A Review of: “Greenwald,R. (2009). Treating Problem Behaviors: A Trauma-Informed Approach.”

This single-case-design experiment examined an intervention to decrease bed-sharing in children, ages 2 through 6. Three sessions were conducted with each parent. At the first session, parents described bed-sharing history and began collecting baseline data. At the second session, parents were instructed to employ the intervention. At the third session, parents were interviewed and offered continued support, if needed. Participants included three children who slept with their parents 4 or more nights per week. The intervention resulted in a substantial decrease in bed-sharing behavior for all participants, and parents reported being satisfied with the intervention.     

The role of spatial frequency in emotional face classification

Previous studies with emotional face stimuli have revealed that our ability to identify different emotional states is dependent on the faces’ spatial frequency content. However, these studies typically only tested a limited number of emotional states. In the present study, we measured the consistency with which 24 different emotional states are classified when the faces are unfiltered, high-, or low-pass filtered, using a novel rating method that simultaneously measures perceived arousal (high to low) and valence (pleasant to unpleasant). The data reveal that consistent ratings are made for every emotional state independent of spatial frequency content. We conclude that emotional faces possess both high- and low-frequency information that can be relied on to facilitate classification.     

Modelling visual detection: Luminance response non-linearity and internal noise

Two experiments that investigate the effect of various display factors on the detectability of a thin line signal in random visual noise are described. Three statistical decision models are described, together with their ability to account for the results. The first is an “ideal detector” model, the second an “energy integrator” model, and the third a model based upon the operation of retinal ganglion cells which incorporates a gain control mechanism. The ideal detector model fails to give a good account of human performance, whereas the other two models provide a good fit to the data. The digital Laplacian with gain control model has the slight advantage over the energy integrating model in being able to account for a small superiority in the detection of dark as opposed to bright signals. Finally, both models require the inclusion of an estimate of the internal noise of the human visual system to account for the pattern of performance observed under changing conditions of display contrast.     

Direct evidence for the existence of energy-based texture mechanisms

Two classes of models have been proposed to explain how the visual system processes texture modulations. In 'feature models', abstract representations of the featural properties of local texture regions (eg orientation, spatial frequency, contrast) are first generated, after which differences in individual feature properties across space are detected. In 'energy models', on the other hand, differences across space in the response energies of linear simple-cell-like filters are detected. This model thus processes the existing differences between texture regions directly without generating a full representation of the individual texture regions. We provide here direct evidence for the existence of the second, energy model, using an adaptation paradigm in conjunction with textures simultaneously modulated in two dimensions--orientation and spatial frequency. We found that the mechanism that processed the conjoint modulation was tuned to orientations and spatial frequencies that could not be predicted by any feature model, but which were precisely predicted by the energy model.     

Colour vision brings clarity to shadows

We have revealed a new role for colour vision in visual scene analysis: colour vision facilitates shadow identification. Shadows are important features of the visual scene, providing information about the shape, depth, and movement of objects. To be useful for perception, however, shadows must be distinguished from other types of luminance variation, principally the variation in object reflectance. A potential cue for distinguishing shadows from reflectance variations is colour, since chromatic changes typically occur at object but not shadow boundaries. We tested whether colour cues were exploited by the visual system for shadow identification, by comparing the ability of human test subjects to identify simulated shadows on chromatically variegated versus achromatically variegated backgrounds with identical luminance compositions. Performance was superior with the chromatically variegated backgrounds. Furthermore, introducing random colour contrast across the shadow boundaries degraded their identification. These findings demonstrate that the visual system exploits inbuilt assumptions about the relationships between colour and luminance in the natural visual world.