The Set of Fear Inducing Pictures (SFIP): Development and validation in fearful and nonfearful individuals

Michałowski, Jarosław M.; Droździel, Dawid; Matuszewski, Jacek; Koziejowski, Wojtek; Jednoróg, Katarzyna; Marchewka, Artur

doi:10.3758/s13428-016-0797-y

Cited by 34 publications

(27 citation statements)

References 35 publications

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“…There is a lot of picture databases with known evaluations of self-reported emotional responses (usually valence, arousal, and dominance, supplemented with emotional categories that include fear and disgust), e.g., IAPS (Lang et al, 1988; Mikels et al, 2005; Libkuman et al, 2007), NAPS (Marchewka et al, 2014; Riegel et al, 2016), SFIP (Michałowski et al, 2017), DIRTI (Haberkamp et al, 2017), or GAPED (Dan-Glauser and Scherer, 2011). However, these databases consist of pictures with very varying properties including background, colors, lightness, focus on detail, etc.…”

Section: Introductionmentioning

confidence: 99%

Snakes Represent Emotionally Salient Stimuli That May Evoke Both Fear and Disgust

et al. 2019

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Humans perceive snakes as threatening stimuli, resulting in fast emotional and behavioral responses. However, snake species differ in their true level of danger and are highly variable in appearance despite the uniform legless form. Different snakes may evoke fear or disgust in humans, or even both emotions simultaneously. We designed three-step-selection experiments to identify prototypical snake species evoking exclusively fear or disgust. First, two independent groups of respondents evaluated 45 images covering most of the natural variability of snakes and rated responses to either perceived fear ( n = 175) or disgust ( n = 167). Snakes rated as the most fear-evoking were from the family Viperidae ( Crotalinae , Viperinae , and Azemiopinae ), while the ones rated as the most disgusting were from the group of blind snakes called Typhlopoidea ( Xenotyphlopinae , Typhlopinae , and Anomalepidinae ). We then identified the specific traits contributing to the perception of fear (large body size, expressive scales with contrasting patterns, and bright coloration) and disgust (thin body, smooth texture, small eyes, and dull coloration). Second, to create stimuli evoking a discrete emotional response, we developed a picture set consisting of 40 snakes with exclusively fear-eliciting and 40 snakes with disgust-eliciting features. Another set of respondents ( n = 172) sorted the set, once according to perceived fear and the second time according to perceived disgust. The results showed that the fear-evoking and disgust-evoking snakes fit mainly into their respective groups. Third, we randomly selected 20 species (10 fear-evoking and 10 disgust-evoking) out of the previous set and had them professionally illustrated. A new set of subjects ( n = 104) sorted these snakes and confirmed that the illustrated snakes evoked the same discrete emotions as their photographic counterparts. These illustrations are included in the study and may be freely used as a standardized assessment tool when investigating the role of fear and disgust in human emotional response to snakes.

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Section: Introductionmentioning

confidence: 99%

Snakes Represent Emotionally Salient Stimuli That May Evoke Both Fear and Disgust

et al. 2019

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“…A similar pattern of dependency is found for ratings of acceptance of internal and external norms for the GAPED-A and GAPED-H subsets (Dan-Glauser and Scherer, 2011). An opposite pattern of dependency on arousal and valence ratings was observed for the fear ratings in the NAPS-SFIP dataset (Michalowski et al, 2017) and DIRTI dataset (Haberkamp et al, 2017), respectively, and for the disgust ratings in the DIRTI dataset ( Table 3; Haberkamp et al, 2017).…”

Section: Ratingsmentioning

confidence: 84%

“…Moreover, some basic physical properties (luminance, contrast, complexity and entropy of the gray-level intensity histograms) are available for this dataset. The authors also published a partially overlapping dataset of 886 pictures, which focused on fear induction and can be used in phobia research (NAPS-SFIP;Michalowski et al, 2017), as well as a dataset of 200 unrated erotic pictures (NAPS-ERO; Wierzba et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Global Image Properties Predict Ratings of Affective Pictures

et al. 2020

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Affective pictures are widely used in studies of human emotions. The objects or scenes shown in affective pictures play a pivotal role in eliciting particular emotions. However, affective processing can also be mediated by low-level perceptual features, such as local brightness contrast, color or the spatial frequency profile. In the present study, we asked whether image properties that reflect global image structure and image composition affect the rating of affective pictures. We focused on 13 global image properties that were previously associated with the esthetic evaluation of visual stimuli, and determined their predictive power for the ratings of five affective picture datasets (IAPS, GAPED, NAPS, DIRTI, and OASIS). First, we used an SVM-RBF classifier to predict high and low ratings for valence and arousal, respectively, and achieved a classification accuracy of 58-76% in this binary decision task. Second, a multiple linear regression analysis revealed that the individual image properties account for between 6 and 20% of the variance in the subjective ratings for valence and arousal. The predictive power of the image properties varies for the different datasets and type of ratings. Ratings tend to share similar sets of predictors if they correlate positively with each other. In conclusion, we obtained evidence from non-linear and linear analyses that affective pictures evoke emotions not only by what they show, but they also differ by how they show it. Whether the human visual system actually uses these perceptive cues for emotional processing remains to be investigated.

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“…Many such databases have so far been developed. The most popular ones, which are free for use by researchers, are: the International Affective Picture System (IAPS) [9,10], the Nencki Affective Picture System (NAPS) [12] (with its extensions NAPS Basic Emotions [13] and NAPS Erotic Subset [14]), the Geneva Affective Picture Database (GAPED) [15], the Open Library of Affective Foods (OLAF) [16], the DIsgust-RelaTed-Images (DIRTI) [17], the Set of Fear-Inducing Pictures (SFIP) [18], the Open Affective Standardized Image Set (OASIS) [19], and the most recent one, the Children-Rated Subset to the NAPS [20]. In addition, a recent list of affective picture databases with different emotionally-annotated multimedia formats and exemplars of the research conducted with these database is available in [8].…”

Section: Affective Multimedia Databases: Definition Architecture Andmentioning

confidence: 99%

Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

2020

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Evaluation of document classification is straightforward if complete information on the documents’ true categories exists. In this case, the rank of each document can be accurately determined and evaluated. However, in an unsupervised setting, where the exact document category is not available, lift charts become an advantageous method for evaluation of the retrieval quality and categorization of ranked documents. We introduce lift charts as binary classifiers of ranked documents and explain how to apply them to the concept-based retrieval of emotionally annotated images as one of the possible retrieval methods for this application. Furthermore, we describe affective multimedia databases on a representative example of the International Affective Picture System (IAPS) dataset, their applications, advantages, and deficiencies, and explain how lift charts may be used as a helpful method for document retrieval in this domain. Optimization of lift charts for recall and precision is also described. A typical scenario of document retrieval is presented on a set of 800 affective pictures labeled with an unsupervised glossary. In the lift charts-based retrieval using the approximate matching method, the highest attained accuracy, precision, and recall were 51.06%, 47.41%, 95.89%, and 81.83%, 99.70%, 33.56%, when optimized for recall and precision, respectively.

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The Set of Fear Inducing Pictures (SFIP): Development and validation in fearful and nonfearful individuals

Cited by 34 publications

References 35 publications

Snakes Represent Emotionally Salient Stimuli That May Evoke Both Fear and Disgust

Snakes Represent Emotionally Salient Stimuli That May Evoke Both Fear and Disgust

Global Image Properties Predict Ratings of Affective Pictures

Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

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