Scaling Baroclinic Eddy Fluxes: Vortices and Energy Balance

The eddy heat flux generated by the statistically equilibrated baroclinic instability of a uniform, horizontal temperature gradient is studied using a two-mode f-plane quasigeostrophic model. An overview of the dependence of the eddy diffusivity D on the bottom friction κ, the deformation radius λ, the vertical variation of the large-scale flow U, and the domain size L is provided by numerical simulations at 70 different values of the two nondimensional control parameters κλ/U and L/λ. Strong, axisymmetric, well-separated baroclinic vortices dominate both the barotropic vorticity and the temperature fields. The core radius of a single vortex is significantly larger than λ but smaller than the eddy mixing length ℓ_mix. On the other hand, the typical vortex separation is comparable to ℓ_mix. Anticyclonic vortices are hot, and cyclonic vortices are cold. The motion of a single vortex is due to barotropic advection by other distant vortices, and the eddy heat flux is due to the systematic migration of hot anticyclones northward and cold cyclones southward. These features can be explained by scaling arguments and an analysis of the statistically steady energy balance. These arguments result in a relation between D and ℓ_mix. Earlier scaling theories based on coupled Kolmogorovian cascades do not account for these coherent structures and are shown to be unreliable. All of the major properties of this dilute vortex gas are exponentially sensitive to the strength of the bottom drag. As the bottom drag decreases, both the vortex cores and the vortex separation become larger. Provided that ℓ_mix remains significantly smaller than the domain size, then local mixing length arguments are applicable, and our main empirical result is ℓ_mix ≈ 4λ exp(0.3U/κλ)

Differences in holistic processing do not explain cultural differences in the recognition of facial expression

The aim of this study was to investigate the causes of the own-race advantage in facial expression perception. In Experiment 1, we investigated Western Caucasian and Chinese participants’ perception and categorization of facial expressions of six basic emotions that included two pairs of confusable expressions (fear and surprise; anger and disgust). People were slightly better at identifying facial expressions posed by own-race members (mainly in anger and disgust). In Experiment 2, we asked whether the own-race advantage was due to differences in the holistic processing of facial expressions. Participants viewed composite faces in which the upper part of one expression was combined with the lower part of a different expression. The upper and lower parts of the composite faces were either aligned or misaligned. Both Chinese and Caucasian participants were better at identifying the facial expressions from the misaligned images, showing interference on recognizing the parts of the expressions created by holistic perception of the aligned composite images. However, this interference from holistic processing was equivalent across expressions of own-race and other-race faces in both groups of participants. Whilst the own-race advantage in recognizing facial expressions does seem to reflect the confusability of certain emotions, it cannot be explained by differences in holistic processing. </jats:p

Natural variability is essential to learning new faces

We learn new faces throughout life, for example in everyday settings like watching TV. Recent research has shown that image variability is key to this ability: if we learn a new face over highly variable images, we are better able to recognize that person in novel pictures. Here we asked people to watch TV shows they had not seen before, and then tested their ability to recognize the actors. Some participants watched TV shows in the conventional manner, whereas others watched them upside down or contrast-reversed. Image variability is equivalent across these conditions, and yet we observed that viewers were unable to learn the faces upside down or contrast-reversed - even when tested in the same format as learning. We conclude that variability is a necessary, but not sufficient, condition for face learning. Instead, mechanisms underlying this process are tuned to extract useful information from variability falling within a critical range that corresponds to natural, everyday variation