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Want to lie convincingly? Get practicing!

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Lying, the deliberate attempt to mislead someone, is a processes that we all engage in at some time or another. Indeed research has found that the average person lies at least once a day, suggesting that lying is a standard part of social interaction (1). Despite its common occurrence lying is not an automatic process. Instead it represents an advanced cognitive function; a skill that requires more basic cognitive abilities to be present before it can emerge. To lie an individual first needs to be able to appreciate the benefits of lying (e.g. a desire to increase social status) so that they have the motivation to behave deceitfully. Successful lying also requires ‘theory of mind’ or the ability to understand what another person knows. This is necessary so that the would-be liar can spot firstly the opportunity to lie, and secondly what sort of deception might be required to produce a successful lie. Finally lying also requires the ability to generate a plausible and coherent, but nonetheless fabricated description of an event. Given these prerequisites it is unlikely that we are ‘born liars’. Instead the ability to lie is believed to develop sometime between the ages of 2 and 4 (2). The fact that the ability to lie develops over time suggests that the our performance of the ‘skill’ of lying should be sensitive to practice. Do people who lie more often become better at it?

Lying is tiring!
Lying is considered more cognitively demanding that telling the truth due to the extra cognitive functions that need to be utilised to produce a lie. The idea that lying is cognitively demanding is supported both by behavioural data showing that deliberately producing a  misleading response takes longer, and is more prone to error, than producing a truthful response (3) and by neurological data showing that lying requires additional activity in the prefrontal areas of the brain when compared to truth telling (4). These observable differences between truth telling and lying allow a measure of ‘lying success’ to be created. For example a successful, or skilled liar, should be able to perform lies more quickly and accurately than a less successful liar, perhaps to the extent that there is no noticeable difference in performance between truth telling and lying in such individuals. Likewise, if the ability to lie is affected by practice, then practice should make lies appear more like the truth in terms of behavioural performance.

Practice makes perfect (but is this a lie)?
Despite the intuitive appeal of the idea that lying becomes easier with practice, much past research has failed to find an effect of practice on lying, either when measuring behavioural (3) or neuroimaging (5) markers of lying. Such results have led to the conclusion that lying may always be significantly more effortful than truth telling, no matter how practiced an individual is at deception.

A recent study (6) has re-examined this issue. They used a version of the ‘Sheffield Lie Test’ where participants are presented with a list of questions that require a yes/no response (e.g. ‘Did you buy chocolate today?’). The experiment involved three main phases. In the first, baseline phase, participants were required to respond truthfully to half the statements and to lie in response to the other half of the statements. In the middle, training phase, the statements were split into two groups. For a control group of statements the proportion that required a truthful response remained at 50% for all participants. For an experimental group of statements the proportion that required a truthful response was varied between participants. Participants either had to lie in response to 25%, 50% or 75% of these statements, thus giving the participants differing levels of ‘practice’ at lying. The final, test phase, was a repeat of the baseline phase. This design allowed two research questions to be assessed. Firstly the researchers could identify whether practice at lying reduced the ‘lie effect’ on reaction time and error rate (e.g. the increased reaction time and error rate that occurs when a participant is required to lie, compared to when they are required to tell the truth). Secondly the researchers could identify whether any reduction in the lie effect applied just to the statements on which the groups had experienced differing practice levels, or whether it also generalised to those statements where all groups had the same level of practice.

The results revealed that practice did produce an improvement in the ability to lie during the period when the training was actually taking place, and that this improvement applied to both the control statements and the experimental statements. The participants who had to lie more demonstrated reduced error rates and reaction times compared to those who had to lie less during the training phase. However in the test phase this improvement was only maintained for the set of statements where the frequency of lying had been manipulated. The group who had practiced lying on 75% of the experimental statements were no faster or more accurate at lying on the control statements than the group who had to lie in response to just 25% of the experimental statements. These results suggest that practice can make you better at lying, but this improvement is only sustained over time for the specific lies that you have rehearsed.

Some lies may be better than others!
One important criticism of most studies on the effect of practice on lying is that they tend to use questions or tasks that require binary responses (i.e. yes/no questions). However in real life lying often involves the concoction of complex false narratives,a form of lying that is likely to be far more cognitively demanding than just saying ‘No’ in response to a question whose answer is ‘Yes’. Likewise the lies tested in laboratory studies tend to be rehearsed, or at least prepared lies. In contrast many real-life lies are concocted at short notice, with the deceptive narrative being constructed in ‘real-time’, whilst the person is in the process of lying. It is likely that the effect of training, and how that training generalises to other lies, will be different for these more advanced forms of lying than it is for the more simple types of lies that tend to be tested under laboratory conditions. Given this, if a psychologist tells you that we know for certain how practice impacts on the ability to deceive, you can be sure that they are lying!

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References

(1) DePaulo, B.M., Kashy, D.A., Kirkendol, S.E., Wyer, M.M. & Epstein, J.A. (1996) Lying in everyday life. Journal of Personality and Social Psychology, 70 (5) 979-995. http://smg.media.mit.edu/library/DePauloEtAl.LyingEverydayLife.pdf
(2) Ahern, E.C., Lyon, T.D. & Quas, J.A. (2011) Young Children’s Emerging Ability to Make False Statements. Developmental Psychology. 47 (1) 61-66. http://www.ncbi.nlm.nih.gov/pubmed/21244149
(3) Vendemia, J.M.C., Buzan,R.F., & Green,E.P. (2005) Practice effects, workload and reaction time in deception. American Journal of Psychology. 5, 413–429. http://www.jstor.org/discover/10.2307/30039073?uid=3738032&uid=2129&uid=2&uid=70&uid=4&sid=21101917386241
(4)Spence, S.A. (2008) Playing Devil’s Advocate: The case against MRI lie detection. Legal and Criminological Psychology 13, 11-25. http://psychsource.bps.org.uk/details/journalArticle/3154771/Playing-Devils-advocate-The-case-against-fMRI-lie-detection.html
(5) Johnson,R., Barnhardt,J., & Zhu, J.(2005) Differential effects of practice on the executive processes used for truthful and deceptive responses: an event-related brain potential study. Brain Research: Cognitive Brain Research 24, 386–404. http://www.ncbi.nlm.nih.gov/pubmed/16099352
(6) Van Bockstaele, B., Verschuere, B., Moens, T., Suchotzki, K., Debey, E. & Spruyt, A. (2012) Learning to lie: effects of practice on the cognitive cost of lying. Frontiers in Psychology, November (3) 1-8. http://www.ncbi.nlm.nih.gov/pubmed/23226137

Neuroscientists Make Declaration On Animal Consciousness

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Scientists have officially acknowledged that birds have consciousness, and can experience emotions.

On 7/7/2012 a group of prominent neuroscientists signed a declaration supporting the view that non-human animals experience consciousness. The statement claims to be a ‘re-evaluation of previously held preconceptions’. It states that:

‘Convergent evidence indicates that non-human animals have the neuroanatomical, neurochemical and neurophysiologial substrates of conscious states, along with the capacity to exhibit intentional behaviours‘.

Unfortunately the declaration doesn’t define clearly what exactly the ‘consciousness’ they are referring to is. Instead the text switches between referring to different elements of conscious experience, such as arousal (e.g. levels of sleep and attentiveness), conscious decision making, perceptual distortions (e.g. hallucinations) and the experience of emotional states. As the concept of consciousness is a notoriously difficult one to define, the lack of an operational definition makes the declaration somewhat difficult to interpret.

A further peculiarity of the declaration is that it states something which I suspect the vast majority of scientists working in the fields of neuroscience, psychology and animal behaviour have believed for some time. Indeed I suspect a significant proportion of the ‘general public’ would accept that most animals have some level of conscious understanding, especially mammals. The declaration isn’t therefore heralding a breakthrough in scientific understanding, even if it does contradict certain religious and philosophical standpoints that propose consciousness as a uniquely human characteristic.

Despite these reservations, the declaration may prove to be of importance. It focuses on the commonalities between different animals in the neural structures supporting various conscious experiences, and discusses the implications this may have for understanding the development of consciousness through evolution. It represents an official acknowledgement that a larger range of animals experience consciousness that many may have previously believed, based off the proposition that absence of a cerebral cortex does not preclude conscious thought. Those animals considered ‘conscious’ can therefore include non-mammalian creatures such as insects and cephalopods. The declaration may hopefully lead to greater discussion of both the nature of consciousness, and the relationship between humans and other animals.  More importantly it may facilitate political changes to ensure the more humane treatment of animals.

A full text of the declaration can be found at http://fcmconference.org/img/CambridgeDeclarationOnConsciousness.pdf

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

Scientists discover bees that can reverse brain aging

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By Maria Panagiotidi

Scientists at Arizona State University have discovered that older honey bees can reverse brain aging when they take on nest responsibilities typically handled by much younger bees.

This finding could provide alternative interventions for the treatment of age-related dementia. Current research focuses mainly on potential new drug treatments.

The study was published in the scientific journal Experimental Gerontology by a team of scientists from ASU and the Norwegian University of Life Sciences, led by Gro Amdam. The researchers found that tricking older, foraging bees into doing social tasks inside the nest causes changes in the molecular structure of their brains.

Previous research on honey bees has found that bees that stay in the nest and take care of larvae – the baby bees – remain mentally competent. However, after a period of nursing, bees fly out looking for food and begin aging very quickly. The effects of aging are visible after two weeks in the appearance of the foraging bees (worn wings, hairless bodies) and more importantly, in their brain function. Specifically, these bees lose the ability to learn new things.

Influenced by recent studies on brain plasticity, Amdam and colleagues wanted to see what would happen if the foraging bees returned to the nest and took care of the larval babies again.

The results of the experiment were fascinating. After 10 days, about 50 percent of the older bees caring for the nest and larvae had significantly improved their ability to learn new things.

The change observed in the older bees was not just behavioural but also physiological; Amdam and colleagues discovered a change in proteins in the bees’ brains. After comparing the brains of the bees that improved to those that did not, they found that two proteins had noticeably changed: Prx6 and “chaperone” protein. Both proteins have been previously found to protect the brain against diseases such as Alzheimer’s.

This finding could lead to the development of a drug that could help older people maintain brain function. However, many years of basic research and trials will be needed before such a drug becomes commercially available.

For now, Amdam and colleagues propose that social interventions might help our brains stay younger. Since the proteins being researched in people are the same as those found in bees, it is possible that these proteins may be able to respond to specific social experiences. Further research is needed on mammals in order to confirm that the same molecular changes occur on other species’ brains.

 

Reference

Nicholas Baker, Florian Wolschin, Gro V. Amdam. Age-related learning deficits can be reversible in honeybees Apis mellifera. Experimental Gerontology, 2012; DOI: 10.1016/j.exger.2012.05.011

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

Can a neuroscientist read your mind?

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Are the contents of your mind really 'confidential' or will your thoughts one day be accessible to others?

Media reports into recent research have claimed that neuroscientists are now effectively able to perform ‘mind reading’. Such reporting inevitable raises ethical questions about what applications such research might eventually be put to, and, judging by some of the comments that the on-line versions of these articles have provoked, have alarmed some people regarding the eventual path that such research might take. But how accurate is the claim that neuroscientific techniques can read minds?

Early this year an article in the Guardian  ( http://www.guardian.co.uk/science/2012/jan/31/mind-reading-program-brain-words ) reported that:

‘Scientists have picked up fragments of people’s thoughts by decoding the brain activity caused by words that they hear.’

Reporting on the same experiment the Daily Mail ( http://www.dailymail.co.uk/sciencetech/article-2095214/As-scientists-discover-translate-brainwaves-words–Could-machine-read-innermost-thoughts.html ) claimed:

 ’It’s a staggering development that could have tremendous implications….judges could use mind-reading machines to find out if murder suspects are telling the truth….mind reading devices might be used to eavesdrop covertly on the most private thoughts and dreams.’

The experiment in question, conducted by Dr Brian Pasley and colleagues (1) involved the recruitment of patients who were to undergo brain surgery. The researchers placed electrodes upon the auditory areas of the brain during the period when the patients’ skulls were open and their cerebral cortex exposed. They then played the patients a sequence of different words and recorded the electrical activity generated by the auditory cortex in response to this speech. Using complex modeling procedures they were able to reconstruct the spoken words solely from the neural signals recorded by the electrodes. Furthermore they were able to successfully apply this model to the electrical responses generated by a separate set of words that had not been used in creation of the model (e.g. which were in effect ‘novel’ to the model) suggesting that the model could theoretically be applied to reconstruct any speech heard by the patient.

While these results are undoubtedly impressive, has the media coverage of them been accurate? In terms of the Guardian’s report, their claim that this represents a decoding of ‘fragments of thoughts’ seems to depend on a rather broad definition of the term ‘thoughts’. What the research did was to reconstruct auditory stimuli that the auditory cortex was in the process of analysing. What has been achieved therefore is the decoding, at a detailed level, of the perceptual process, NOT the reading of internally generated thoughts. This is a significant step away from ‘decoding thoughts’ as the  process being decoded is entirely dependent on the presentation of an external stimulus. This doesn’t therefore represent ‘mind reading’ because the same result could theoretically be achieved without reference to the brain, e.g. by taking measurements from the relevant sensory organ or by just observing the sensory stimulus itself (2). Even if the research did represent mind reading, there seems little justification for the Daily Mail’s claim that the research could lead to ‘covert eavesdropping’. It should be obvious that the methodology required not only the opening up of the participant’s skull, but also the co-operation of the participant in allowing data to be taken for the construction of the model. Furthermore what is not mentioned by either article is that the reconstructed words were not actually intelligible to a human listener, but had to be ‘recognised’ via a speech recognition algorithm (an example of the reconstructed speech can be heard here:  http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001251#s5).

Actual Mind Reading?

While the results of Dr Pasley’s study required the participant’s brains to be exposed, other neuroimaging methods are not so intrusive, and could therefore be considered closer to the covert mind-reading reported by the Mail. Magnetic Resonance Imaging (MRI) allows brain activity to be measured in a non-invasive way, so that no surgery of any kind is required (although lying down in a scanner which costs millions of pounds and is the size of a small boat, is still required, making it far from ‘covert’!). MRI studies have produced some equivalent results to that of Pasley’s study, but using visual stimuli; with images (3) and short movies (4) having been reconstructed purely from data obtained from MRI scans. Of course such results don’t represent mind reading any more than Dr Pasley’s study, since they reflect a reconstruction of external sensory information. However other MRI studies have produced results that have allowed scientists to predict processes occurring within a participant’s brain that are not directly tied to the characteristic of external stimuli. A couple of studies by Yukiyasu Kamitani and Frank Tong (5,6) have shown that models can be created that allow an observer to identify to which stimulus a participant is (covertly) attending to. In effect these studies, and others like them, use the output from the perceptual processing mechanisms of the brain to identify how ‘top-down’ influences (such as expectation and attention) are driving perception. Strictly speaking they represent mindreading as although the mental processes in question are still involved in analysing external stimuli, it is not necessarily possible to garner the information provided by the MRI data in any other way (short of asking the person themselves). This is because the ‘top-down influences’ in question arise internally from the brain, rather than being a function of the external stimulus. Neuroimaging has enabled the concept of mind reading to be taken further however, into the realms of decoding mental events that don’t rely on any external stimulation at all. Recent studies have found that it is possible to decode what broad categories of objects someone is imagining, in the absence of any coincident external stimulation (7) although the performance level of the model is reasonably modest (~ 50%). Similarly, it also appears that the results of basic decision making processes can be identified from brain activity, with decisions relating to which button to press and when to press it (8) and whether a participant in lying (9) being decipherable using models constructed in a similar way to those already described. Interestingly the neural information that allows these decisions to be decoded occurs many seconds BEFORE the decision has actually been made, highlighting how conscious actions are likely driven by brain processes that are outside conscious awareness, rather than being the result of conscious ‘free will’. Most recently such work has been extended to more complex scenarios, with MRI data being used to predict at what point in solving an algebraic problem a child is at, and whether they are performing the calculation correctly (10).

The possibility of covert mind reading?

Clearly the aforementioned examples reflect mind reading, but do they represent the top of a ‘slippery slope’ that will lead to technology that will allow the sort of covert eavesdropping envisioned by the Daily Mail? The first impediment to such technology is the process of neuroimaging itself. MRI scanners are far from being portable enough to allow forced or covert application of brain scanning. Furthermore MRI scanning involves the production of a large magnetic field and the firing of electromagnetic pulses towards the object being imaged, both functions that would be totally impractical outside a controlled, isolated environment. Other neuroimaging methods, such as EEG, function by recording the electrical remnants of brain activity from outside the skull, and are therefore cheaper and more portable than MRI. However they lack the spatial resolution that would be required for any sophisticated mind reading application, and in any case they are extremely sensitive to external noise, again making them unsuitable for use outside of controlled environments.

Even if we assume that future technological advances would allow systems to be developed that would enable covert collection brain activity data, would such technology enable your innermost thoughts to be deciphered? There are a number of reasons to doubt that this would be possible. Current mind reading models are only able to distinguish between very broad categories of thoughts, or between very coarse categories of decisions (e.g. lie/truth, attending to one or other stimulus). To be able to read the specific details of an individual’s thoughts you would need models that distinguished between the literally billions of different things that someone could be thinking about, and the multitude of different decisions that they could make. To even create such models would involve the co-operation of individuals in a data collection process that would take an incalculable length of time. Even if such data were collected, and the subsequent required level of computation to create accurate models were possible, the ability to generalize such models to the brain activity of other individuals would rely on an assumption that every person’s brain being identical in terms of where different individual thoughts and memories are stored. This seems extremely unlikely, and is in fact counter to what we know about individual differences in brain anatomy and function. Thus while it is possible to aggregate data across participant to produce mind-reading for coarse decisions, it would be impossible to replicate such a method to distinguish between more subtle categories of thought. Even in situations where co-operation of the participant is attained, and only a coarse distinction between different psychological states is required, such mind reading techniques are problematic. Taking the example of the mooted ‘MRI Lie detector’ such a system will always be somewhat unreliable because, just like the current physiological lie detectors, they could be easily deceived if the participant can train themselves to act as if the truth is a lie (or vice versa). This is because the brain activity which is associated with lying most likely relates to the emotional and cognitive processes involved in creating a false story, rather than to lying per se. It follows that simply engaging in these same emotional and cognitive processes while telling the truth should produce neural activity which mimics that produced by a lie. If even the decoding of simple decisions can be subverted easily, it would seem impossible that attempts at more subtle discriminations of different thoughts would not be subject to even greater uncertainty. Finally it is important to note that all the forms of mind reading reviewed here are the result of probabilistic calculations. The parts of the brain that are deemed active at a certain point in time are the result of statistical computations as to whether a small signal is reflective of task-related neural activity or noise. Likewise the classification of such activity as belonging to one category of thought/decision over another is also based off probabilistic inference. There is no certainty in such a process; in fact it is fraught with uncertainty.

To conclude it seems very unlikely that neuroimaging methods will ever be able to perform the sort of mind reading predicted by scare stories in the press. In some cases such methods may not even represent a particular improvement on the sort of mind reading applications that already exist. What the mind reading research discussed in this article does allow is a greater understanding of how the brain works, which in turn provides insight into how the brain achieves the myriad feats it performs so frequently with apparent ease. The most fruitful practical application of such knowledge is likely to be in the treatment of patients with brain damage. For example the limited mind reading functions possible from existing neuroimaging methods may allow technology to be developed that would allow patients who suffer from brain damage to the extent that they cannot communicate using their peripheral nervous system, some primitive form of communication through their brain activity. In contrast your private thought and memories are likely to remain safe from the prying eyes of neuroscientists!

Image (top right) courtesy of Idea Go:  http://www.freedigitalphotos.net/images/view_photog.php?photogid=809

References

(1) Pasley BN, David SV, Mesgarani N, Flinker A, Shamma SA, et al. (2012) Reconstructing Speech from Human Auditory Cortex. PLoS Biol 10(1): e1001251. doi:10.1371/journal.pbio.1001251 http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001251

(2) Tong, F. & Pratte, M.S. (2012) Decoding Patterns of Human Brain Activity. Annual Review of Psychology, 63: 483-509.  http://www.ncbi.nlm.nih.gov/pubmed/21943172

(3)  Miyawaki, Y. Uchida, H. et al (2008) Visual Image Reconstruction from Human Brain Activity using a Combination of Multi-scale Local Image Decoders.. Neuron 60, 915–929, http://iopscience.iop.org/1742-6596/197/1/012021

(4)  Nishimoto, S., Vu, A.T., et al (2011) Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies. Current Biology 21, 1641–1646 http://www.sciencedirect.com/science/article/pii/S0960982211009377

(5) Kamitani Y, Tong F. 2005. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8:679–85  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1808230/

(6) Kamitani Y, Tong F. 2006. Decoding seen and attended motion directions from activity in the human visual cortex. Curr. Biol. 16:1096–102 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1635016/

(7) Reddy, L., Tsuchiya, N. & Serre, T. (2010). Reading the mind’s eye: Decoding category information during mental imagery. Neuroimage. 50(2) 818-825  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2823980/

(8) Soon CS, Brass M, Heinze HJ, Haynes JD. 2008. Unconscious determinants of free decisions in the human brain. Nat. Neurosci. 11:543–45  http://www.nature.com/neuro/journal/v11/n5/full/nn.2112.html

(9) Davatzikos C, Ruparel K, Fan Y, Shen DG, Acharyya M, et al. 2005. Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 28:663–68  http://www.sciencedirect.com/science/article/pii/S1053811905005914

(10) Anderson, J.R. (2012) Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model algorithms. Neuropsychologia, 50(4) 487-498. http://www.sciencedirect.com/science/article/pii/S0028393211003605

 

Anxiety enhances sense of smell

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By Maria Panagiotidi

Anxious people have a heightened sense of smell, when it comes to sniffing out a threat, according to a new study by Elizabeth Krusemark and Wen Li from the University of Wisconsin-Madison in the US. The results of their study will be published online in the journal Chemosensory Perception.

The sense of smell is an essential tool for survival in animals. It allows them to detect, locate and identify predators in the surrounding environment. In fact, the olfactory-mediated defence system is so important in animals, that the mere presence of predator odours can evoke potent fear and anxiety responses.

Smells also evoke powerful emotional responses in humans. Krusemark and Li hypothesized that in humans, detection of a particular bad smell may signal danger of a noxious airborne substance, or a decaying object that carries disease. Also, they speculated that the level of response to the above could underlie phobias or anxiety related disorders.

The researchers tested their hypotheses by combining assessment of state-level anxiety, psychophysical testing, and functional magnetic resonance imaging (fMRI) techniques.  They recruited 14 young adult participants who were exposed to three types of odours: neutral pure odor, neutral odor mixture, and negative odor mixture. The participants were asked to detect the presence or absence of an odour in an MRI scanner. During scanning, the researchers also measured skin conductance response (a measure of arousal level), and monitored the subjects’ breathing patterns. After completing the odour detection task, the participants were asked to rate their current level of anxiety using a standardised clinical test.

The authors found that as anxiety levels rose, so did the subjects’ ability to discriminate negative odours accurately – suggesting a ‘remarkable’ olfactory acuity to threat in anxious subjects. The same pattern was found in the skin conductance results which showed that anxiety also heightened emotional arousal to smell-induced threats.

Krusemark and Li uncovered amplified communication between the sensory and emotional areas of the brain in response to negative odours, particularly in anxiety. This increased connectivity could be responsible for the heightened arousal to threats.

These findings could help researchers elucidate the aetiology of the unfortunate and debilitating symptoms that perpetuate anxiety disorders.

 

Reference:

Krusemark EA & Li W (2012). Enhanced olfactory sensory perception of threat in anxiety: an event-related fMRI study. Chemosensory Perception. DOI 10.1007/s12078-011-9111-7

You can find the article here: http://www.springerlink.com/content/a268t518p1x59v68/

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