Perceptual plausibility of exaggerated realistic motion.

The informal heuristic practices of the fine arts have much to offer to our understanding of the appearance of phenomenological reality. One interesting example is the use of exaggeration to enhance the illusion of liveliness in both living and nonliving subjects. This further eases the uncomfortable sense that the motion is somehow uncanny - especially with inanimate objects. We performed a series of experiments to test the effects of exaggeration on the phenomenological perception of simple animated objects - bouncing balls. A physically plausible model of a bouncing ball was augmented with a frequently used form of exaggeration known as squash and stretch. Observers were shown a series of animated balls, depicted using systematic parameterizations of the exaggeration model, and asked to rate their plausibility. A range of rendering styles provided varying levels of information as to the type of ball. In all cases, balls with small amounts of exaggeration were seen as plausible as those without any exaggeration (e.g., with veridical motion). Furthermore, when the type of ball was not specified, observers tolerated a large amount of exaggeration before judging them as implausible. When the type of ball was indicated, observers narrowed the range of acceptable exaggeration somewhat but still tolerated exaggeration well beyond that which would be physically possible. We contend that, in this case, exaggeration acts to bridge the so-called uncanny valley for artificial depictions of physical reality.

Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech.

People of all ages display the ability to detect and learn from patterns in seemingly random stimuli. Referred to as statistical learning (SL), this process is particularly critical when learning a spoken language, helping in the identification of discrete words within a spoken phrase. Here, by considering individual differences in speech auditory-motor synchronization, we demonstrate that recruitment of a specific neural network supports behavioral differences in SL from speech. While independent component analysis (ICA) of fMRI data revealed that a network of auditory and superior pre/motor regions is universally activated in the process of learning, a frontoparietal network is additionally and selectively engaged by only some individuals (high auditory-motor synchronizers). Importantly, activation of this frontoparietal network is related to a boost in learning performance, and interference with this network via articulatory suppression (AS; i.e., producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on SL from speech and reconciles previous contrasting findings. These findings also highlight a more general need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.