinate experiences (sometimes already referred to as “victimization”) as well as dominant behavior (sometimes referred to as “perpetration”); if both occurred, the case was considered as dynamic behavior rather than victimization, due to an assumed difference in situational appraisal ( Kemeny, 2003 ). Further, assumptions had to be made about what adversity may have occurred during the gap years between waves, since no assessment was conducted then ( Fig. S1 ). Also, to define an unaffected control group represented in model intercepts, it had to be taken into account that participants may also have faced other impactful adversities, such as sexual adversity—which was not consistently measured—and online and dating adversity—which were not measured at all. Finally, declining trends in adversity severity across the decade of measurement had to be considered. Download figure Open in new tab Figure S1 Study timeline showing the schedule of peer adversity assessments in the z-proso panel. Each bar’s right edge corresponds to a survey wave, while the leftward gradient represents the 12-month retrospective reporting period. Gaps indicate periods when no data were collected. To implement this classification, the following approach was adopted to classify victims of peer adversity: (1) consistently measured incidence across the primary adversity domains—verbal, social, and physical aggression—was assessed using Likert scales (1 = never, 2 = 1 time, 3 = 3 times, 4 = about once a month, 5 = about once a week, 6 = about every day) and summed and normalized separately for experienced and exerted adversity; (2) severe occurrence at each measurement point was defined as scoring above the 90th percentile (subordinate experiences: 2009 = 0.36, 2011 = 0.36, 2013 = 0.28, 2015 = 0.20, 2018 = 0.20; dominant behavior: 2009 = 0.24, 2011 = 0.36, 2013 = 0.28, 2015 = 0.20, 2018 = 0.16), capturing the downward trend; (3) after subtracting dominant behavior, 23 participants with positive scores only in subordinate experiences were coded as victims in the dummy variable and 168 as controls. Controls consisted of 90 individuals (53.6%) with zero primary adversity, 32 (19.0%) with dominant behavior, and 46 (27.4%) with both subordinate experiences and dominant behavior. These behaviors, along with known sexual adversity, were statistically controlled in the model to ensure that the control group (represented in the intercept) was not confounded by other forms of adversity. Download figure Open in new tab Figure S2 Longitudinal patterns of peer adversity. Each row corresponds to one participant, with subordinate experiences (blue) and dominant experiences (orange) displayed across survey waves spanning ages 11 to 20. Individuals with repeated subordinate experiences and no dominant behavior were classified as peer-victimized ( n = 23), comprising the victim group at age 22. Inverse probability weighting (IPW) Across all analyses, IPW ( Hernán & Robins, 2020 ) was applied to account for both treatment (PV) selection bias and stratified sampling. This counterfactual approach generates a pseudo-population approximating a randomized trial and aids causal interpretation and is generally recommended for observational studies, especially in PV research ( Baldwin et al., 2023 ). To further reduce confounding, weights were genetically informed by psychiatric polygenic risk scores (PRSs), genetic ancestry, and biopsychosocial baseline confounders. Confounders were defined as influencing both PV and outcome ( Rubin, 1997 ). Weights for estimating the average treatment effect on the treated (ATT, Eq. 1 ) were derived from propensity scores for PV calculated via a logit model, where D denotes the PV dummy, and e the propensity score ( Morgan & Winship, 2015 ). The ATT was chosen as actual victims represent the primary population of interest. To reduce the influence of extreme values, weights were trimmed at the 5th and 95th percentiles. Baseline data for the propensity score model were taken from: (i) self-reports (adolescents, teachers, and parents) from the first three z-proso waves (ages 7–9) prior to the measurement of PV; (ii) PRS for attention-deficit/hyperactivity disorder (ADHD), generalized anxiety disorder (GAD), major depressive disorder (MD), and panic disorder (PD); and (iii) first four principal components from genotyping based on eigenvalue analysis, which showed a clear inflection point at the fourth component with an eigenvalue near 1 ( Fig. S3 ) ( Cattell, 1966 ; Price et al., 2006 ) to account for population structure within the sample. Missing values (0.5–6% across all variables) were imputed using random forests [missRanger 2.6.0; ( Stekhoven & Buhlmann, 2012 )]. A full list of confounders and corresponding references of expert literature is provided in Table S1 . Download figure Open in new tab Figure S3 Scree plot of eigenvalues from genotyping data. An elbow at the fourth component suggests that the first four dimensions capture ancestry-related structure. To minimize extrapolation bias, ATT estimation was restricted to victims with propensity scores within the range of the controls (common support) and excluded two of the original 25 victims due to lack of appropriate matches ( Austin & Stuart, 2017 ; Stuart, 2010 ), resulting in the final group of 23 victims. IPW weights w were applied as regression weights throughout the analyses using bootstrap standard errors with 100,000 replications and bias-corrected and accelerated confidence intervals (BCa) to account for the pseudo-population variance ( Davison & Hinkley, 1997 ). IPW diagnostics The maximum absolute standardized difference was 0.198 ( Fig. S4A ). Mean of expected weights (0.259) and estimated weights (0.250) differed by -0.009, indicating good agreement and stability ( Reifeis & Hudgens, 2022 ). Propensity scores of victims and controls demonstrated sufficient overlap ( Fig. S4B ). Download figure Open in new tab Figure S4 ( A ) Covariate balance before and after IPW. Absolute standardized differences (ASD) are shown for each covariate used in weighting. Vertical dashed line marks the maximum ASD after weighting (0.198), indicating successful covariate balancing. ( B ) Propensity score distributions. Histogram of propensity scores for treated and control groups in the final analytic sample, indicating sufficient overlap for group comparability. View this table: View inline View popup Download powerpoint Table S1 Confounders relevant to the relationship between PV and health outcomes, identified through expert knowledge and literature. Acknowledgments This work was supported by funding from the Swiss National Science Foundation (Grants 10531C-197964 to MJS, 405240-69025, 100013 116829, 100014 132124, 100014 - 149979, 10FI14 170409), the Jacobs Foundation (Grants 2010-888, 2013-1081-1), the Jacobs Center for Productive Youth Development, the Swiss Federal Office of Public Health (Grants 2.001391, 8.000665), the Canton of Zurich’s Department of Education, the Swiss Federal Commission on Migration (Grants 03-901 (IMES), E-05-1076), the Julius Baer Foundation, and the Visana Foundation. We gratefully acknowledge all individuals from the z-proso and its z-GIG subsample for their voluntary participation. During the work on this paper, Jens Heumann was a fellow of the International Max Planck Research School on the Life Course (LIFE; https://www.imprs-life.mpg.de/ ). Funder Information Declared Swiss National Science Foundation , 10531C-197964 , 405240-69025 , 100013_116829 , 100014_132124 , 100014_149979 , 10FI14_170409 Jacobs Foundation , 2010-888 , 2013-1081-1 Swiss Federal Office of Public Health , 2.001391 , 8.000665 Swiss Federal Commission on Migration , 03-901 (IMES) , E-05-1076 Jacobs Center for Productive Youth Development Footnotes * © 2025 The Authors. Licensed under CC BY-NC-ND 4.0. References ↵ Armstrong , T. , & Olatunji , B. O. ( 2012 ). Eye tracking of attention in the affective disorders: A meta-analytic review and synthesis . Clinical Psychology Review , 32 ( 8 ), 704 – 723 . doi: 10.1016/j.cpr.2012.09.004 . OpenUrl CrossRef PubMed ↵ Arseneault , L. ( 2018 ). Annual Research Review: The persistent and pervasive impact of being bullied in childhood and adolescence: Implications for policy and practice . Journal of Child Psychology and Psychiatry , 59 ( 4 ), 405 – 421 . doi: 10.1111/jcpp.12841 . 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