Past exam of the philosophy of science supplementary subject of the University of Oxford / 2021 / 1

Scientists should take sceptical challenges seriously when they reveal unsupported assumptions or distinguishable sources of error. They need not obtain a proof against every conceivable deception before conducting an experiment. This position concedes the force of some philosophical scepticism while rejecting certainty as the normal standard of scientific warrant.

There are several importantly different challenges. Global philosophical scepticism questions whether an external world exists at all. The problem of induction questions the justification for extending observed regularities to unobserved cases. Local criticism questions a particular instrument, sample or inference. A response appropriate to the last challenge need not answer the first. Repeating a measurement with a second instrument can expose a faulty sensor; it does not prove that all experience is independent of a deceiving agency.

David Hume's challenge to inductive reasoning is especially relevant. A finite record does not deductively entail a universal generalization. Nor can the inference be justified simply by saying that past uses of inductive reasoning succeeded: that argument itself extends a past regularity beyond the observed cases. The possibility that an observed regularity fails tomorrow is not a contradiction. This identifies a real gap between deduction and empirical projection.^[ref]

For example, suppose a metal's measured resistance agrees with $R(T)=R_0[1+\alpha (T-T_0)]$ throughout a tested temperature interval. Both that expression and a rival with an extra term zero throughout the interval fit those measurements exactly, yet can disagree outside it. Additional measurements can discriminate specified rivals, but no finite collection excludes every possible future deviation. The scientific lesson is to state the tested domain, physical assumptions and uncertainty. A linear electrical resistance model can be useful without being a universal law; a phase transition is an independently intelligible reason why extrapolation might fail.

One might reply that successful prediction and scientific replication justify confidence. They certainly distinguish a tested model from an arbitrary guess under the ordinary standards of inquiry. They do not supply the noncircular proof demanded by radical philosophical scepticism. A Bayesian inference similarly makes the dependence on prior probabilities and likelihoods explicit; it does not deduce the correctness of those commitments from no premises. Acknowledging this limit is preferable to claiming that a mathematical updating rule dissolves the problem of induction.

The sceptic nevertheless owes us an account of the practical consequence. If two global possibilities give exactly the same observable consequences under every available intervention, their difference does not identify which laboratory procedure to change. A scientist can suspend judgement about that difference while comparing models that make different predictions. This is a defence of proceeding under fallible standards, rather than a refutation of global philosophical scepticism.

By contrast, local criticism can reorganize an entire research programme. If an apparent signal might be instrument drift, useful responses include independent calibration, reversing a controllable input, measuring an appropriate control and conducting scientific replication. Those procedures make the proposed error account answerable to evidence. Merely imagining that every control could also be deceptive adds no further discriminating test.

The defensible policy is therefore fallibilism: confidence proportionate to the testing and domain, openness to identifiable error, and no claim to absolute foundations. Sceptical arguments matter most when they improve the questions asked of a model. Their inability to be conclusively defeated does not make every model equally warranted or make scientific work irrational.

A distinction between physical constitution and perceptual response is plausible and scientifically useful. A division into wholly objective primary qualities and wholly unreal secondary qualities is much harder to defend. These are different interpretations of the primary and secondary qualities distinction.

John Locke attributes extension, solidity, shape, number and motion to bodies as primary qualities. Secondary qualities are powers to produce sensations through the constitution of bodies: a body's power to produce a colour experience is distinct from the experience itself. This distinction need not make the power unreal. Nor should the further resemblance thesis, that ideas of primary qualities resemble their causes, be confused with the more modest claim that bodily constitution explains experience.^[ref]

A modern model of colour perception shows what can be retained. For a nonfluorescent surface in fixed viewing geometry, a simplified cone cell response isHere $L$ is the incident spectral irradiance, $R$ the spectral reflectance, and $S_i$ an effective sensitivity for one of three cone cell classes. Neural processing and adaptation also matter, so these responses are not a complete identification of perceived colour. Nevertheless, the model separates the surface, illumination and observer rather than assigning every experienced variation to a changing surface.

It follows that one surface can produce different responses under different illumination. Conversely, distinct spectral reflectance functions can produce the same three responses for a specified illuminant and observer, a case of metamerism. Three weighted integrals do not in general uniquely determine a whole function. Thus identifying experienced colour with one intrinsic surface property loses both the dependence on the observer and the many-to-one character of the response.

The scientific benefits are concrete. A comparison of paints should specify illumination and observer conditions. A measurement intended to characterize a material can instead estimate its spectral reflectance, preserving information that a single colour match discards. In both cases the distinction tells an investigator what to hold fixed and what to vary. It organizes experiments and explains disagreement without requiring that one observer be hallucinating.

However, perceptual variability alone does not distinguish primary qualities from secondary qualities. George Berkeley points out that apparent extension and shape also change with viewpoint and perceiver.^[ref] A circular disc yields different projected shapes at different viewing angles. The stable object is inferred through a model of that variation; it is not simply copied into experience. Primary qualities therefore have no automatic exemption from the interpretative work involved in perception.

Nor is relationality equivalent to subjectivity. In special relativity, measured length depends on an inertial frame, but relations between such measurements are tightly constrained. Conversely, a disposition to affect a specified perceiver under specified conditions can be an objective physical fact. Classifying a property as relational does not settle whether it is real, measurable or explanatory.

The best distinction is consequently functional and revisable: distinguish the experience to be explained from physical properties and relations used to explain it. That retains the scientific benefits of separating constitution, environment and response. It does not establish an immutable list of primary qualities, a simple resemblance relation between experience and object, or a proof of scientific realism.