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Research Articles

Real is the new sexy: the influence of perceived realness on self-reported arousal to sexual visual stimuli

ORCID Icon, ORCID Icon, ORCID Icon, , ORCID Icon & ORCID Icon
Pages 348-360 | Received 14 Jul 2023, Accepted 29 Nov 2023, Published online: 16 Jan 2024
 

ABSTRACT

As state-of-art technology can create artificial images that are indistinguishable from real ones, it is urgent to understand whether believing that a picture is real or not has some import over affective phenomena such as sexual arousal. Thus, in two pre-registered online studies, we tested whether 60 images depicting models in underwear elicited higher self-reported sexual arousal when believed to be (N = 57) or presented as (N = 108) real photographs as opposed to artificially generated. In both cases, Realness correlated with significantly higher scores on self-reported sexual arousal. Consistently with the literature on downregulation of emotional response to fictional works, our result indicates that sexual images that are perceived to be fake are less arousing than those believed to portray real people.

Acknowledgements

We would like to gratefully thank Prof. Massimiliano Pastore (Department of Developmental and Social Psychology, University of Padua) and Emiliano Ricciardi (IMT School for Advanced Studies Lucca) for their insightful advice in the modelling phase.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval

All procedures performed were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all participants. The study was approved by the Joint Ethics Committee of the Scuola Normale Superiore and the Scuola Superiore Sant'Anna.

Data availability statement

The data that support the findings of this study may be made available from the corresponding author upon reasonable request.

Notes

1 Two alternative GLMMs were built by employing inverse and identity link functions. We opted for the log link function model after comparing them in terms of their Akaike's and Bayesian Information criteria (i.e. AIC and BIC).