Visual impressions of active and inanimate resistance to impact from a moving object

ABSTRACT

Images of moving objects presented on computer screens may be perceived as animate or inanimate. A simple hypothesis, consistent with much research evidence, is that objects are perceived as inanimate if there is a visible external contact from another object immediately prior to the onset of motion, and as animate if that is not the case. Evidence is reported that is not consistent with that hypothesis. Objects (targets) moving on contact from another object (launcher) were perceived as actively resisting the impact of the launcher on them if the targets slowed rapidly. Rapid slowing is consistent with the laws of mechanics for objects moving in an environment that offers friction and air resistance. Despite that, ratings of inanimate motion were lower than ratings of active resistance for objects that slowed rapidly. The results are consistent with the hypothesis that there is a perceptual impression of active (animate) resistance that is evoked by the kinematic pattern of rapid slowing from an initial speed after contact from another object.     

Vector Semiotic Model for Visual Question Answering

In this paper, we propose a Vector Semiotic Model as a possible solution to the symbol grounding problem in the context of Visual Question Answering. The Vector Semiotic Model combines the advantages of a Semiotic Approach implemented in the Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model represents information about a scene depicted on an input image in a structured way and grounds abstract objects in an agent’s sensory input. We use the Vector Symbolic Architecture to represent the elements of the Sign-Based World Model on a computational level. Properties of a high-dimensional space and operations defined for high-dimensional vectors allow encoding the whole scene into a high-dimensional vector with the preservation of the structure. That leads to the ability to apply explainable reasoning to answer an input question. We conducted experiments are on a CLEVR dataset and show results comparable to the state of the art. The proposed combination of approaches, first, leads to the possible solution of the symbol-grounding problem and, second, allows expanding current results to other intelligent tasks (collaborative robotics, embodied intellectual assistance, etc.).     

Inductive countersupport

The basic idea by means of which Popper and Miller proved the non-existence of inductive probabilistic support in 1983/1985/1987, is used to prove that inductive probabilistic countersupport does exist. So it seems that after falsification has won over verification on the deductive side of science, countersupport wins over support on the inductive side.