The theoretical notion of ‘construal’ captures the idea that the way in which we describe a scene reflects our conceptualization of it. Relying on the concept of ception – which conjoins conception and perception – we operationalized construal and employed a Visual World Paradigm to establish which aspects of linguistic scene description modulate visual scene perception, thereby affecting event conception. By analysing viewing behaviour after alternating ways of describing location (prepositions), agentivity (active/passive voice) and transfer (NP/pp datives), we found that (...) the linguistic construal of a scene affects its spontaneous visual perception in two ways: either by determining the order in which the components of a scene are accessed or by modulating the distribution of attention over the components, making them more or less salient than they naturally are. We also found evidence for the existence of a cline in the construal effect with stronger expressive differences, such as the prepositional manipulation, inducing more prominent changes in visual perception than the dative manipulation. We discuss the claims language can lay to affecting visual information uptake and hence conceptualization of a static scene in the light of these results. (shrink)

Significant advances have been made in understanding human face recognition. However, a fundamental aspect of this process, how faces are located in our visual environment, is poorly understood and little studied. Here we examine the role of color in human face detection. We demonstrate that detection performance declines when color information is removed from faces, regardless of whether the surrounding scene context is rendered in color. Furthermore, faces rendered in unnatural colors are hard to detect, suggesting a role beyond simple (...) segmentation. When faces are presented such that half the surface is colored appropriately, and half unnaturally, performance declines. This suggests that observers are not simply using the presence of skin color “patches” to detect faces. Rather, our data suggest that detection operates via a face template combining diagnostic color and face‐shape information. These findings are consistent with color‐template approaches used in some computer‐based face detection systems. (shrink)