“Fitness” Beats “Truth”

The “Fitness-Beats-Truth Theorem” provides a quantitative measure of the extent to which the fitness-only strategy dominates the truth strategy, and of how this dominance increases with the size of the perceptual space. The FBT Theorem supports the Interface Theory of Perception.

The Interface Theory of Perception is discussed and described in detail in 2015 by Donald D. Hoffman, Manish Singh & Chetan Prakash can be summarized as:

Natural selection has shaped our perceptions in ways that help us survive:
– Our perceptions have been shaped by natural selection to make it easier for us to act effectively in the world, so that we can survive and reproduce … – Our perceptions have not been shaped to make it easy to know the true structure of the world but instead to hide its complexity.

The basic ideas were already presented by Hoffmann and Prakash in Objects of consciousness.

… when we actually study the evolution of perception …, we find that natural selection does not, in general, favor perceptions that are true reports of objective properties of the environment. Instead, it generally favors perceptual strategies that are tuned to fitness.
Why? Several principles emerge from the simulations.
First, there is no free information. For every bit of information one obtains about the external world, one must pay a price in energy, e.g., in calories expended to obtain, process and retain that information. And for every calorie expended in perception, one must go out and kill something and eat it to get that calorie. So natural selection tends to favor perceptual systems that, ceteris paribus, use fewer calories. One way to use fewer calories is to see less truth, especially truth that is not informative about fitness.
Second, for every bit of information one obtains about the external world, one must pay a price in time. More information requires, in general, more time to obtain and process. … natural selection tends to favor perceptual systems that, ceteris paribus, take less time. One way to take less time is, again, to see less truth, especially truth that is not informative about fitness.
Third, in a world where organisms are adapted to niches and require homeostatic mechanisms, the fitness functions guiding their evolution are generally not monotonic functions of structures or quantities in the world. Too much salt or too little can be devastating; something in between is just right for fitness. The same goldilocks principle can hold for water, altitude, humidity, and so on. In these cases, perceptions that are tuned to fitness are ipso facto not tuned to the true structure of the world, because the two are not monotonically related; knowing the truth is not just irrelevant, it can be inimical, to fitness.
Fourth, in the generic case where noise and uncertainty are endemic to the perceptual process, a strategy that estimates a true state of the world and then uses the utility associated to that state to govern its decisions must throw away valuable information about utility. It will in general be driven to extinction by a strategy that does not estimate the true state of the world, and instead uses all the information about utility.
Fifth, more complex perceptual systems are more difficult to evolve. … there is a combinatorial explosion in the complexity of the search required to evolve more complex perceptual systems. This combinatorial explosion itself is a selection pressure toward simpler perceptual systems.

In short, natural selection does not favor perceptual systems that see the truth in whole or in part. Instead, it favors perceptions that are fast, cheap, and tailored to guide behaviors needed to survive and reproduce.
Perception is not about truth, it’s about having kids.

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If our reasoning has been sound, then space-time and three-dimensional objects have no causal powers and do not exist unperceived. Therefore, we need a fundamentally new foundation from which to construct a theory of objects. Here we explore the possibility that consciousness is that new foundation, and seek a mathematically precise theory. The idea is that a theory of objects requires, first, a theory of subjects.

Conscious agent is a technical term, with a precise mathematical definition. To understand the technical term, it can be helpful to have some intuitions that motivate the definition.

A key intuition is that consciousness involves three processes: perception (P),  decision (D), and action (A).

• In the process of perception, a conscious agent interacts with the world and, in consequence, has conscious experiences.
• In the process of decision, a conscious agent chooses what actions to take based on the conscious experiences it has.
• In the process of action, the conscious agent interacts with the world in light of the decision it has taken, and affects the state of the world.

A conscious agent, C, is a six-tuple
C=((X,X),(G,G),P,D,A,N)), where:
(1) (X, X) and (G, G) are measurable spaces (σ-algebras);
(2) P: W × X → [0, 1], D: X × G → [0, 1], A: G × W → [0, 1] are Markovian kernels; and
(3) N is an integer.

For convenience we will often write a conscious agent C as
C=(X,G,P,D,A,N),   omitting the σ-algebras.

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Belief in object permanence commences at 3 months of age and continues for a lifetime. It inclines us to assume that objects exist without subjects to perceive them, and therefore that an account of objects can be given without a prior account of subjects.
However, studies with evolutionary games and genetic algorithms indicate that selection does not favor veridical perceptions, and that therefore the objects of our perceptual experiences are better understood as icons of a species-specific interface rather than as an insight into the objective structure of reality. This requires a fundamental reformulation of the theoretical framework for understanding objects.
This reformulation cannot assume that physical objects have genuine causal powers, nor that space-time is fundamental, since objects and space-time are simply species-specific perceptual adaptions.
If we assume that conscious subjects, rather than unconscious objects, are fundamental, then we must give a mathematically precise theory of such subjects, and show how objects, and indeed all physics, emerges from the theory of conscious subjects. This is, of course, a tall order. …
But if it succeeds, H. sapiens might just replace object permanence with objects of consciousness.

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The Interface Theory of Perception complements the standard Bayesian approach. Given a particular perception, the Bayesian posterior B defines a probability distribution on scene interpretations in X. The choice of a loss function then allows one to pick a single best interpretation based on this full posterior distribution on X. Given the two probabilistic sources of information embodied in the likelihood and the prior, Bayes’ Theorem provides a provably optimal way to combine them.
In the standard Bayesian framework space X plays two distinct roles. First, X corresponds to the set of objective world states. Second, X corresponds to the space of interpretations (or hypotheses) from among which the visual system must select. 
The dual role played by space X clarifies the way in which the standard Bayesian framework for vision embodies the assumption that the human perception has evolved to perceive veridically.
Clearly, it is not the case that a Bayesian observer always makes veridical inferences.  A Bayesian observer must rely on assumptions of statistical regularities in the world.

Clearly, what matters in evolution is fitness, not objective truth; and even perceptual systems that compute only simple, “subjective” properties can confer sufficient fitness for an organism to survive—even thrive.

There is a more fundamental sense, however, in which the standard Bayesian framework assumes veridicality: it assumes that the hypothesis space X—the observer’s representational space, which contains the possible scene interpretations from which it must select—corresponds to objective (i.e., observer-independent) reality.
In other words, it assumes that the observer’s representational language of scene interpretations X is the correct language for describing objective reality. Even if the observer’s estimate might happen to miss the “correct” interpretation in any given instance, the assumption is nevertheless that the representation space X contains somewhere within it a true description of the world. 

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The main message in the FBT paper is that, contrary to this prevalent view, attempting to estimate the “true” state of the objective world corresponding to a given sensory input confers no evolutionary benefit whatsoever.
Specifically: If one assumes that perception involves inference to states of the objective world, then the FBT Theorem shows that a strategy that simply seeks to maximize expected-fitness payoff, with no attempt to estimate the “true” world state, does consistently better. In an evolutionary competition, this “Fitness-only” strategy would drive the “Truth” strategy to extinction.

These considerations strongly undermine the standard assumptions that seeing more veridically enhances fitness, and that therefore one can expect that human perception is largely veridical. As human observers, we are prone to imputing structure to the objective world that is properly part of our own perceptual experience. Our perceived world is three-dimensional and populated with objects of various shapes, colors, and motions, and so we tend to conclude that the objective world is as well.
But if, as the Fitness-beats-Truth Theorem shows, evolutionary pressures do not push perception in the direction of being increasingly reflective of objective reality, then such imputations have no logical basis whatsoever.

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