The present study examines face-scanning behaviors of infants at 6, 9, and 12 months as they watched videos of a woman describing an object in front of her. The videos were created to vary information in the mouth (speaking vs. smiling) and the eyes (gazing into the camera vs. cueing the infant with head turn or gaze direction to an object being described). Infants tended to divide their attention between the eyes and the mouth, looking less at the eyes with age and more at the mouth than the eyes at 9 and 12 months. Attention to the mouth was greater on speaking trials than on smiling trials at all three ages, and this difference increased between 6 and 9 months. Despite consistent results within subjects, there was considerable variation between subjects. This raises the question of whether a developmental “norm” of face scanning in infancy ought to be pursued. Rather, these data add to emerging evidence suggesting that individual differences in face scanning might reliably predict aspects of later development.

The current model of three-dimensional perception hypothesizes that the brain integrates the depth cues in a statistically optimal fashion through a weighted linear combination with weights proportional to the reliabilities obtained for each cue in isolation (Landy, Maloney, Johnston, & Young, 1995). Even though many investigations support such theoretical framework, some recent empirical findings are at odds with this view (e.g., Domini, Caudek, & Tassinari, 2006). Failures of linear cue integration have been attributed to cue-conflict and to unmodelled cues to flatness present in computer-generated displays. We describe two cue-combination experiments designed to test the integration of stereo and motion cues, in the presence of consistent or conflicting blur and accommodation information (i.e., when flatness cues are either absent, with physical stimuli, or present, with computer-generated displays). In both conditions, we replicated the results of Domini et al. (2006): The amount of perceived depth increased as more cues were available, also producing an over-estimation of depth in some conditions. These results can be explained by the Intrinsic Constraint model, but not by linear cue combination.

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