Cognitive influences on the affective representation of touch and the sight of touch in the human brain

We show that the affective experience of touch and the sight of touch can be modulated by cognition, and investigate in an fMRI study where top-down cognitive modulations of bottom-up somatosensory and visual processing of touch and its affective value occur in the human brain. The cognitive modulation was produced by word labels, 'Rich moisturizing cream' or 'Basic cream', while cream was being applied to the forearm, or was seen being applied to a forearm. The subjective pleasantness and richness were modulated by the word labels, as were the fMRI activations to touch in parietal cortex area 7, the insula and ventral striatum. The cognitive labels influenced the activations to the sight of touch and also the correlations with pleasantness in the pregenual cingulate/orbitofrontal cortex and ventral striatum. Further evidence of how the orbitofrontal cortex is involved in affective aspects of touch was that touch to the forearm [which has C fiber Touch (CT) afferents sensitive to light touch] compared with touch to the glabrous skin of the hand (which does not) revealed activation in the mid-orbitofrontal cortex. This is of interest as previous studies have suggested that the CT system is important in affiliative caress-like touch between individuals.

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck’s cargo occurs. The threats range from explicit, e.g. a person attacking the truck driver, to implicit, e.g. somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person’s activity, presence in a particular zone and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The aim of this representation was to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person’s activities are the most important elements of this semantic representation, followed by the person’s trajectory. The proposed threat detection system is very accurate: 96.6 % of the tracks are correctly interpreted, when considering the temporal context.