Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces

Attention Modulates the Ensemble Coding of Facial Expressions

Ensemble coding and attention are two mechanisms utilized by our visual system to overcome the limitation of visual processing when confronted with the overwhelming visual information. Recent evidence in ensemble coding of size suggests that the attended items contributed more to the averaging. On the other hand, some new evidence also indicates that reduced attention jeopardies the perceptual averaging of stimuli. What is the relationship between attention and ensemble coding? To answer this question, in the current study, we tested whether an exogenous attentional cue would influence the reported mean emotion of a crowd. We showed participants a group of four faces with different emotions. Participants' attention was guided to the happiest or saddest face (attention conditions), or not to any specific face (baseline condition). The results supported the notion that the attention alters the ensemble perception of the facial expression by elevating the weight of that face in the ensemble representation. This opens the question for the neural mechanisms of ensemble coding and its connection to visual attention.

The emotional adaptation aftereffect discriminates between individuals with high and low levels of depressive symptoms

The adaptation aftereffect plays a critical role in human development and survival. Existing studies have found that, compared with general individuals, individuals with learning disability, autism and dyslexia show a smaller amount of non-affective-based cognitive adaptation aftereffect. Nevertheless, it is unclear whether individuals with depression or depression tendency show similar phenomenon in the adaptation aftereffect, and whether such depression tendency occurs in the non-affective-based cognitive or emotional adaptation aftereffect. To address this question, the present study conducted two experiments. Experiments 1A and 1B used the emotional facial expression adaptation paradigm to examine whether Chinese participants showed the emotional adaptation aftereffect and whether the emotional adaptation aftereffect was influenced by physical features of faces, respectively. Experiment 2 recruited two groups of participants, with high and low depression, respectively, to examine whether they showed differences in the emotional or cognitive adaptation aftereffect. Results showed that Chinese participants showed the typical emotional adaptation aftereffect, which was not influenced by physical features of faces. More importantly, compared to the low-depression group, the high-depression group showed a smaller emotional adaptation aftereffect, but the two groups showed a similar cognitive adaptation aftereffect. These results suggest that level of depressive symptoms is associated with the emotional adaptation aftereffect.

Brief facial emotion aftereffect occurs earlier for angry than happy adaptation

Prolonged exposure to an emotional face biases our judgement of subsequent face stimulus toward the opposite emotion. This emotion aftereffect has been suggested to occur as early as 35 ms exposure duration in cartoon faces. In the current study, we are interested in investigating the time-course of brief emotional face adaptation, and the relationship between brief emotional face adaptation and prolonged emotional face adaptation. We adapted the subjects from 17 ms to 1000 ms with a happy or angry adapting face. We found that a facial emotion adaptation aftereffect started from 17 ms adapting duration for angry face adaptation, and from 50 ms for happy face adaptation. Factor analysis on the adaptation effects highlighted three different components: brief angry adaptation (17 ms, 34 ms, and 50 ms), prolonged angry adaptation (100 ms and 1000 ms), and happy face adaptation (from 17 ms to 1000 ms). We found that the brief angry face adaptation was negatively associated with the awareness of the adapting face, and the prolonged angry face adaptation was stronger in subjects who perceived the angry adapting face as more negative in valence. Together, these findings suggest that (1) facial emotion adaptation can be induced by brief (17 ms) adapting face presentation; (2) brief angry face adaptation may be related to early visual processing, whereas prolonged angry face adaptation may be related to adaptation at later and higher-level visual emotional processing; and (3) brief and prolonged adaptations may adapt different neural populations. Our findings thus shed light on the current understanding of the neural mechanisms of emotional face adaptation.

Two Ways to Facial Expression Recognition? Motor and Visual Information Have Different Effects on Facial Expression Recognition

Motor-based theories of facial expression recognition propose that the visual perception of facial expression is aided by sensorimotor processes that are also used for the production of the same expression. Accordingly, sensorimotor and visual processes should provide congruent emotional information about a facial expression. Here, we report evidence that challenges this view. Specifically, the repeated execution of facial expressions has the opposite effect on the recognition of a subsequent facial expression than the repeated viewing of facial expressions. Moreover, the findings of the motor condition, but not of the visual condition, were correlated with a nonsensory condition in which participants imagined an emotional situation. These results can be well accounted for by the idea that facial expression recognition is not always mediated by motor processes but can also be recognized on visual information alone.

Adaptation in the visual cortex: a case for probing neuronal populations with natural stimuli

The perception of, and neural responses to, sensory stimuli in the present are influenced by what has been observed in the past—a phenomenon known as adaptation. We focus on adaptation in visual cortical neurons as a paradigmatic example. We review recent work that represents two shifts in the way we study adaptation, namely (i) going beyond single neurons to study adaptation in populations of neurons and (ii) going beyond simple stimuli to study adaptation to natural stimuli. We suggest that efforts in these two directions, through a closer integration of experimental and modeling approaches, will enable a more complete understanding of cortical processing in natural environments.