Facing up to Complexity: Implications for Our Social Experiments

Biological systems are highly complex, and for this reason there is a considerable degree of uncertainty as to the consequences of making significant interventions into their workings. Since a number of new technologies are already impinging on living systems, including our bodies, many of us have become participants in large-scale “social experiments”. I will discuss biological complexity and its relevance to the technologies that brought us BSE/vCJD and the controversy over GM foods. Then I will consider some of the complexities of our social dynamics, and argue for making a shift from using the precautionary principle to employing the approach of evaluating the introduction of new technologies by conceiving of them as social experiments.     

Assessment of information content in visual signal: analysis of optical flow fractal complexity

We make a first attempt at distinguishing an information-carrying visual signal by comparing visual characteristics of American Sign Language to everyday human motion, to identify what clues might be available in one but not in the other. The comparison indicated significantly higher fractal complexity in sign language across tested frequency bands (0.01–15?Hz), as compared to everyday motion. A comparison of our results with other work showing high fractal complexity in the speech signal allows us to suggest the underlying properties of linguistic signals which allow babies to “tune into” a specific channel, or modality, during language acquisition.     

Dealing with Possible Baseline Inequalities Between Experimental Groups – The Case of Motor Learning

Abstract

One important concept of experimental design is the random assignment of participants to experimental groups. This randomization process is used to prevent selection bias, as well as to provide a strong basis for a cause-and-effect relationship between the independent variable/s and the dependent variable/s. In small sample sizes, simple randomization may not provide equal groups at baseline for one or more of the variables, and therefore more restricted types of randomization, such as the stratified permuted-block randomization, can be used. A code was written to calculate the probability that simple randomization will not lead to equality between groups at baseline, and then an example of stratified permuted-block randomization was examined. The findings suggest that for certain variables that are commonly measured in experiments in motor learning, there is a relatively high probability that groups will not be equal at baseline after simple randomization. This observation reflects the small sample sizes usually found in the literature on motor learning. However, stratified permuted-block randomization does lead to greater equality among groups. Implications for researchers are discussed, and a flowchart is proposed that will allow researchers to decide whether to use simple or stratified randomization.