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7 Neural Network Models of Workload We trained neural network models (NNs) to predict subjective workload measures using both condition and performance measures as input variables. Essentially the models attempted to learn how condition and performance factors relate to differences in subjective workload measures. There are several possible approaches to developing a neural net model of mental workload. Because of the extensive development and frequent application of the multi-layer feed-forward network architecture (or multi-layered perceptron, MLP), it is a reasonable place to start. With that architecture, it is possible to compare the performance of a linear system (a perceptron which has no hidden layers and can only represent linear solutions; see Rosenblatt, 1962) with the nonlinear systems that can be realized by including one or more layers of hidden units. If there is no performance gain with the inclusion of hidden units, the problem has a linear solution (or the best solution to the problem is linear). In such cases, the nonlinear solution with the hidden layer(s), tends to be unstable, and it generalizes to new cases poorly. In our modeling work, we routinely compared more complex models to the linear ones. To give the linear models a reasonable chance of performing well, it is important to code inputs such that the expected output has a monotonic relation with the input code. For example, in coding different conditions, the difficulty of the conditions should be represented by the order of values in the variable coding the conditions so, for example, coding the tracking task conditions might use 0, 0.33, 0.67, and 1.0 to code no tracking, and slow, medium, and fast cursor movement, respectively. In contrast to rule-based models, NNs compute by using interconnected networks of simple processing units. These simple units, called nodes, receive information from external sources (i.e., from input to the network or from other nodes), sum this information, and then propagate an activation level to all connected nodes. The advocates of NNs frequently mention the ability of such systems to learn the appropriate mapping of inputs to outputs from examples and to successfully generalize that learning to new examples. Perhaps the crux of neural network modeling is the application of appropriate learning algorithms to appropriate processing network topologies such that a set of connection weights is found that lead to desired performance. One of the most basic learning algorithms found in NN models is the Hebbian contiguity, or associative rule (Hebb, 1949). This simple learning rule states that if two simple processors are simultaneously active and are connected, then the relationship between them should be strengthened. This type of learning rule is associated with networks that rely on external teachers for feedback. In such networks, learning occurs in an iterative feedback loop composed of four parts (Lippmann, 1987). First, a pattern is presented and activation is propagated through the layers of the network. Second, the output activation is compared against the correct output (i.e., the true output information associated with a given input pattern), and an error term is computed. Third, interconnections (i.e., weights) are modified using some scheme that reduces the error measure computed in part two. Finally, go back to step one and repeat this process. This iterative learning scheme continues until all training patterns produce the correct output. NNs are able to generalize from previously learned responses to incomplete or novel instances of stimuli. To the extent that a new stimulus is similar to a stimulus pattern that has already been trained into the network, a similar pattern of processing will occur across the network resulting in a similar response (Arbib, 1986). 7

18 Table 1 Pairwise Correlations for the Variables in the Study track seq tone trker trkvr seqrt seqer tner numov anyov tmwl efwl stwl wlsm wlg wli rl TRACK SEQ TONE TRKER TRKVR SEQRT SEQER TNER NUMOV ANYOV TMWL EFWL STWL WLSM WLG WLI RL

19 Table 2 Description of the Variables in Table 1 Variable Description TRACK Presence and level of Tracking Task SEQ Presence and level of SRT Task TONE Presence of Tone Counting Task TRKER Tracking Error TRKVR Tracking Variance SEQRT SRT Reaction Time SEQER SRT Error Rate TNER Tone Counting Error NUMOV Number of Performance Measures over Mean ANYOV Are any Performance Measures over Mean? TMWL Time Workload Rating EFWL Effort Workload Rating STWL Stress Workload Rating WLSM Sum of tmwl, efwl, stwl WLG Group Scaled Workload WLI Individual Scaled Workload RL Redline: Does Group Workload exceed 40? 19

20 Table 3 Tracking Error as a Function of Tracking, SRT, and Tone-Counting Conditions No SRT Task Structured SRT Task Random SRT Task No Tones Tones No Tones Tones No Tones Tones Means No Tracking x x x x x x x Slow Tracking 0.05 x Medium Tracking 0.09 x Fast Tracking 0.16 x Means 0.10 x

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