Human and machine factors in algae monitoring performance

Abstract

We all take our visual systems for granted, and often assume we are always ‘near perfect’ observers. This is not the case; expert visual recognition is complex and can be error prone. Starting with examples that define the problem I will explore some of the issues of recognition where expert judgements are required. In addition to ‘expert’ effects, there are a number of cognitive factors that can severely affect performance, including fatigue, boredom, recency effects, positivity bias and short-term memory effects. Experimental evidence of the impact of these on performance are presented and discussed. The specimen identifications generated by experts are useful not only to ecology, but to researchers developing systems for automatic labelling of marine plankton. Comparisons of performance are presented, where human experts have been pitted against machines to label plankton. Consensus of opinion is important in reducing errors, yet it is the norm for experts to operate alone. The shortcomings of man and machines engaged in plankton recognition are reviewed and the future of automation is assessed.