Abstract
Unlike static images of facial expressions routinely used in most experiments, natural expressions unfold over time, providing observers with richer and ecologically more valid signals. Our previous findings revealed greater recognition accuracy for dynamic expressions in young and elderly populations (Richoz et al., 2017), an advantage driven by a suboptimal performance for static images in older adults. Interestingly, it has also been shown that patients suffering from mild cognitive impairment (MCI) are impaired for the recognition of static facial expressions. Yet, the very nature of such a deficit and its presence for dynamic faces remains to be clarified. To this aim, we tested a group of MCI patients and an age-matched healthy control group while they performed a facial expression recognition (FER) task of the six basic expressions in three conditions: static, shuffled (temporally randomized frames) and dynamic (Gold et al., 2013). We observed greater and comparable FER accuracy for dynamic vs. static expressions in MCI patients and the controls. Crucially, however, the MCI patients were significantly more impaired in the decoding of the static expressions of fear, disgust and anger compared to the controls. While static faces may be more sensitive to detect expression recognition deficits in MCI patients, the results obtained in the dynamic condition suggest that their FER ability in their daily life is spared. The deficit in the MCI patients might thus selectively relate to a suboptimal functioning of the ventral face-selective network, which is dedicated to static face processing, while dynamic face processing involves a diffuse network of brain regions. Altogether, these findings not only underline the critical importance of assessing FER with dynamic faces in clinical populations, but also pave the way for the development of future diagnostic tools that may link FER deficits with static images to specific facets of cognitive decline.
Meeting abstract presented at VSS 2018