Psychotic-like experiences are also reported in the general population, particularly during adolescence and young adulthood (Kelleher et al., 2012; van Os et al., 2009). Understanding what gives rise to these symptoms might be important as their presence during late adolescence correlates with an increased risk of developing psychotic (Poulton et al., 2000) and other psychiatric conditions (Bourgin et al., 2020) later in life. Similarly, it is helpful to map out the downstream effects of psychosis proneness (PP) so that informed, targeted interventions focus on the mechanisms more likely to reduce disease burden.

Previous research has indicated that environmental adversity not only elevates the risk of developing schizophrenia but also influences the lower end of the psychosis continuum, such as phenotypic psychotic proneness in healthy populations and unaffected siblings (Varese et al., 2012). Various environmental risk factors for psychosis have been identified, including obstetric complications (Clarke and Cannon, 2020), minority group position (Bourque et al., 2011), urban birth or upbringing (Krabbendam and van Os, 2005), trauma (Varese et al., 2012), and cannabis use (Arseneault et al., 2002). These factors have been associated with neural disruptions, including the prefrontal cortex leading to greater vulnerability (Teicher et al., 2016; van Os et al., 2010).

Alongside environmental factors, cognitive deficits have emerged as a crucial feature of psychotic disorders. These deficits, commonly found among healthy relatives of people with schizophrenia, (Goldberg et al., 1995; Toulopoulou et al., 2003) are thought to precede psychotic symptoms (Reichenberg et al., 2010) and may play a causal role (Toulopoulou et al., 2015). Cognitive dysfunction may, therefore, serve as an early indicator of individuals vulnerable to psychosis before the full onset of the disease. Moreover, disruptions in the cognitive framework that shapes beliefs about oneself (self-schema), have frequently been reported in psychotic disorders. For example, negative self-perception and negative self-concepts have been found to be associated with paranoia (Bentall et al., 2008; Humphrey et al., 2021; Palmier-Claus et al., 2011; Smith et al., 2006), hallucinations (Aaron T. Beck and Rector, 2003; Close and Garety, 1998; Thomas et al., 2015), and delusions (et al., 2001) and may interact with environmental factors (Garety et al., 2007), although the nature and direction of this interaction remain unclear.

Despite progress in understanding PP's neural and environmental mechanisms, comprehensive assessments in the same population are lacking. Furthermore, previous research investigating PP largely employed traditional statistical methods, which fall short of capturing potential causal factors. To address this gap, we took a comprehensive approach to assessment spanning environmental, cognitive, clinical, and neurobiological data. We used causal discovery analysis (CDA), a novel method that differentiates causation from association, to identify causal pathways (cause and effect) to and from PP in the general population. CDA is a data-driven approach used to estimate causal relationships between variables, producing a directed graphical output that represents the cause-and-effect relationships within the data. Unlike traditional methods, CDA does not require pre-defined assumptions by an expert. It identifies the most suitable model for the given data and can accommodate many relevant variables. Since our approach was data-driven, while we anticipated discovering connections between PPS, environmental risk, negative self-schema, and neurobiological deviations, we were mostly agnostic about the direction of causation.