The Ivory Tower of Primitives in Second Life

‘Architectural education is built upon analogic reality: the student is given a problem and asked to pretend that it is real. To pretend is to make believe, or construct fiction, and schools construct, pretend or analogue worlds on two levels: first, projects are inevitably hypothetical, a surrogate for the real world; second, there is complicit agreement that students be only expected to produce a pretend or artificial product, so-called “student work,” while professors produce the real academic article—publishable, credible, and contributory work.’¹

Over the past four centuries, pedagogical approaches have developed design knowledge from four perspectives: 1) academic pedagogy emphasized the importance of compositional and formal design theories (e.g., Beaux Arts); 2) craft training  stressed the achievement of proficiency in building (e.g., Bauhaus), 3) technical schools focused on the pragmatic application of scientific principles to specific problems (e.g., Bartlett) and 4) social pedagogy stressed the social and contextual implications of architecture (e.g., AA). Despite the various approaches attempting to modify design practice, the design studio remains central to creative exploration and knowledge acquisition.

Few serious attempts have responded to the lack of research on design pedagogy. For example, until the mid 2000s, only three notable instances are referred to: Changing Architectural Education: Towards and New Professionalism (Nichol & Pilling, 2000) looked at how students prepare themselves for changes in the profession; Architectural Educational Today, Cross-cultural perspectives (Salama, O’Reilly, Noschis eds., 2002) called for architectural pedagogy to respond to the contemporary societal needs, and The Redesign of Studio Culture (Koch et al., 2002) focused on how values integral to studio pedagogy may stimulate effective engagement. Design Studio Pedagogy: horizons for the future (Salama & Wilkinson eds., 2007)  is the most recent attempt to advance the  debate by examining ‘how studio teaching invigorates the attitudes of future architects and designers, and how it may contribute to the creation of better built environments’.

Yet such critique is surely not the reserve of academics or designers, or indeed bounded by the design studio alone. In a networked economy, creative exploration and knowledge acquisition extends far beyond traditional spheres of expertise. How academia negotiates the distribution of educational technology would seem imperative if the profession has any chance of a bright future.

1. Miller, Robert, ‘The Analogue and the Real: Two Paradigms for Architectural Education,’ in Carpenter, William (ed), Learning by Building ,New York, Van Nostrand Reinhold, 1997, p. 86.

In attempting to understand the interactions between formal and informal communities, Cynthia Kurtz and Dave Snowden’s sense-making model the Cynefin framework challenges three key assumptions currently held within organizational theory: 1) ‘Order’ – that human interactions and markets possess fundamental cause and effect relationships; 2) ‘Rational choice’ – that rational decision making based on minimizing pain or maximizing pleasure (Skinner’s ‘operant conditioning’) can be manipulated through education and thus determine possible outcomes; and 3) ‘Intent’ – that individuals or communities acquiring capabilities show an intention to use that capability. While the above assumptions may be true in some cases, Snowden’s sense-making approach contends they are not true universally, despite the fact that the methods commonly used assume that they are. Data is frequently skewed by the fact that people not only have multiple identities of which they are often blind, but they do not follow rules or act on local patterns.

Snowden, D., & Kurtz, C. F. (2003). The new dynamics of strategy: Sense-Making in a complex and complicated world. IBM Systems Journal, 42(3), 35-45.

In A biased history of CAAD¹ Alexander Koutamanis traces a bibliographic history of CAAD, positing that it emerges from two distinct ambitions: 1) a bottom-up technology-driven evolution of architectural computer graphics; and 2) a top-down domain of theory-minded design automation; including a subcategory of the previous two emphasizing computerization of analysis and evaluation. Results of the analysis are summarized in the above timeline, indicating parallel approaches during the broad adoption of CAAD in the 1980s, and a diversification during the democratic and populist 1990s, ranging from support, computational theory and collaborative projects with other specialisms. Algorithmic Architecture² by Kostas Terzidis shares a similar point that architectural computing is framed by two conditions: 1) bottom-up design realized through abstracted high level programming, i.e. computerization processes already conceptualized in the designer’s mind which are entered, manipulated or stored on a computer; and 2) top-down design realized through little or lower level programming, i.e. computation processes which apply scripting languages available in 3D packages like Maya Embedded Language (MEL), 3dMaxScript and FormZ 4.0.  While valid arguments can be made for all architects to have a broader understanding of computation in general, I would argue that the algorithmic approach is merely a shift towards an extreme formalist grammar. In fact on closer inspection, much of the recent work conceived using such methods has a recognizably visual bias of surface over space. In contrast to web and product design which seems more concerned with how things feel rather than look, architectural theory on the other hand remains trapped in a formalist cul-de-sac, conditioned by an increasingly formulaic geometricism. While I would agree with Terzidis that CAAD’s graphical interface incurs significant constraints on ideation (a view many design educators would share) a paradigm shift is unlikely unless a radical new way of interfacing with computer graphics is developed. Until this happens, computational architecture will remain largely faithful to its formalist roots.

1. Koutamanis, A. (2005). A biased history of CAAD. In Digital Design: The Quest for New Paradigms, 23nd eCAADe Conference Proceedings, Lisbon, September

2. Terzidis, K. (2006). Algorithmic architecture. Architectural Press

Emerging from a 1990s inquiry into the effects of emotion on cognitive health, CBU‘s Philip Barnard and John Teasdale constructed a macro-theory of mental architecture – Interacting Cognitive Subsystems¹ or ICS (above left). Accounting ‘for all the intricacies of human cognition and affect’, ICS is a highly parallel and modular structure comprising of nine interactive systems which have been further distilled into four subsystems – ‘acoustic’, ‘body state’, ‘effectors’ and ‘visual’ (above right).

In a recent series of tests into the affective qualities of images, Nick Halper et al. attempted to clarify the cognitive processing of ‘invariants’ (Gibson’s notion of stable entities) by applying ICS, making distinctions between propositional and implicational meaning – i.e. between semantic (facts about the world) and schematic (ideational and affective) content. In the experiments, participants were required to make rapid aesthetic judgements on selections of either high or low resolution images. While the earlier Fordham experiment similarly showed affective variance between cold and hot media, disseminating the effects via the ICS model potentially offers a more critical approach beyond the purely descriptive (see Gestaltism). For example – as Halper et al. explain, subsystems receive data from multiple sources but only invariants within the incoming representations become coherent. Transformations only function on coherent products and if these are absent, output becomes unusable. Transformation disengages and thus operates either on data most recently copied or from deeper within the experiential record.²

Like Fordham, Halper’s tests confirmed differences between visualization media and how they control meaning and influence judgement. Paradoxically however, while applications of ICS correctly highlight the crossmodal nature of visual perception and learning, the methods chosen to test participant feedback were largely modular (unimodal). Arguably, mapping crossmodal interaction will become an increasingly critical element for future research if such models are to be more sucessfully adopted in practice. As the most recent crossmodal studies remind us:

‘…visual processing does not appear to take place in a module independently of other sensory processes. It appears to interact vigorously with other sensory modalities in a wide variety of domains.’³

1. Philip Barnard and John Teasdale (1991) Interacting cognitive subsystems: A systematic approach to cognitive affective interaction and change. Cognition and Emotion, 5(1):1 39

2. David Duke, Philip Barnard, Nick Halper, Mara Mellin (2003) Rendering and affect. Computer Graphics Forum, 22 (3), pp. 359-368

3. Ladan Shams & Robyn Kim (2010) Crossmodal influences on visual perception. Physics of Life Reviews Vol. 7, Issue 3, pp. 269-284