Recognizing facial expressions of emotion amid noise: A dynamic advantage.

Humans communicate internal states through complex facial movements shaped by biological and evolutionary constraints. Although real-life social interactions are flooded with dynamic signals, current knowledge on facial expression recognition mainly arises from studies using static face images. This experimental bias might stem from previous studies consistently reporting that young adults minimally benefit from the richer dynamic over static information, whereas children, the elderly, and clinical populations very strongly do (Richoz, Jack, Garrod, Schyns, & Caldara, 2015, Richoz, Jack, Garrod, Schyns, & Caldara, 2018b). These observations point to a near-optimal facial expression decoding system in young adults, almost insensitive to the advantage of dynamic over static cues. Surprisingly, no study has yet tested the idea that such evidence might be rooted in a ceiling effect. To this aim, we asked 70 healthy young adults to perform static and dynamic facial expression recognition of the six basic expressions while parametrically and randomly varying the low-level normalized phase and contrast signal (0%-100%) of the faces. As predicted, when 100% face signals were presented, static and dynamic expressions were recognized with equal efficiency with the exception of those with the most informative dynamics (i.e., happiness and surprise). However, when less signal was available, dynamic expressions were all better recognized than their static counterpart (peaking at ∼20%). Our data show that facial movements increase our ability to efficiently identify emotional states of others under the suboptimal visual conditions that can occur in everyday life. Dynamic signals are more effective and sensitive than static ones for decoding all facial expressions of emotion for all human observers.

Controlling the trajectory of a moving object substantially shortens the latency of motor responses to visual stimuli.

Motor responses to visual stimuli have shorter latencies for controlling than for initiating movement. The shorter latencies observed for movement control are notably believed to reflect the involvement of forward models when controlling moving limbs. We assessed whether controlling a moving limb is a "requisite" to observe shortened response latencies. The latency of button-press responses to a visual stimulus was compared between conditions involving or not involving the control of a moving object, but never involving any actual control of a body segment. When the motor response controlled a moving object, response latencies were significantly shorter and less variable, probably reflecting a faster sensorimotor processing (as assessed fitting a LATER model to our data). These results suggest that when the task at hand entails a control component, the sensorimotor processing of visual information is hastened, and this even if the task does not require to actually control a moving limb.

Face-Information Sampling in Super-Recognizers.

Perceptual processes underlying individual differences in face-recognition ability remain poorly understood. We compared visual sampling of 37 adult super-recognizers-individuals with superior face-recognition ability-with that of 68 typical adult viewers by measuring gaze position as they learned and recognized unfamiliar faces. In both phases, participants viewed faces through "spotlight" apertures that varied in size, with face information restricted in real time around their point of fixation. We found higher accuracy in super-recognizers at all aperture sizes-showing that their superiority does not rely on global sampling of face information but is also evident when they are forced to adopt piecemeal sampling. Additionally, super-recognizers made more fixations, focused less on eye region, and distributed their gaze more than typical viewers. These differences were most apparent when learning faces and were consistent with trends we observed across the broader ability spectrum, suggesting that they are reflective of factors that vary dimensionally in the broader population.

The Facespan-the perceptual span for face recognition.

In reading, the perceptual span is a well-established concept that refers to the amount of information that can be read in a single fixation. Surprisingly, despite extensive empirical interest in determining the perceptual strategies deployed to process faces and an ongoing debate regarding the factors or mechanism(s) underlying efficient face processing, the perceptual span for faces-the Facespan-remains undetermined. To address this issue, we applied the gaze-contingent Spotlight technique implemented in an old-new face recognition paradigm. This procedure allowed us to parametrically vary the amount of facial information available at a fixated location in order to determine the minimal aperture size at which face recognition performance plateaus. As expected, accuracy increased nonlinearly with spotlight size apertures. Analyses of Structural Similarity comparing the available information during spotlight and natural viewing conditions indicate that the Facespan-the minimum spatial extent of preserved facial information leading to comparable performance as in natural viewing-encompasses 7° of visual angle in our viewing conditions (size of the face stimulus: 15.6°; viewing distance: 70 cm), which represents 45% of the face. The present findings provide a benchmark for future investigations that will address if and how the Facespan is modulated by factors such as cultural, developmental, idiosyncratic, or task-related differences.