Oferta Económica

Observation 3 shows that there is some potential for Keyebernates as a design interface, however

a number of issues must be overcome before it can become a practical interface, a subject of further work.

The results of experiment 2 clearly show that the gaze directed navigation is only possible across small parametric distances. As Keyebernates relies on changes in the peripheral vision to be slow and continuous, the speed of navigation in parameter space is necessarily very limited. One of the issues is that only a small amount of parameter-space can be shown on screen at any one time. This problem gets worst the more parameters the generative process has; as there is a limited, and constant, amount of screen-real estate, for each additional parameter added to the generative process, the span of values for each parameter renderable on screen decreases as the number of dimensions to be navigated increases.

The design of Keyebernates is such that it is more suitable to slow detailed explorations of a small area of state-space. Further, the issue with the afore mentioned disconnect between parametric similarity and phenomenal similarity makes it hard to anticipate in what ‘direction’ in parameter space one is to navigate to reach a particular phenomenal result.

Mitigating Fatigue

Keyebernates is gaze-contingent; it behaves and appears differently with respect to how it is

gazed upon. However there is nothing gaze-contingent in nature; nowhere is there a situation whereby something moves only when not directly pointed at with gaze. It is in this sense a highly unnatural situation. It is then perhaps unsurprising, as noted in observation 4, that inter- facing with Keyebernates is fatiguing; the viewer’s visual system is continually trying to build a coherent representation of the scene, while the scene is shifting constantly, but only in the peripheral vision.

It may be that solving this issue would involve changing the dynamics of Keyebernates. In particular, if the changes in the peripheral vision were such that they were more subtle, perhaps interfacing with it would be less fatiguing. Further work could explore this by changing the rate of changes in Keyebernates. It is possible that making change happen an order of magnitude slower could mitigate the fatigue issue, as the activity in the peripheral vision may be harder to detect. This would conflict with making the interface practical in any sense, as the navigation through parameter space would be slower still. However, if Keyebernates is only to be considered

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an artistic object, as discussed below in section 4.4.3, than practicality may not necessarily be a concern.

Another possibility is that working with a higher resolution displays might mitigate fatigue. As the sizes of the circles change, they naturally quantise to the nearest pixel. These quantisation steps are noticeable sudden changes, but have been minimised through blurring of the output, as shown in figure 4.4. A higher resolution display – such as modern 4K ultra-high definition screen – would allow for smoother, more continuous and even less noticeable changes.

If the aim is to make an interface to assist designers in parameter discovery, these experi- ments, and the literature, show clearly that eye tracking as the sole input modality is not practical. Fatigue could be mitigated if eye-tracking was one of a number of ways of interacting with the system . For instance, a physical haptic controller, such as motorised sliders12, could allow the designer to quickly navigate to any point in the state space with their hands. By assigning each slider to a parameter value, it could either be moved by the designer or could be made to move by themselves to reflect the state of the system. The designer could for instance find an approximate design manually, and then put one or more of the parameters under gaze control to explore a de- tailed area of the state-space. Designers do not have clear search targets in mind when working with parametric designs (then they would have already finished their design!), but if they ‘know it when they see it’, perhaps this framework could assist in a design context.

In response to observation 1, an intriguing possibility that could help further improve the performance of the system involves leveraging the overall gaze patterns outlined in section 4.3.2. These patterns could be used to construct a ‘map’ to give greater or less importance to fixations at different areas of the screen. For instance if a fixation occurs near the lower/middle left edge of the screen, it could be given greater importance than a fixation at the centre of the screen.

Re-mapping Parameter Space

Another possible approach to improve the performance of Keyebernates is to provide an interme- diate mapping layer between the parameter planes and the generative artefacts. Currently sets of parameters that are far apart in parameter space can yield outputs that are similar (as an extreme case consider the reflections in the offset in x and offset in y across the 0.5 axis), conversely some small differences in parameters values can yield perceptually large differences. There is a non- linear mapping from the parameters to the qualities of the work; a viewer does not experience

12_{As commonly seen on modern mixing desks, where the sliders can be made to move in response to}

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‘offset in y-ness’, but rather might experience ‘floweriness’ or ’row/column-ness’ for example. If one could identify properties of the conceptual spaces(G¨ardenfors, 2004b) of the genera- tive system at hand, then it could be used to create an alternate parameter mapping. The outputs of the generative processes used in these experiments, as can be seen from the examples, can sometimes look a bit like flowers of sort, that could be labeled by an observer as ‘flowery’. How- ever there is nothing in the definition of this generative system per se that implies floweriness, that an output is ‘flowery’ is entirely a property of the perceiver’s interpretation of this output.

If the parameter mapping could be re-aligned to match the relevant quality dimensions, then it would be possible to place all the ‘flowery’ parameter values in adjacent areas of this re-mapped parameter space (defining what G¨ardenfors calls a ‘natural concept’). The mapping between the ‘floweriness’ quality dimension and the parameter space is non-linear and discontinuous; roughly speaking flowery outputs will be the outputs with a circles size within a certain bounds (the circles need to overlap, but not too much), with the offset in x and offset in y being of roughly equal distance from 0.5.

If such a mapping were implemented, this would make it such that similarity would coincide with parametric proximity, and then searching for a particular type of design could potentially be much easier and faster; navigations would be less likely to get stuck in ‘dead-ends’ and it would be easier to determine in what direction in parameter space one is to travel.

Such quality dimensions could conceivably be applied to any generative process. An aware- ness of the relevant quality dimensions is of value to the designers and composers who work with any particular generative system, and is naturally built up as the designer/composer be- comes more familiar with the generative process or the formalisms within which they operate. The Melody Triangle was designed to explicitly provide a linear mapping between input pa- rameters and the ‘quality dimension’ of predictability. The mating algorithm of the EvoColour interactive evolutionary system project described in the next chapter operates with respect to the quality dimensions of the perceptual similarity of colours.

However it is unclear how one could build such a ‘quality dimension mapping’ for Keyebernates. For the Melody Triangle it was based on extensive knowledge and theory of the process at hand. For EvoColour, the quality dimensions of colour leveraged in its algorithms were identified through the history and evolution of colour theory over centuries of research.

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makes it clear that quality dimensions and conceptual spaces are based one phenomenal similar-

ity(G¨ardenfors, 2004b). One could conjecture that if a large number of similarity measurements

were made on randomly selected outputs of a generative system, these measurements could then be used to build this re-mapping of parameters. This proposition is the subject of further work.