Monthly Archives: March 2012

Animal Testing: pros, cons, and a bit of ethics thrown in for good measure

This is one of those subjects that often invokes strong opinionated debates, but at the end of the day if we didn’t disagree with each other we wouldn’t be human. Personally I kind of sit on the fence on some aspects of this debate; of course animal testing for cosmetic products etc is wrong, but what about animal testing in medicine? Is it ethical to test cures for diseases on animals? Are animals similar enough to humans to test on anyway? I’ll be addressing all of these questions within this blog, and will cover the pros, cons and take into account the ethical implications of testing on animals.

There are copious reasons why researchers choose to use animals to test upon. There are so many studies using animals it would be ridiculous to name them all but in psychology there are a few key things we use animal studies for. For instance, classical conditioning (Pavlov, 1927), operant conditioning (Skinner, 1947), aggressive tendencies in males and females (Wright and Wrangham, 1998; Wagner et al 1980; Carlson, 1998) … I could go on forever (but I won’t).

I will however discuss Skinner (1947) and his box. Skinner wanted to investigate the concept of operant conditioning through the use of his Skinner Box and either rats of pigeons. In the experiment the animals were required to complete behaviours such as pressing a lever, in the case of the rats, in order to release a food reward. The animals learnt through differential reinforcement (or punishment); i.e. if they pressed the leaver by accident they got a food reward and from this they began to learn that pressing the lever delivered the reward. As the image below shows there were both rewards and punishments within the box. From this piece of research Skinner was able to conclude that consequences such as rewards and punishments help to shape and also to predict human behaviour. We can see this happening in everyday life. For example, when you were a child if you put your hand on the front of the oven and it hurt you probably learnt not to do it again because the pain acted as a punisher. Whereas if you got a good school report when you were younger and your parents bought you sweets you would probably want to be rewarded again and so would continue to do well at school.

Experiments such as this have given us such an insight into the human mind, and also demonstrate our similarities with animals. However, as I usually like to do I will start with the cons associated with animal testing, and where better to start than with generalisability? Can we really generalise findings from animals to humans? It can be difficult to make generalisations from one species to another, however some people may disagree with that point reminding us that humans are in fact animals and have developed and changed over time just as rats, pigeons, and monkeys etc have. Starkey (2008) suggested that the obvious differences between humans and animals make it difficult to make generalisations. For example, it is not always appropriate to generalise findings from the brains of rats due to biological differences across species. We know that rats have a significantly smaller neo-cortex to brainstem ratio. We also know that primates, such as chimpanzees, have a larger neo-cortex but the proportions are still different to human beings. This makes any generalisations we may wish to make from the brains of one species to another difficult to do reliably.

Further disadvantages of using animal studies have been discussed in the literature (Stubblefield, 2009). Firstly, one interesting fact I discovered whilst researching this blog is that researcher’s bias towards gender is not just confined to human studies. Zucker and Beery (2010) suggested that many researchers avoid using female animals, just like in the past they avoided female humans. This bias can have just as many implications for research using animals in that, like with humans, we may not get a true representation of behaviour if we only look at one gender. If we’re measuring levels of aggression in rats we would need to observe males and females in order to compare if there is a difference between genders, and also within genders.

Another disadvantages or “con” of using animals for research is that it can be very costly. For example it’s very costly to house the animals for testing to ensure that they are healthy and ready to be studied. Similarly it is difficult for researchers to determine whether it is necessary to use animals; so is the product a necessity? Will the behaviour be reproducible in humans? These are both common questions asked by researcher when determining whether to use animals in their research. Murnaghan (2010a) has suggested that different methods of research are needed in order to reduce the number of animal studies. It was suggested that in vitro techniques may help to reduce the number of animal studies, but unfortunately the human body may be far more complex than we can study in this way. In later work Murnaghan (2010b) suggested that we can help to reduce ‘treating’ the animal by instead using computer technology to simulate answers to research questions (in other words we can use data from previous animal studies to predict findings using computer programs in the present).

I wanted to discuss the positives of animal research as well but have really struggled to find much support for animal studies, but this is what I did find: Animals are much easier to find than humans and researchers do not have to worry about the animals withdrawing from the study (which some may argue is also a disadvantage). Animals generally breed a lot quicker than humans which also aids studies into hereditary behaviours etc.

It’s also a lot easier for researchers to control and manipulate the situations and conditions that animals are in. Such things as how much food they’ve eaten, what they’ve drank, how much they weigh, how much exercise/activity they do, how long they sleep for etc etc. For example, one study I stumbled upon whilst looking for something else used flatworms to demonstrate memory transfer through cannibalism in flatworms. Basically, flatworms were taught that when a light came on above where they were they should expect a small electrical shock. The worms were described to have a conditioned response if they contracted their body at the first sign of light, even without an electrical shock. The worms were then cut in half and left to regenerate for 4 or so weeks before being retested in the light/shock condition. Results showed that both the head and tail sections retained what they had previously learnt, an interesting finding if you consider that we would expect the head half with the brain to retain previous learnt behaviours. It sounds cruel to cut the worms in half, and obviously this is not something that researchers can repeat with humans (definitely not ethical to cut people in half!) but it did provide an insight into different structures within the DNA of planarians, and if you’re interested in the weird and wonderful the link to the paper is below in the references (McConnell, 1962). This study required a lot of control, and it’s pretty difficult to get such a high level of control in a study with humans.

This leads neatly back to a few more issues with animal studies. Most animal studies are conducted in laboratory settings; which of course means high control and high internal validity. If you look at this from one point of view, high control and internal validity are good as we can be relatively confident that we are testing what we intend to test. However, laboratory experiments have very low ecological validity and low external validity as it’s very difficult to see how the behaviour may occur in the real world. For example, if you cage a chimpanzee its behaviour is going to be very different when it’s in a cage compared to when it’s in the wild. Imagine if you were confined in a cage and couldn’t do anything you wanted to do. You’d probably be pretty grouchy and not your normal self, thus a bad representation of real life behaviours.

Right before I drone on even longer I will quickly mention the ethics bit. In recent years there have been much stricter rules put in place to ensure the protection of animals in experiments. Animals should not be subjected to harm, just like humans, which helps to protect animals from cruelty in present day experiments. In the past animals were treated as if they did not have feelings and so were subjected to environments that were extremely damaging to them just for the progression of psychology. For example Harlow (1950s) conducted a relatively well known experiment in which he separated baby monkeys from their mothers and replaced the mothers with either a wire monkey with food or a furry, warn monkey. It was found that the monkeys that had a choice between either of the two “surrogate” mothers generally chose the furry monkey as they felt safer with it. The monkeys formed attachments with the furry surrogate, but still ate from the wire monkey. This study has given us valuable evidence for attachment in human infants, but at what cost? Many of the monkeys that were deprived of feelings of safety and comfort were unable to develop at the typical rate, often becoming aggressive or depressed, suggesting that children too need to form emotional attachments with their primary caregivers.

So, should we use animals in experiments? It’s a difficult question really. At the start of this blog I thought I was undecided about animal testing. Personally, now, I think that animals should be left alone as much as possible. They don’t have the choices that humans have when participating in an experiment. They can’t ask the experimenter to stop or tell them that they don’t want to participate as they simply don’t have the language. Animal testing in recent years has been monitored better to try and ensure animals are not damaged in experiments. Their use in psychology is something I’m undecided on as it’s difficult to generalise and if something is too unpleasant to test on humans then should we really do it to animals? Testing cosmetics on animals is a big no no for me, as for medicines I’m not too sure. I think if someone you knew well had a serious disease that animal testing may be able to provide a cure for your opinion may be very different that if you wanted to know if the latest shampoo has been tried and tested on animals.


Carlson (1998). Physiology of Behaviour, 6th edition.

Harlow, H. (1950s). Retrieved:

McConnell, J. V. (1962). Memory transfer through cannibalism in planarians. Journal of neuropsychology.

Murnaghan, I. (2010a). About Animal Testing. Retrieved from

Murnaghan, I. (2010b). New Technologies as Alternatives to Animal Testing. Retrieved from:

Pavlov, I. P. (1927). Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex. Translated and Edited by G. V. Anrep. London: Oxford University Press

Skinner, B. F. (1947). Substitution in the pigeon. Journal of Experimental Psychology, 38, 168-172.

Starkey, G. (2008). Animal Models of the Brain: Ethical Considerations and Alternatives

Stubblefield (2009). The Pros and Cons of Animal Testing. Medical Science

Wagner et al. (1980). Aggressive Behaviour, 6, 1-7.

Wright & Wrangham. (1998). Morals, Demonic Males and Evolutionary Psychology. In Information and Biological Revolutions: Global Governance Challenges.

Zucker, I., & Beery, A. K. (2010). Males still dominate animal studies. Nature, 465.

Image 1 from: scheme _01.png/300px-Skinner_box_scheme_01.png

Image 2 from:


Should we use correlations in research?

What is a correlation? Well, a correlation is defined as a relationship between two variables. And just like any kind of relationship there are both positive and negative aspects to correlational designs. I’m going to start with a bit of correlation basics before discussing the negative aspects to correlations (to get them out of the way first), and then I will talk about the more positive side to this type of design before finally reaching a conclusion to the question “should we use correlations in research?”

I’m pretty sure that by now everyone knows that we can have positive or negative relationships between variables or even no relationship at all. However, we need to be able to measure how strong the relationship between the two variables is. If you look at the image below it shows the different relationships that can occur and their strength. The strength of relationship is assigned a numerical value with -1.00 being a perfect negative correlation and +1.00 being a perfect positive correlation. The first image (top left) shows no correlation (a value of 0.00 shows no relationship between the two variables being measured). However the bottom right image shows a correlation equal to 0.99 which is suggestive of a very strong positive relationship between variables. Similarly a value of -0.99 shows a strong negative relationship.

Now that little introduction is out of the way we can get into the more interesting stuff (can’t believe I’ve just said that!) As I just mentioned above, a correlation of 1.00 (+/-) shows a perfect correlational relationship between two variables. But even so we cannot infer causation from correlational research. For instance, we may see that there is a relationship between A and B but we do not know whether A causes B.  The first of the negative aspects of correlation studies I will discuss. One of the main problems to do with causation is that we often do not have tight control over variables so we may not always know whether the two variables we aim to study are the only variables at effect. The third variable problem suggests that there may be a third variable at play in a study that you are not aware of! So we might think that there is a relationship between A and B, when in fact a third variable (let’s call it C) is affecting A, or B, or both! I presented a piece of evidence in one of my comments the other week that shows the third variable problem in action and probably helps to get across what I mean. Li (1975)* wanted to find out which variables were the best predictors of the use of birth control in Taiwan. To cut a long story short it was found that the variable that correlated the most highly with the use of birth control was the number of electrical items that there were in the house! Clearly the researchers could tell something was amiss there, no way is owning a kettle going to increase the use of birth control right? Exactly, and this is why researchers realised that there was a third variable contributing to the correlation they were seeing. After some more research they discovered that the third variable was actually how well educated the individuals were; those who attended school regularly learnt about birth control, they probably got better jobs and so could afford more electrical appliances. So therefore it wasn’t actually whether you owned a toaster (A) causing the use of contraceptives (B) but actually our third variable, education (C). Whilst this piece of research was quite easy to spot that there was something else contributing to the correlation that was seen it isn’t always that easy and often things such as this can go unnoticed.

And unfortunately the third variable problem isn’t the only negative aspect to correlations. We can’t see which way the relationship goes; does A affect B or is it B that affects A? Often it is difficult to know which direction the relationship goes for definite. Gentile and Anderson (2003)** were interested in studying the relationship between aggression and the use of video games. The results of their study found that the amount of time that children spent playing violent video games (D) correlated positively with aggressive behaviour (E). However there is no way that we can say that the violent video games were causing children to act aggressively. Yes, possibly violent video games can increase aggressive tendencies, but it is also just as likely that children who are already more aggressive may choose to play violent video games. In other words, it is a “bi-directional model” as we don’t know which the determining factor is.

Now we’ve got two of the main negatives out of the way I’m going to show you that correlations aren’t all bad. Correlations are used throughout research as they are an easy way to determine if there is a relationship between variables.  Correlation studies are often used in medicine. For example, McNeal and Cimbolic (1986)*** noticed a correlation between depression and low serotonin levels. This has consequently led to the development of new drugs to treat depression, such as Selective Serotonin Reuptake Inhibitors (SSRIs) that increase the levels of serotonin in the brain. Without correlation studies, we might miss relationships like this!

Correlations are also good because they allow researchers to study naturally occurring relationships between variable that it would be unethical to manipulate in, for example, a laboratory experiment. One study found that there was a correlation between increasing unemployment levels and instances of alcohol abuse, suicides and homicides. You can read more about it in the link above, but the study collected information from various sources such as the World Health Organisation (WHO). It was found that unemployment increases of 3% were correlated with a 28% increase in alcohol related deaths. The reason I mention this piece of research is because we couldn’t possible test it in a laboratory as it would be extremely unethical to make people unemployed to see how they’re health deteriorated as a result. Therefore researchers have to use the information that is available for them to observe. This is why correlational studies can be a great benefit to researchers as they show us things that we may otherwise miss. They’re relatively easy to run and can produce some extremely useful results without manipulating any variables and simply observing natural interactions between different variables.

I suppose I should really conclude before this gets even longer: Correlational research is a pain in the neck when it comes to inferring causation- we just can’t do it. But do we always need to know if one thing causes another? The issue of the third variable problem is, let’s face it, similar to problems that arise in laboratory experiments. We say we are better able to infer causation in lab experiments because they are controlled, however extraneous variables can still go unnoticed. They’re good at showing relationships, and can lead to further research once a relationship is established.


The End.


(Oh, actually I haven’t answered the question: “should we use correlations in research?”… My simple answer is yes. Why not? As my conclusion shows, there are strengths and weaknesses but nothing that’s bad enough to completely dismiss correlational research all together.)


Image from:

*Li (1975) in S. L. Jackson’s Research Methods and Statistics: A Critical Thinking Approach

**Gentile, D.A. and Anderson, C.A. (2003). Violent video games: the newest media violence hazard. In D. A. Gentile (Ed.) Media violence and children.

***McNeal, E.T. and Cimbolic, P. (1986). Antidepressants and biochemical theories of depression