What is Animacy in Dynamical Movement

What sort of movements of an object make it look alive? How do we feel aliveness in a robot moving? This sense, animacy, is experimentally investigated for moving of objects. There are many artifacts that remind us animacy, e.g., animation movie characters, dolls or human figures in fine arts. We often feel animacy for non-living things, or even abstract objects (Tremoulet and Feldaman, 2000) (Nakamrura and Sumi, 2002). One possible reason is personification. Johansson (Johansson, 1973) showed that even complicated shape can be easily recognised when moving objects can be viewed as a human movement. However, this concept is not concerned about the quality of movements, which we are interested in. We performed a psychophysical experiment where a dynamical system evolves to life-like system by interactive evolution computation method. In the model, the genotype codes the parameters of a dynamical model. The model consists of combined oscillators on two dimension space with harmonic interaction (i.e., linear spring), see fig.1A. The boundary condition is periodic. The number of points are 3, 4 and 5. For each point, there are four parameters: kS (spring constant for oscillation of itself), kI (spring constant for interaction with nearest neightbour), x (initial position) and v (initial velocity). The points move only in horizontal direction. The phenotype dynamics, is shown to a subject as a set of moving dots on a computer display. Display image area is divided into 12 compartments where individual models are presented. Evolutional process is based on the selection by the subject. At each generation, one or two individual models out of 12 are selected for next generation and subjects do not explicitly tune parameters of the model. Mutation processes are random bit flip mutation or crossover, depending on the number of selected individuals. We adopted Gray coding for encoding dynamical parameters and gradual evolution is obtained by mutation. 15 healthy subjects without visual difficulty participated in the experiment (ages: 23 to 28). In typical session, a subject was asked to select one or two most “lifelike” individuals in each generation (up to 40 genelations). The experiment consisted of 3 trial runs, followed by 3 sessions, where the number of dots increased by one after each session (from 3 to 5 ). After the experiment, the subject was asked to fill out a questionnaire. All subjects reported that they felt animacy in their products, of which the majority of the subjects reported a set of dots that resembles a “body”. This may be due to personification. We examined what kind of movements of the “dots” gives animacy. The evolutionary process is logged and analysed. According to the reports of subjects, we defined stagnating points, where the same genotype is selected for more than 5 generations, as products. 77 products are obtained (3 to 8 per subject). Alternate data is randomly generated products by using random numbers as genotypes. The number of sample is equal to total number of trials, 135. By visually observing these products, we classified 9 sorts of dynamical features as follows. (SA) Stroke. Like a fish swimming, dots form a body (i.e., almost in phase) and stroke is obviously seen. (SB) Stroke and cluster coexist. (SC) Stroke is not clearly seen. (CA) Clustering. Each dot form in-phase or antiphase clustering with neightbour. Periodic trajectory is obviously seen. (CB) Partially disordered clustering. (CC) Shows reorgnisation of clusters. (CD) The dots look broken into two clusters (i.e., bodies), due to large difference of amplitude. (CE) Clustering. Top and bottom dots are in phase. (J) Jerky. Cluster is formed but jerky movement is often seen. (Other) Unclassifiable. Their relative frequencies are shown in fig.1B. The classification is subjectively done but based on the results of questionaires. The subjects suggested they felt animacy for stroking pattern. This pattern is defined as travelling wave pattern. Also, a “walking human” like pattern is suggested. We regard this pattern as alternating oscillation and defined it as cluster pattern. Based on these two patterns, we visually observed time serieses of the pheno type dynamics and identified 9 features shown above. We are currently working on a numerical method to classify the results will be reported in elsewhere. It is important to note that human impression is based on implicit filtering. For example, clustered movement can be seen when all points are moving in the same direction with small differences. In such case, trend must be subtracted. Then before we adopt statistical testing, many possible patterns of transformation of coordinates