Tracking multiple perspectives: Spontaneous computation of what individuals in high entitative groups see.

Perspective-taking ability is crucial for supporting social interactions. It has been widely suggested that the calculation of an individual's perspective is spontaneous. Nevertheless, people typically engage with more than one individual, and computing what individuals in a crowd see is important. The current study explored whether people spontaneously compute the perspectives of individuals displayed in a crowd. The classic visual perspective-taking task was adopted, but the picture of the room was presented with four human avatars facing two walls. The results showed that if the crowd of individuals was treated as a high entitative group, when none of the perspectives of the individuals contained the same number of discs as that from the perspective of the participant, the judgment of the participant's perspective was slower than when a proportion of the perspectives of the individuals displayed in the crowd were consistent with the participant's perspective, even if the perspectives of the multiple individuals in a crowd were not explicitly noticed. This altercentric intrusion effect was not present when the crowd had low entitativity. These findings were replicated by using different methods to operationalize group entitativity. Hence, this study demonstrates that spontaneously tracking the perspectives of individuals displayed in a crowd has a boundary condition and that people can spontaneously compute what individuals in high entitative groups see.

Analyzing Liver Surface Indentation for In Vivo Refinement of Tumor Location in Minimally Invasive Surgery.

Manual palpation to update the position of subsurface tumor(s) is a normal practice in open surgery, but is not possible through the small incisions of minimally invasive surgery (MIS). This paper proposes a method that has the potential to use a simple constant-force indenter and the existing laparoscopic camera for tumor location refinement in MIS. The indenter floats with organ movement to generate a static surface deformation on the soft tissue, resolving problems of previous studies that require complicated measurement of force and displacement during indentation. By analyzing the deformation profile, we can intraoperatively update the tumor's location in real-time. Indentation experiments were conducted on healthy and "diseased" porcine liver specimens to obtain the deformation surrounding the indenter site. An inverse finite element (FE) algorithm was developed to determine the optimal material parameters of the healthy liver tissue. With these parameters, a computational model of tumorous tissue was constructed to quantitatively evaluate the effects of the tumor location on the induced deformation. By relating the experimental data from the "diseased" liver specimen to the computational results, we estimated the radial distance between the tumor and the indenter, as well as the angular position of the tumor relative to the indenter.

Mechanical characterization of porcine liver properties for computational simulation of indentation on cancerous tissue.

An accurate characterization of soft biological tissue properties is essential for a realistic simulation of surgical procedures. Unconfined uniaxial compression tests with specimens affixed to the fixtures are often performed to characterize the stress-stretch curves of soft biological tissues, with which the material parameters can be obtained. However, the constrained boundary condition causes non-uniform deformation during the uniaxial test, posing challenges for accurate measurement of tissue deformation. In this study, we measured the deformation locally at the middle of liver specimens and obtained the corresponding stress-stretch curves. Since the effect of the constrained boundary condition on the local deformation of specimen is minimized, the stress-stretch curves are thus more realistic. Subsequently, we fitted the experimental stress-stretch curves with several constitutive models and found that the first-order Ogden hyperelastic material model was most suitable for characterizing the mechanical properties of porcine liver tissues. To further verify the characterized material properties, we carried out indentation tests on porcine liver specimens and compared the experimental data with computational results by using finite element simulations. A good agreement was achieved. Finally, we constructed computational models of liver tissue with a tumor and investigated the effect of the tumor on the mechanical response of the tissue under indentation. The computational results revealed that the liver specimen with tumor shows a stiffer response if the distance between the tumor and the indenter is small.