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Behav Res Methods ; 55(6): 3078-3099, 2023 09.
Artigo em Inglês | MEDLINE | ID: mdl-36018484


Faces convey a wide range of information, including one's identity, and emotional and mental states. Face perception is a major research topic in many research fields, such as cognitive science, social psychology, and neuroscience. Frequently, stimuli are selected from a range of available face databases. However, even though faces are highly dynamic, most databases consist of static face stimuli. Here, we introduce the Sabanci University Dynamic Face (SUDFace) database. The SUDFace database consists of 150 high-resolution audiovisual videos acquired in a controlled lab environment and stored with a resolution of 1920 × 1080 pixels at a frame rate of 60 Hz. The multimodal database consists of three videos of each human model in frontal view in three different conditions: vocalizing two scripted texts (conditions 1 and 2) and one Free Speech (condition 3). The main focus of the SUDFace database is to provide a large set of dynamic faces with neutral facial expressions and natural speech articulation. Variables such as face orientation, illumination, and accessories (piercings, earrings, facial hair, etc.) were kept constant across all stimuli. We provide detailed stimulus information, including facial features (pixel-wise calculations of face length, eye width, etc.) and speeches (e.g., duration of speech and repetitions). In two validation experiments, a total number of 227 participants rated each video on several psychological dimensions (e.g., neutralness and naturalness of expressions, valence, and the perceived mental states of the models) using Likert scales. The database is freely accessible for research purposes.

Expressão Facial , Reconhecimento Facial , Humanos , Fala , Universidades , Emoções
Atten Percept Psychophys ; 84(3): 781-794, 2022 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-35138578


Perceptual averaging refers to a strategy of encoding the statistical properties of entire sets of objects rather than encoding individual object properties, potentially circumventing the visual system's strict capacity limitations. Prior work has shown that such average representations of set properties, such as its mean size, can be modulated by top-down and bottom-up attention. However, it is unclear to what extent attentional biases through selection history, in the form of value-driven attentional capture, influences this type of summary statistical representation. To investigate, we conducted two experiments in which participants estimated the mean size of a set of heterogeneously sized circles while a previously rewarded color singleton was part of the set. In Experiment 1, all circles were gray, except either the smallest or the largest circle, which was presented in a color previously associated with a reward. When the largest circle in the set was associated with the highest value (as a proxy of selection history), we observed the largest biases, such that perceived mean size scaled linearly with the increasing value of the attended color singleton. In Experiment 2, we introduced a dual-task component in the form of an attentional search task to ensure that the observed bias of reward on perceptual averaging was not fully explained by focusing attention solely on the reward-signaling color singleton. Collectively, findings support the proposal that selection history, like bottom-up and top-down attention, influences perceptual averaging, and that this happens in a flexible manner proportional to the extent to which attention is captured.

Viés de Atenção , Recompensa , Humanos , Tempo de Reação