Why our best theories of perception lead to Anti-reductionism

Basic percepts and observation sentences, such as "the voltmeter is at 7A", provide the ground truth for realistic theories. Reduction is the second backbone of these theories linking, for example, neuroscience to physics. First, we will show, by mathematical proof, that reduction is impossible if ontology is complex. We will provide a toy example which illustrates this point: a hypothetical animal has a sensor, which reacts to red and green light only. When red light is presented, the animal deterministically lifts the right back limb. For green light, the left one. Inputs and outputs are causally linked by a “brain” with just a few (binary) neurons. Even though inputs, outputs, and brain activity are fully available for millions of observations of a “scientist”, it is impossible to decode the output from the brain activity. Hence, neither sensation nor motor actions can be reduced to the underlying neural activity- even though input and output are perfectly correlated. Next, we outline the challenges any perceptual system needs to meet. For example, the light (luminance), which arrives at a photo-receptor of the retina, is a combination of the light shining on an object (illuminance) and the material properties of the object (reflectance). For a given luminance value, there are infinitely many illuminance-reflectance pairs, giving rise to this luminance value (an ill-posed problem). Hence, perception cannot be based on the raw input values. Second, we show how perceptual systems can solve such ill-posed problems. One conclusion of this analysis is that perception is inherently subjective, i.e., the metric of the perceptual system is not isomorphic to the metric of the physical space. We will argue that perception has evolved subjective metrics to exactly cope with the abundant complexity of the physical, non-reducible external world. In conclusion, we propose that reduction is neither possible nor desirable.