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Food for the brain: How diet affects mental health

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Is a diet of junk food bad for your mental health?

In a recent article published in the Guardian (originally available on his personal website) George Monbiot looked at recent scientific evidence suggesting a link between ‘junk food’ and Alzheimer’s disease (1). This prompted me to think about the wider subject of nutrition and mental health. It’s an uncomfortable subject to consider especially if, like me, you enjoy a trip to the local takeaway and the ‘occasional’ alcoholic beverage. Nevertheless the availability and popularity of processed foods in modern industrial societies (2, 3) makes the impact of diet on brain function an issue that we all need to seriously consider.

Despite a significant amount of research being undertaken into how diet affects the brain, there appears to be little discussion of the subject in public discourse. This may due to the scientific uncertainties inherent in the study of diet and mental processes, especially when contrasted with the strong influence that the commercial interests of food manufacturers and retailers hold over government decision making. Here I intend to briefly review the difficulties researchers face in studying this topic, and what we know so far about how diet may alter mental health.

The problems of studying nutrition

A major problem with the study of diet is that it is really particular nutrients within food (e.g. vitamins and minerals) that influence our brains, rather than the foods themselves.  As people can only really report their diets in terms of the foodstuffs they consume, and as each foodstuff contains a variety of chemicals in varying levels, each of which may be harmful, beneficial or neutral to our health to differing extents, it is not straightforward to map the relationship between foodstuffs and changes in health.

A second problem is that the impact of individual nutrients is likely to be mediated by other factors, such as the nutrient’s baseline level in the body, or the presence or absence of other nutrients. For example nutrients that are known to be beneficial to human health when consumed in food often fail to produce positive results when consumed in supplementary form (e.g. vitamin pills) an effect that is most likely due to the absence (in supplements) of naturally co-existing chemicals that facilitate the body’s uptake of the nutrient when it is consumed via foods (4). Likewise other factors that are independent of diet, such as age, genetics, and the level of physical activity, are likely to influence the effect of nutrition on health (e.g. 5). It is unethical to systematically control and manipulate a person’s entire diet over the period of time necessary to identify changes in mental processes likely to be triggered by diet. It is also impossible to fully control for the influence of other non-diet factors over a similar time frame. Therefore it is not possible to establish causality between individual foods and health outcomes with any certainty. Of course it is possible to perform such experiments on laboratory animals, but as such animals lack many of the cognitive functions that are disrupted in neurological diseases such as dementia, such studies are of limited use when considering the impact of nutrition on mental health in humans.

In light of these problems, the effect of nutrition on health is often studied via ‘cohort studies’, where large numbers of people are surveyed as to their dietary habits and health over an extended period of time. Such studies are not only expensive and time-consuming to complete, but also rely on potentially unreliable self-report measures (see (6) for a discussion). Alternatively, the influence of individual nutrients is sometimes studied by giving one group of participants supplements containing the nutrient, and others a placebo. This approach lacks the ecological validity of cohort studies, but allows a tighter control over the intake level of the nutrient involved, thus allowing its effects to be isolated. Neither method however overcomes the previously mentioned problems regarding establishing causality.

What we do know?

Given the complex relationship between food and nutrition, and the imprecision of self-report measures, diet is often characterised in cohort studies in broad terms. One relative common distinction that is used is between the so-called ‘Mediterranean Diet’ and the ‘Western Diet’. The former involves the high intake of fruit, vegetables, fish, cereals and unsaturated fats (e.g. the type of fat that tends to be found in nuts and seeds). In contrast the ‘Western Diet’ involves the frequent consumption of foods with high levels of saturated fats, such as red meats, dairy products as well as other processed foods such as confectionery and ‘convenience’ foods.  Studies tend to show that those who have diets that more closely resemble the Mediterranean Diet have lower instances of both dementia and mild cognitive impairment, even after confounding factors like age, socio-economic status and physical activity are controlled for (7). More specifically it has been shown that high intake of fruit and vegetables, as well as omega-3 fats (dietary rather than through supplements) predict a reduced likelihood of dementia (8); dementia levels in those with diets high in fruit and vegetables being 2.6%, compared with 5.7% for those with diets poor in fruit, vegetables and omega-3 fats.

The neurological effects of diet are not just restricted to dementia however. There is increasing evidence that diets high in saturated fat and sugars may contribute to behavioural problems in children and adolescents, including ADHD (9, 10). Similarly artificial food additives, such as the colourings and preservatives commonly added to confectionery and soft drinks, appear to increase hyperactivity in children (11). For example in a double-blind placebo trial (12) it was found that children regularly given a drink containing additives became more hyperactive (as measured by parent and teacher ratings, and through performance on a computerised attention task) than those given a placebo drink with the same frequency. This effect was present in both 3 year old and 8 year old children, suggesting that the influence of additives is not restricted to one particular stage of development.

Evidence also exists which suggests that deficiencies in a variety of vitamins and minerals within the body may encourage depressive symptoms. For example double-blind placebo trials consistently show that Thiamine supplements improve mood (13) while other studies have suggested that low levels of vitamins B6 and E are implicated in depression (14). The effect of diet on mood may be self-reinforcing as depressed individuals often turn to ‘comfort eating’ (13) which is likely to involve foods that are high in saturated fats, and which in turn may promote obesity which could further depress mood and self-esteem over the long term.

In what way do nutrients affect the brain?

Due to the aforementioned complexities in identifying the contribution of different nutrients, it has proven difficult to identify the exact mechanisms by which the under or over abundance of certain nutrients might affect the brain. However two interrelated systems are thought to be most vulnerable to dietary factors; the neuroinflammatory response of brain neurons, and the processes surrounding insulin signalling within the brain (15). Neuroinflammation is the immune response to neuron damage. It acts to preserve the damaged neuron and promote its recovery, but it can also cause damage to surrounding neurons. It is thought that the beneficial effect of diets high in fruit and vegetables may partly be due to the polyphenols present in plant matter working to limit neuroinflammation in the brain (e.g. 16). In terms of the second system, Insulin is involved in regulating the uptake of glucose by neurons, as well as maintaining their function and structure (17).  Diets that are high in saturated fats appear to promote ‘insulin resistance’ which reduces the body’s ability to utilise Insulin (hence the association between obesity and type II diabetes). This in turn negatively impacts on the ability of neurons to function properly and to adapt to changes in the signalling patterns of other connecting neurons. This leads to reduced neural plasticity and an increased likelihood of chronic, maladaptive neuroinflammation, both of which are likely to interfere with normal cognitive functioning. This may be the mechanism by which frequent consumption of junk foods leads to a greater risk of dementia (1).

Should I change what I eat?

While it is never possible to rule out the influence of confounding factors, the basic message one can take from these studies seems pretty intuitive. We are better off eating foods that can be thought of as ‘natural’ for humans to eat. Throughout history the human race have presumably mainly relied on fruits, vegetables, nuts and cereals, supplemented with small amounts of fish and meat. It therefore makes sense that these foods would be conducive to both our physical and mental health, as research seems to suggest. In contrast the convenience and affordability of seemingly unnatural foods such as confectionery, processed meats and ‘ready meals’ belies their damaging impact on our health. We could do our future selves a favour by avoiding the temptation these foods provide, and making the extra effort to eat healthily.

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Image courtesy of www.freedigitalphotos.net

References

  1. http://www.monbiot.com/2012/09/10/the-mind-thieves/ (retrieved 24/09/2012).
  2. Popkin, B. M. (2004). The nutrition transition: An overview of world patterns of change. Nutrition Reviews, 62(7), S140-S143. <link>
  3. Thow, A. M. (2009). Trade liberalisation and the nutrition transition: mapping the pathways for public health nutritionists. Public Health Nutrition, 12(11), 2150-2158. <link>
  4. Morris, M. C. (2012) Nutritional determinants of cognitive aging and dementia. Proc Nutr Soc, 71(1), 1-13. <link>
  5. Dauncey, M. J. (2009). New insights into nutrition and cognitive neuroscience. Proceedings of the Nutrition Society, 68(4), 408-415 <link>
  6. http://www.sciencebrainwaves.com/uncategorized/the-dangers-of-self-report/ (retrieved 24/09/2012)
  7. Sofi, F., Abbate, R., Gensini, G. F., & Casini, A. (2010). Accruing evidence on benefits of adherence to the Mediterranean diet on health an updated systematic review and meta-analysis. American Journal of Clinical Nutrition, 92(5), 1189-1196. <link>
  8. Barberger-Gateau, P., Raffaitin, C., Letenneur, L., Berr, C., Tzourio, C., Dartigues, J. F., et al. (2007). Dietary patterns and risk of dementia – The three-city cohort study. Neurology, 69(20), 1921-1930 <link>
  9. Oddy, W. H., Robinson, M., Ambrosini, G. L., O’Sullivan, T. A., de Klerk, N. H., Beilin, L. J., et al. (2009). The association between dietary patterns and mental health in early adolescence. Preventive Medicine, 49(1), 39-44 <link>
  10. Howard, A. L., Robinson, M., Smith, G. J., Ambrosini, G. L., Piek, J. P., & Oddy, W. H. (2011). ADHD Is Associated With a “Western” Dietary Pattern in Adolescents. Journal of Attention Disorders, 15(5), 403-411 <link>
  11. Schab, D.W & Trinh, N.T. (2004). Do Artificial Food Colors Promote Hyperactivity
    in Children with Hyperactive Syndromes? A Meta-Analysis of Double-Blind
    Placebo-Controlled Trials. Developmental and Behavioral Pediatrics, 25(6), 423-434 <link>
  12. McCann, D., Barrett, A., Cooper, A., Crumpler, D., Dalen, L., Grimshaw, K., . . . Stevenson, J. (2007). Food additives and hyperactive behaviour in 3-year-old and 8/9-year-old children in the community: A randomised, double-blinded, placebo controlled trial. Lancet, 370, 1560-1567. <link>
  13. Benton, D., & Donohoe, R. T. (1999). The effects of nutrients on mood. Public Health Nutr, 2(3A), 403-409. <link>
  14. Soh, N. L., Walter, G., Baur, L., & Collins, C. (2009). Nutrition, mood and behaviour: a review. Acta Neuropsychiatrica, 21(5), 214-227 <link>
  15. Parrott, M. D., & Greenwood, C. E. (2007). Dietary influences on cognitive function with aging: from high-fat diets to healthful eating. Ann N Y Acad Sci, 1114, 389-397. <link>
  16. Lim, G. P., Chu, T., Yang, F., Beech, W., Frautschy, S. A., & Cole, G. M. (2001). The curry spice curcumin reduces oxidative damage and amyloid pathology in an Alzheimer transgenic mouse. J Neurosci, 21(21), 8370-8377. <link>
  17. http://www.thealzheimerssolution.com/insulin-brain-function-and-alzheimers-disease-is-insulin-resistance-to-blame-for-alzheimers/ (retrieved 28/09/2012)

How delusions occur, and why they may be widespread!

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Why do many people believe that Crop Circles are created by alien life forms?

It is a common occurrence to come across people who believe things that seem extraordinary, and who maintain that belief even in the face of huge amounts of contradictory evidence. For example despite vast amounts of evidence suggesting otherwise, there are people who believe that aliens create crop circles, that astrology can predict their future, and that the next Adam Sandler movie will be any good. A delusion can be defined as an extraordinary belief that is strongly held despite the presence of seemingly overwhelming evidence to the contrary. They are of particular interest to psychologists and neuroscientists because they occur in a number of neurological disorders, as well as in seemingly healthy individuals. For example a variety of paranoid or grandiose delusions frequently occur in psychotic disorders such as schizophrenia. Delusions relating to various bizarre forms of misidentification, such as the belief that a loved one is an imposter (the Capgras delusion) can also occur, often in forms of dementia such as Alzheimer’s Disease, and even in old age populations who do not exhibit any other noticeable cognitive impairment (1). Delusions of various types also occur in Parkinson’s disease, depression and as a result of other brain traumas such as those caused by strokes.

One error or two?
On a theoretical level there has traditionally been a distinction between 1-step and 2-step theories of delusions. 1-step theories (e.g. 2) suggest that a single perceptual deficit causes delusions. The delusion represents the most logical response to the bizarre perceptual information the brain is receiving as a result of the perceptual deficit. For example paranoid delusions may be caused by a perceptual bias towards threat signals which makes the sufferer conclude that some overbearing threat must be present to explain the constant warnings coming from the sensory environment. In contrast 2-step models (e.g. 3) argue that in addition to a perceptual deficit, there must also be a second, cognitive deficit. Such theories are motivated in part by the finding that there are some individuals who exhibit very similar perceptual deficits to those with delusions, but nevertheless do not hold delusional beliefs. For example there are individuals with bilateral damage to specific parts of the frontal lobe who, like patients with the Capgras delusion, experience a lack of familiarity when they come into contact with a particular close relative. However in contrast to the Capgras patients, the frontal lobe patients do not hold the belief that the relative is an imposter (4). Instead they are able to understand that it is their experience that has changed, rather than their relative. While 1-step theories suggest that delusions are caused by a single neuro-perceptual deficit, which varies in its nature depending on the nature of the delusion, 2-step theories require that an additional, separate deficit exists within the neural system involved in the formation and evaluation of beliefs. Variances in this second cognitive stage explain the likelihood of adopting a delusional belief in the context of disrupted perceptual experiences, and hence the difference between the Capgras and frontal lobe patients.

How are beliefs formed and updated?
If delusions are underpinned by a 2-step deficit, with the second, cognitive step being similar across delusional disorders, then the question arises as to what is the exact nature of this cognitive deficit? Recently an answer to this question has been proposed based off the insight that our ability to navigate the world is achieved through a process of inferential learning (e.g. 5). In short it is proposed that the brain creates representations as to how the external world is organized based off the information it receives. These models of the world by their nature encapsulate our belief system, as they contain representations of how different information is related, and what is likely to occur in any given situation. These models also allow the brain to predict both upcoming external stimulation, and internal experience. When actual experience differs from that which is expected, signals communicating this discrepancy (referred to as prediction-error signals) are sent back to the areas that generated the prediction, with the purpose of updating the model from which the original prediction arose. This process, when working optimally, allows us to adapt to new, unexpected information while at the same time enabling the majority of unexceptional information we encounter to be processed quickly and with minimum effort (because it has been predicted in advance).
Within this system the updating of beliefs can be framed using the principles of Bayesian inference, whereby the decision as to whether to adopt one of (say) two explanations to account for an unexpected stimulus is taken by balancing the inherent probability of each explanation (based off the current model of the world that the individual holds) with the likelihood of the unexpected stimulus having occurred if each explanation were true. When in the presence of a surprising or anomalous experience, such as those caused by the perceptual deficits believed to underpin the first step of delusion formation, an alteration in the belief pattern will only occur if the difference between the probability of the sensation occurring given that the new belief is true, compared to its probability of it occurring if the existing belief is true, is greater than the difference in the inherent probability of the two beliefs. In order to adopt an atypical or delusional belief, whose inherent probability would usually be very low, new evidence would have to appear that is almost inexplicable within the current belief system, while being fully explainable using the new belief. For example to believe that the moon is made of cheese would probably require you to actually travel to the moon, dig a bit of it up, put it in your mouth and taste cheese. Any lesser form of evidence would be discarded as a coincidence or trick, as the inherent probability of the moon being made of cheese given your existing belief system is (or at least should be) extremely low!

Delusions: A problem with prediction error?
In delusions it is proposed that this process of error-dependent updating of beliefs is disrupted. Most likely this occurs through a process whereby the weight (or importance) given to various prediction error signals is sub-optimal (e.g. 6, 7). If prediction error signals are given undue weight then potentially unimportant variances from expectation will become flagged as being highly salient. This in turn would mean that they are given unnecessary influence in updating our belief system. An anomalous experience that would normally not be treated as particularly relevant to understanding how the world works, either because of the unusual context in which it occurred, or its infrequency, would, if this deficit existed, be treated as important enough to warrant a change in the individual’s belief system. In terms of Bayesian inference, a system which gives undue weight to prediction errors would be one that had a bias towards accepting the influence of the new anomalous experiences without taking fully into account the relative inherent probabilities of the competing potential beliefs (which would usually strongly favour the non-delusional belief) (8). A less convincing anomalous experience would therefore be required in order to successfully challenge an existing non-delusional belief.
As an example, reconsider the aforementioned difference between patients with frontal lobe lesions and those with the Capgras delusion. In both types of patient the feeling of familiarity that is expected to appear on the physical recognition of a known person is absent. In the non-deluded individual, while this discrepancy is noted, it is not used to adopt the ‘imposter explanation’ because the correct weight is given to the prediction error and it is therefore not strong enough to overturn an otherwise functioning belief that the individual is who they claim to be (a belief that would be supported by several other pieces of information). In contrast the deluded individual gives far too much weight to the unexpected experience of non-familiarity, and the model is changed to accommodate it through the acquisition of the belief that the person is an imposter. As the prediction error deficit in such cases is restricted to the perceptual system dedicated to familiarity processing, other evidence that is contradictory to the imposter hypothesis, but which comes from a different source (e.g. people telling the deluded individual that they are wrong) is not treated with the same weight as the experience of absent familiarity. The delusion is therefore maintained even in light of strong contradictory evidence.

More widespread delusions
Whereas the Capgras delusion tends to be monothematic (i.e. it relates to just one known person having been replaced by an imposter, rather than people in general being imposters) faulty prediction error signalling can also be used to explain more widespread delusional thinking such as paranoia. For example one potentail consequence of the incorrect updating of belief systems is that the model of the world that the individual holds will itself become further divorced from reality, making it less able to accurately predict upcoming stimulation. This in turn will lead to a further increase in the frequency of prediction errors; to the extent that surprising or anomalous information would appear to occur with seemingly baffling frequency. If the deficit in prediction error exists across more than one perceptual domain, the inferential response to this might be to adopt a paranoid outlook to explain this constant uncertainty in the world. For example a delusion that MI5 are spying on the sufferer might be the best explanation for a world where objects and strangers seem to take on a sinister level of salience, and unexpected events seem to happen with alarming frequency (6).

Is healthy belief formation optimal, or are we all deluded?
The strength of a model of delusions based off deficits in the processes of inferential learning is that it can be used to explain the characteristics of general belief formation. For example deficits in prediction-error signaling may explain why some otherwise healthy individuals tend to adopt a wide variety of irrational beliefs. Such people may lack the perceptual deficit that causes the bizarre but specific anomalous experiences suffered by individuals with clinical delusions, but they may share with the clinical group a general deficit in inferential reasoning which results in a tendency to accept unusual beliefs that are poorly supported by available evidence. Along similar lines, variances from optimal processing (in terms of Bayesian inference) may explain more general cognitive biases that seem to be present in most people (including scientists!) and which are therefore presumably hard wired in the human brain because they have some adaptive evolutionary advantage. For example most people display a ‘belief bias’, the tendency to evaluate the validity of evidence based on their prior beliefs, rather than on the inherent validity of the evidence as could be assessed through logical reasoning (9). This bias could be said to be the result of our system of inferential learning being sub-optimal (in Bayesian terms) but in the opposite direction to that seen in delusion, such that we have a bias towards evaluating beliefs more in terms of their inherent probability (as we see it) without fully taking into account new evidence.
More generally the processes of inferential learning and belief formation may be able to explain why people who have had relatively similar types of upbringing and experience can often exhibit very different sets of beliefs. These differences are likely to be in part due to differences in the process of belief formation between individuals. It would seem very unlikely that anybody’s brain is able to process information in strict accordance with Bayesian inference, given that neural signals are coded through the transmission of neurotransmitters between groups of neurons, a process that is naturally susceptible to a significant amount of noise. Differences in beliefs between people are presumably therefore inevitable, as is the likelihood that we all, at some time, adopt irrational convictions. Of course these are just things that I believe, and I may be deluded in believing them!

Image courtesy of www.freedigitalphotos.net

References
(1) Holt, A.E., & Albert, M.L. (2007) Cognitive Neuroscience of delusions in aging. Neuropsychiatric disease and treatment, 2 (2) 181-189. Link
(2) Maher, B.A. (1974) Delusional thinking and perceptual disorder. Journal of Individual Psychology, 30:98-113. Link
(3) Coltheart, M, Langdon, R. & McKay, R. (2011) Delusional Belief. Annual Review of Psychology, 62, 271-298 Link
(4) Tranel, D., Damasio, H. & Damasio, A.R. (1995) Double dissociation between overt and covert face recognition. Journal of Cognitive Neuroscience, 7(4) 425-432. Link
(5) Friston, K. (2003). Learning and inference in the brain. Neural Networks, 16(9), 1325-1352. Link
(6) Fletcher, P. C., & Frith, C. D. (2009). Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews Neuroscience, 10(1), 48-58. Link
(7) Corlett, P. R., Taylor, J. R., Wang, X. J., Fletcher, P. C., & Krystal, J. H. (2010). Toward a neurobiology of delusions. Progress in Neurobiology, 92(3), 345-369. Link
(8) McKay, R. (2012). Delusional Inference. Mind & Language, 27(3), 330-355. Link
(9) Markovits, H. & Nantel, G. (1989). The belief bias effect in the production and evaluation of logical conclusions. Memory & Cognition, 17(1) 11-17. Link

Consciousness In The Brain

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You see, but you do not observe…

A Scandal in Bohemia, The Adventures of Sherlock Holmes:  Arthur Conan Doyle

Can neuroscience provide an explanation as to how the brain enables us to consciously process information?

What is the distinction between seeing and observing? The term ‘seeing’ suggests a passive process, whereas observation clearly requires something additional; the attention to a particular detail or details within the visual scene, the extraction of salient information and perhaps the further evaluation of that information. Neuroscience has made great strides in understanding the functioning of our basic sensory mechanisms, such as those that allow seeing. This work has reached such a level that we are now coming close to being able to create ‘bionic eyes’; mechanical replicas which can mimic the workings of damaged parts of the visual system (1). However is it a much harder task to fully understand the myriad of different ‘higher order’ functions that serve to differentiate observation from merely seeing. These functions are the reason that human experience is much more than the sum of the output from our sensory systems. At the heart of this problem is the need to understand the phenomenon of consciousness. Consciousness can be difficult to define precisely, with different philosophers breaking consciousness down into different sets of features (2) producing concepts that, perhaps inevitably, tend to be somewhat vague and potentially overlapping. However the most fundamental aspect of consciousness would appear to be our ability to experience awareness of (certain) sensory information, and to impose our higher order abilities on that information. In short, given that the majority of sensory processing is performed outside of consciousness, how is it that certain information can be sectioned off and subject to processes such as attention, evaluation and reflection, and how is it that we are aware of both the selected data, and the cognitive processes we perform on it?

Brain waves and synchronisation
The simplest way of addressing the issue of consciousness is to compare the response of the brain during circumstances where the level of consciousness awareness is different. It has long been known that states of consciousness (such as wakefulness, sleep and coma) are marked by differences in the pattern of ‘brain waves’; the oscillating electrical signals that are produced by the brain. It would seem sensible therefore to assume that such changes in the pattern of brain waves reflect, at least in part, changes in the functioning of the mechanism that enables consciousness. Similar changes in brain oscillations are also seen in a wide variety of different brain areas during performance of cognitive tasks, which of course also require the conscious processing of information. In general cognitive processes appear not only to alter the power of such oscillations, but also to evoke an increase in synchronisation between these oscillations (such that the phase difference between the signals generated from the brain areas activated by the task remains constant over time). Such synchronisation is believed to allow communication between disparate brain areas; so-called ‘communication through coherence’ (3). If one takes the simple example of one neuronal population passing a signal to another, then to provide the greatest likelihood of that signal being received, the sending neurons must all fire at the same time (hence the oscillating nature of brain waves) thus maximising the signal sent to the receiving neurons. However the timing of this signal is also important. To maximise the chance of the signal being propagated, the firing of the sending neurons must be timed so that the signal arrives at a time when the receiving neurons are optimally receptive to the signal (or alternatively, if inhibition of signalling is required, at a time when the receiving neurons are optimally insensitive of the signal). Therefore when different brain areas need to communicate in order to facilitate cognitive processing their pattern of neuronal firing much achieve coherence, so they tend to synchronise with (for unidirectional, excitation signals at least) the conduction delay between the two areas being equal to the phase difference between the two oscillating signals.

Global Neuronal Workspace
As the cognitive tasks that produce neural synchrony all require conscious processing of some sort, we would expect that the experience of consciousness in general must rely on changes in synchrony between brain areas. Indeed studies that have directly compared conscious vs non conscious processing (e.g. comparing instances where the same stimulus is consciously perceived versus instances where it is not) have found an increase in synchronisation between distant cortical sites not directly related to the processing of the relevant sensory information (e.g. 4). Evidence from several MRI studies suggests that the location of these synchronising sites is consistent across different tasks, involving a specific set of areas in the frontal and parietal lobes as well as the thalamo-cortical circuits that control the flow of sensory information to and from the cortex (see 5 for a review). The relevance of this finding to consciousness is supported by evidence that the source of the altered brain response between different states of consciousness appears to be generated by a similar set of areas (6). This has led to the idea that these brain areas represent a ‘global neuronal workspace’ (GNW: 5,7) that supports consciousness. The GNW system is thought to be able to orchestrate synchronisation between different sensory processing areas in such a way as to allow certain sensory representations to be amplified and maintained, while inhibiting others. As synchronisation facilitates neuronal communication it may allow the specific information being held within different sensory areas to form a single, multi-sensory representation within the workspace, explaining how the conscious experience of perception is of a unified sensation, despite the fact that information from each sense is analysed separately (8 – the ‘perceptual binding’ problem). In addition the parietal and frontal areas of the GNW contain a large number of neurons with long axons which allow these areas to project information to a wide variety of disparate brain areas. This in turn is thought to allow them to make the representation held within the GNW available to the areas of the brain involved in higher processing functions. In effect the amplified representation that is maintained by the GNW is also broadcast to these other processing sites, thus allowing higher order processing of conscious information. It is this selection and amplification of a specific representation, and it’s subsequent global availability (to other brain areas) which we experience as consciousness. The concept of synchronous firing and a global neuronal workspace may also help explain other aspects of the conscious experience, such as metacognition (our ability to perform mental processing on the outputs of other mental processing e.g. to know what we know). Metacognition may simply be the conscious component of a much larger perceptual system that is continuously reflecting on our own activity and its likely consequences (9). The metacognition we experience consciously may therefore simply be the instances where this process reaches conscious access via the GNW and is therefore exposed to other higher order processing functions.

The consequences a neural explanation of consciousness
The study of the neural basis of consciousness is an exciting, but complex subject. It also however raises significant philosophical questions. The idea that consciousness is merely a manifestation of the firing patterns of neurons and their arrangement vis-a-vis each other is not a particularly controversial conclusion from a neuroscience perspective, as one would expect every aspect of human cognition to manifest via changes in brain physiology. However the topic is controversial in general because it suggests that if something as core to our being, to our experience of being ‘human’, as consciousness is in fact solely reliant on biological mechanisms, then concepts such as the mind,  the soul and free are redundant. If there is no ‘ghost in the machine’ driving our conscious behaviour then are we really nothing more than just a collection of tissue; are we really just, in effect, extremely complex machines? The consequences of this discussion has important implications for philosophy and morality (for an interesting discussion on this topic see 10). More optimistically however, the ability to understand the biological underpinnings of consciousness can lead to greater understanding of the basis of neurological disorders that cause the loss of conscious abilities, and of psychiatric symptoms that relate to the disruption of consciousness. For example many people suffering from forms of psychosis can experience what could be termed failures of consciousness, such that patterns of conscious thought become disordered, or that they may feel that their thoughts are being read or even controlled by others. An understanding as to how the brain generates consciousness is surely an important step in identifying what has gone wrong in these situations, and potentially how they can be remedied.

                                                                                                                                                   

Image ‘Idea and Creative Concept’ by ‘Mr Lightman’, courtesy of freedigitalphotos.net http://www.freedigitalphotos.net/images/view_photog.php?photogid=3921

References
1. Mathieson et al (2012). Photovoltaic retinal prosthesis with high pixel density. Nature Photonics, 6, 391-397. http://www.nature.com/nphoton/journal/v6/n6/full/nphoton.2012.104.html
 2. Gok, S.E., and Sayan, E. (2012) A philosophical assessment of computational models of consciousness. Cognitive Systems Research 17–18 (2012) 49–62. http://www.sciencedirect.com/science/article/pii/S1389041711000635
3 Fries, P. (2005) A mechanisms for cognitive dynamics: neuronal communication through neuronal coherence. Trends in cognitive sciences. 9(10) 474-480. http://www.sciencedirect.com/science/article/pii/S1364661305002421
4. Doesburg, S.M., Green, J.J., McDonald, J.J., and Ward, L.M. (2009). Rhythms of consciousness: Binocular rivalry reveals large-scale oscillatory network dynamics mediating visual perception. PLoS ONE 4, e6142. http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0006142
 5. Dehaene, S. and Changeux, J.P., (2011). Experimental and Theoretical Approaches
to Conscious Processing. Neuron 70, 201-227. http://www.cell.com/neuron/abstract/S0896-6273%2811%2900258-3
6. Boly, M et al (2008) Intrinsic brain activity in altered states of consciousness – How conscious is the default mode of brain function? Annals of the New York Academy of Sciences. 1129, 119-129. http://www.ncbi.nlm.nih.gov/pubmed/18591474
7. Dehaene, S. & Naccache, L. (2001) Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework, Cognition 79 1–37. http://www.jsmf.org/meetings/2003/nov/Dehaene_Cognition_2001.pdf
8. Varela, F., Lachaux, J.P., Rodriguez, E., and Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2, 229–239. http://www.nature.com/nrn/journal/v2/n4/abs/nrn0401_229a.html
9. Timmermans, B., Schilbach, L., Pasquali, A., and Cleeremans, A. (2012) Higher order thoughts in action: consciousness as an unconscious re-description process. Phil. Trans. R. Soc. B (2012) 367, 1412–1423. http://rstb.royalsocietypublishing.org/content/367/1594/1412.abstract
10. http://www.time.com/time/magazine/article/0,9171,1580394-1,00.html

The addicted brain

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Addictive behaviours include, but are not limited to, the abuse of drugs

If you’ve got the money honey, we’ve got your disease — Guns n’ Roses: Welcome to the Jungle

One of the key challenges of cognitive neuroscience is to gain an understanding of the neural mechanisms behind the various psychiatric disorders that can blight mankind. Knowledge of the how various brain mechanisms work in health, and how, and in what way, they become defective is crucial for the development of neurological treatments for such conditions. Such an approach doesn’t imply tacit acceptance of the idea that all behaviour is guided by changes in the brain, or that psychiatric problems are solely of a biological origin. Indeed it is well established that social and psychological factors can drive changes in brain function (for example purely cognitive therapies can alter the patterns of neural firing [1]). What understanding the neural basis of disease does allow, is the development of better methods of tackling such conditions at a neurological level, which is important because in many patients the social and psychological factors that have triggered their condition may prove to be either impractical or impossible for clinicians to alter (e.g. changing the structure of society).

Addiction is an extremely prevalent problem in modern society. Alcohol and opiate addictions alone are estimated to affect 15million Europeans, costing around 65 million Euros a year in both health and non-health related costs (2). Addiction can be defined as the persistent, compulsive dependence on a behavior or substance (3) and therefore spans not just drug dependencies, but also ‘behavioural addictions’ such as gambling, overeating, sex addiction and compulsive shopping (oniomania).  Although the definition of addiction is reasonably straightforward, the process of addiction needs to be broken down into its constituent cognitive parts before it can be fully understood. Addiction, and indeed all psychiatric problems, are not unitary constructs; they reflect abnormalities in several different facets of human cognition. For example unipolar depression can involve not just low mood, but also failure to respond to pleasurable experiences (anhedonia), low energy, anxiety and loss of appetite. Breaking down such conditions into their components parts is crucial if we are to be able to understand how they develop and how they can be treated. From a clinical perspective, focusing on the array of symptoms rather than the overall condition can help identify sub-types of the condition, which in turn can allow treatments to be modified to address the particular set of symptoms presented by an individual patient.

So which cognitive processes may be at fault when an individual becomes addicted? While opinions vary on this subject, in general it can be said that addiction involves abnormalities in the following interconnected processes:

  • Reward processing
  • Motivation and learning
  • Decision Making
  • Cognitive control
  • Insight

By their nature addictive behaviours have, at least initially, a rewarding effect. Moreover these effects are felt both by those who later become addicted and those that do not. Clearly therefore something in the processing of rewarding events must either change during addiction, or be naturally defective in the addicted individual. Unfortunately, while there are a number of different theories concerning how reward processing is disrupted in addiction, the exact nature of the deficiency is as yet uncertain. For example do people become sensitized to a drug and thus gradually require more to be able to maintain a balanced physiological state, or are people at risk of addiction more naturally prone to negative emotions and therefore have a greater tendency to seek out rewarding stimuli despite the risk? Despite this uncertainty around the exact nature of the cognitive deficiencies in reward processing, neurological research has revealed that experience of reward (e.g. intoxication) is strongly associated with activity within circuits of the brain that make use of the neurotransmitter dopamine (neurotransmitters are the chemicals that facilitate communication between different neurons in the brain). This dopaminergic system encompasses subcortical areas directly related to processing of motivationally relevant stimuli, such as the striatum and amygdala, as well as cortical areas such as the prefrontal cortex which are involved in the prediction of future reward, the evaluation of existing rewards and decision making (4). Various addictive drugs appear to alter the balance of dopamine within this system, usually increasing it, presumably creating the feeling of high associated with drug taking. Over the long term an ‘exhaustion’ effect may occur, whereby the brain is unable to maintain its previous tonic (standard) level of dopamine because of the effect on dopamine levels of frequent performance of the  addictive behaviour. This may then lead to the withdrawal state and to a situation where the addicted user becomes trapped in a cycle of repeating the addictive behaviour, not to achieve the high that the behaviour was initially associated with, but merely to maintain a acceptable tonic level of dopamine, thus avoid the ‘low’ that occurs with withdrawal from the behaviour.  Other neurotransmitter systems which also innervate similar brain areas, such as the noradrenergic system, also play a part in addiction, although they have in general been less widely studied regarding their role in reward processing.

Stimuli that are not directly rewarding, but are predictive or otherwise associated with the positive effects of the addictive behaviour, act to induce cravings for the addictive behaviour. The processing of such ‘addictive cues’, in comparison to similar stimuli unassociated with the addiction, tend to provoke greater activity in a wide variety of brain areas including those involved in the actual processing of reward, alongside frontal-cortical circuits involved in the regulation of thoughts and actions,  and areas involved in memory, sensory processing and the engagement of motor actions (5). This suggests that contextual factors that induce cravings can not only evoke brain activity in the reward centers of the brain, but also engage greater perceptual processing and attention, and even trigger motor activity, presumably in preparation for seeking out or performing the addictive behavior. Dysfunctions within these circuits are likely to have a knock-on effect on the processes such as learning and memory. Persistent performance of the addictive behaviour after exposure to addictive cues will lead to a strengthening of the association between the cue and the behaviour, and between both the cue and behaviour and the subsequent hedonic effects of the reward. The strengthening of such associations can lead to a behaviour that was previously under conscious control becoming habitual. The more habitual or automatic a behaviour becomes, the more effort is required to control it, and ultimately the more likely the behaviour is to be performed regardless of its utility in a particular circumstance. In short, it becomes compulsive. Indeed the ease with which a behaviour can become habitual may distinguish addicts from those who remain ‘casual users’.

In addition to the neural circuits involved in reward and learning, the frontal areas of the brain which are also activated by both the addictive behaviour itself and during craving are crucial in the process of addiction. Such areas are broadly believed to be involved in ‘cognitive control’; they act to regulate activity from the more primal, sub-cortical brain areas which are involved in motivation, emotional and learning. This effectively meaning that they provide control over thoughts and behavior. Perhaps unsurprisingly, the (partially separate) systems within the frontal cortex that are involved in decision making and in inhibiting pre-potent (i.e. habitual or natural) responses are both found to be deficient in addicted populations, thus explaining why addicts make decisions that are counter-productive to their health, even when they are fully aware of the likely consequences of their actions (8). Increasing sensitivity, or reactivity from the subcortical reward circuits, coupled with a weakening of the control exerted on them by the frontal control areas is likely to be behind the habituation of addictive behavior, and the subsequent failure to regulate that behavior. In some senses the addict (or more accurately, the frontal control areas of the addict’s brain) loses control over their instinctive behavior.

One of the most serious problems with addiction can be what is termed ‘insight’ or the ability to understand that you are ill. Lack of insight is a severe challenge for clinicians as it can be nearly impossible to effectively implement any treatment when the patient is unaware that the treatment is needed. Again frontal areas, most notably the Insula and anterior cingulate cortices, appear to be crucially involved in the lack of insight (6). The Insula is involved in monitoring internal body states (interoceptive awareness) and producing the ‘subjective experience’ relating to this. It also is involved in deriving salience from sensory information and, along with the anterior cingulate, influencing behavior accordingly (7) thus providing a crucial system for the expression of the effect of addictive cues on behaviour. Addiction-induced dysfunctions in this system may therefore lead to an inability to properly process and respond to changes in body state caused by the performance of (or withdrawal from) the addictive behavior, and may stop the individual from fully appreciating that addictive cues are provoking the cravings which are driving the addictive behaviour. Thus insight into the problematic nature of their condition is lost to the individual.

This article represents a very brief overview of the sorts of cognitive and neural structures involved in addiction. It isn’t unfortunately possible to do justice to the full scope of research into addictive behavior in a short article. What should be clear however is that drug abuse can induce changes in a multitude of different interconnected neural circuits, affecting a multitude of different cognitive functions. This effect can be somewhat different depending on the drug of abuse, but nevertheless also applies to a significant extent to non-drug addictions, implying that such neurological changes can occur without the direct influence of external chemical agents. It follows that these changes must therefore be at least partly the consequence of purely internal, cognitive shifts in the workings of the brain, which do, of course, also occur in drug based additions, thus exacerbating the natural neurochemical effects of the drug. Despite the complexity of the processes involved, increased understanding of the neurological and cognitive basis of addiction should enable, in time, more advanced and effective treatments to be designed. Future research into addiction will also hopefully enable ‘markers’ for the condition to be identified; biological or cognitive indices that predict those who are at potential risk of addiction. This in turn would improve our ability to take preventative measures to reduce the prevalence of this debilitating problem.

 

References

1) Porto, P. R., Oliveira, L., Mari, J., Volchan, E., Figueira, I., & Ventura, P. (2009). Does Cognitive Behavioral Therapy Change the Brain? A Systematic Review of Neuroimaging in Anxiety Disorders. Journal of Neuropsychiatry and Clinical Neurosciences, 21(2), 114-125. http://neuro.psychiatryonline.org/article.aspx?articleID=103678

2) Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H. U., Jonsson, B., Grp, C. S., et al. (2012). The economic cost of brain disorders in Europe. European Journal of Neurology, 19(1), 155-162.  http://onlinelibrary.wiley.com/doi/10.1111/j.1468-1331.2011.03590.x/full

3) http://medical-dictionary.thefreedictionary.com/addiction

4) Parvaz, M. A., Alia-Klein, N., Woicik, P. A., Volkow, N. D., & Goldstein, R. Z. (2011). Neuroimaging for drug addiction and related behaviors. Reviews in the Neurosciences, 22(6), 609-624. http://www.bnl.gov/medical/Personnel/Rita-Goldstein/files/Parvaz_RNS2011.pdf

5) Yalachkov, Y., Kaiser, J., & Naumer, M. J. (2012). Functional neuroimaging studies in addiction: Multisensory drug stimuli and neural cue reactivity. Neuroscience and Biobehavioral Reviews, 36(2), 825-835. http://www.sciencedirect.com/science/article/pii/S0149763411002119

(6) Goldstein, R. Z., Craig, A. D., Bechara, A., Garavan, H., Childress, A. R., Paulus, M. P., et al. (2009). The Neurocircuitry of Impaired Insight in Drug Addiction. Trends in Cognitive Sciences, 13(9), 372-380. http://www.sciencedirect.com/science/article/pii/S1364661309001466

7) Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct, 214(5-6), 655-667. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2899886/

8 ) Duka, T., Crombag, H. S., & Stephens, D. N. (2011). Experimental medicine in drug addiction: towards behavioral, cognitive and neurobiological biomarkers. Journal of Psychopharmacology, 25(9), 1235-1255.  http://jop.sagepub.com/content/25/9/1235.short

The dangers of self-report

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A common methodology in behavioural science is to use self-report questionnaires to gather data. Data from these questionnaire can be used to identify relationships between scores on the variable(s) that the questionnaire is assumed to measure and either performance on behavioural tasks, physiological measures taken during an experiment, or even scores obtained from other questionnaires (some studies just report on the correlations between batches of self-report measures!). Self-report measures are popular for a number of reasons. Firstly they represent a ‘cheap’ way (in terms of both time and cost) of obtaining data. Secondly they can be easily implemented to large samples, especially with the advent of on-line questionnaire distribution sites such as Survey Monkey. Finally they can be used to measure constructs that would be difficult to obtain with behavioural or physiological measures (for example facets of personality such as introversion). This issue of self-report methodology is important because studies that use this method are regularly reported in the media (see http://www.bbc.co.uk/news/health-17209448 for a recent example) and therefore have a significant impact on how the general public perceive scientific research. I therefore think it is important to discuss potential problems with self-report measures.

Most (but certainly not all) questionnaires that are used in behavioural research undergo  testing for reliability, to check that they produce consistent results when applied to the same population over time. More importantly they are normally also tested for validity, to check that the questionnaire measures what it claims to measure. Such tests are done following the logic that the questionnaire should be able to discriminate participants in a similar way to relevant non-self report measures. For example scores on a questionnaire measuring depression should be able to discriminate between depressed patients and controls, while scores on a questionnaire measuring diet should be able to predict the ‘Body Fat Percentage’ of respondents with reasonable accuracy. While such tests can act to increase confidence that a questionnaire is measuring what it claims to measure they are not foolproof. For example just because a depression questionnaire can discriminate between patients and controls does not mean that it measures depression well, as the two groups will likely vary in several different ways. Likewise a questionnaire that distinguishes between patients and controls may not be able to identify the (presumably) more subtle differences between depressed and non-depressed healthy individuals, or the range of depressive tendencies within the healthy population. In fact that are a large number of reasons why questionnaire may not be entirely valid, including the following:

Honesty/Image management – researchers who use self-report questionnaires are relying on the honesty of their participants. The degree to which this is a problem will undoubtedly vary with the topic of the questionnaire, for example participants are less likely to be honest about measures relating to sexual behaviour, or drug use, than they are about caffeine consumption, although it is unwise to assume, even when you are measuring something relatively benign, that participants will always be truthful. Worse, the level at which participants will want to manage how they appear will no doubt vary depending on personality, which means that the level of dishonesty may vary significantly between different groups that a study is trying to compare.

Introspective ability – Even if a participant is trying to be honest, they may lack the introspective ability to provide an accurate response to a question. We are probably all aware of people who appear to view themselves in a completely different light to how others see them. Undoubtedly we are all to some extent unable to introspectively assess ourselves completely accurately. Therefore any self-report information we provide may be incorrect despite our best efforts to be honest and accurate.

Understanding – Participants may also varying regarding their understanding or interpretation of particular questions. This is less a problem with questionnaires measuring concrete things like alcohol consumption, but is a very big problem when measuring more abstract concepts such as personality. From personal experience I have participated in an experiment where I was asked at regular intervals to report how ‘dominant’ I felt. As I can honestly say I don’t monitor my feelings of ‘dominance’ and how they change over time, I know that my responses to the question were pretty random. Even if I could conjure an understanding of what the question was getting at, it would be impossible to ensure that everyone who completed the questionnaire interpreted that question in the same way that I did.

Rating scales – Many questionnaires use rating scales to allow respondents to provide more nuanced responses than just yes/no. While yes/no questions do often appear restrictive in terms of how you can respond, using rating scales can bring their own problems. People interpret and use scales differently, what I might rate as ’8′ on a 10 point scale, someone with the same opinion might only rate as a ’6′ because they interpret the meanings of the scale points differently. There is research which suggests that people have different ways of filling out ratings scales (1). Some people are ‘extreme responders’ who like to use the edges of the scales, whereas other like to hug around the midpoints and rarely use the most outer points. This naturally produces differences in scores between participants that reflects something other than what the questionnaire was designed to measure. A related problem is that of producing nonsense distinctions. For example studies sometimes appear where participants are given a huge rating scale to choose from, for example a scale of 1-100 to rate the confidence of a decision as to whether two lines are the same length (2).  Is anyone really capable of segmenting their certainty over such a decision into 100 different units? Is there really any meaningful difference, even within the same individual, between a certainty of 86 and a certainty of 72 in such a paradigm? Any differences found in such experiments therefore run the risk of being spurious.

Response bias – This refers to individual’s tendency to respond a certain way, regardless of the actual evidence they are assessing. For example on a yes/no questionnaire asking about personal experiences, some participants might be biased towards responding yes (i.e. they may only require minimal evidence to decide on a yes response, so if an experience has happened only once they may still respond ‘yes’ to a question relating to whether they have had that experience). Alternatively other participants may have a conservative response bias and only respond positively to such questions if the experience being inquired about has happened regularly. This is a particular problem when the relationship between different questionnaires is assessed, as a correlation between two different questionnaires may simply reflect the response bias of the participants being consistent across questionnaires, rather than any genuine relationship between the variables the questionnaire is measuring.

Ordinal Measures – Almost all self-report measures produce ordinal data. Ordinal data is that which only tells you the order that units can be ranked in, not the distances between them. It is contrasted with interval data which tells you the exact distances between different units. This distinction is easiest to define by thinking of a race. The position in which each runner finishes in is an ordinal measure. It tells you who is fastest and slowest, but not the relative differences between the different runners. In contrast the finishing time is an interval measure, as it provides information relating to the relative differences between the runners. Even when the questionnaire measures something that could be measured in SI units, and is therefore theoretically an interval scale (i.e. alcohol consumption) it is doubtful whether the responses can really be treated as interval because of the problems relating to response accuracy raised above. More pertinently most self-report measures in behavioural science relate to constructs, such a personality measures, that can’t be measured in interval units and are therefore always ordinal. The problem with ordinal data is not the data itself, but the common practice of using parametric statistical techniques with such data, because these tests make assumptions about the distribution of the data that cannot be met when said data is ordinal. Deviations from such assumptions can lead to incorrect inferences being made (3) bringing the conclusions of such studies into question.

Control of sample – this has become more of an issue with the advent of online questionnaire distribution sites like Survey Monkey. Previously a researcher had to be present when a participant completed a questionnaire, now with these tools the researcher need never meet any of their participants. While this allows much bigger samples to be collected much more quickly, it does cause several concerns over the sample make up. For example there are few controls to stop the same person filling in the same questionnaire multiple times. There is also little disincentive for participants to respond with spurious responses, and there is little control over how much attention the participant pays to various parts of the questionnaire. Conversely, from personal experience, I know that sometimes it is hard to complete these questionnaires because there is no way of asking the researcher for clarification as to the meaning of various questions. Finally as the researcher has lost control over the make up of their sample, they may end up with a sample which is vastly skewed towards a certain type of person, as only certain types of people are likely to fill in such questionnaires. These issues existed even before the advent of online data collection (e.g. (4)), but collecting data ‘in absentia’ exacerbates the size of such problems.

Although there are many problems with using self-report questionnaires they will continue to be a popular methodology in behavioural science because of their utility. While it might be preferable for every variable a researcher wants to investigate to be manipulated systematically using behavioural techniques, this is in practice impossible as it would severely restrict what each individual research design could achieve, and would make certain topics effectively impossible to research. Self-report measures are therefore a necessary tool for behavioural research. Furthermore some of the problems listed above can be countered through the careful design and application of self-report measures. For example response bias can be removed by ‘reversing’ half the questions on a questionnaire so that the variable is scored by positive responses on half the questions and negative responses on the other half, thus cancelling out any response bias. Likewise statistical techniques are being devised to attempt to pick out dishonest reporting, a problem that can also be attenuated by ensuring anonymity and confidentiality of responses (e.g. the researcher leaving the room when the participant is completing the questionnaire). Given this it would be wrong to dismiss any findings that are reliant on self-report measures. However whenever you read about research where self-report measures have been used to draw conclusions about human behaviour, it is always worth bearing in mind the multitude of problems associated with such measures, and how they might impact on the validity of the conclusions that have been drawn.

(1) Austin, E. J., Gibson, G. J., Deary, I. J., McGregor, M. J., & Dent, J. B. (1998). Individual response spread in self-report scales: personality correlations and consequences. Personality and Individual Differences, 24, 421–438. http://www.sciencedirect.com/science/article/pii/S019188699700175X

(2) Balakrishnan, J. D. (1999). Decision processes in discrimination: Fundamental misrepresentations of signal detection theory. Journal of Experimental Psychology: Human Perception & Performance, 25, 1189-1206. http://psycnet.apa.org/psycinfo/1999-11444-002

(3) Wilcox, R. R. (2005). Introduction to robust estimation and hypothesis testing. Academic Press. ISBN: 0127515429

(4) Fan, X., Miller, B. C., Park, K., Winward, B. W., Christensen, M., Grotevant, H. D., et al. (2006). An exploratory study about inaccuracy and invalidity in adolescent self-report surveys. Field Methods,18, 223–244. http://fmx.sagepub.com/content/18/3/223.short

Humans as data sources!

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I have recently begun collecting data for an experiment.  Data collection is the ‘bread and butter’ of science, without it there is no data, and therefore no results, conclusions or theories. While scientists can collect data from almost anything, as I am involved in behavioural science the data I require almost always comes from people; volunteers who agree to participate in an experiment. Using human participants (volunteers were previously referred to as ‘subjects’, but this term was dropped because it suggests that the volunteer is ‘subject’ to the experiment, rather than a willing participant) as your main data source produces additional (or at least different) problems to that presented from other data sources. I presume that in natural sciences, materials are ordered from a supplier, and therefore can be (hopefully) acquired to a predetermined timescale at a predictable cost. This is not the case with using participants, whose availability depends on the willingness of the local (normally student) population to submit to your study. Likewise whereas physical data-sources presumably perform reasonably consistently (i.e. putting the same quantity of lithium into the same quantity of water will always produce similar results, as long as other relevant variables are held constant) the same cannot be said for humans. The performance of two participants, tested under identical conditions, can vary drastically, even when the participants are from very similar backgrounds. Similarly an individual participant’s performance can vary widely during an experiment as concentration and motivation fluctuate. These factors produces a large amount of variance in the resulting data that is not due to the experimental manipulations the study is designed to investigate. The consequence of this is that the amount of data that needs to be collected in order to overcome such variance, and therefore provide a valid result, increases.

The variability in human performance also generates the further problem of generalisation. How can you be sure that the participants you have used in your study provide data that can be generalised to humans in general, given that individuals vary widely on how they perform the task? Larger samples (more data collection!) can make a sample more representative, but as undergraduates are usually the easiest source of data, inevitably most studies involving humans utilise samples that are non-representative of the general population to a greater or lesser extent. You could write an entire book on the issues around sampling and generalisation (indeed many have (1)) suffice to say that when you read any behavioural science research, especially that which is weighted towards the ‘social science’ end of the spectrum, it is worth considering the sort of people who may have participated in the research, and how that may effect the results that were found.

There are other, more basic problems with using humans as a data source.  Participants may fail to show up for the study, they may fail to understand what is required of them in ways that you couldn’t predict, they may even not take the experiment seriously, making little effort or deliberately producing nonsensical data. In physical science I suspect the main problem that can occur with an experiment is equipment failure. This is also a danger with behavioural experiments, but ‘participant failure’ is often a more pressing concern.

A final issue with using humans as a data source is that any study involving humans requires ethical approval, meaning that the research design is scrutinized by a committee prior to data collection for anything that might be deemed unacceptable. Ethical procedures are in place for a good reason, as in the past certain scientists were subjecting volunteers to all sorts of unpleasant and/or morally dubious procedures in the name of science (2). However perhaps inevitably ethical checks tend towards the cautious in terms of their application. While for many behavioural and social science research, ethical approval is merely a formality, it can restrict scientific enquiry for those of us that are interested in the facets of human behaviour that can only be evoked through manipulations of the participant’s emotional state or physical comfort.

So, given that I have just spent 700 words complaining about the problems of using humans as data sources,  why have I chosen a career path which relies so heavily on collecting data from humans? Well there are some advantages of performing research on humans. Most importantly humans are (to me at least) the most interesting subject in science. You can keep your chromatography, your mutagenesis and your particle accelerators, nothing they produce will ever be as interesting to me as investigations into human mind and behaviour. The variability in human performance which causes us so many problems is actually the main reason the subject of psychology is so interesting. A second advantage to behavioural research is that it allows you to meet a lot of different people who volunteer for your study for a variety of different reasons. The fact that certain people are prepared to give up their time and submit themselves to the often unpleasant or tedious tasks that make up your research project has helped reaffirm my faith in human nature after years of working in soul-destroying office jobs. Apart form anything else, the actual data collection part of a behavioural study certainly helps to break up a research process which would otherwise mainly consist of reading journal articles and staring at a matrix of numbers on a computer screen.

I’ll be coming to the end of the data collection process soon. I will then have weeks of grappling with the resultant data to look forward to!! As a final plea, if there are any men out there who fancy participating in my research then get in contact, as I still need a few human ‘data sources’ to complete my study!

(1) Rao (2000) Sampling methodologies with applications. Chapman & Hall
(2) See the early chapters of Naomi Klein’s book “The Shock Doctrine” (Penguin, 2008) for a description of some particularly unethical experiments performed in the US.

What is cognitive neuroscience, and why should anyone care?

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I often have trouble explaining to people what I am doing for my PhD. This is not a consequence of the topic being so fiendishly complex that no-one else can understand it. Instead it comes from a fact that the area of study seems to fall between several difference subject areas. When I tell people that I am doing my PhD within the Neuroscience department I imagine this provokes images of test-tubes, microscopes and pipettes, and perhaps associations with genetics, animal testing and stem cells. In reality I have little knowledge or experience of any of these topics, having last done ‘traditional’ lab work while I was at secondary school. If you asked me to dissect something, I would probably run a mile! When I instead say that I work within the psychiatry department this probably brings up an altogether different set of images, of drug therapies, ECT and perhaps of ‘talking therapies’ such as CBT (cognitive behavioural therapy). In fact both the above statements regarding my PhD are true, as the Psychiatry department sits within the Neuroscience department, but neither appear to give an accurate impression of what I actually do.

The best description of my area of research is ‘cognitive neuroscience’, but what does this mean? Cognitive Neuroscience relates to the study of the neural basis of behaviour. Roughly, it bridges the gap between biological sciences, and behavioural sciences such as psychology and psychiatry. It attempts to determine how the brain achieves the legion of processes that it performs – crudely ‘what part of the brain does what’! Cognitive neuroscience has only been seen as a separate area of study relatively recently, partly because the advanced brain imaging techniques which the discipline now heavily relies on have only been developed within the last 30 years (according to Wikipedia the term ‘cognitive neuroscience’ itself was coined in the back of a taxi in 1979!!). However scientists from various disciplines have been trying to understand how the brain functions, using whatever methods were available, since at least the 19th century.

Cognitive Neuroscience relies heavily on work done within behavioural sciences, which have served to define how human behaviour and cognition can be classified into concepts that can be studied. Unsurprisingly therefore, cognitive neuroscience research normally involves the application of a behavioural task which has already been utilised without the use of brain imaging techniques. One question this raises is what does knowing how the brain achieves it function tell us that purely behavioural science does not?  Psychologists have been ably investigating the details of mental processes for well over a century without knowing (or even caring) what part(s) of the brain are involved. The knowledge that spatial processing is largely dependent on the Hippocampus is not necessary for studying the intricacies and individual differences in spatial processing. So what does an understanding of the neural basis of mental processes achieve?

Firstly understanding the neural basis of a mental process can help distinguish between different theories relating to how that process is performed. Behavioural data is often not sufficient to distinguish between competing theories (e.g. whether a particular process is performed in totality, or whether it is split into components processes that are dealt with separately, and whether such component processes are performed in parallel or in series). Neuroimaging data can be used to provide strong evidence in relation to these questions (1).  Secondly cognitive neuroscience can provide insight into areas of cognition that were difficult or impossible to address without neuroimaging techniques. For example much work has been done on trying to understand what the brain does ‘at rest’ (i.e. when no task is being performed, effectively ‘mind wandering’) which can allow us to understand how the brain might work as an self-contained integrative mechanism. As, by definition, non-task related mental processes can’t be manipulated systematically, it is hard to investigate these processes from a purely behavioural standpoint. Similarly neuroimaging has enabled scientists to begin to uncover the neural basis of ‘consciousness’, raising interesting questions about how our experience of the world is constructed (3). These achievements of cognitive neuroscience help elucidate the nature of human thought and behaviour, shedding light on why we act the way that we do. 

On a larger scale, understanding how the brain is able to processes such a large variety of information, and produce such a wide variety of responses, can help guide the design of artificial intelligence systems intended to mimic human abilities, facilitating advances in medicine and engineering. Finally, and perhaps most importantly, knowing how the brain produces certain responses can lead to the development of interventions to alter the functioning of the appropriate brain areas when those responses become problematic (e.g. during mental health disorders). One of the major aims of cognitive neuroscience is to identify the neural deficiencies that mark various psychiatry and neurodegenerative disorders. From this information it becomes potentially possible to identify methods of combating such deficiencies. Indeed biological interventions are being developed that can target specific brain areas, potentially offering great hope for improving the therapeutic treatment of mental disorders.  

References

(1) Jonides et al (2006). What has Functional Neuroimaging told us about the Mind? So many examples, so little space. Cortex, 42, 414-417 http://www-personal.umich.edu/~jjonides/pdf/2006_3.pdf

(2) Van den Heuval & Pol (2010) Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519-534 http://www.sciencedirect.com/science/article/pii/S0924977X10000684

(3) Dehaene & Changeux (2011) Experimental and Theoretical Approaches to Conscious Processing. Neuron, 70. 200-225 http://www.sciencedirect.com/science/article/pii/S0896627311002583