In both cases we assume that the robot knows some a priori response to low level sensors (i.e. to contact sensors in the case of obstacles or to proximity target sensors in the case of targets) and has to learn the response to high level stimuli (i.e. distance sensors or visual input).
On the other hand, studies manipulating expectations of higher level stimuli, such as faces and houses31,32,33 and letter shapes34,35 have generally reported effects in high-level visual areas specialised in processing such stimuli.
However, this article investigates the potential of employing such elements as viable alternatives to higher level stimuli such as facial expression and movie scenes.
Rhode [ 5] pointed out that the BM response to sinusoidal stimuli is less frequency selective for higher level stimuli.
We used behavioral and neuroimaging data to test the hypothesis that adaptation to high-level stimulus features decorrelates stimulus representations.
Memories were reproducible within and across individuals, showed the typical decay with time elapsed between encoding and testing, were fallible yet accurate, and were insensitive to low-level stimulus manipulations but sensitive to high-level stimulus properties.
On the contrary, both the cognitive factors and the high-level stimulus (e.g., face [22], person and car [23]) are considered in top-down attention.
Focal-attentional selection, in turn, mediates high-level stimulus analysis and response decision processes.
Age-related deficits in visual temporal processing have been found with both low-level and high-level stimuli.
The OHC nonlinearly amplifies the small basilar membrane motions, and this action consequently enhances the sensitivity of IHC to weak stimuli and compresses high-level stimuli.
