Classification techniques (using different feature sets) and ROC curves:

Section 7.3.1 in chapter 7 outlined the specific results predicted by the model for the four novel conditions created by manipulating two levels of conceptual layer activation and two levels of perceptual analyzer activation. These four conditions were implemented in experiment 1 (described in chapter 8).

10.1.1

The evidence for this prediction can be found in section 8.7.4 in Chapter 8. The analysis of the data showed a significant difference in this regard with the SUS and PQ scales, but not the experimental COCI. In the case of the SUS (Table 8.5) and PQ (Table 8.6), subjects in the High stimulus quality condition experienced more presence than in the Low stimulus quality condition. As two of the three scales used to measure presence in this experiment produced the predicted results, this prediction can be regarded as having being satisfied by the data. This difference, however, only occurred in presence of VE relevant priming. This may indicate either that VE experiences are optimal under certain conditions of user priming, or that the presence measures used produce more distinct results under certain conditions of user priming.

10.1.2

The evidence for this prediction is in Chapter 8, section 8.7.5, and summarized on Table 8.7. The post-hoc analyses of the High stimulus quality condition reveals that as measured by the SUS, subjects in the High stimulus quality/VE relevant priming group experienced significantly more presence than those in the High stimulus quality/VE irrelevant priming group. This finding is repeated in the PQ, but

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not on the experimental COCI. This prediction can also be regarded as having being satisfied, as it holds for two of the three measures used.

10.1.3

The evidence for this statement is far more obscure. The details are presented in Chapter 8, section 8.7.5 (summary on Table 8.7). The COCI results do not follow the prediction, as the Low stimulus quality/VE relevant priming and the Low stimulus quality/VE irrelevant priming conditions show a significant difference, with the VE relevant priming imparting an advantage to the subjects in that condition. In the case of the SUS, this pattern is reversed; the significant difference exists, but the VE relevant priming detracts from the presence experienced in the low quality condition. Finally, the PQ shows the predicted pattern – no significant difference between these conditions. With only one of the three measures showing the predicted pattern, it cannot be said, while claiming deference to scientific parsimony, that the prediction has been satisfied by the data.

An item of interest arising from this data, is the SUS result. While there is a difference (which violates the prediction) it is important to note that adding VE relevant priming decreased the level of presence experienced by subjects in that condition. This implies that the contribution of conceptual layer activation to presence is not a simple summation or subtraction from the experience of presence (as is the case with the contribution of the perceptual analyzers, which on their own could be modeled by a simple sum). The conceptual layers act in a far more complex way, their effect apparently being to mediate the perceptual context in which the virtual environment is processed.

10.1.4

The comparison of the predicted patterns of presence to the empirical findings is better understood by considering the data graphically. Figure 8-10 in chapter 8 presented the predicted presence experiences of the four novel conditions graphically, while Figures 8-4 and 8-5 of chapter 8 represent the means profiles for the measurements made using the SUS and PQ respectively. The COCI results are not used in this analysis, as the COCI failed to match the results of either of the two established scales used. By overlaying these graphs, it is possible to better understand the relationship between these three sets of data more easily.

10.1.5

Before this comparison can take place, it is important to note that the sets of data in question use three different scales. The PQ scores have a maximum score of 224; the SUS a maximum of 42, and the predicted scores were not predicted numerically, but rather in a relative way (see section 7.3 in chapter 7 for details). To plot these three data sets onto one graph, it is necessary to transform these scores onto a common scale. We propose the simple method of converting each mean into a proportion of the total possible score on the scale in question for the PQ and SUS scores (these transformations are shown in Table 10-1). Inserting the predicted scores into the graph is more difficult, as only relative scores exist.

As no quantities exist for these values, it would be incorrect to compare these directly with the empirically obtained values. Rather, as the purpose of the comparison is to compare the pattern of data distribution rather than the magnitude of the scores, the predicted values are simply superimposed over the center of the empirically obtained values to allow the comparison of the data patterns more easily.

The resulting graph is presented in Figure 10-1.

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Scale Stimulus

quality Priming Score Maximum

possible score

Proportion score

PQ High VE relevant 164 224 0.73

PQ High VE irrelevant 151 224 0.67

PQ Low VE relevant 141 224 0.63

PQ Low VE irrelevant 149 224 0.67

SUS High VE relevant 29 42 0.69

SUS High VE irrelevant 25 42 0.59

SUS Low VE relevant 20 42 0.47

SUS Low VE irrelevant 25 42 0.60

Table 10-1: Transformation of PQ and SUS mean scores to proportion scores for each of the four conditions

0.80

0.75

0.70

0.65

0.60

0.55

0.50

0.45

VE relevant

priming VE irrelevant

priming

Presence score as proportion of scale total

Figure 10-1: The predicted results for the four novel conditions compared to the empirical findings. The blue lines are measurements taken by the PQ, the orange lines measurements made by the SUS, and the green lines the results predicted by the model. For each data set, the

upper line shows the high stimulus quality condition, and the lower line shows the low

CHAPTER 10:EVALUATING THE EVIDENCE FOR THE CONNECTIONIST MODEL OF PRESENCE 135

10.1.6

With the three datasets superimposed, it is possible to notice that each dataset displays the same basic pattern. Examining the High stimulus quality condition for each of the three datasets (the upper line in each case), it is clear that both the PQ (square data points in Figure 10-1) and SUS (circular data points in Figure 10-1) show the pattern predicted by the model – The subjects experiencing VE relevant priming reported more presence than those who experienced VE irrelevant priming. When examining the low stimulus quality conditions (lower line in each data set) however, it is obvious that the model’s predictions were not realized. The model predicts the same level of presence regardless of priming conditions, and this is indeed the case for the PQ (the slight slope visible is not statistically significant, and can thus be disregarded). The SUS, on the other hand, shows a distinct and significant positive slope, which implies that relevant priming proved detrimental to the presence experience for those in the low stimulus quality condition only. As the measurements do not agree in this regard, the evidence must be regarded as contradictory, and for the low stimulus quality condition no positive conclusions can be drawn. However, it is possible to conclude a weaker form of the prediction, namely that for the low stimulus quality condition, VE relevant priming will not increase the presence experienced by subjects.

Another pattern is also discernable in the graph, although it does not form part of the set of predictions made in chapter 7. In the case of both measures, the difference between the high stimulus quality and low stimulus quality (distance between the two lines in each data set), is marked and statistically significant in the case of VE relevant level of priming but insignificant (both in the statistical and literal senses) at the VE irrelevant level of priming. The difference between these two priming conditions underscores the role of priming as a mediator rather than as a direct causal agent. This pattern also underscores the importance of priming as a possible source of error variance in experimentation. Figure 10-1 suggests that by controlling the priming state of the subjects, it is possible to exclude mental context as a third variable, and effectively accentuate the difference in presence scores between high and low stimulus quality display conditions.

10.2 Empirical evidence for the independence of O node activation