Maintaining internal representations: The role of the human superior parietal lobe

In sensorimotor integration, sensory input and motor output signals are combined to provide an internal estimate of the state of both the world and one's own body. Although a single perceptual and motor snapshot can provide information about the current state, computational models show that the state can be optimally estimated by a recursive process in which an internal estimate is maintained and updated by the current sensory and motor signals. These models predict that an internal state estimate is maintained or stored in the brain. Here we report a patient with a lesion of the superior parietal lobe who shows both sensory and motor deficits consistent with an inability to maintain such an internal representation between updates. Our findings suggest that the superior parietal lobe is critical for sensorimotor integration, by maintaining an internal representation of the body's state.

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the Active Appearance Model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases. © 2009 IEEE.

Contingency awareness in human aversive conditioning involves the middle frontal gyrus.

In contrast to the wealth of data describing the neural mechanisms underlying classical conditioning, we know remarkably little about the mechanisms involved in acquisition of explicit contingency awareness. Subjects variably acquire contingency awareness in classical conditioning paradigms, in which they are able to describe the temporal relationship between a conditioned cue and its outcome. Previous studies have implicated the hippocampus and prefrontal cortex in the acquisition of explicit knowledge, although their specific roles remain unclear. We used functional magnetic resonance imaging to track the trial-by-trial acquisition of explicit knowledge in a concurrent trace and delay conditioning paradigm. We show that activity in bilateral middle frontal gyrus and parahippocampal gyrus correlates with the accuracy of explicit contingency awareness on each trial. In contrast, amygdala activation correlates with conditioned responses indexed by skin conductance responses (SCRs). These results demonstrate that brain regions known to be involved in other aspects of learning and memory also play a specific role, reflecting on each trial the acquisition and representation of contingency awareness.