These are the notes I made whilst watching the video recording of Paul Meehl’s philosophy of science lectures. This is the fifth episode (a list of all the videos can he found here). Please note that these posts are not designed to replace or be used instead of the actual videos (I highly recommend you watch them). They are to be read alongside to help you understand what was said. I also do not include everything that he said (just the main/most complex points).
- Operationism states all misible concepts in scientific theory must be operationally defined in observable predicates BUT that’s incorrect, don’t need all theoretical postulates to map to observable predicates.
- Don’t need constants to be able to use functions and see if the components are correct. Given the function forms you can know the parameters (ideal case is to derive parameters). Weaker version: I can’t say what a, b, and c are but I know they are transferable or that a tends to be twice as big as b. If theory permits that it’s a risky prediction (could be shown to be wrong). Theories are lexically organised (from higher to lower parts). You don’t ask questions about lower points before answering the higher up ones in a way that makes the theories comparable. If two theories have the same entities arranged in the same structure with the same connections, with the same functions that describe the connections between them, and the parameters are the same: t1 and t2 are empirically the same theory. If we can compare two theories, we can compare our theory (tI) to omniscient Jones’ theory (tOJ) and see verisimilitude of our theory (how much it corresponds with tOJ).
- People can become wedded to theories or methods. This results in demonising the “enemy” & an unwillingness to give up that theory/method.
- Lakatosian defence (general model of defending a theory): 1) (t^At^Cp^Ai^Cn) follows deductively that [sideways T, strict turnstile of deducibility] (o1, ⊃ [if, then], o2)
AND absent the theory P(o2/[conditional on]o1)bk[background knowledge] is small
– this extension allows you to say you have corroborated the theory by the facts (because without this small prior it’s formally invalid logic). When P is very small, meets Salmon criteria for a damn strange coincidence
t= theory we are interested in
At= theoretical auxiliaries we’ve tied to our initial theory (almost always more than 1)
Cp= ceteris paribus clause (all other things being equal). No systematic other factors (they have been randomised/controlled for) but there will be individual differences.
Ai= instrumental auxiliaries. Theories about some controlling or measuring instruments. You distinguish between At and Ai by which field it’s in (if it’s in the same science then it’s an At)
Cn= conditions, experimenter describes to you what they did, very thorough methodology (often incompletely described).
*If the theory is true and the auxiliaries are true, the ceteris paribus clause is true and the instruments are accurate and you did what you said you did, it follows deductively that if you observe o1 you will observe o2
- This only works left to right; can never deduce the scientific theory from the facts.
- Sometimes you can’t assume the main theory to test the auxiliary theories; you are testing both of them. So if it’s corroborated, then you’ve corroborated both.
- Can be validating a theory and validating a test at the same time. Only works if the conjunction of the two leads to a damn strange coincidence.
- Strong use of predictions=to refute the theory.
- Suppose: (o1,-o2). Modus tollens: P>Q, ~Q therefore ~P
- Lakatosian criticism: Modus tollens only tells us the whole of the left side is false, not which specific part is.
- To deny: (p x q x r x s is equivalent to p is false or q is false or r is false or s is false.
- Formal equivalent of ~ on top a conjunction is disjunctions between statements on the left.
- Short form: the denial of a conjunction is a disjunction of the denial of the conjuncts.
- So when we falsify the right in the lab, we falsify the left but because its a conjunction it only tells us something on the left is wrong. But we are testing T so we want to specify whether that is false or not.
- Randomness is essential for Fisherian statistics.
- In soft psychology, probability that Cp is literally true is incredibly small.
- If you start distributing confidence levels to the different conjuncts you work towards “robustness”, can see how by how much Cp is false.
- Often can’t tell (from an experiment) whether a finding is due to what is reported or a confounding variable. Have to consider all potential confound variables and escape from logically invalid 3rd syllogism by exploring all of them.
- Different methods result in different Cp’s & At’s, something not often considered.
- Lakatosian defence of theory is only worthwhile if it has something going for it; it has been falsified in a literal sense but has enough verisimilitude that it’s worth sticking with.
- When examining part of the conjunct, look at Cn first. Can say: “Let’s wait to see if it replicates”.
- Ai isn’t a great place to start for psychologists.
- Cp is good (can almost assume it’s false). When we have different types of experiments over different qualitative domains of data, by challenging Cp in one experiment it doesn’t threaten the theories success in other domains.
- If you challenge At, if that auxiliary plays a role in derivation chain to experiments in other domains and you try to fix up failed experiments by challenging auxiliaries then all derivation chains that worked in past will now be screwed (because you’re undermining one of the links). Cp is more likely to be domain specific (violated in different ways in different settings).
- Can modify Cp by adding a postulate (as you don’t want to fiddle with At) because you may have changed subjects or environment etc.
- Progressive movement: Can turn falsifier into a corroborator by adding auxiliaries that allow you to predict new (previously un-thought of) experiments. Not just post-hoc rationalisation of a falsifier (ad-hoc 1). Ad-hoc 2: when you post-hoc rationalise a falsifier by adding auxiliaries & make new predictions but those predictions are then falsified.
- Honest ad-hocery: ad-hoc rationalisations that give new predictions (which are found to be correct) that are risky and are a damn strange coincidence.
Yonce, J. L., 2016. Philosophical Psychology Seminar (1989) Videos & Audio, [online] (Last updated 05/25/2016) Available at: http://meehl.umn.edu/video [Accessed on: 06/06/2016]