Morphological Complexity and Conceptualization : The Human Body

In this squib, I want to argue that the morphological structure of words is, at least to some extent, motivated. As an example I have choosen the partonomic (and for the less part taxonomic) nomenclature of the human body. While important work by Brown et alii (1973), Anderson (1978) and Schladt (1997) exists on this topic, these analyses focus on the conceptualization of body-parts and their semantics, but not on their morphological representation. In the following, I want to check two predictions about the morphological complexity of lexical items denoting parts of the human body. The first assumption is that the most canonical body-parts are always expressed by mono-lexematic items. The second one consists in the assumption that body-parts of the lowest levels in the hierarchy are always morphologically complex. A set of six body-parts has been analysed in 27 languages. The set consists of two canonical (HEAD and EAR) and of one from the lowest level of the hierarchy (TOENAIL). For this I have adopted a sample from Schladt (1997) and a small one compiled by mysel

The Complexity of Human Walking: A Knee Osteoarthritis Study

This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space

Human variability, task complexity and motivation contribution in manufacturing

This paper is a preliminary study of the human contribution to variability in manufacturing industry and how motivation and learning play a key role in this contribution. The longer term aim is to incorporate this understanding in a methodology, using principles and guidelines, that aims to help in the design of intelligent automation that reduces product variability. This paper reports on the early stages that are concerned with understanding relationships between human-induced product variability, task complexity and human characteristics and capabilities. Two areas have been selected for initial study in manufacturing industry: (a) the relationship between manual task complexity and product variability and (b) the relationship between employee motivational factors and learning behaviours. The paper discusses the progress to date in conducting initial empirical studies and surveys in industry and draws tentative conclusions of the value of this knowledge to the overall objective of intelligent automation

Natural scene statistics mediate the perception of image complexity

Humans are sensitive to complexity and regularity in patterns. The subjective perception of pattern complexity is correlated to algorithmic (Kolmogorov-Chaitin) complexity as defined in computer science, but also to the frequency of naturally occurring patterns. However, the possible mediational role of natural frequencies in the perception of algorithmic complexity remains unclear. Here we reanalyze Hsu et al. (2010) through a mediational analysis, and complement their results in a new experiment. We conclude that human perception of complexity seems partly shaped by natural scenes statistics, thereby establishing a link between the perception of complexity and the effect of natural scene statistics

Depth, balancing, and limits of the Elo model

-Much work has been devoted to the computational complexity of games. However, they are not necessarily relevant for estimating the complexity in human terms. Therefore, human-centered measures have been proposed, e.g. the depth. This paper discusses the depth of various games, extends it to a continuous measure. We provide new depth results and present tool (given-first-move, pie rule, size extension) for increasing it. We also use these measures for analyzing games and opening moves in Y, NoGo, Killall Go, and the effect of pie rules

Approach to the Organisational Complexity in Terms of Network and Intellectual Capital Concepts

The viability of the systems depends on the way of adaptation of the internal complexity to the environmental complexity. Under structural aspect, any complex system represents a network. Complexity may be estimated on the basis of density and of the non-redundant character of the network. The capacity of the networks to create and diffuse knowledge is essential. Comparing the change speed of the environment with the knowledge processing speed in the system, we can determine the maximum complexity that can be absorbed. A close image of the internal complexity is the level of the human and structural capital. The external complexity may be expressed by means of the relational capital.requisite variety, endogenous complexity, exogenous complexity, network, intellectual capital

First anatomical network analysis of fore- and hindlimb musculoskeletal modularity in bonobos, common chimpanzees, and humans

Studies of morphological integration and modularity, and of anatomical complexity in human evolution typically focus on skeletal tissues. Here we provide the first network analysis of the musculoskeletal anatomy of both the fore- and hindlimbs of the two species of chimpanzee and humans. Contra long-accepted ideas, network analysis reveals that the hindlimb displays a pattern opposite to that of the forelimb: Pan big toe is typically seen as more independently mobile, but humans are actually the ones that have a separate module exclusively related to its movements. Different fore- vs hindlimb patterns are also seen for anatomical network complexity (i.e., complexity in the arrangement of bones and muscles). For instance, the human hindlimb is as complex as that of chimpanzees but the human forelimb is less complex than in Pan. Importantly, in contrast to the analysis of morphological integration using morphometric approaches, network analyses do not support the prediction that forelimb and hindlimb are more dissimilar in species with functionally divergent limbs such as bipedal humans