Overview of Development and Optimisation Steps

The model described in this document presents many opportunities for improvement and further investigation. This section presents some directions of research we feel should be followed as a first step towards improving the model. We present these suggestions in an order which we believe represents their relative importance, beginning with the most pressing.

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12.6.1

As noted in chapter 10 (in section 10.2), the two experiments performed failed to clearly demonstrate the independence of presence from O node activation. This issue needs to be addressed with some urgency, as the structure of the perceptual analyzers is created under the assumption that O and R node activation can be equivalent under the right conditions.

To establish that presence can arise as a result of either O or R node activation, it would be necessary to replicate the basic design of experiment 1, but using several levels of priming and stimulus quality rather than simply two levels of each. Particularly, it would be necessary to widen the range of each variable, to include more extreme cases. For example, for the stimulus quality variable, immersive devices such as head-mounted displays could be used to maximize the degree of O node activation, while priming could be maximized by the use of multimedia shows rather than simple text. Using extreme levels of these variables can be helpful, because this allows one to explore the possibility that the independence occurs only at very high or very low levels of stimulus quality.

Based on the findings of such research, it might become necessary to amend the model. If it is found that R nodes are not capable of contributing to presence, or that they can only contribute under extreme conditions, the necessary changes might be effected by changing the strength of the connections between the R nodes and the action layer. If the R nodes do not contribute at all to presence, this might be modeled by connecting all R nodes to a small subset of action nodes which represent behaviours that are usually unsuitable for operating in an environment. If it is found that R nodes only contribute to presence under extreme conditions (e.g. under high levels of R node activation only), this could be modeled by reducing the strength of the connections between the R nodes and the action layer.

12.6.2

As described in section 5.2 (in chapter 5), the conceptual layers in the current model represent a more complex structure, which was not elaborated on due to a lack of relevant empirical evidence. Modeling the contribution of mental state to presence by means of a single source of priming does not allow for an understanding of how different elements of cognition, such mental models of environments and previous experience, each contributes to presence. Once some components of the conceptual layer have been identified, it might be possible to better understand the negative effect of priming on presence in low quality displays. Knowing the components which make up the conceptual layers will allow for the establishment of a priming theory, which can be used to determine the appropriate type of priming to maximize presence for a particular perceptual situation.

12.6.3

The COCI was conceived as a measure of cognitive presence, and the connectionist model mapped cognitive presence onto the activation of specific action nodes. However, as discussed in section 11.3 of Chapter 11, the COCI lacks the required degree of construct validity. This leaves the requirement for a measure of cognitive presence unfilled.

Although the other presence measures used in this study lead to satisfactory results, they should not be regarded as suitable measures of cognitive presence or of action layer activation. The success of these other measures in this role comes from the fact that action layer activation is related to but not synonymous with traditional conceptions of presence. These measures doubtless include aspects of their own conceptions of presence which are not related to action layer activation (such as the measures of display system features in the PQ). These can act as confounds and reduce reliability when used to measure action layer activation. It is necessary to search again for a measure of action layer activation.

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As refinement of the connectionist model is by necessity driven by empirical research, it is important to have an accurate, direct measure of the degree to which experimental manipulations are affecting the model. The basic concept behind the COCI still merits investigation to determine if it is a useful method for measuring action layer activation.

12.6.4

As argued in section 11.3.3 and 11.3.4 of Chapter eleven, with some refinements the COCI could be used as a measure of priming. The current version of the COCI is already quite effective, but it can doubtless be improved. The major flaw of the current COCI seems to be its dependence on the content of the VE on which it is to be used, effectively precluding the creation of a “universal COCI” to work on any environment. Based on the theory of priming, it seems unlikely that priming can be measured without making reference to the content of the mental state, as priming is defined in terms of content itself; to say “the subject is primed” is really a contraction of “the subject is primed for x”, and as such measuring priming without reference to x is impossible. Requiring a new version of the COCI for each environment can however create a serious confound, as each version of the COCI might posses differing psychometric qualities.

This problem can be partially circumvented by establishing a rigorous method for creating COCI variants. This method would aim to reduce possible error variation associated with the choice of particular items over other possible candidate. One solution to this already exists in a related problem in psychometrics, namely the parallel forms problem. The problem of parallel forms is to create two separate tests of the same subject, with equivalent psychometric properties, and of equal difficulty. The standard solution to this problem is to create one test of twice the length, and to use a random selection method to select items for each of the two tests. A similar procedure could be applied to the COCI – rather than selecting ten words for the test, a longer list is created, and ten words are chosen at random from the list. As it is likely that only a subset of all words will be problematic, this method reduces the probability that problematic words will be selected for inclusion in the test.

12.6.5

Before a connectionist network can be used to make quantitative predictions, the strengths of the connections and the unit functions must be determined (Martindale, 1990). By way of assumption, the model currently treats all connections as if they were of equal strength. However, it is unlikely that this is the case, due to the effects of learning and experience on connection strength.

An advantage of working with a model that specifies the connection strengths is that it makes it possible to incorporate individual differences into the model, which allows the modeling of particular networks (i.e. particular people). To successfully predict the presence that a particular individual will experience, an “average network” would be used, which models the mean performance of subjects.

Then, some method of quantifying the difference between the individual in question and the average network (such as, for instance, Witmer & Singer’s Immersive Tendencies Questionnaire) can be used to calibrate the network for the individual in question.

To determine connection strengths, an accurate measure of each construct attached to a particular connection is required. For example, to determine the connection strength between the O node of the visual analyzer and the action node encoding visiting a restaurant, a measure of each is required. After activating one of the nodes to varying levels, the activation of the other connected nodes can provide the required information to determine the strength of the connection as well as the parameters of the unit function.

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12.6.6

Once accurate measures exist and the network parameters have been calculated, it will be possible to implement a simulation of the network. This will allow a quick method of predicting presence quantitatively for a given set of initial conditions. A simulator can be a useful tool for designers of virtual environments, as it would allow them to determine quantitatively what effect a change in the VE or priming manipulation would have on the presence experienced by users in that environment.

A network simulator is also a useful tool for research purposes, as it allows detailed examinations of the models. With detailed analyses possible, researchers working in applied fields such as VR therapy and collaborative shared spaces could quantitatively investigate the relative benefits of presence. Also, as detailed analyses of how activation spreads to the conceptual layers would become possible, it would become feasible to analyze the possible contribution of presence to performing a particular task, given that one knows the cognitive components required to complete the task.

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Appendix A

The DAVE tool

We developed the DAVE tool to support the experiments described in this dissertation. DAVE is an acronym which stands for Dave’s Application for Virtual reality Experiments. DAVE runs on the Win32 platform, and has been tested and found to work on Windows 98 sp2, Windows 2000 sp 1 and Windows XP. DAVE requires DirectX 7 or later to be installed. Although it is not a requirement, a great increase in performance is obtained from using a Direct3D compatible graphics accelerator. We used version 1.1 of the Genesis 3D SDK, and compiled DAVE using Microsoft Visual C++ version 6.0 sp 1.

Figure A-1: An example of the rendering capabilities of DAVE. This scene can be rendered with a refresh rate of 20Hz on an AMD Athlon 700MHz with a GeForce 2MX based graphics

card.