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Hoffman's Conscious Realism:
A Critical Review

3. Interface Theory of Perception (ITP)

3.4 Parasitic on Realism

Book cover: Who's in Charge?: Free Will and the Science of the Brain by Michael S. Gazzanig

In this section, I want to advance the objection that Hoffman's Interface Theory of Perception (ITP) borrows from his opponents' metaphysical framework without paying its dues; that is, that his scheme is parasitic on realism. Consider the answer that Hoffman offered in response to an objection put to him. The objection he deals with is this:

If natural selection did not design our senses and brain to construct a relatively accurate model of reality, then how is it we can land a spacecraft on Mars with pinpoint accuracy, or put satellites in space so finely tuned with relativity theory that they can detect a device on earth within less than a meter of accuracy?

[Hoffman 2018: 8]

Hoffman's reply to this objection makes it seem as if successfully landing a spacecraft on Mars is simply a matter of getting our hand-to-eye coordination right. As he puts it: 'one can have perceptions of 3D space and 1-D time together with perfectly coordinated actions in that perceived space and time that are entirely predictable to arbitrary accuracy' [2018: 8f]. Even though, for Hoffman, this ability 'entails absolutely nothing about the nature of objective reality', we manage to get around using that interface, much like the subject who manages to get around quite well after a while when fitted with upside down goggles that reverse the visual image. (See, for example, Abrahams [2012]).

Here, Hoffman invokes his and his team's Invention of Symmetry Theorem (IOS) to demonstrate mathematically how we can map, using symmetry, planarity and compactness, perceived shapes in a 3D space to another non-actualized dimension. Their system of perception-behaviour is highly operationalized: 'the regularities of our perceptions are an evolutionarily designed interface that guides adaptive behavior and hides the true regularities of the objective world' [Hoffman 2018: 9].

That mathematical mapping scheme may be adequate for explaining the successful manipulation of 'icons' within a spatio-temporal field. When it comes to landing a spacecraft on Mars, what Hoffman et al ignore is the essential role that theory construction plays in scientific achievements.

The feat of landing a spacecraft on a planet millions of kilometres from Earth was not achieved by simply using a joystick to manipulate the 'icon' of a spacecraft within a virtual reality game. That achievement is not like playing a space video game in which our only task is to co-ordinate our eyes and hands within a set of pre-established game rules. Landing a spacecraft on Mars is the end result of centuries of hypothesizing and testing models of reality in the fields of physics, astronomy and cosmology against predicted future perceptions. To put a spacecraft on Mars, scientists first needed to understand what lies under the bonnet of our everyday perceptual world, so to speak. Space scientists only got to know how to build the spacecraft, send it into space and control its landing by first theorizing and understanding the underlying physics that govern planets, rockets and spacecraft.

It is only after the theoretical and experimental hard work had been done that Hoffman et al are able to reverse engineer the mapping of manipulations of physical objects by scientists in space-time to achieve the Mars landing. What scientists did over many years was uncover the inner workings of nature so that they could predict how the Mars spacecraft would perform in hitherto unknown environments. What they did is much like a video gamer working out how the microprocessors, power supply, display, memory devices, and so on, interact with software code to display the visual image she sees in her headset. No amount of looking more closely at and manipulating the icons on her screen will allow her to predict with much success what will happen if she performs a motion that is not within her known game rules. For that, she must understand how the virtual reality apparatus really works and the programming rules that govern its behaviour.

Hoffman is well familiar with how scientists create and validate predictive models. In his interview with Tsakiris [2020], he speaks about one of those crucial understandings space scientists needed to have under their belt if they were to be successful in landing a spacecraft on Mars. Speaking of Einstein, he said:

... in 1905 and then in his general theory of relativity, he said, 'Here's my equation, I'm going to predict exactly how much light will bend and precisely where Mercury will be and we'll test that against Newton.' And it was when the experiments confirmed the precise mathematical predictions of his equation of general relativity, that's when he burst on the scene

Here, Einstein was not postulating about how some unknown substance behaves in some unknown substrate. He was theorizing in particular about how the mind-independent body we know as Mercury deflects light. Einstein was theorizing about how mass and energy behave in space and time independently of how we perceive them. That's why predictive success signals a theory's veracity and why Hoffman wants to emulate such success. Under Bayesian inference rules, the more novel the prediction (i.e., the more unlikely the predicted event will occur if the theory from which it is derived is false), the more epistemic weight it lends to the theory while reducing the probability of the rival theory. In this case, the stunning confirmation of the predicted value of the perihelion of Mercury provided independent evidence that mind-independent bodies behave as General Relativity describes.

Book cover: The Problems of Philosophy by Bertrand Russell

Hoffman recognizes this objective feature of novel confirmations when he says in the same interview: 'once you write down a mathematical theory, the theory becomes smarter than the person who wrote it down'. In Einstein's case, as Hoffman relates: 'he had no idea that it was going to predict black holes. He didn't know that. But the equations do predict black holes, and he didn’t like it'. Again, the prediction of hitherto undreamed of black holes pointed to new real objects out there. With Einstein's successful prediction of black holes, Hoffman tantalizingly seems to accept a realist interpretation of physical theories when he says: 'the theory had a deeper, in some sense a deeper insight into reality than the person who wrote it down'.

It is this deep insight into reality that our most successful physical theories afford us and that enable scientists to make incredible accomplishments. And it is this window into reality that, historically, realism has given us that Hoffman et al's ITP fails to acknowledge in its reverse engineering of these successes. Their operationalization of the activities of scientists and post hoc cross-mapping of hand-eye co-ordination is reminiscent of the logical positivists and phenomenalists of the mid-twentieth century with their reconstructions of 'physical object' talk into 'permanent possibilities of sensation'. As with their predecessors' failed logical positivist and phenomenalist programme, their operationalized translations of scientists' successes is parasitic on the realist theories that enabled those achievements. (For more on parasitic phenomenalism, see my Allan [2016a].)

The ability of scientists to land a spacecraft on Mars raises another problem for Hoffman. He argues that evolution shaped our perception not to mirror truth, but to enhance our reproductive fitness. As he puts it: 'Perception is not about seeing truth, it's about having kids' [Hoffman 2018: 5]. If our perceptions are shaped by fitness payoffs—with opportunities to bear more children—then that raises a question: How does our ability to manipulate 'icons' with such precision to enable scientists to land a spacecraft on a body millions of kilometres from anyone aid reproductive fitness? Through advances in technology, scientists also now have the capacity to see molecules and atoms and to see distant galaxies. How does seeing these 'icons' help scientists have more children? Prima facie, such abilities appear to enable the opposite; a time and resource consuming distraction away from finding a mate and reproducing plentifully. In fact, surveys reveal that increasing education levels are a reliable predictor of reduced fertility [Roser 2017] and that female scientists abandon their career to have children [Elaine and Lincoln 2011; Else 2019]. This counter-intuition to evolutionary theory is explained easily on the standard view that Homo sapiens' ability to model reality co-evolved with the ability to find a mate and reproduce. On Hoffman's scheme, it is another mystery waiting to be solved.

Copyright © 2020, 2022

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