7 Studying psychology | I-C
I-C: This chapter is compulsory independent reading.
Congratulations! You’ve chosen the most interesting subject that one can study at university! (At least in my humble opinion.) What I love about psychology is that it uses scientific methods to answer questions about human cognition and behaviour. The scientifc methods point is an extremely important one. Psychological hypotheses and theories based on opinions that are not confirmed using rigorous tests are worthless (even if it was, say, Freud who came up with these hypotheses).
7.1 Numerical literacy
The data analysis associated with this rigorous testing very often requires dealing with numbers, so a certain degree of numerical literacy is a non-negotiable part of psychology. That said, I would argue that the mathematical concepts required in your first year are not too advanced. Looking at the first-year labs, I would argue that you need to have an understanding of the following things:
- Addition
- Subtraction
- Multiplication (incl. squaring numbers, extracting a square root)
- Division
- Percentages
- Linear equations of the form \(y = ax + b\).
- The idea of integration (calculating the area under a curve)—but software will do the actual integrating for you (which is one of the reasons why computer literacy is also important).
- Some basic concepts of probability - see below.
A useful visualisation of some basic concepts related to probability is the Galton board. Have a look at this Galton board animation and play around with it for a bit (you might want to increase the speed). Set size to 2 to get a basic understanding of why extreme outcomes are less likely. They are less likely because there’s only one way to get there: left → left to end up on pile 0, and right → right to end up on pile 2. On the other hand, there are two ways to end up on pile 1: left → right and right → left. Therefore, after many rounds, there should be twice as many balls on pile 1 as on pile 0 and pile 2. So, the height of the pile tells you how likely an event is. If you understand this, you’ve already taken a big step towards understanding a lot of statistics!
If you now increase the size of the board, the same idea still applies. Now there are just many more paths a ball can take. If you increase the size to 10 or more, the resulting distribution begins to resemble a normal distribution or bell curve. The bell curve, for better or worse, has had a strong influence on psychology. If you look at, say, personality traits or intelligence, it is a way of explaining why most people end up somewhere in the middle of the distribution and very few at the extremes: There are many “paths” that lead to the middle of distribution, whereas there are very few “paths” leading to the extremes (e.g., most genetic and environmental factors increasing intelligence would need to “fall to the right side of the peg” for someone to be highly intelligent).
7.2 Surface-level vs in-depth psychology
Surface-level psychology is like going on a boat tour in the arctic ocean and watching icebergs from the safety and comfort of a boat. It’s nice and you might learn some interesting facts, but you only ever get to see the part of an iceberg that is above the surface. In-depth psychology is like scuba diving in the cold water and exploring the 90% of the iceberg that is below the surface. It requires more preparation and more skills. It is more challenging. But it is also more rewarding, because you get to see things you would not get to see otherwise.
Let’s look at an example. This is what we learn from the AQA Psychology for A Level book by Flanagan et al. (2020) about the study by Maguire et al. (2000) (p. 42):
Eleanor Maguire et al. (2000) studied the brains of London taxi drivers and found significantly more volume of grey matter in the posterior hippocampus than in a matched control group. This part of the brain is associated with the development of spatial and navigational skills in humans and other animals. As part of their training, London cabbies must take a complex test called The Knowledge, which assesses their recall of the city streets and possible routes. Maguire et al. found that this learning experience alters the structure of the taxi drivers’ brains. They also found that the longer the taxi drivers had been in the job, the more pronounced was the structural difference (a positive correlation).
That is certainly an interesting finding. But this brief description leaves open a number of key questions, most importantly perhaps how Maguire et al. (2000) were actually able to measure the volume of grey matter in the posterior hippocampus. This is what Maguire et al. (2000) have to say about this (pp. 4398-4399):
Image Acquisition. Structural MRI scans were obtained with a 2.0 Tesla Vision system (Siemens GmbH, Erlangen, Germany) by using a T1-weighted three-dimensional gradient echo sequence (TR 9.7 ms; TE 4 ms; flip angle 12°; field of view 256 mm; 108 partitions; partition thickness 1.5 mm; voxel size 1 x 1 x 1.5 mm).
Image Analysis Method 1: Voxel-based morphometry (VBM). Data were analyzed by using VBM implemented with Statistical Parametric Mapping (SPM99, Wellcome Department of Cognitive Neurology) executed in MATLAB (Mathworks, Sherborn, MA). Detailed descriptions of the technique are given elsewhere (9, 10). Briefly, the subjects’ data were spatially normalized into stereotactic space (11) by registering each of the images to the same template image by minimizing the residual sums of squared differences between them. The template was generated from the structural scans of 50 healthy males acquired in the same scanner used to collect the data for the current analysis (the scans of 13 of the control subjects used in the VBM analysis were included in the creation of this template). The spatially normalized images were written in voxels of 1.5 x 1.5 x 1.5 mm and segmented into gray matter, white matter, and cerebrospinal fluid by using a modified mixture cluster analysis technique. To reduce confounds caused by individual differences in gyral anatomy, the gray matter images were smoothed by using an isotropic Gaussian kernel of 4-mm full width at half maximum. The statistical model included a measure of total amount of gray matter in each brain as a confound (essentially the original values before normalization). Statistical tests involved locating regionally specific differences in gray matter between subject groups and were based on t tests and the general linear model. Significance levels were set at P < 0.05 (small volume correction for multiple comparisons, with 62 resolution elements comprising the volume of interest).
Whoa! Now, that’s an entirely different beast! Note that the point here is to illustrate that underlying the simple description of “more volume of grey matter” is a whole new world involving magnetic resonance imaging, anatomical registration, voxel-based morphometry and statistical testing. We don’t expect you to reach a level of expertise required for understanding the above in your undergraduate studies, but I would like you to be able to appreciate, metaphorically speaking, the presence of this submerged part of the iceberg.
In keeping with the metaphor, we could perhaps say that the aim in Year 1 is to become an open-water diver (i.e., someone who can go diving on their own). We would like you to use your first year to build a strong base across psychological disciplines and acquire the necessary skills for becoming a competent independent learner. Hopefully, as you progress through your studies, you will then acquire more and more expert knowledge. But note that the aim is not to become an expert in all fields in psychology. The aim is to become an expert in your chosen field. Not everyone will be interested in neuroscience, but every other field in psychology will similarly have a new world to discover below the surface.
7.3 Building the base
So, how do you build this base for the labs and ensure you do well on this module?:
- Read the HHG (and, unless you have an excellent memory1, read it multiple times).
- Read the required chapters in Beth’s book (see Section 10.1). Ideally, also read some of the other chapters. They’re all relevant, we just don’t have the time to cover all of them in the labs.
- Think about the content: Critically question what you’ve read. Try to make connections between what you’ve read and what you already know. Dive deeper and read some of the original studies that are being mentioned.
- Ask questions: Ask us. Ask AI chatbots. Ask your fellow students.
- Talk to others. Explain things to others. Have others explain things to you. Discuss the content with others.
- Read some of the books recommended as wider reading in Section 10.2.
- Attend the PSGY1001 workshops to learn more about study techniques.
7.4 A word on AI
In my view, AI chatbots can supercharge your learning. Of course they’re not perfect and I wouldn’t trust their statements without independent evidence, but they’re still incredibly useful. Use them to learn. Then stop using them and do your piece of coursework/exam. Using AIs for assessments is academic misconduct. Period. (Unless of course the assessment instructions explicitly allow the use of AI.)
I would recommend to at least have free accounts with OpenAI (for ChatGPT) and Anthropic (for Claude). Google’s Gemini is also pretty good. See here for a leaderboard comparing AIs. If you have too many questions for a free account, I would recommend getting an API key (if you’re not sure what I’m talking about, ask your favourite AI chatbot what this means), rather than paying for a subscription (unless you find you have so many questions that paying for a subscription is actually cheaper :)). If you have an API key, you will also need some sort of chat interface. At the time of writing (09/2024), I would recommend LibreChat, a free and open source chat platform.
References
Which many people believe they have, but very few actually have—see Section 12.1.↩︎