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The proposed study aims to observe the detection of Event-Related Potentials’ components and correlated neural phenomena under the visual presentation of complex stimuli and devise processing methods that would generalize their classification for applications in Computer-Aided Architectural Design, where visual complexity becomes an intrinsic feature of the tasks. It aims to investigate the cardinality of discriminative neural patterns correlated with the presentation of complex visual stimuli by detecting subcomponents of these neural phenomena using the designed system on short and prolonged periods of time and involving participants from architecture, visual arts or related fields. Subsequent results will bring to further discussion the role of visual experience in such system and the range it might address in the population segment. 

This paper focuses on a specific aspect of human visual discrimination from computationally generated solutions. The bottleneck at work here concerns informational ratios of discriminative rates over generative ones. The amount of information that can be brought to a particular sensory modality for human perception is subject to both bandwidth and dimensional limitations. This particular problem is well known in the field of Brain-Computer Interfaces, where the flow of relevant information must be maintained through such interaction for applicative ends and adoption of use in many fields of human activity. While architectural modeling conveys a high level of complexity in its processes, let alone in the presentation of its generated design solutions, promises in applicative potentials of such interfaces cannot escape from such basic issues and need developments of appropriate sophistication. This paper will address this informational bottleneck by introducing methods to retrieve spatial information from the rapid serial visual presentation of generated pictures. This method will be explained and defined as an inverse modeling method based on inverse graphics and its relation to human visual processing.

This paper focuses on the application of visual Event-Related Potentials (ERP) in better generalisations for design and architectural modelling. It makes use of previously built techniques and trained models on EEG signals of a singular individual and observes the robustness of advanced classification models to initiate the development of presentation and classification techniques for enriched visual environments by developing an iterative and generative design process of growing shapes. The pursued interest is to observe if visual ERP as correlates of visual discrimination can hold in structurally similar, but semantically different, experiments and support the discrimination of meaningful design solutions. Following bayesian terms, we will coin this endeavour a Design Belief and elaborate a method to explore and exploit such features decoded from human visual cognition.