Motion-defined pattern

The Correlation Analyses between fMRI and Psychophysical Results Contribute to Certify the Activated Area for Motion-Defined Pattern Perception... 

Keywords— motion-defined pattern, random dot kinematogram, fMRI, correct rate, correlation analysis. 

Motion-defined pattern perception is realized, when we watch an area composed of coherently moving dots surrounded by sand storm region of incoherently moving dots. According to psychophysical experiments, this type of pattern perception includes two processes perception of motion itself and that of shape or contour [1]. The activated brain area for detecting motion itself has been established to be in the middle temporal area (MT/V5) or area V3A [2]. In contrast, the area for motion-defined pattern perception has not been clearly identified. The results of many conventional fMRI studies suggested that various areas 6, 9, 19, 32 and 38 in Brodmann s area (BA) were activated by motion-defined pattern stimuli [3]. However, it is not evident from these results whether they were elicited for motion perception itself or for the pattern perception. 

Fig. 1 Schematic drawings of motion-defined pattern stimuli...

For each moving velocity level, the measurement was repeated for 3 cycles. In each cycle, the resting block of static random dot presentation was continues for 30 sec (10 scans) and was then followed by the stimulating block, during which the same motion-defined pattern stimuli as those in psychophysical experiments continuing for 30 sec were presented. The order of velocity levels was randomized. 

The activated areas determined as described above (n, B-3) are indicated as red regions in Fig.3. Most of the activated areas were in the left occipital lobe. These activated areas were considered to include the processing areas for both motion itself and motion-defined pattern perception. We then tried to discriminate the areas for pattern perception by utilizing the correlation analyses between the sizes of effect and the correct rates for pattern discrimination task at 4 velocity levels. The only one voxel significantly correlated in such analyses was the area designated by the black square at BA19 in Fig.3 (t(3) = 5.185, p<0.05). The correlation coefficient ( r = 0.967) revealed very strong correlation. The location of this area is listed in Table. 1. 

The motion-defined pattern perception should be performed by interconnecting the magncellular system, which deals with location and moton, and the parvocellular system, which processes shape and color. BA19 is identical to V3 (visual area 3), which has also been suggested to represent an important area for integration and transformation of visual signals processed by magno- and parvo-cellular systems ([5], [6]). It is then plausible that motion-defined pattern perception is eventually accomplished at V3. 

One of the most striking experimental observations is that the amplitude and phase of the slip vary in a well-defined pattern over many thousands of cycles 10), This indicates that the film remembers its previous motion over extremely long times. A similar conclusion comes firom intermittent sliding experiments (5,5) as we discuss in the next section. 

Dendrites can grow at constant speed at arbitrarily small undercooling A, but usually a non-zero value of the anisotropy e is required. The growth pattern evolving from a nucleus acquires a star-shaped envelope surrounding a well-defined backbone. The distances between the corners of the envelope increase with time. For small undercooling we can use the scaling relation for the motion of the corners as for free dendrites [103-106] with tip... 

Analytical solutions for the equations of motion are not possible because of the difficulty of specifying the flow pattern and of defining the precise nature of the interaction between the phases. Rapid fluctuations in flow frequently occur and these cannot readily be taken into account. For these reasons, it is necessary for design purposes to use correlations which have been obtained using experimental data. Great care should be taken, however, if these are used outside the limits used in the experimental work. 

Figure 26 Different flow patterns for batch and continuous fuel beds. The motion of the fuel bed is defined relative to a fixed coordinate system.

As with the flow regimes in fluid dynamic theory, that is, the stagnation, laminar flow and turbulent flow, it is obvious that a solid phase can exhibit the corresponding flow pattern regimes, which herein are referred to as fixed, moving and mixed, respectively. The terms fixed, moving and mixed are defined as the relative motion of the particle phase with respect to a fixed coordinate system (see Figure 26). Examples of commercial PBC systems with different fuel-bed movement are found in section B.3.4. A comparison between theoretical and practical conversion systems. 

It is difficult to define turbulence. Intuitively, we associate it with the fine-structure of the fluid motion, as opposed to the flow pattern of the large-scale currents. Although it is not possible to describe exactly the distribution in space and time of this small-scale motion, we can characterize it in terms of certain statistical parameters such as the variance of the current velocity at some fixed location. A similar approach has been adopted to describe the motion at the molecular level. It is not possible to describe the movement of some individual molecule, but groups of molecules obey certain characteristic laws. In this way the individual behavior of many molecules sums to yield the average motion in response to macroscopic forces. 

The main difficulty with the first mode of oxidation mentioned above is explaining how the cation vacancies that arrive at the metal/oxide interface are accommodated. This problem has already been addressed in Section 7.2. Distinct patterns of dislocations in the metal near the metal/oxide interface and dislocation climb have been invoked to support the continuous motion of the adherent metal/oxide interface in this case [B. Pieraggi, R. A. Rapp (1988)]. If experimental rate constants are moderately larger than those predicted by the Wagner theory, one may assume that internal surfaces such as dislocations (and possibly grain boundaries) in the oxide layer contribute to the cation transport. This can formally be taken into account by defining an effective diffusion coefficient Del( = (1 -/)-DL+/-DNL, where DL is the lattice diffusion coefficient, DNL is the diffusion coefficient of the internal surfaces, and / is the site fraction of cations located on these internal surfaces. 

Once the hydrocarbons have been solubilized in the formation water, they move with the water under the influence of elevation and pressure (fluid), thermal, electroosmotic and chemicoosmotic potentials. Of these, the fluid potential is the most important and the best known. The fluid potential is defined as the amount of work required to transport a unit mass of fluid from an arbitrary chosen datum (usually sea level) and state to the position and state of the point considered. The classic work of Hubbert (192) on the theory of groundwater motion was the first published account of the basinwide flow of fluids that considered the problem in exact mathematical terms as a steady-state phenomenon. His concept of formation fluid flow is shown in Figure 3A. However, incongruities in the relation between total hydraulic head and depth below surface in topographic low areas suggested that Hubbert s model was incomplete (193). Expanding on the work of Hubbert, Toth (194, 195) introduced a mathematical mfcdel in which exact flow patterns are... 

Diffusion Pattern from a Continuous Point Source—The distribution of particles from a point source in a moving fluid can be determined provided we assume that the concentration gradients in the direction of fluid motion are small compared to those at right angles to it. If C, is defined as the concentration of particles over a unit area of a plane horizontal surface downstream and to one side of the mean path of the diffusing stream from a point source, then the equation of diffusion at any point x downstream and at a distance y from the mean path is... 

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