Also, for the monkey avatar, this variance was smaller than for all other conditions. Moreover, the difference of the perceived <a href="https://pubmed.ncbi.nlm.nih.gov/23966296/">search Acetyl Pentapeptide-1</a> expressiveness condition for equilibration with the DF measure.Equilibration was based on creating morphs between the original motioncaptured expressions and a neutral expression, varying the morphing weight of the neutral expression in a way that resulted in a matching of the amount of motion flow. Equilibration was realized separately for the two avatars and also for the different view conditions.Figure B shows an example of the effect of equilibration on the extreme frames of a monkeythreat expression.The equilibration also reduces the very salient mouth opening motion of the monkey, which, due to anatomical differences, cannot be realized by a real human face.These functions are more symmetrical along the axes of the morphing space than for the original stimuli for example, this reduces the amount of confusions of human anger and monkey fear expressions that occurs for intermediate levels of the style parameters, especially for the human avatar, potentially due to the subtlety of the monkey fear expression.Compared to the original stimuli, we found an even higher similarity of the discriminant functions between the two avatar types and the different view conditions.Most importantly, also for these equilibrated stimulus sets, we found a narrower tuning for the human than for the monkey dynamic expressions. Mean perceived expressivity ratings for stimulus sets that were equilibrated using different types of measures for the amount of expressive lowlevel information: OF: optic flow computed with an optic flow algorithm; DF: shape difference compared to the neutral face; MF: twodimensional motion of the polygons on the surface of the face.This analysis shows that the equilibration procedure effectively balances the steepness of the tuning functions between the human and the monkey expressions, which is apparent in the nonequilibrated stimuli.Contrasting with this result, the ANOVA for the stimuli with equilibrated motion does not show any significant <a href="https://www.targetmol.com/compound/Acetyl%20Pentapeptide-1%20acetate">buy Acetyl Pentapeptide-1</a> effects, neither of the avatar type and view, nor of an interaction. Summarizing, these results show that the high similarity of the classification data of the stimuli between the two different avatar types, and between the different view conditions, was not fundamentally changing if the expressiveness of the stimuli was controlled.Also, the tendency for a narrower tuning for human ownspecies expressions was robust against this manipulation.However, balancing expressiveness leveled out the differences in the steepness of the computed speciestuning functions.Exploiting advanced methods from computer animation with motion capture across species and machinelearning methods for motion interpolation, our study reveals fundamental insights about the perceptual encoding of dynamic facial expressions across primate species.At the same time, the developed technology lays the ground for physiological studies with highly controlled stimuli on the neural encoding of such dynamic patterns. Our first key observation was that facial expressions of macaque monkeys were learned very quickly by human observers.This was the case even though monkey expressions are quite different from human expressions, so that naive observers could not interpret them spontaneously.This fast learning might be a consequence of the high similarity of the neuromuscular control of facial movements in humans and macaques, resulting in a high similarity of the structural properties of the expression dynamics that can be exploited by the visual system for fast learning.
