How do forewarnings and post-warnings affect misinformation reliance? The impact of warnings on the continued influence effect and belief regression.

People often continue to rely on certain information in their reasoning, even if this information has been retracted; this is called the continued influence effect (CIE) of misinformation. One technique for reducing this effect involves explicitly warning people that there is a possibility that they might have been misled. The present study aimed to investigate these warnings' effectiveness, depending on when they were given (either before or after misinformation). In two experiments (N = 337), we found that while a forewarning did reduce reliance on misinformation, retrospectively warned participants (when the warning was placed either between the misinformation and the retraction or just before testing) relied on the misinformation to a similar degree as unwarned participants. However, the protective effect of the forewarning was not durable, as shown by the fact that reliance on the misinformation increased for over 7 days following the first testing, despite continued memory of the retraction.

Vaccination against misinformation: The inoculation technique reduces the continued influence effect.

The continued influence effect of misinformation (CIE) is a phenomenon in which certain information, although retracted and corrected, still has an impact on event reporting, reasoning, inference, and decisions. The main goal of this paper is to investigate to what extent this effect can be reduced using the procedure of inoculation and how it can be moderated by the reliability of corrections' sources. The results show that the reliability of corrections' sources did not affect their processing when participants were not inoculated. However, inoculated participants relied on misinformation less when the correction came from a highly credible source. For this source condition, as a result of inoculation, a significant increase in belief in retraction, as well as a decrease in belief in misinformation was also found. Contrary to previous reports, belief in misinformation rather than belief in retraction predicted reliance on misinformation. These findings are of both great practical importance as certain boundary conditions for inoculation efficiency have been discovered to reduce the impact of the continued influence of misinformation, and theoretical, as they provide insight into the mechanisms behind CIE. The results were interpreted in terms of existing CIE theories as well as within the remembering framework, which describes the conversion from memory traces to behavioral manifestations of memory.

Classification of nine types of pornographic materials using the sAI 0.3 model. Continuation of the pilot study. / Klasyfikacja dziewieciu typów materialów pornograficznych za pomoca modelu sAI 0.3. Kontynuacja badan pilotazowych.

OBJECTIVES: Legal pornographic materials are a heterogenous group of audiovisual materials that depict one or more person over the age of eighteen engaging in sexual activities. The aim of this study was to train a model that could classify given types of pornographic materials. METHODS: Materials included in the training set (3,600 materials) and the validation set (900 materials) were manually classified and tagged by psychologists-sexologists. Then, a deep neural network was trained on the dataset. Six models based on different architectures of convolutional neural networks were included in the study (ResNet152, ResNet101, VGG19, VGG16, Squeezenet 1.1, Squeezenet 1.0). Each model was trained on the same group of photographs, and fast.ai library was used for the training process. RESULTS: The final model allows for the classification of more types of pornographic materials with greater efficiency than the pilot model, and thanks to the manual labelling of individual photographs, the limitations of the classification are known. CONCLUSIONS: The possible applications of the model in clinical sexology and psychiatry are discussed. The application of deep neural networks in sexology seems to be particularly promising for at least two reasons. Firstly, a tool for automated detection of pornographic materials involving minors can be developed and used during criminal proceeding. Secondly, after retraining the presented model on photographs of men and women not engaging in sexual activity the model could be used to filter content that is inappropriate for minors.