The effect of positive and negative messages on problem solving in computer programming tasks

Many supervisory control systems require the operator to solve any problems that the system’s automation cannot accommodate. Consequently, this class of systems would benefit from designs and methods which improve operator problem solving performance. Currently, human factors researchers develop designs and methods emphasizing the cognitive capacities and abilities of operators. For the most part, these approaches neglect the emotional state of the operator, although emotion has been shown to have an important impact on performance in many other domains. This dissertation introduces the modified Multidimensional Problem Solving (mMPS) Model, a theoretical model predicting how affect, one aspect of emotion, will influence problem solving performance. The model was tested in an experiment in which 32 participants attempted to correct a series of 5 bugs in a computer program. During their task, they received compiler messages with keywords specifically chosen to create a positive or negative affective state. The model predicted that the participants with messages designed to increase positive affect would seek solutions with a more divergent thought process, and this would be indicated with a more diverse set of problem-solving approaches, along with higher scores on a divergent thought measuring test administered throughout the experiment. Those with less positive affect would seek solutions in a smaller, less creative space and demonstrate less divergent thought. Unfortunately, the feedback messages did not appear to evoke an emotional response powerful enough to create a measurable change in emotional state. However, the messages did affect various aspects of the participants’ performance in ways consistent with the model, including 1