Who Has Anomalous Experiences Today? Evidence for the Highly Sensitive Person Paradox

The Regulative Theory of Temperament (RTT)

One of the best established theories describing, classifying, and explaining people’s temperament is the Regulative Theory of Temperament (RTT) (see Strelau, 1983, 1989). This theory considers temperament as a formal structure of character based on action theory (see Tomaszewski, 1978), the theory of conditioned reflexes (Pavlov, 1928), and the concept of arousal (Gray, 1964). This resulted in temperament acquiring two properties: a temporal property (stability over time) and an energetic property (informing about activation and inhibition flows of the organism) (see Strelau, 2008; Strus et al., 2022). RTT conceives temperament as a behavioral marker that allows regulating and defining the individual’s responses to the environment (see Clark & Watson, 2021; Strelau, 1989). This means that the different ways in which a person acts in the face of certain stimuli would not only be justified from the classical and instrumental conditioning theories of learning (e.g., Lee et al., 2021) – they would also be regulated by psychobiological markers intrinsic to each person (i.e., temperament) (Dragan et al., 2022; Fagen et al., 1987). As a consequence, temperament could be measured and used as a predictor of behavior (e.g., Irwin et al., 2018).

RTT describes and explains temperament from seven dimensions (see Cyniak-Cieciura et al., 2018; Strelau, 2008):

(1) Briskness – the agility with which the individual tends to react quickly to environmental stimuli;

Each of these dimensions is validly and reliably measured by the Formal Characteristics of Behaviour-Temperament Inventory (FCB-TI) (see Strelau & Zawadzki, 1993, 1995). In fact, the original version of the FCB-TI only included six of these dimensions (the Rhythmicity dimension was excluded) (see Kantor-Martynuska, 2012). However, Cyniak-Cieciura et al. (2018) developed a revision of the FCB-TI in which they maintained the seven dimensions highlighted above and provided evidence supporting its psychometric properties.

Although Strelau and Zawadzki (1993) recommend distributing the scores of the FCB-TI in two macro-factors (temporal characteristics and energy), Strelau (1983) admitted that both dimensions are related and could form a single macro-factor of temperament. Along this line, the interpretation of the information of this single factor would be centered on the levels of energy or arousal (see Zawadzki & Strelau, 2010). Indeed, in recent reviews of the FCB-TI(R), statistical evidence supported employing the 1-factor solution as a general basis to subsequently extract alternative bifactor solutions (see Cyniak-Cieciura et al., 2018).

RTT allows application of the analysis of temperament influence to almost any field of psychology: for example, in parental educational styles (Mącik, 2020), self-awareness (see Śniecińska, 2020), types of cognitive reasoning (Dragan & Dragan, 2013; Wytykowska et al., 2022), psychopathology (Fruehstorfer et al., 2012; Watson et al., 2022), concentration (Baran et al., 2021), belief systems related to sense of control (Bylinka & Oniszczenko, 2016), and perceptual distortions (Przedniczek & Bednarek, 2021). This evidences the universality of temperament in the prediction of behavior and its importance as a cross-cutting object of study underlying any type of behavior. In this report we will focus on an under-researched area: the effects of temperament on the development of the Sensory Processing Sensitivity (SPS) construct (see Aron et al., 2012), and on the production of anomalous experiences.

The Sensory Processing Sensitivity (SPS) and Highly Sensitive Person (HSP)

SPS is defined as a type of temperament (see Jagiellowicz et al., 2016) characterized by the degree of reactivity to subtle physical stimuli, or as the ease with which a person is overstimulated (Williams et al., 2021). SPS involves sensitivity in signal detection and cognitive processing that allows mental representation and categorization of perceived stimuli (e.g., Greven et al., 2019). Thus, a very high degree of SPS describes a person who is highly susceptible and reactive to subliminal stimuli in the environment (Lionetti et al., 2019). This type of profile is called Highly Sensitive Person (HSP) (see Aron & Aron, 1997) and scientific evidence showed that they are people who easily experience social anxiety (see Hofmann & Bitran, 2007), burnout (Meyerson et al., 2020), and nightmares (Carr et al., 2020), and perceive dreams very lucidly and vividly (Carr et al., 2023). In addition, they may also have a high degree of both empathy for others (see Aron, 2011) and conscientiousness (Acevedo et al., 2014).

In general, HSP individuals exhibit high levels of paranoia and psychoticism compared to low scorers (Konrad & Herzberg, 2019). Based on this trend and other previous evidence (e.g., Fox & Williams, 2000; Irwin, 2009; Irwin et al., 2013), several studies analyzed the relationship between temperament, HSP, and the production of anomalous perceptions (e.g., Gawęda & Kokoszka, 2013; Parra & Argibay, 2016). Specifically, the prevailing evidence holds that people with a tendency toward extraversion and activation tend to develop these types of extraordinary experiences and beliefs (e.g., Andersson et al., 2022; Chauvin & Mullet, 2018). Additionally, evidence also reports that SPS and HSP are not related to anomalous experiences (see Williams et al., 2021). This is not contradictory and is rationally justifiable: people who score high on extraversion biologically possess lower cortical activation than introverted individuals. In order to balance activation levels, extroverted subjects seek stimulation from environmental inputs and, consequently, engage in more socially interactive behaviors (see also Fink & Neubauer, 2004; Stelmack, 1990). In contrast, also following the SPS model, people with an HSP profile would themselves have high levels of cortical activation and would not need environmental stimuli for self-regulation (see Acevedo et al., 2014). Therefore, it is known and proven that the HSP profile is not related to extraversion (Smolewska et al., 2006), and that when this relationship does exist extraversion scores tend to be low (Grimen & Diseth, 2016). So, it is logical, and to be expected, that people with HSP do not have as many anomalous experiences as extroverted subjects. Despite no relationship between HSP and anomalous perceptions, there would remain the question of how to rationally account for high levels of psychoticism (see Konrad & Herzberg, 2019).

The Anomalous Perceptions

The study of anomalous perceptions is important because research focuses on why some people have these perceptions and others do not (see Escolà-Gascón, 2020a, 2020b, 2022a, 2022b, 2022c; Ross et al., 2017). Typically, anomalous perceptions are explained on the basis of the Psychosis Continuum Model (PCM) (e.g., van Os et al., 2009) and justified on the basis of the psychotic phenotype (which includes schizotypal personality) (e.g., Escolà-Gascón, 2022a). The PCM postulates that anomalous perceptions (as positive symptoms of psychosis) fluctuate between two extremes; at one extreme are the more attenuated and non-pathological anomalous perceptions and, at the other, are the more intense, persistent, and dysfunctional perceptual disturbances (e.g., Stefanis et al., 2002). The idea of psychotic phenotypy is to consider the attenuated symptoms of schizotypy as risk factors for future psychotic episodes (Escolà-Gascón & Wright, 2021). Moreover, scientific evidence consistently shows that schizotypy is positively related to anomalous experiences (e.g., Irwin, 2009). The PCM and these robust evidences represent sufficient epistemic and empirical grounds to analyze psychotic symptoms as variables integrated in the classical personality theories, RTT and SPS. PCM symptoms could have a moderating role that rationally justifies when there might be a significant relationship between SPS and anomalous experiences. Despite these rationales, and the numerous model theories published in the literature that purport to predict which people have anomalous experiences and which do not (see Lindeman & Aarnio, 2007; Stone et al., 2018), no integrative model was found that incorporates RTT, SPS, and PCM as predictor and intervening constructs of anomalous experiences.

Integrated Temperamental-Sensitivity Theory of Anomalous Experience (ITSTAE)

The RTT and the SPS framework, along with the empirical evidence previously cited, supports the hypothesis that the temperament dimensions of Briskness, Endurance, and Activity are negatively correlated with SPS (Sobocko & Zelenski, 2015). This is because, as Ujiie and Takahashi (2022) observed, individuals with high SPS tend to exhibit excessively elevated internal arousal, making them less likely to engage in behaviors that would further increase stimulation levels (see also Iimura, 2021). Our hypothesis is that if anomalous experiences are related to certain positive psychotic symptoms (Dagnall et al., 2024), then such experiences would be more likely to occur in individuals with HSP traits only when a psychotic phenotype is also present—typically reflected in high scores on the aforementioned temperament dimensions. Based on this, we propose the Integrated Temperamental-Sensitivity Theory of Anomalous Experience (ITSTAE).

In this model, both temperament and HSP traits exert direct effects on Anomalous Perceived Phenomena (APP), while the PCM plays a central mediating role. Specifically, PCM is influenced by temperament and, in turn, predicts both HSP traits and anomalous experiences. Moreover, HSP traits themselves also directly predict APP. Thus, the ITSTAE model reflects a complex network of both direct and mediated relationships, structured along three main pathways: (a) temperament → PCM → HSP → APP, (b) temperament → APP, and (c) HSP → APP. These parallel and sequential influences interact simultaneously to determine the likelihood of anomalous experiences in HSP individuals, with PCM amplifying the effects of both temperament and HSP traits. Figure 1 illustrates the model, with directional arrows depicting the hypothesized relationships among variables.

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Figure 1.

Flowchart representing the predictive model of anomalous experiences, integrating key theoretical perspectives—Regulative Theory of Temperament (RTT), Sensory Processing Sensitivity (SPS), and the Psychosis Continuum Model (PCM)—into the Integrated Temperamental-Sensitivity Theory of Anomalous Experience (ITSTAE).

As shown in Figure 1, PCM does not serve merely as a mediator between temperament and anomalous experiences, but as a hub variable that connects multiple predictive pathways. It mediates the impact of temperament on both HSP traits and APP, while HSP traits also exert a direct influence on APP. This recursive causal structure, consistent with the type of system described by Lawley and Maxwell (1971), allows for the modeling of simultaneous and interdependent effects across variables. Within this framework, ITSTAE offers a comprehensive and integrative approach to understanding the emergence of anomalous experiences in individuals with heightened sensitivity. This model, inspired by complex mediation designs commonly employed in econometrics, enables the prediction of multifactorial phenomena such as anomalous experiences under overlapping influences. Crucially, the model indicates that without the involvement of PCM, HSP alone would not be sufficient to predict APP. This highlights that HSP traits may only lead to clinically significant or pathological APP when they are shaped or intensified by a psychosis-prone phenotype, as defined by the PCM. Accordingly, ITSTAE provides a preventive and differential framework for identifying when APP represents a benign feature of sensitivity—and when it may instead signal a latent clinical risk.

The primary aim of the present research is to test this multi-pathway model and determine under what conditions anomalous experiences become clinically significant—particularly when PCM-related psychotic features are present. In doing so, ITSTAE provides a theoretically grounded and clinically useful model for the psychological assessment and potential treatment of anomalous experiences, especially those associated with psychotic-like symptoms or altered states of consciousness.

Sample

Responses were recorded from 1215 adult participants (53% female and 47% male). Ages ranged from 21 to 55 years (mean = 39.22; standard deviation = 9.278). Of the participants, 33.6% completed high school, 33.6% also received vocational training and 32.8% completed university studies. 406 participants (33.4%) lived in Madrid, 403 (33.2%) lived in Barcelona and 406 (33.4%) lived in Valencia. All respondents agreed to collaborate with this research on a voluntary basis and after having digitally signed an informed consent form explaining what this research consisted of. All data were recorded completely anonymously and treated only for statistical purposes.

Materials

Procedures

This research has a correlational design based on the statistical control of variables through the application of structural equation models. The collection of responses was done online in collaboration with the logistics company M.G. Integrated Services. It was a private-individual computer application designed specifically for survey studies. Participants were contacted through Telegram, Whatsapp, Twitter and Facebook social network groups. The sampling was non-probabilistic and had a duration of 1 year and 2 months. Before answering the questionnaires, participants had to read and accept the informed consent in which the purposes of the research and the conditions of data collection were detailed; this guaranteed anonymity on all occasions. The data were progressively recorded in an Excel-type file that was subsequently reviewed and configured for data analysis. In this first initial review, no missing values were identified and there were no strange patterns in the responses (e.g., tendency to acquiescence, tendency to denial or neutrality, or both).

Statistical Analysis

The data were processed with the SPSS® statistical package and its AMOS® extension specialized in Structural Equation Modeling (SEM). The R programming language was also used to calculate some reliability coefficients (The R Core Team, 2018). For the SEM models, it was decided to use the Maximum Likelihood Method (MLM) for parameter estimation. This method allowed obtaining a wide catalog of goodness-of-fit indices, which were the following (cut-off points according to Kline, 2013 are specified in parentheses): chi square; Normed χ² = chi square divided by freedom degrees; RMSEA = Root Mean Square Error of Approximation (threshold= <0.08); AGFI = Adjusted Goodness of Fit Index (threshold= >0.9); CFI = Comparative Fit Index (threshold= >0.950); TLI = Tucker-Lewis index (threshold= >0.950); IFI = Incremental Fit Index (threshold= >0.950); NFI = Normed Fit Index (threshold= >0.950); AIC = Akaike Information Criterion; and BIC = Bayesian Information Criterion. Several theoretical models (with and without variable mediation) were analyzed and the explained variances of the main dependent variables (APP and PCM) were calculated for each of them. Likewise, the Analysis of Variance technique (ANOVA) was used to ensure that there were no significant differences between participants from Madrid, Barcelona, and Valencia. The Kruskal-Wallis nonparametric test was also applied simultaneously. If the results were significant, we would have statistical grounds to use factorial invariance analysis between these groups. Otherwise, we could proceed without this analysis on the understanding that there would be no direct differences and, consequently, no bias attributable to the measurement instruments. A confidence level of 1% and 0.1% was established in all analyses.

Structural Equation Modeling and Validity of the ITSTAE

Figure 2 presents the model without mediation, including the temperament variables and the HSP profile. If the effects in this model were not statistically significant, it would make little sense to proceed with testing the mediation model proposed by the ITSTAE theory. To help readers better understand: if the effects were significant, we could then test the extent to which the simultaneous mediations proposed by ITSTAE account for part of the observed effects in predicting APPs. It must be clearly stated that we are not comparing different theoretical models, but rather evaluating a single model—the ITSTAE—using a sequential, recursive, and nested approach. The purpose is to ensure that each methodological step in the SEM analyses yields acceptable fit indices, thereby validating the entire process (and not just the most comprehensive version of the model, which includes the mediators). In contrast, Figure 3 incorporates the simultaneous mediations proposed by ITSTAE, as previously outlined and illustrated in Figure 1, which serves as a conceptual diagram supporting the framework presented in Figure 3.

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Figure 2.

A SEM analysis was conducted on the previous ITSTAE model without simultaneous statistical mediations. The variable HSP was included as the first mediator; however, the model was not nested. The variable APP corresponds to the MMSI scale that measures anomalous perceptions. All parameter estimates were statistically significant, except for the paths from HSP to APP, which were not significantly different from zero. This finding suggests that, in the absence of additional intervening variables, the HSP profile does not predict APP.

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Figure 3.

A SEM analysis was conducted on the ITSTAE model, incorporating the simultaneous mediations of the PCM variable, which represents the psychosis continuum. This variable acts as a dual mediator, influencing both APP and HSP. According to ITSTAE theory, if anomalous experiences occur in individuals with high HSP scores, it is because the psychotic phenotype may mediate effects on both HSP and APP. This accounts for the fact that, even when the psychotic phenotype affects HSP profiles, anomalous experiences do not necessarily increase. It suggests that, if risk factors are involved, they are more likely associated with negative symptoms and social maladaptation, rather than with the positive symptoms of psychosis. All parameter estimates in this more comprehensive specification were statistically significant.

Along with the estimates and specifications shown in Figures 2 and 3, we also obtained the fit indices summarized in Table 2. This table includes the minimum theoretical thresholds required to support the validity of the ITSTAE theory. It should be noted that the chi-square statistic is particularly sensitive to sample size, which inflates the standard errors of parameter estimates and leads to an overestimation of model discrepancies—often identifying mismatches that do not actually exist (see Escolà-Gascón, 2022c). Therefore, this fit index is not the most reliable and should not be the central criterion for assessing the validity of the ITSTAE theoretical model. Rather than focusing on this statistic, it is more appropriate to consider the remaining fit indices, which provide less biased information without compromising statistical inference.

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Table 2.

Fit index models for the Integrated Temperamental-Sensitivity Theory of Anomalous Experience: with and without mediation effects.

Indices	Thresholds (Kline, 2013)	ITSTAE model
		With no mediation	With double mediation
χ2	p > 0.01	122.432 (df = 32) (p < 0.0001)	220.171 (df = 59) p < 0.0001
Normed χ2	—	3.826	3.732
RMSEA	<0.08	0.048 (0.039–0.057)	0.047 (0.041–0.054)
AGFI	>0.90	0.966	0.959
CFI	>0.95	0.990	0.986
TLI	>0.95	0.986	0.982
IFI	>0.95	0.990	0.986
RFI	>0.95	0.981	0.975
NFI	>0.95	0.986	0.981
AIC	—	168.432	284.171
BIC	—	168.852	284.917

The results shown in Table 2 support the validity of the ITSTAE theoretical model for modeling anomalous experiences in individuals with HSP profiles. All fit indices fell within the thresholds typically considered satisfactory for this type of analysis. It is true that the model including the dual mediation (Figure 3) produced slightly lower fit indices compared to the model in Figure 2. Despite these differences, the fit levels for Figure 3 were still satisfactory. Given that ITSTAE is a valid model that enables the prediction of anomalous experiences, we also calculated the proportions of explained variance for the APP variable in both the non-mediated model (Figure 2) and the full model (Figure 3). In the former, the model explained up to 47.1% of the variance in APP. In contrast, when the mediation of PCM and its effects on HSP were included, the explained variance increased to 61.4%. This 14.3% increase in APP variance was attributable to the inclusion of the PCM variable and was statistically significant. Thus, we obtained evidence not only that the ITSTAE model presented in Figure 3 was valid in terms of data fit, but also that it improved the prediction of variance in APP. The implications of these results are discussed further in the discussion section, but one important conclusion can already be drawn: the involvement of the PCM in HSP profiles does not predict an increase in APP—on the contrary (as shown by the negative signs in the estimates presented in Figure 3). APP, as anomalous perceptions, should theoretically be associated with the positive symptoms of psychosis—and in fact, they are, as indicated by a standardized regression coefficient of 0.560 in this case. However, when HSP characteristics are taken into account, the prediction of APP reverses its direction (with a coefficient of −0.550 in Figure 3). Therefore, although there is a positive correlation between PCM and APP, in individuals with HSP profiles, APP levels decrease rather than increase.

This result may appear paradoxical, but it carries important clinical implications. For example, it raises the question of whether HSP profiles might contain protective traits that buffer the expression of anomalous experiences associated with the psychotic phenotype. If so, it would be worth considering clinical pathways that make use of HSP traits from a preventive perspective, rather than treating them as additional clinical risk factors. Could we be observing a personality profile that functions as a potential protective factor against certain positive psychotic symptoms?

Equivalence Analysis between PCM and HSP Using the Multitrait-Multimethod Technique

Multitrait-Multimethod (MTMM) matrices are commonly used in behavioral measurement to evaluate the possibility that two constructs—although qualitatively distinct—may in fact be measuring the same underlying behavior, thus creating a problem of poor discriminant validity. We include this analysis as a complementary statistical control to ensure that PCM and HSP are distinct constructs, not only in terms of their phenomenology or qualitative nature, but also in terms of their quantitative measurement, which refers to the objective assessments employed in the present study. This approach allows us to verify that PCM and HSP are empirically distinguishable constructs. The correlation matrix was calculated using Spearman’s method, and in Figure 4 we visually display the degree to which the dimensions of both constructs are interrelated, and to what extent.

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Figure 4.

Multitrait-Multimethod matrices of the latent variable dimensions used to assess the discriminant validity of the theoretical ITSTAE model. Correlations were calculated using Spearman’s method.

To analyze the matrix presented in Figure 4 with greater rigor, we used two indicators: the Fornell–Larcker criterion, which calculates the Average Variance Extracted (AVE) for each factor, and the Heterotrait–Monotrait Ratio (HTMT), which assesses the ratio between heterotrait-monotrait and monotrait-heteromethod correlations. Discriminant validity is supported when the AVE for a given factor is greater than the squared correlation between that factor and any other. Additionally, an HTMT value below 0.85 is generally considered satisfactory evidence of discriminant validity. Importantly, if discriminant validity is established, it indicates that the constructs are not equivalent—even if their observed scores are highly correlated. In our analysis, the results were AVE = 0.823 > 0.516 and HTMT = 0.715 < 0.85. These findings demonstrate that HSP and PCM are not only qualitatively distinct constructs but also measure different behavioral dimensions, as reflected in the quantitative assessments used in this study.

Why Integrate the Psychosis Continuum Model into the Theorization?

Anomalous experiences can be addressed from traditional psychology through multiple perspectives, and the psychotic phenotype (included in PCM) is only one of the explanatory avenues that has been investigated in recent years (Brett et al., 2013). Some scholars have criticized the use of PCM as a reductionist, psychopathologizing, and stigmatizing framework when applied to anomalous experiences that lack a clear scientific explanation (Moreira-Almeida et al., 2024). In this study, our aim is not to confine anomalous experiences exclusively to what is clinically psychotic or potentially pathological. It is true that certain types of anomalous experiences currently remain unexplained in terms of their origin and developmental mechanisms—for example, phenomena related to quantum-like learning, which have been demonstrated in several experiments using highly sophisticated quantum mathematical procedures (Escolà-Gascón, 2025; Escolà-Gascón & Benito-León, 2025).

The term anomalous can carry several meanings, and across its various manifestations, PCM may indeed constitute a risk factor for personal well-being. While we do not propose PCM as the sole explanatory model, it must nonetheless be considered in order to accurately distinguish between anomalous experiences that are harmless (i.e., without clinical relevance) and those that may reflect an underlying psychotic process. Employing PCM does not entail reducing or stigmatizing anomalous experiences; rather, it enables the development of new theoretical frameworks that help determine when such experiences warrant clinical attention—and when they do not. It is precisely this distinction that generates the scientific rigor necessary for advancing psychological assessment practices within the clinical domain.

Researchers or theorists who misinterpret this contribution are likely influenced by ideological biases that compromise objective judgment. To avoid such misreading, we wish to clarify that while we observed a positive correlation between PCM and anomalous experiences, a paradox emerges: when PCM interacts with HSP, the predictive relationship between HSP and anomalous experiences becomes negative. This suggests that high levels of HSP reduce the likelihood of experiencing psychosis-related anomalous phenomena. Our findings point to something critical: although the HSP profile and the underlying SPS theory may involve certain vulnerabilities or challenges, they also encompass traits that appear to serve as protective factors against the positive symptoms of psychosis. This interpretation is supported for two key reasons. First, if the interpretation were simply incorrect, we would not have found a negative correlation between HSP and APP. The fact that such a correlation emerges only in the presence of PCM suggests that HSP modulates the positive symptoms of psychosis (including anomalous experiences) in a way that neutralizes their detrimental impact—thus protecting individuals from associated psychotic risks. Second, the three temperament dimensions cited—each negatively correlated with HSP—also show negative correlations with PCM. This evidence, derived from the ITSTAE model, supports the view that HSP functions as a protective factor against psychotic symptomatology. ITSTAE posits that temperament influences HSP, which in turn adjusts in ways that help prevent the emergence of APP and reduce its frequency. In short—and to state this clearly—temperament appears to foster an expression of the HSP profile that acts as a protective factor against anomalous experiences associated with psychotic symptoms.

Although the two reasons previously discussed clarify that HSP and its associated personality dimensions may serve as a therapeutic asset in the psychological evaluation of anomalous experiences, it is important to highlight that PCM includes two additional dimensions—depressive symptoms and the negative symptomatology of psychosis—which could negatively affect the quality of life of individuals with an HSP profile. Put more plainly: excluding positive psychotic symptomatology from HSP profiles does not imply that the other dimensions of the psychotic phenotype should also be excluded.

At this stage, we consider it necessary to recommend that psychological assessments of individuals with an HSP profile explore more deeply the potential clinical relevance of these subclinical psychotic dimensions. Although the ITSTAE model supports the view that PCM ceases to represent a subclinical risk for positive symptoms in this population, it remains essential to rule out whether depressive and negative symptoms may still contribute to psychological distress in highly sensitive individuals. To investigate this more thoroughly, we suggest that future research move beyond the APP construct and include behavioral indicators related to these two dimensions of the psychotic phenotype, in order to examine how HSP scores might predict them. While ITSTAE posits that HSP functions in a complex way within personality—sufficiently so to prevent certain anomalous experiences typically linked to positive psychotic symptoms—further significant insights may emerge from examining precisely the alternative possibility we now propose.

If future studies were to show that HSP positively predicts depressive traits or behaviors associated with negative psychotic symptomatology, we propose that the hypothesis of a link between HSP and the psychoticism trait of personality be taken seriously, in line with work by Konrad and Herzberg (2019). Such a finding would be especially intriguing, as it could open up new lines of inquiry into the connections between HSP profiles and certain underexplored autistic traits, which may also fall within the attenuated spectrum of psychotic symptoms (Golay et al., 2020; Liss et al., 2008).

Thus, although our proposal is valid within the theoretical framework of ITSTAE, further empirical research is needed before concrete clinical applications for HSP assessment can be recommended. That said, we already have a first line of stable evidence suggesting a potential protective function of HSP against a specific type of psychotic symptomatology—an effect that, while modest, may help reduce the risk of future psychotic episodes in this population.

Limitations

The main limitations of this study are related to the quality of the inferences (which cannot be applied in pure experimental terms) and the generalization of the results. Regarding the first limitation, it is clear that the focus of this study and causal inferences should be understood exclusively at a statistical and not empirical level. Therefore, the results should be replicated in future research using experimental designs that control variables. The second limitation refers to the type of sample used. The spectrum of psychosis can be measured in a non-clinical population, but it would be convenient to include clinical samples to contrast whether the covariations between variables and the observed effects remain stable between groups of healthy people and groups of patients diagnosed with psychosis. Therefore, the generalization of inferences should be limited to the healthy population without psychiatric history. Other cultural variables could also be included, and population groups from different countries could be analyzed. This would add a new source of variation that should be controlled for, but which would contribute positively to a more extensive and ambitious generalization. These proposals should also be applied in future research.

Another complementary limitation—though important to highlight for the value of future research—is that, in this study, the temperament construct was operationalized based on a single higher-order factor derived from three specific dimensions. This is not incorrect, and the literature provides several examples of the usefulness of theorizing the FCB-TI using a single macro-factor (see Cyniak-Cieciura et al., 2018; Strelau, 1983; Zawadzki & Strelau, 2010). However, this approach represents a narrowly focused and specific modeling strategy, rather than a generalist one. While this specificity served our goal of maximizing conceptual precision within the ITSTAE model, future research should explore how other temperament dimensions may influence the structure and predictive validity of ITSTAE. We selected the unidimensional model intentionally to synthesize the observed effects and present them in a clearer, more pedagogical way—especially for researchers aiming to replicate or extend our work. Nonetheless, we recommend that future studies consider temperament models that incorporate two or more second-order factors, as these may offer a more comprehensive understanding of the interplay between temperament, PCM, and APPs within the ITSTAE framework.

Conclusions

The present study, its results and the discussion offered allow us to highlight 3 main conclusions that are crucial in the field of temperament theories and the production of anomalous experiences:

(1) The psychotic phenotype (as measured by the PCM model) positively predicts the development of certain anomalous experiences, though not all types and not in their entirety. Low scores in the temperament dimensions of Briskness, Endurance, and Activity may increase the predisposition to subclinical psychotic states, which in turn could lead to an increase in anomalous experiences (referred to as APP).