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$A conceptual double-exposure photo showing a young adolescent's face blended with a cracked, weathered wall texture, symbolizing political alienation and disillusionment. The left half reveals the youth's serious expression while the right dissolves into fractured, peeling surfaces, visually representing distrust, powerlessness, and psychological fragmentation associated with the pathway from system-disillusionment toward political radicalism in Swedish adolescents.$

System-disillusioned youth radicalize: The role of political alienation in the formation of political radicalism

$A photo showing a fragmented, close-up human face in muted blue-gray tones, overlaid with distorted textures and streaks of vivid red. The image is heavily manipulated with cracked, glass-like fractures and double-exposure effects that obscure the eyes, evoking themes of psychological rupture, obscured identity, and violence—visually paralleling the article's discussion of ideology-free extremism fixated on aggression itself.$

Extremism without ideology

A photo showing a close-up impasto oil painting of a human face composed of fragmented, palette-knife patches in blue, orange, yellow, red, and white. The mosaic-like brushstrokes assemble piercing blue eyes, nose, and mouth from disparate color blocks, visually evoking the article's "salad bar" metaphor of composite, hybrid extremist worldviews built from diverse ideological fragments.

Why the ‘salad bar’ might actually help extremism research – A reply to Horgan and Shayler

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Maarten Marsman

Department of Psychology, University of Amsterdam; Centre for Urban Mental Health, University of Amsterdam

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Maarten Marsman is an associate professor in the Department of Psychology at the University of Amsterdam, where he is also affiliated with the Centre for Urban Mental Health. Marsman's research centers on Bayesian statistical methods and their application to psychological networks, including work on Markov random fields, the Ising model, and parameter estimation approaches such as maximum likelihood and pseudolikelihood. Much of this work addresses practical problems in network psychometrics, including model comparison using Bayes factors, prior sensitivity analysis, and the development of accessible computational tools for applied researchers.