Abstract
Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects' self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.
Highlights
Imagine you were abandoned on an uninhabited planet and you could move around on the surface, thereby generating motion trajectories that can be observed by some non-human intelligence
We found that participants could change the randomness of their behavior depending on context and feedback
Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories
Summary
Imagine you were abandoned on an uninhabited planet and you could move around on the surface, thereby generating motion trajectories that can be observed by some non-human intelligence. Previous studies have used the concept of Kolmogorov complexity, for example, to evaluate the complexity of animal behavioral patterns, such as ants’ hunting behavior (Panteleeva et al, 2010; Reznikova et al, 2012). In these studies the authors assessed the regularity of behavioral sequences and found that successful hunting behavior was associated with higher stereotypy. Behavioral studies in psychology have indicated that the randomness of human-generated random number sequences might be dependent on the feedback provided to human subjects (Neuringer, 1986; Persaud, 2005; Figurska et al, 2008)
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