A Bottom-Up Validation of the IAPS, GAPED, and NAPS Affective Picture Databases: Differential Effects on Behavioral Performance

Colored valence in a lexical decision task

Color influences behavior, from the simplest to the most complex, through controlled and more automatic information elaboration processes. Nonetheless, little is known about how and when these highly interconnected processes interact. This study investigates the interaction between controlled and automatic processes during the processing of color information in a lexical decision task. Participants discriminated stimuli presented in different colors (red, blue, green) as words or pseudowords. Results showed that while color did not affect the faster and more accurate recognition of words compared to pseudowords, performance was influenced when examining words and pseudowords separately. Pseudowords were recognized faster when presented in blue or red, suggesting a potential influence of evolutionary color preferences when processing is not guided by more controlled processes. With words, emotional enhancement effects were found, with a preference for green independent of valence. These results suggest that controlled and more automatic processes do interact when processing color information according to stimulus type and task.

Development of a Universal Validation Protocol and an Open-Source Database for Multi-Contextual Facial Expression Recognition

Facial expression recognition (FER) poses a complex challenge due to diverse factors such as facial morphology variations, lighting conditions, and cultural nuances in emotion representation. To address these hurdles, specific FER algorithms leverage advanced data analysis for inferring emotional states from facial expressions. In this study, we introduce a universal validation methodology assessing any FER algorithm's performance through a web application where subjects respond to emotive images. We present the labelled data database, FeelPix, generated from facial landmark coordinates during FER algorithm validation. FeelPix is available to train and test generic FER algorithms, accurately identifying users' facial expressions. A testing algorithm classifies emotions based on FeelPix data, ensuring its reliability. Designed as a computationally lightweight solution, it finds applications in online systems. Our contribution improves facial expression recognition, enabling the identification and interpretation of emotions associated with facial expressions, offering profound insights into individuals' emotional reactions. This contribution has implications for healthcare, security, human-computer interaction, and entertainment.

Development and validation of a highly dynamic and reusable picture-based scale: A new affective measurement tool

Emotion measurement is crucial to conducting emotion research. Numerous studies have extensively employed textual scales for psychological and organizational behavior research. However, emotions are transient states of organisms with relatively short duration, some insurmountable limitations of textual scales have been reported, including low reliability for single measurement or susceptibility to learning effects for multiple repeated use. In the present article, we introduce the Highly Dynamic and Reusable Picture-based Scale (HDRPS), which was randomly generated based on 3,386 realistic, high-quality photographs that are divided into five categories (people, animals, plants, objects, and scenes). Affective ratings of the photographs were gathered from 14 experts and 209 professional judges. The HDRPS was validated using the Self-Assessment Manikin and the PANAS by 751 participants. With an accuracy of 89.73%, this new tool allows researchers to measure individual emotions continuously for their research. The non-commercial use of the HDRPS system can be freely accessible by request at http://syy.imagesoft.cc:8989/Pictures.7z. HDRPS is used for non-commercial academic research only. As some of the images are collected through the open network, it is difficult to trace the source, so please contact the author if there are any copyright issues.

An EEG study on the effect of being overweight on anticipatory and consummatory reward in response to pleasant taste stimuli

Two-thirds of adults in the United Kingdom currently suffer from overweight or obesity, making it one of the biggest contributors to health problems. Within the framework of the incentive sensitisation theory, it has been hypothesised that overweight people experience heightened reward anticipation when encountering cues that signal food, such as pictures and smells of food, but that they experience less reward from consuming food compared to normal-weight people. There is, however, little evidence for this prediction. Few studies test both anticipation and consumption in the same study, and even fewer with electroencephalography (EEG). This study sought to address this gap in the literature by measuring scalp activity when overweight and normal-weight people encountered cues signalling the imminent arrival of pleasant and neutral taste stimuli, and when they received these stimuli. The behavioural data showed that there was a smaller difference in valence ratings between the pleasant and neutral taste in the overweight than normal-weight group, in accordance with our hypothesis. However, contrary to our hypothesis, the groups did not differ in their electrophysiological response to taste stimuli. Instead, there was a reduction in N1 amplitude to both taste and picture cues in overweight relative to normal-weight participants. This suggests that reduced attention to cues may be a crucial factor in risk of overweight.