Tunability of CogInfoCom Channels
This chapter investigates the need for designers and users to be able to customize CogInfoCom channels. It is argued that the availability of tools for this purpose is important due to the specificities of the CogInfoCom modality that is used—especially with respect to the input device and the noise level characteristic of the transfer medium. However, the task of creating such a model is rendered difficult due to the fact that the function which links all possible combinations of generation parameter values to perceptual qualities (referred to as f eval in Chap. 9) is both difficult to compute and also practically impossible to invert. One possible solution to this challenge is to apply a tuning model that allows users to interactively explore the parametric space used to generate CogInfoCom messages. The chapter introduces the spiral discovery method (SDM)—a tuning model that fulfills these requirements and also empirically aims to support flexibility and interpretability.
KeywordsGeneration Parameter Perceptual Quality Tensor Representation Rank Reduction Cognitive Artifact
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