We try to answer the question: ‘What is this thing called "science"?’
The title of this course is “Psychology as a Science”. - But what is this thing called “Science”? - Is it a special way of learning about the world? - And if it is then what makes it special? - How does science advance - And does science give us a true and objective view of the world
We’re going to try and tackle a few of these questions in today’s lecture.
The common-sense view might go something like this:
Science is special because it is knowledge based on facts
Science is often contrasted with other forms of knowledge that might be based on authority (e.g., coming from celebrities, religious and political leaders), revelation (e.g., personal religious or spiritual experiences) or superstition (e.g., “knowledge of the ancients”).
But this raises two questions. First, if science is based on facts, then where do “facts” come from? And once you have the “facts”, how do you go about deriving knowledge from these. We’ll try and tackle each of these questions in turn.
The common-sense view of science was formalised by two schools of thought: The empiricists and the positivists. Together they held a view that went something like this:
Knowledge should be derived from the facts of experience
We can break this idea down into three parts. First, the idea that careful and unbiased observers can directly access facts through the senses/observation. Second, that facts come before and are independent of, theories. And finally, that facts form a firm and reliable base for scientific knowledge. But is this view true?
A simple story of how the senses work is that there are some external physical causes (light, sound waves etc.) that produce some physical changes in our sense organs (e.g., our eyes) that are then registered by the brain. This account implies direct and unmediated access to the world through our senses. But this is not actually the case.
The above image could be seen as an old woman or a young woman. Some of you might see one and not the other while some might be able to see be both and even switch between them. The physical causes (the light hitting our eyes) is more-or-less the same for everyone, but you might “see” different things. Although this is just a toy example, it reveals a larger point. Namely, two scientists might “observe” something different even when looking at the same thing. In some fields, being able to make “observations” actually requires training. For example, training in how to observe objects using a microscope, training in how to distinguish different kinds of behaviour or training in how to read an x-ray. So a simple claim that observations are “unbiased” or “straightforwardly given by the senses” seems to be false.
Furthermore, when we think of a “fact” there are two things we could mean. “Fact” could refer to some external state of the world, or it could refer to statements about those external states. For example, the fact that this university is in East Sussex could refer to this actual university, and its actual being in East Sussex or it could refer to the statement: “This university is in East Sussex.” When we talk about “facts” as the basis for science, we’re talking about these statements. (We’ll call this type of fact an “observation statement”). Is it possible to make “observation statements” in a way that is free from theoretical assumptions, prior knowledge, or our conceptual framework? That is, can they just enter our brains in an unmediated way?
To unpick this, let’s look at an example. Think of a child learning the word apple. First, they might initially imitate the word “apple” when shown an apple by their parent. Next, they might use the word “apple” when pointing to apples. But then one day they might see a tennis ball and say “apple”. When this happens the parent would then correct them, and show them that a tennis ball isn’t an apple because you can’t, for example, bite into it like an apple.
Further errors might result in further corrections with new demonstrations about the properties of apples. As a result, by the time the child can make accurate “observation statements” about the presence of apples they might already know a lot of about the properties of apples (have an extensive “theory of apples”). The same goes for scientists; formulating observation statements might require substantial background knowledge or a conceptual framework to place them in. So observation statements are not completely independent of theory. They’re not just out there in the world for us to pick up through the senses.
However, let’s say that we’ve been able to acquire some facts. Will any old facts do? Again, let’s take a simple example. Let’s say that you observe that grass grows longer among the cowpats in a field. You think this is because the dung traps moisture that helps the grass grow. Your friend, however, thinks that the grass grows longer among the cowpats because the dung acts as a fertiliser. Observations alone can’t distinguish these. To tell which is correct you need to intervene on the situation in some way. For example, you could grind up the dung so that it still fertilises the ground or use something else to trap the moisture. Intervening, for example, through experiment allows you to tell what the relevant facts of your observation are. If science is made of facts, then they must be the relavent facts.
By intervening on the system, we can tell which facts are relevant. But scientific theories may play a part in helping to determine what is and isn’t relevant. Let us take an example from the history of cognitive psychology. In certain kinds of reading tasks psychologists thought it was relevant that people made errors, but they didn’t think the exact nature of the errors was relevant. But after certain kinds of theories were developed (ones based on neural network models) they came to realise that the particular kinds of errors (e.g., whether people swapped letters between words) was relevant. Again, observations can’t be completely divorced from theories. And, further, experiments will presume the truth of certain theories (e.g., brain imaging experiments assume the validity of certain theories about brain function), which means that experiments and observations are infused with all kinds of theoretical assumptions.
There is an idea that science is objective in a simple sense of the word. That no matter what you believe, the facts are just out “there”. But thinking of “objectivity” in this sense is misleading. Your conceptual framework, and theoretical assumptions, and even your knowledge and training can play a part in what kinds of observations you can make or what types of observation statements you can formulate. “Objectivity” doesn’t mean making observations free from theoretical assumptions (“the view from nowhere”). The idea of “objectivity” is more complex.
Instead of thinking about “objectivity” in terms of the existence of atheoretical facts, we should think of objectivity as:
1. Relying on publicly and independently verifiable methods;
2. Recognising theoretical assumptions;
3. Theory/data that are open to revision and improvement; and
4. Science free from avoidable forms of bias (confounds, cherry-picking data, experimenter bias).
Science is also objective in the sense that despite all this, when you make the observations, either the behaviour will happen or it won’t, the detector will flash or it won’t etc. Your theory can’t make things happen.
The second part of the common-sense view of science is that scientific knowledge is derived from facts. Usually, this idea of derived means something like logically derived. To understand what it might mean to logically derive scientific knowledge, we need to know a bit about logic.
A deductive argument is called valid if the conclusions follow from the premises.
1. All research methods lectures are boring
2. This is a research methods lecture
3. (Therefore) this lecture is boring
In this example, if we accept that (1) and (2) are true, then we have to accept (3) as true. We cannot accept (1) and (2) as true and then deny that (3) is true because we would be contradicting ourselves.
Let us change the example slightly.
1. Most research methods lectures are boring
2. This is a research methods lecture
3. (Therefore) this lecture is boring
In our new example, we can accept (1) and (2) as true without accepting (3) as true. That is, (3) does not necessarily follow from (1) and (2). This might just be a case of a research methods lecture that isn’t boring.
Deduction is only concerned with whether (3) follows from (1) and (2). It is not concerned with determining whether (1) and (2) are true or false. The argument assumes that (1) and (2) are true, but it doesn’t establish their truth.
Importantly, an argument can be valid while being false. Again, let us make a slight change.
1. All pigs can fly
2. Percy is a pig
3. (Therefore) Percy can fly.
This conclusion is valid. However, it is also false because (1) is false. It is valid because if we accept (1) and (2) we have to accept (3). But it is also plainly false because pigs can’t fly.
Logic only tells us what follows from what. If there is truth in our premises, then there is truth in our conclusions. If our premises are false, then our conclusions will also be false. Deductive logic is truth-preserving, but it can’t tell us what is true and what is false, because the conclusion is just a re-statement of the information contained in the premises.
Because deductive logic just involves re-statement of information deductive logic can’t create new knowledge. So what can we do if we want to create new knowledge?
To create new knowledge, we need a way to go from particular observations to generalisations. This process is called induction.
To create new knowledge, we need a way to construct arguments of the following form:
1. Emily the swan is white
2. Kevin the swan is white
3. …. the swan is white
All swans are white
But the problem with arguments like this is that all the premises may be true and yet the conclusion can be false. Maybe we just haven’t observed the one swan that isn’t white?
But surely there are good and bad inductive arguments? For example, maybe more observations are better than fewer observations—but if so, then how many is enough? Maybe it’s better when observations are made in many different contexts—_but what makes a context different and what makes the differences relevant? Different contexts should be novel in some sense and changes should not just be trivial changes. Maybe another criterion is that there should be no contradicting observations—but what about probabilistic phenomena? Clear and simple rules aren’t easy to come by. But the bigger problem is induction can never establish truth. So how do we ever prove anything for certain in science? The short answer is, we don’t. We can never be certain of truth.
So where do we go from here? One option is that instead of just collecting confirmations we can employ induction and deduction together.
This approach would involve something like, collecting observations and using induction to come up with general laws and theories from these observations. We could then use deduction to figure out what logically follows from these general laws and theories. This approach nicely captures the idea of testability. Our theories should make predictions about what we expect to find and we can test these predictions with more observations.
The philosopher Karl Popper also saw trouble with relying on induction. He wanted to put science on a firmer logical footing. To do this, he suggested that instead of deduction to figure out what is true, scientists could use deduction to figure out what is false, and he suggested that a key quality of scientific theories is that they should be falsifiable.
For Popper, theories could come into existence through any means (wild speculation, guesses, dreams, or whatever); however, once a theory has been proposed it has to be rigorously and ruthlessly tested by attempting to falsify it.
Here is an example of how deduction can work to tell us what is false. But first an example of induction, just to refresh how it works.
Premise: A swan that was white was spotted in London at time t
Conclusion: All swans are white.
The conclusion might be true or false, but it doesn’t logically follow from the premise.
And now deduction working to falsify a statement.
Premise: A swan, which was not white, was spotted in Australia at time t.
Conclusion: Not all swans are white.
The conclusion logically follows from the premise, so if the premise is true the conclusion is true.
We can’t prove the claim “all swans are white”, but we can reject it.
Not all theories are equally falsifiable. Good theories are falsifiable, but better theories are more falsifiable. Let’s take a look at three example theories.
Of these three theories, (1) is the least falsifiable and (3) is the most falsifiable. Why? For theory (1) only an observation of Mars could falsify it. But for theory (3), an observation of Mars, Venus, Saturn, Neptune, or any other yet undiscovered planet would falsify it.
Bad theories are ones that can seemingly accommodate any observation. If two outcomes are possible and the theory can explain outcome one and outcome two then this is bad, because what then counts as evidence against the theory?. Good theories are also broad in their applicability but precise in their predictions, because this makes them more falsifiable.
Once we have a falsifiable theory, then what happens when you make an observation that falsifies the theory? That is, what do you do when you observe something that contradicts the theory you’re testing?
In this situation, you have at least two options. One option might be to abandon the theory. But what about probabilistic theories? And what about auxiliary assumptions? Remember, theories don’t exist in isolation. Another option might be to modify or amend the theory. But are their better ways and worse ways to do this?
Let’s first think about the situation of probabilistic theories. Theories in psychology tend to be probabilistic. They make claims about how things are on average, not claims about how things are in every case. A probabilistic claim might say something like on average “men are taller than women”, but of course there are shorter men and taller women. As a result, a single contradictory observation can’t falsify a probabilistic claim because we will sometimes expect contradictions with probabilistic claims.
Much of what we do with statistics is figuring out how to test and specify probabilistic claims. For example, statistics will help us figure out what it means for things to differ on average. Or it can help us to figure out how many cases you have to observe before you have evidence for a probabilistic claim. Or to know how many contradictory cases you have to observe before you have evidence against a probabilistic claim (that you might previously have believed).
But putting aside the probabilistic nature of claims (or assuming you’ve seen enough contradictory examples), should these contradictory observations lead you to abandon the theory? Again, any experiment is not testing one theory in isolation but also relies on a range of auxiliary assumptions and other support theories. For example, an experiment on memory using brain imaging is also making assumptions about the truth of theories related to physics, brain functioning, besides testing the theory about memory).
Therefore, it may be the case that what is actually at fault is one of these auxiliary assumptions and not your theory. Telling which part of the interconnected web of theories is at fault can be tricky. (Philosophers call this the Duhem-Quine problem.) Popper didn’t have a good answer on how to figure out where to lay the blame for an apparent falsification.
But Popper also didn’t think that theories should be abandoned too quickly. He suggested some dogmatism because at the start scientists might still be figuring out the details, and therefore they might just need to make some tweaks to modify their theories.
Let’s say we do decide to amend a theory, then how do we do this? Let’s take a look at a theory, and then think about how to modify it in the face of a falsifier.
Theory: All bread is nourishing
Observation: Bread eaten in a French village in 1951 was not nourishing1
One way to modify it might be as follows:
All bread except bread eaten in a French village in 1951 is nourishing
The problem with this modification is that it has fewer tests: The original theory can be tested by eating any bread. The modified theory can be tested by eating any bread except that particular bread.
But there is a way to make an acceptable modification. It might go something like this:
All bread except bread made with flour containing ergot fungus is nourishing
This modification is acceptable because the modification leads to new tests. You could, for example, test the bread for the presence of the fungus. You could cultivate the fungus and make bread with it and test whether it nourishes. Or you could analyse the fungus for poisons.
Popper’s falsificationism is not without its problems. At least two problems arise because of the focus on falsifying theories. First, as we’ve already discussed, it can be difficult to figure out when to abandon theories and when to amend theories. In fact, you might question whether all parts of the theoretical web are of the same status? And second, it can be difficult to compare two theories to see which is “better”. For example, if you have Theory A and Theory B and neither has been falsified, which is the better theory? The one with more confirming observations? But then won’t trivial theories always win?
The philosopher Imre Lakatos developed his idea of research programmes as a reaction to these two problems2.
One key aspect of Lakatos’s idea of research programmes is that not all parts of a science are on par. For example, some laws or principles are so fundamental they might be considered a defining part of the science. Other parts, in turn, might be more peripheral. Lakatos called these fundamental parts the hard core and the more peripheral parts the protective belt.
He suggested that the hard core is resistant to falsification, so when an apparent falsifier is observed the blame is placed on theories in the protective belt. Research programmes are defined by what is in their hard core.
What is in the hard core and what is in the protective belt might not always be explicit, but these might be some examples: In Cognitive Science the hard core might include the theory that mind/brain is a particular kind of computational system and the protective belt might include specific theories about how memory works. In the biomedical view of mental illness, the hard core might include the theory that mental illness can be explained biochemically and the protective belt might include the dopamine theory of schizophrenia. When apparent falsifications occur the protective belt is up for revision but the hard core stays intact. Falsifying the hard core amounts to abandoning the research programme.
On Latakos’s view, scientists work within a research programme, and he splits guidelines for working within a research programme into a negative and positive heuristic, specifying what scientists ** shouldn’t** but also what they should do. The negative heuristic includes things like not abandoning the hard core. The positive heuristic is harder to specify exactly, but it includes suggestions on how to supplement the protective belt to develop the research programme further—that is, it should specify a programme of research (the research programme should identify problems to solve).