Abstract:This study examined how cells in the temporal cortex code orientation and size of a complex object. The study focused on cells selectively responsive to the sight of the head and body but unresponsive to control stimuli. The majority of cells tested (19/26, 73%) were selectively responsive to a particular orientation in the picture plane of the static whole body stimulus, 7/26 cells showed generalisation responding to all orientations (three cells with orientation tuning superimposed on a generalised response)… Show more
“…On the other hand, as the parameters of the posture-selective neurons have to match the retinal size of the stimulus, one needs templates of different sizes or ways to establish size invariance, as for other types of object recognition. Indeed, body-shape-selective neurons have only limited size invariance (Ashbridge et al, 2000), suggesting that indeed posture-selective neurons for different sizes might be needed.…”
The visual recognition of action can be obtained from the change of body posture over time. Even for point-light stimuli in which the body posture is conveyed by only a few light points, biological motion can be perceived from posture sequence analysis. We present and analyze a formal model of how action recognition may be computed and represented in the brain. This model assumes that motion energy detectors similar to those well-established for the luminance-based motion of objects in space are applied to a cortical representation of body posture. Similar to the spatio-temporal receptive fields of regular motion detectors, these body motion detectors attain receptive fields in a posture-time space. We describe the properties of these receptive fields and compare them with properties of body-sensitive neurons found in the superior temporal sulcus of macaque monkeys. We consider tuning properties for 3D views of static and moving bodies. Our simulations show that key properties of action representation in the STS can directly be explained from the properties of natural action stimuli. Our model also suggests an explanation for the phenomenon of implied motion, the perceptual appearance, and neural activation of motion from static images.
“…On the other hand, as the parameters of the posture-selective neurons have to match the retinal size of the stimulus, one needs templates of different sizes or ways to establish size invariance, as for other types of object recognition. Indeed, body-shape-selective neurons have only limited size invariance (Ashbridge et al, 2000), suggesting that indeed posture-selective neurons for different sizes might be needed.…”
The visual recognition of action can be obtained from the change of body posture over time. Even for point-light stimuli in which the body posture is conveyed by only a few light points, biological motion can be perceived from posture sequence analysis. We present and analyze a formal model of how action recognition may be computed and represented in the brain. This model assumes that motion energy detectors similar to those well-established for the luminance-based motion of objects in space are applied to a cortical representation of body posture. Similar to the spatio-temporal receptive fields of regular motion detectors, these body motion detectors attain receptive fields in a posture-time space. We describe the properties of these receptive fields and compare them with properties of body-sensitive neurons found in the superior temporal sulcus of macaque monkeys. We consider tuning properties for 3D views of static and moving bodies. Our simulations show that key properties of action representation in the STS can directly be explained from the properties of natural action stimuli. Our model also suggests an explanation for the phenomenon of implied motion, the perceptual appearance, and neural activation of motion from static images.
“…The neural implementation of the two models is inspired by neurophysiological evidence suggesting a continuous representation of stimuli in feature maps (Ashbridge et al, 2000;Cisek & Kalaska, 2005;Schwartz et al, 1988). In such neural populations, neurons generally respond to external stimuli with broad tuning curves of activity.…”
Humans' capacity to imitate has been extensively investigated through a wide-range of behavioral and developmental studies. Yet, despite the huge amount of phenomenological evidence gathered, we are still unable to relate this behavioral data to any specific neural substrate. In this paper, we investigate how principles from psychology can be the result of neural computations and therefore attempt to bridge the gap between monkey neurophysiology and human behavioral data, and hence between these two complementary disciplines.Specifically, we address the principle of ideomotor compatibility, by which 'observing the movements of others influences the quality of one's own performance' and develop two neural models which account for a set of related behavioral studies [Brass, M., Bekkering, H., Wohlschläger, A., & Prinz, W. (2000). Compatibility between observed and executed finger movements: comparing symbolic, spatial and imitative cues. Brain and Cognition 44,[124][125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143]. We show that the ideomotor effect could be the result of two distinct cognitive pathways, which can be modeled by means of biologically plausible neural architectures. Furthermore, we propose a novel behavioral experiment to confirm or refute either of the two model pathways. q
“…These data suggest that, for normal recognition tasks, size is not encoded. Ashbridge, Perrett, Oram, and Jellema (2000) tested for size invariance in single cells of the object recognition area of rhesus macaques. They first recorded from 16 cells in the anterior part of the superior temporal sulcus that responded selectively to images of human forms; they later presented these images at several different sizes.…”
The authors investigated the pigeon's ability to generalize object discrimination performance to smaller and larger versions of trained objects. In Experiment 1, they taught pigeons with line drawings of multipart objects and later tested the birds with both larger and smaller drawings. The pigeons exhibited significant generalization to new sizes, although they did show systematic performance decrements as the new size deviated from the original. In Experiment 2, the authors tested both linear and exponential size changes of computer-rendered basic shapes to determine which size transformation produced equivalent performance for size increases and decreases. Performance was more consistent with logarithmic than with linear scaling of size. This finding was supported in Experiment 3. Overall, the experiments suggest that the pigeon encodes size as a feature of objects and that the representation of size is most likely logarithmic.
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