Abstract
Introduction : Face recognition is supposed to be fast. However, the actual speed at which faces can be recognized remains unknown. To address this issue, we report two experiments run with speed constraints. In both experiments, famous faces had to be recognized among unknown ones using a large set of stimuli to prevent preactivation of features which would speed up recognition, hence they assessed what we call "bottom-up recognition". Methods: In the first experiment (31 participants), recognition of famous faces was investigated using a go/no-go task. In the second experiment, 101 participants performed a highly time constrained recognition task using the Speed and Accuracy Boosting (SAB) procedure (Besson et al, in press). Results: Results of both experiments converge and indicate that the fastest speed at which a face can be recognized is around 360-390 ms (minimum reaction time). Discussion : Such latencies are about 100 ms longer than the latencies recorded in similar tasks in which subjects have to detect faces among other stimuli. We discuss which model of activation of the visual ventral stream could account for such latencies. These latencies are not consistent with a purely feed-forward pass of activity throughout the visual ventral stream. An alternative is that face recognition relies on the core network underlying face processing identified in fMRI studies (OFA, FFA and pSTS) and reentrant loops to refine face representation. However, the model of activation favoured is that of an activation of the whole visual ventral stream up to anterior areas, such as the perirhinal cortex, combined with parallel and feed-back processes. Further studies are needed to assess which of these three models of activation can best account for face recognition.
Meeting abstract presented at VSS 2013