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In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus "observation") in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools.KEYWORDS: observation-level interaction, visual analytics, statistical models. INDEX TERMS: H.5.0 [Human-Computer Interaction] INTRODUCTIONVisual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces" [1]. The goal of visual analytics (VA) is to extract information, perform exploratory analyses, and validate hypotheses through an interactive exploration process known as sensemaking [2]. In this sensemaking loop, users proceed through a complex combination of proposing and evaluating hypotheses and schemas about their data, with the ultimate goal of gaining insight (i.e. "making sense of" the data). A wide variety of statistical models have been specifically designed for visualizations of this purpose. Thus, many visual analytic systems are fundamentally based on interaction with statistical models and algorithms, using visualization as the medium for the communication (i.e. where the interaction occurs). This communication is performed via direct interaction with the parameters of the model. For example, Interactive Principal Component Analysis, iPCA [3], allows the user to change the weight for each dimension in calculating the direction of projection using multiple sliders (one slider per dimension). Also, in an interactive visualization using MDS [4], the user can weight the dissimilarities in the calculation of the stress function through similar visual controls.In both instances, the model is made aware of the user input through a formal and direct modification of a parameter (i.e. parameter level interacti...

In the primate visual system, area V4 is located in the ventral pathway and is traditionally thought to be involved in processing color and form information. However, little is known about its functional role in processing motion information. Using intrinsic signal optical imaging over large fields of view in V1, V2, and V4, we mapped the direction of motion responses in anesthetized macaques. We found that V4 contains direction-preferring domains that are preferentially activated by stimuli moving in one direction. These direction-preferring domains normally occupy several restricted regions of V4 and tend to overlap with orientation- and color-preferring domains. Single-cell recordings targeting these direction-preferring domains also showed a clustering, as well as a columnar organization of V4 direction-selective neurons. These data suggest that, in contrast to the classical view, motion information is also processed in ventral pathway regions such as area V4.

Screw shaped implants of commercially pure (c.p.) titanium and titanium-6aluminum-4vanadium (Ti6A14V) were blasted with particles of TiO2 of mean sizes of 25 microns (Group I) and 75 microns (Group II) and inserted in rabbit bone for 3 months. The surface roughness of the implants was examined and quantified with an optical scanning 3-dimensional instrument (TopScan 3D system), revealing the two alloy surfaces in each group had similar surface roughness. Biomechanical (removal torque) tests showed the c.p. titanium implants to be significantly more stable in the bone bed than those of Ti6A14V. In Group I, the c.p. titanium implants demonstrated a mean removal torque of 38 N cm while the Ti6A14V demonstrated a mean removal torque of 27 N cm (P = 0.004). Group II implants revealed a mean removal torque of 70 N cm for the c.p. ti and 50 N cm for the alloy samples (P = 0.003). The removal torque values were converted to shear forces/strengths by three calculation methods, based on (a) the entire length of the implant surface in the cortical region, (b) the thickness of the cortical bone measured in close vicinity to the thread peaks and (c) the bone-metal contact length measured on the non-unscrewed neighbouring implants. Group I: (a) the c.p. ti implants revealed a mean shear force of 4 vs a mean of 3 N/mm2 for the alloy samples. Shear strengths based on (b); were 8 for c.p. ti vs 6 N/mm2 for the alloy. The mean shear strength/force if calculated according to (c) revealed 23 for c.p. ti vs 18 N/mm2 for the alloy. Corresponding numbers for Group II; (a) c.p. ti 8 compared to 6 N/mm2 for the alloy, (b) c.p. ti demonstrated a mean value of 17 vs 11 N/mm2 for the alloy. According to method (c); c.p. ti had a mean shear strength of 26 vs 22 N/mm2 for the alloy samples. Histomorphometrical comparisons were performed on 10 microns thick undecalcified ground sections in the light microscope. In both Group I and Group II, the calculations of the mean bone-to-metal contact demonstrated more bone in contact to the c.p. titanium implants than to the Ti6A14V ones. Whereas comparisons of the bone volume inside the threads demonstrated slightly higher bone volumes around the alloy samples, no statistically significant difference was obtained between the two materials histomorphometrically.