Strength of visual percept generated by famous faces perceived without awareness: effects of affective valence, response latency, and visual field

Participants who were unable to detect familiarity from masked 17 ms faces ([Stone and Valentine, 2004] and [Stone and Valentine, in press-b]) did report a vague, partial visual percept. Two experiments investigated the relative strength of the visual percept generated by famous and unfamiliar faces, using masked 17 ms exposure. Each trial presented simultaneously a famous and an unfamiliar face, one face in LVF and the other in RVF. In one task, participants responded according to which of the faces generated the stronger visual percept, and in the other task, they attempted an explicit familiarity decision. The relative strength of the visual percept of the famous face compared to the unfamiliar face was moderated by response latency and participants’ attitude towards the famous person. There was also an interaction of visual field with response latency, suggesting that the right hemisphere can generate a visual percept differentiating famous from unfamiliar faces more rapidly than the left hemisphere. Participants were at chance in the explicit familiarity decision, confirming the absence of awareness of facial familiarity.

ORBS and the limits of static slicing

Observation-based slicing is a recently-introduced, language-independent, slicing technique based on the dependencies observable from program behaviour. Due to the wellknown limits of dynamic analysis, we may only compute an under-approximation of the true observation-based slice. However, because the observation-based slice captures all possible dependence that can be observed, even such approximations can yield insight into the limitations of static slicing. For example, a static slice, S that is strictly smaller than the corresponding observation based slice is guaranteed to be unsafe. We present the results of three sets of experiments on 12 different programs, including benchmarks and larger programs, which investigate the relationship between static and observation-based slicing. We show that, in extreme cases, observation-based slices can find the true static minimal slice, where static techniques cannot. For more typical cases, our results illustrate the potential for observation-based slicing to highlight unsafe static slices. Finally, we report on the sensitivity of observation-based slicing to test quality.