Nisha Cooch, PhD – Brain Blogger http://brainblogger.com Health and Science Blog Covering Brain Topics Wed, 30 May 2018 15:00:03 +0000 en-US hourly 1 https://wordpress.org/?v=4.9.6 Waterboarding the Brain – The Neural Effects of Enhanced Interrogation Techniques http://brainblogger.com/2014/12/11/waterboarding-the-brain-the-neural-effects-of-enhanced-interrogation-techniques/ http://brainblogger.com/2014/12/11/waterboarding-the-brain-the-neural-effects-of-enhanced-interrogation-techniques/#respond Thu, 11 Dec 2014 19:32:41 +0000 http://brainblogger.com/?p=17904 The Question of Morality vs. The Question of Efficacy

The recent Senate Intelligence Committee report detailing the CIA’s use of enhanced interrogation techniques (EITs) like waterboarding has reinvigorated debate over the appropriateness of such methods for counterterrorism efforts. Many protest the use of EITs on moral or legal grounds, citing the inhumanity of the physical and emotional pain imparted by these tactics. Further corroborating protesters’ arguments is ample scientific evidence demonstrating that the aversive effects of exposure to stressors like those involved in EITs are not limited to the duration of those experiences. Instead, enduring high stress circumstances can lead to life-long struggles with post-traumatic stress disorder (PTSD), anxiety, and depression, effects that are likely mediated by physiological changes in the amygdala, an area of the brain that processes emotional information.

Given the harm associated with EITs, it is perhaps important to assess whether EITs provide the desired outcome of enhancing national security. Indeed, harming an individual to spare no one is morally distinct from harming an individual to save many. Though we may determine that neither case is acceptable, the former case is clearly less acceptable than the latter.

Unfortunately, the actual impact of EITs is not clear. Defenders of EITs claim that EITs are critical tactics that have helped the United States thwart terrorist attacks, while opponents argue that the techniques have not significantly contributed to actionable intelligence. Because there are few data that can be used to address the efficacy of EITs as they are currently employed, it may be helpful to draw upon behavioral science research to identify any potential for these EITs to fulfill their stated goal of extracting valuable information.

Are Enhanced Interrogation Techniques Effective?

What are the critical brain processes that determine whether a prisoner will divulge constructive information to his interrogators?

Memory – To convey accurate information, memory must be intact.

Some arguments against the efficacy of EITs have focused on the deleterious effects of stress and sleep deprivation on memory and have pointed to imaging studies that suggest that people suffering from PTSD have different patterns of activation in areas of the brain involved in memory compared to those without the disorder. These arguments emphasize the potential for those undergoing EITs to supply false information.

Much of the literature on the effects of stress and sleep deprivation on memory actually show that these factors influence working memory and diminish the ability to learn new information. However, it is possible that memories of the past could also be affected, particularly if those memories are complex or not stably encoded in the brain. Nonetheless, it is unlikely for stress to demolish highly engrained information. Given that such information, such as the names of family members or close friends, may be valuable for counterterrorism, arguments against the efficacy of EITs that are built on the impact of stress on memory are perhaps not highly convincing.

Executive Functions – The surrender of withheld information may be facilitated by disruption of executive functioning.

The scientific research that is probably most relevant to the efficacy of EITs is that addressing the effects of stress on executive functions. Stress significantly affects the prefrontal cortex (PFC), which is critical for these functions. Executive functions that are likely relevant for those undergoing EITs are the disciplined control over behavior and the ability to keep track of different versions of fact sets.

When the PFC is damaged, people have a harder time regulating their behavior and tend to resort to behavior that is more habitual and less goal-oriented. Thus, it could be argued that the stress-induced diminution of discipline could increase the likelihood that withheld information becomes shared. Nonetheless, there is no evidence to suggest that the extreme measures that appear to accompany EITs are necessary to induce the type of stress needed for this effect.

Motivation – Choosing to share information requires that one deem the value of sharing that information as higher than the value of not sharing that information.

If we consider the effects of EITs on motivation and valuation, the argument of their effectiveness begins to crumble. Stressful circumstances, particularly those that may be interpreted as life threatening, elicit what is known as the fight or flight response, in which the body’s physical and mental resources are focused on escaping or demolishing the perceived threat. In the case of EITs, these responses likely manifest as instincts to survive and to avoid pain.

Some researchers have suggested that those enduring EITs are motivated to talk because time spent talking is time where interrogation, and the associated stress and pain, are avoided. These researchers further claim that there is little correlation between the accuracy of information provided and the degree of pain endured. When this is the case, there is clear incentive to talk but no added value of providing truthful information. Even if we could articulate how EIT practices could incentivize the sharing of accurate information, the argument for the justification of EITs would still suffer from our inability to illustrate how these practices are superior to others that would be considered more civilized.

How Can We Ensure National Security Without EITs?

Behavioral science suggests that the potential for EITs to lead to the acquisition of accurate actionable intelligence is limited. Thus, there is great potential to develop a new system for procuring desired information that is superior to current EITs in both a moral sense and a practical sense.

A significant weakness of EITs is that they are not conducive to scientifically rigorous experimentation. Ethical considerations prevent us from conducting controlled human studies on the impact of EIT implementation, which precludes optimization of these approaches. However, research from a number of disciplines, including neuroscience, computer science, psychology, and economics provide considerable insight into how to affect decision making. If we apply this knowledge to build new innocuous systems for interrogation, we have the opportunity to collect data and optimize these systems accordingly.

In addition to providing general information on the efficacy of specific negotiation tactics, this type of research could also uncover details on how to customize interrogation to individual cases. For example, neuroscience research demonstrates that stress differentially affects male and female decision making tendencies, suggesting that different coercive approaches could be strategically targeted to men and women.

Conclusion

It is difficult to condone EITs given that the aversive neural effects do not appear to be accompanied by significant societal benefits. Further, even if we could establish moral grounds for EITs, our focus should be on how to effectively obtain the information we need to keep our citizens safe. EITs are not conducive to science-backed optimization because they do not allow for controlled experimental manipulations. On the other hand, other persuasive tactics, which may be equivalent or superior to EITs in their efficacy, can be experimentally tested and improved upon as more data are collected. Employing the scientific method to understand specific effects of different coercive techniques could allow us to more effectively acquire the information needed to keep our nation secure.

References

1          Amat, J. et al. Medial prefrontal cortex determines how stressor controllability affects behavior and dorsal raphe nucleus. Nat Neurosci 8, 365-371, doi:10.1038/nn1399 (2005).

2          Arnsten, A. F. Stress signalling pathways that impair prefrontal cortex structure and function. Nature reviews. Neuroscience 10, 410-422, doi:10.1038/nrn2648 (2009).

3          Bremner, J. D., Krystal, J. H., Southwick, S. M. & Charney, D. S. Functional neuroanatomical correlates of the effects of stress on memory. Journal of traumatic stress 8, 527-553 (1995).

4          Dias-Ferreira, E. et al. Chronic stress causes frontostriatal reorganization and affects decision-making. Science 325, 621-625, doi:10.1126/science.1171203 (2009).

5          Elbert, T. & Schauer, M. Burnt into memory. Nature 419, 883, doi:10.1038/419883a (2002).

6          Fernandez, M. et al. Brain function in a patient with torture related post-traumatic stress disorder before and after fluoxetine treatment: a positron emission tomography provocation study. Neurosci Lett 297, 101-104 (2001).

7          Ferry, B., Roozendaal, B. & McGaugh, J. L. Role of norepinephrine in mediating stress hormone regulation of long-term memory storage: a critical involvement of the amygdala. Biol Psychiatry 46, 1140-1152 (1999).

8          Flor, H. & Nees, F. Learning, memory and brain plasticity in posttraumatic stress disorder: context matters. Restorative neurology and neuroscience 32, 95-102, doi:10.3233/rnn-139013 (2014).

9          Henckens, M. J., Hermans, E. J., Pu, Z., Joels, M. & Fernandez, G. Stressed memories: how acute stress affects memory formation in humans. J Neurosci 29, 10111-10119, doi:10.1523/jneurosci.1184-09.2009 (2009).

10       Hermans, E. J. et al. How the amygdala affects emotional memory by altering brain network properties. Neurobiology of learning and memory 112, 2-16, doi:10.1016/j.nlm.2014.02.005 (2014).

11       Jones, D. Beyond waterboarding: the science of interrogation. New Scientist 205, 40-43 (2010).

12       Manikandan, S. et al. Effects of chronic noise stress on spatial memory of rats in relation to neuronal dendritic alteration and free radical-imbalance in hippocampus and medial prefrontal cortex. Neurosci Lett 399, 17-22, doi:10.1016/j.neulet.2006.01.037 (2006).

13       McNally, R. J. Psychological mechanisms in acute response to trauma. Biol Psychiatry 53, 779-788 (2003).

14       Mizoguchi, K., Kunishita, T., Chui, D. H. & Tabira, T. Stress induces neuronal death in the hippocampus of castrated rats. Neurosci Lett 138, 157-160 (1992).

15       Morgan, C. A., 3rd, Doran, A., Steffian, G., Hazlett, G. & Southwick, S. M. Stress-induced deficits in working memory and visuo-constructive abilities in Special Operations soldiers. Biol Psychiatry 60, 722-729, doi:10.1016/j.biopsych.2006.04.021 (2006).

16       O’Mara, S. Torturing the brain: On the folk psychology and folk neurobiology motivating ‘enhanced and coercive interrogation techniques’. Trends in Cognitive Science 13, 497-500 (2009).

17       O’Mara, S. On the imposition of torture, and extreme stressor state, to extract information from memory. Journal of Psychology 219, 159-166 (2011).

18       Parsons, R. G. & Ressler, K. J. Implications of memory modulation for post-traumatic stress and fear disorders. Nat Neurosci 16, 146-153, doi:10.1038/nn.3296 (2013).

19       Ray, W. J. et al. Decoupling neural networks from reality: dissociative experiences in torture victims are reflected in abnormal brain waves in left frontal cortex. Psychological science 17, 825-829, doi:10.1111/j.1467-9280.2006.01788.x (2006).

20       Reul, J. M. & Chandramohan, Y. Epigenetic mechanisms in stress-related memory formation. Psychoneuroendocrinology 32 Suppl 1, S21-25, doi:10.1016/j.psyneuen.2007.03.016 (2007).

21       Shin, L. M., Rauch, S. L. & Pitman, R. K. Amygdala, medial prefrontal cortex, and hippocampal function in PTSD. Ann N Y Acad Sci 1071, 67-79, doi:10.1196/annals.1364.007 (2006).

22       Sotres-Bayon, F., Bush, D. E. & LeDoux, J. E. Emotional perseveration: an update on prefrontal-amygdala interactions in fear extinction. Learning & memory (Cold Spring Harbor, N.Y.) 11, 525-535, doi:10.1101/lm.79504 (2004).

23       Thomaes, K. et al. Increased activation of the left hippocampus region in Complex PTSD during encoding and recognition of emotional words: a pilot study. Psychiatry research 171, 44-53, doi:10.1016/j.pscychresns.2008.03.003 (2009).

24       van den Bos, R., Harteveld, M. & Stoop, H. Stress and decision-making in humans: performance is related to cortisol reactivity, albeit differently in men and women. Psychoneuroendocrinology 34, 1449-1458, doi:10.1016/j.psyneuen.2009.04.016 (2009).

25       Vermetten, E., Vythilingam, M., Southwick, S. M., Charney, D. S. & Bremner, J. D. Long-term treatment with paroxetine increases verbal declarative memory and hippocampal volume in posttraumatic stress disorder. Biol Psychiatry 54, 693-702 (2003).

26       Xenakis, S. N. Neuropsychiatric evidence of waterboarding and other abusive treatments. Torture : quarterly journal on rehabilitation of torture victims and prevention of torture 22 Suppl 1, 21-24 (2012).

27       Yen, P. M. Waterboarding is not torture: a physician’s response. Lancet 371, 1838, doi:10.1016/s0140-6736(08)60795-3 (2008).

Image via jerryjoz / Shutterstock.

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If Brain Training Won’t Help the Elderly, What Will? http://brainblogger.com/2014/12/02/if-brain-training-wont-help-the-elderly-what-will/ http://brainblogger.com/2014/12/02/if-brain-training-wont-help-the-elderly-what-will/#respond Tue, 02 Dec 2014 12:00:16 +0000 http://brainblogger.com/?p=17650 In October, the Stanford Center on Longevity published a statement, signed by about 70 psychologists and neuroscientists, that likely shocked millions of consumers and disgruntled a number of tech companies.

The statement, which criticized brain training companies’ claims to improve cognitive abilities included the following: “We object to the claim that brain games offer consumers a scientifically grounded avenue to reduce or reverse cognitive decline when there is no compelling scientific evidence to date that they do.” Soon after the statement was released, the Dana Foundation posted an interview with Professors Walter Boot and Arthur Kramer regarding the recent scrutiny of brain training.

On the heels of this public degradation of apparently scientifically-backed strategies to enhance cognition, Aging2.0 held a pitch event in Washington, DC for start-up companies aiming to improve the lives of our aging citizens. Over 100 people assembled in downtown DC on November 7th for the event, many wondering how recent statements about brain games might undermine some of the participants’ products.

Aging 2.0 Pitch Event at Washington, DCOnly a couple of presenters claimed that their products affect the brain. However, their pitches may have been influenced by recent news that brain scientists are taking a more active role in assessing the validity of relevant claims made in the private sector. While these individuals effectively conveyed the value associated with the ability to induce certain types of neural activity, they did little to persuade us that they are actually able to do so.

One company representative, for example, alluded to the “very, very complex algorithm” the company uses to make its recommendations, while a gerontologist representing a company that deploys kits developed to stimulate the brain evaded questions about what the kit actually contains.

Brain training is estimated to be about a $1.3 billion a year industry, regardless of whether the task-specific improvements in performance that are demonstrated by brain training companies actually represent global cognitive changes that will help consumers with other tasks that are actually relevant to their daily lives. The market share that relevant companies enjoy speaks to the significant market opportunity surrounding both strategies for impacting the brain and strategies for helping the growing elderly demographic.

Though the recent statement put out by scientists may deter some young companies from attempting to create products that are grounded in hard science, science does provide some clues for addressing complaints commonly made by older members of society. For instance, cognitive decline is associated not only with a reduction in mental exercise but also with reductions in physical activity, sleep, and proper nutrition. Nutrition in particular is often overlooked as a critical component of brain health, even though certain nutrients are required for the maintenance and growth of the very brain cells that support cognition. In addition, brain changes that occur with age do much more than promote cognitive decline. They also alter peoples’ moods and behaviors, and helping Grandma feel less depressed may improve her quality of life more than helping her remember where the remote control is.

As the leaders of Aging 2.0 and several participants in Friday’s event pointed out, the brain is not the only worthy target for start-ups interested in helping the elderly. Quality of life results from a combination of factors, including mental and physical health, mobility, and social engagement. Each of these factors was addressed by at least one company at Aging 2.0’s event. The winner of the event, Luna Lights, prevents and detects falls.

Though the companies that pitched ideas more relevant to the social aspects of older peoples’ lives did not get as much attention from panel members, much of the panel conversation that followed pitch presentations focused on ways to reduce the isolation that often comes with aging. Anne Marie Kilgallon, the AARP’s Director of Corporate Relations and Business Development emphasized that technology is a “hugely important way to connect people.”

Sandra Timmermann, a gerontologist, reiterated Ms. Kilgallon’s point, pointing out that older women don’t have the same community networks they used to have when they were more likely to have lived in the same town for years and years. Technology, she said, can provide means for companionship.

The panel also talked about the potential for technology to support older folks’ general desire to stay at home as they age. Even better, the panel agreed, would be for people to start sharing houses, as such a solution would provide companionship, as well as safety and economic benefits. Andrew Carle, Executive Director of George Mason University’s Program in Senior Housing Administration, said that this idea is particularly appealing because baby boomers like to congregate more than members of previous generations did.

Regardless of how companies choose to address the needs of our aging citizens, Scott Collins, President and CEO of Link-age, said that he likes to invest in companies that use technology as enablers for the elderly. The mission of such companies is likely well-aligned with that of Aging2.0.

Stephen Johnston, Aging2.0 Co-Founder, explained that he believes we need to shift our mindsets from Aging1.0, where the older generations represented a problem dealt with by the government, to Aging2.0, where we recognize the opportunity to create a “holistic community that connects medical, lifestyle, financial services, etc… that make life worth living.” Creative applications of science and technology should certainly bolster this opportunity.

Image via Firma V/Shutterstock.

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Halloween Special – Why Does the Brain Love a Scary Holiday? http://brainblogger.com/2014/10/31/halloween-special-why-does-the-brain-love-a-scary-holiday/ http://brainblogger.com/2014/10/31/halloween-special-why-does-the-brain-love-a-scary-holiday/#respond Fri, 31 Oct 2014 11:00:34 +0000 http://brainblogger.com/?p=17457 Why is a holiday filled with creepy ghosts, goblins, and haunted houses so much fun? Research in neuroscience may provide some answers.

The Department of Biological and Clinical Psychology recently teamed with the Institute of Diagnostic and Interventional Radiology at the Friedrich Schiller University of Jena, Germany, in an attempt to understand what happens in our brains when we view scary scenes.

While scanning subjects’ brains using functional magnetic resonance imaging (fMRI), the researchers showed people threatening clips from movies such as Aliens, Jaws, The Exorcist, The Shining, and Silence of the Lambs, as well as neutral scenes that do not normally illicit fear responses. Researchers also collected information on each subject’s tendency to seek out scary scenarios, as well as their reactions to the scenes to which they were exposed.

Compared to those who do not like getting spooked, those who seek out eerie circumstances showed less activity in the thalamus when exposed to neutral scenes and more activity in the visual cortex when exposed to scary scenes. The pattern of activity in these brain regions, involved in sensory processing, suggests that the enjoyment of scary situations may be partially explained by the enriched sensory experience that accompanies them.

Interestingly, the amygdala, which contributes to emotional processing, was not differentially activated by scary versus neutral scenes in either group. In fact, activity in the prefrontal cortex, important for executive functions, was the only brain area whose activity correlated with subjects’ reported anxiety during exposure to scary movie scenes.

Hyperactivity in the prefrontal cortex is associated with certain anxiety disorders that involve rumination, like obsessive-compulsive disorder. The worry that is experienced in these disorders may be physiologically and psychologically similar to the feelings invoked by scary movies, which would help explain the heightened prefrontal activity that is experienced during anxiety-provoking movie clips.

This type of neural activity is different from activity seen in disorders that involve intense fear, such as panic disorder or phobias, wherein prefrontal activity is actually diminished. Reduced prefrontal activity disinhibits the amygdala (which receives information from the prefrontal cortex) and leads to fear responses that are not as likely to be observed in a movie theater or a haunted house. Unlike what we typically observe in movie theaters or on Halloween, people exposed to phobia cues show extreme avoidant or escape behaviors, as if they were enduring a real biological threat.

Our brains are so sophisticated that they can usually distinguish situations that appear threatening from those that actually are. Though the scary scenes we see on Halloween may resemble scenes that would be threatening in other contexts, we are able to quiet our anxiety because other parts of our brain tell us that we are not in danger. But apparently the sensation elicited by stimulation that bears some likeness to threat can be lots of fun.

Happy Halloween!

References

Gilmartin, M., Balderston, N., & Helmstetter, F. (2014). Prefrontal cortical regulation of fear learning Trends in Neurosciences, 37 (8), 455-464 DOI: 10.1016/j.tins.2014.05.004

Kaufmann, C., Beucke, J., Preuße, F., Endrass, T., Schlagenhauf, F., Heinz, A., Juckel, G., & Kathmann, N. (2013). Medial prefrontal brain activation to anticipated reward and loss in obsessive–compulsive disorder NeuroImage: Clinical, 2, 212-220 DOI: 10.1016/j.nicl.2013.01.005

Roozendaal, B., McEwen, B., & Chattarji, S. (2009). Stress, memory and the amygdala Nature Reviews Neuroscience, 10 (6), 423-433 DOI: 10.1038/nrn2651

Sladky, R., Höflich, A., Atanelov, J., Kraus, C., Baldinger, P., Moser, E., Lanzenberger, R., & Windischberger, C. (2012). Increased Neural Habituation in the Amygdala and Orbitofrontal Cortex in Social Anxiety Disorder Revealed by fMRI PLoS ONE, 7 (11) DOI: 10.1371/journal.pone.0050050

Straube, T., Preissler, S., Lipka, J., Hewig, J., Mentzel, H., & Miltner, W. (2009). Neural representation of anxiety and personality during exposure to anxiety-provoking and neutral scenes from scary movies Human Brain Mapping DOI: 10.1002/hbm.20843

Image via g-stockstudio / Shutterstock.

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Thinking Slow About Thinking Fast – Part IV – A New Perspective on the Framing Effect http://brainblogger.com/2014/09/27/thinking-slow-about-thinking-fast-part-iv-the-framing-effect/ http://brainblogger.com/2014/09/27/thinking-slow-about-thinking-fast-part-iv-the-framing-effect/#respond Sat, 27 Sep 2014 11:00:16 +0000 http://brainblogger.com/?p=17162 Our tendency to choose options that appear less valuable than alternative options (such as choosing to stick with our original choice in the Monty Hall Problem) is often cited as evidence for our irrationality. However, the view that we are irrational derives too from inconsistency in our preferences.

Nobel Prize winner, Daniel Kahneman, and his colleague, Amos Tversky, described such inconsistency with the following example of the Framing Effect:

In response to the news that a disease outbreak is expected to kill 600 people, 2 programs are proposed:

  • Program 1 allows 200 people to be saved
  • Program 2 provides a 33.3% chance that 600 people will be saved and a 66.67% chance that 0 people will be saved

If asked to choose the superior program, which would you choose? Most people say Program 1, which ensures the safety of 1/3 of those in danger.

Imagine that instead of Programs 1 and 2, these programs are offered:

  • Program A results in the death of 400 people
  • Program B provides a 33.3% chance that no one will die and a 66.67% chance that 600 people will die

Which option do you prefer? A or B? People tend to choose Program B.

If we examine the programs, it is clear that Programs 1 and A are equivalent, and Programs 2 and B are equivalent, but people reliably choose different options depending on how the options are framed. That our preferences in these scenarios are inconsistent makes us, by several definitions, irrational.

But which program is actually superior? 1/A or 2/B? The question is perhaps philosophical, as there is no way to determine which choice will save the most lives/result in the least deaths. As economists point out, the options are equivalent from a utility perspective. Just as in Program 1, the expected average outcome in Program 2 is 200 lives saved (33.3% x 600 people). Similarly, 400 deaths is the expected average outcome of both Program A and Program B (66.67% x 600 deaths). This analysis leads to 2 essential questions:

(1) If all options are equivalent, why don’t we choose Programs 1/A  as often as we choose Programs 2/B?

(2) If all options are equivalent, does our vulnerability to frames carry any significant implications?

Let’s start with Question 1.

When there is no difference in the value of options, we expect choice to occur randomly. Accordingly, we should expect people to choose Program 1/A half the time and Program 2/B half the time. Many scholars point to psychological factors to explain why our behavior deviates from this prediction. Specifically, they describe us as “risk averse” with respect to gains and “risk seeking” with respect to losses. In this context, “risk” means having an uncertain outcome. In the positive frame above (“lives saved”), psychologists say we are risk averse because we choose the option where the outcome is certain (save 200 people). On the other hand, when the frame is negative (“deaths”), we appear risk seeking because we choose the uncertain outcome.

I think that the pattern of our choices illustrated through the Framing Effect is actually quite practical, considering the computational complexity of risk and our use of rules, or heuristics, to facilitate decision making. Heuristics such as “guarantee gains” and “avoid guaranteed losses” would reliably result in asymmetric preferences that have led researchers to conclude that we feel losses more deeply than we feel gains (more on this when we discuss the Endowment Effect).

In the context of the Framing Effect, considering whether to save some lives or take a gamble that involves a chance that no one is saved, a “guarantee gains” approach makes Program 1 the obvious choice. When the frame is switched so that the focus is on loss of lives rather than the saving of lives, an “avoid guaranteed losses” heuristic makes Program B (the risky option) appear superior. Conscious application of such heuristics is even plausible in the Situation Room (“You have to save people” ; “You can’t just let people die.”)

Question 2.

Does our vulnerability to frames even matter if outcomes are roughly equivalent? Rather than exposing a weakness in our decision making abilities, I think that the Framing Effect demonstrates our adaptive tendency to save time and energy when expending those resources would be fruitless. If outcomes are roughly equivalent, then allowing frames to focus us on an aspect of choice for the sake of efficient decision making should not be detrimental. Indeed, when the value of outcomes differs significantly, frames tend to have less of an impact on our choices.

Image via Sandro Donda / Shutterstock.

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Thinking Slow About Thinking Fast – Part III – The Monty Hall Problem http://brainblogger.com/2014/08/20/thinking-slow-about-thinking-fast-part-iii/ http://brainblogger.com/2014/08/20/thinking-slow-about-thinking-fast-part-iii/#respond Wed, 20 Aug 2014 11:00:42 +0000 http://brainblogger.com/?p=16990 To wrap our minds around human behavior it’s helpful to consider why certain behaviors may have evolved. Natural selection tells us that behaviors that increase our chances of passing along our genes will continue to show up in future generations. It therefore follows that aspects of our behavioral tendencies at some point likely conferred an advantage over alternative behaviors. Efficiency may be the specific advantage afforded to us by our so-called irrational behaviors.

Before delving into classic examples of “irrational” behavior, I’d like to share my favorite example of how our brains are not built to choose optimally in certain scenarios wherein choosing optimally requires a large amount of energy.

In 1990, a magazine columnist named Marilyn vos Savant was approached with a question about the best strategy to use on the game show Let’s Make A Deal. On the show, a player is told that there is a large prize behind one of three doors, and if he chooses the correct door, he wins the prize. Once the player has selected a door, the host opens one of the two remaining doors, revealing no prize behind that door. The host then gives the player the option to change his guess as to which door leads to the prize.

If you were a contestant, would you stick with your original choice or switch your choice at the last minute, given the opportunity? According to Marilyn, switching to the other door would make you more likely to win the prize. Don’t believe it? Neither did dozens of mathematicians who read Marilyn’s response. But it turns out that Marilyn was right, and here’s why:

When the player originally chooses a door, there is a 1/3 probability that he has chosen the door that will win him the prize and a 2/3 probability that he has not. The key to the problem is that the host will always open a door that does not lead to the prize. Thus, regardless of where the prize actually is, the host, by eliminating one of the two doors that does not lead to the prize, gives the player the opportunity to switch their bet from a 1/3 probability of winning the prize to a 2/3 probability of winning.

So – the “rational” choice in Let’s Make A Deal is to switch doors before the door with the prize is revealed. But our brains do not readily identify the advantage associated with the switch. We have to almost turn off our fast brain to grasp the solution to this problem, which has come to be known as The Monty Hall Problem. Before Marilyn’s publication, Let’s Make A Deal players reliably defaulted to their original choice – an example of what is known as the “status quo bias”.

Why are there scenarios in which we tend to choose the seemingly less optimal option? My suspicion is that the relative advantage afforded by the “optimal” option does not outweigh the energy cost associated with fully assessing the options, particularly in light of the fact that the option we choose against in these scenarios does not guarantee a gain (remember, switching doors increases one’s chances of winning the prize but does not guarantee a win).

If we employ a rule of thumb, or heuristic, such as “go with your gut”, that can be applied habitually to all choice situations where outcomes are probabilistic, we can save an amount of energy that may be more valuable than forgone gains. And indeed, choosing the status quo is associated with increased activity in the parts of the brain that are active while performing habits, while choosing against the status quo activates the parts of the brain important for slow, laborious thinking. It therefore usually takes more effort and energy to NOT choose the status quo.

One last consideration – before moving on to the Framing Effect in the next post – is why our brains did not evolve to more efficiently identify advantageous choices like switching doors in Let’s Make A Deal. Throughout human history, we have dealt with the inundation of stimuli, the meaning of which we have learned through experience. However, gambling-type choices with known risk probabilities represent modern choices without obvious prehistoric analogues. Our brains thus likely did not evolve to assess such choices with high precision. Perhaps they would have had our ability to acquire food and other resources depended on identifying subtle statistical advantages.

Image via Designsstock / Shutterstock.

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Thinking Slow About Thinking Fast – Part II http://brainblogger.com/2014/08/11/thinking-slow-about-thinking-fast-part-ii-2/ http://brainblogger.com/2014/08/11/thinking-slow-about-thinking-fast-part-ii-2/#respond Mon, 11 Aug 2014 11:00:53 +0000 http://brainblogger.com/?p=16909 The idea that we are irrational stems from several observations where our decisions are inconsistent with those that would maximize gains. In other words, we often choose options that appear less beneficial than alternative options.

Though irrationality implies lack of logic or order, the behaviors we deem irrational actually have order and follow reliable patterns. Many animal species too display the same type of “irrational” behaviors that humans do, suggesting that the nature of our decision making may have evolutionary roots.

Daniel Kahneman recently described our decision making processes in his book Thinking Fast and Slow. Though many choices can rely on fast thinking, occurring almost automatically and requiring little attention, other decisions require slow, deliberative thinking. For example, when moving to a new house in an unfamiliar neighborhood, we have to pay attention to how to get to the house. We’ll likely follow directions or use a map, which requires focus.

Different parts of the brain mediate choices resulting from focused attention and those that are habitual. Slow thinking engages the prefrontal areas, which are evolutionarily newer parts of the brain and involved in executive functions, whereas fast thinking relies on deeper, more primitive brain structures.

With repetition, choices that once required slow thinking can come to rely on fast thinking. For instance, though learning the directions to our new house will require mental effort in the beginning, eventually we’ll be able to navigate to and from our home while conversing with others, singing along to music, or planning our day. As our drive home becomes automatic, or habitual, the deeper parts of our brains that mediate fast thinking will take over, freeing up the frontal areas to focus elsewhere.

Efficiency is therefore a significant benefit of habits. The downside to habits is their lack of flexibility. Once we’ve driven home from work hundreds of times, we may find ourselves on our normal route on a day when we were supposed to do a favor for a friend on the other side of town. Strong habits do not allow much incorporation of new information, and so our reliance on them does not always result in optimal outcomes.

Our behavior is a balance of slow thinking, which improves the likelihood of making optimal decisions, and fast thinking, which enhances our decision making efficiency. When we use our slow brain to analyze our choices, we identify behaviors that appear irrational because they are not the choices a purely slow brain would make. But given the benefit of efficiency, these choices are not particularly surprising.

As we go through a number of examples of our so-called irrational behavior, it should become apparent that our behavior is a reasonable product of a system balancing the goal of accuracy with the goal of practicality. That our choices are often “predictably irrational” suggests to me that, by some measure, these choices are the most valuable choices. This notion becomes more intuitive when we consider time and energy as innately valuable.

Image via CWB / Shutterstock.

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Thinking Slow About Thinking Fast – Part I http://brainblogger.com/2014/07/03/thinking-slow-about-thinking-fast-part-i/ http://brainblogger.com/2014/07/03/thinking-slow-about-thinking-fast-part-i/#comments Thu, 03 Jul 2014 11:00:47 +0000 http://brainblogger.com/?p=16788 In recent years, our so-called irrational behavior has become a popular topic, and research from behavioral economics, psychology, and neuroscience has begun to be applied to marketing, finance, and political science. At the same time, many consumers and citizens, reading popular books such as Predictably Irrational, Thinking Fast and Slow, and Nudge, have begun to appreciate that their own decision making may be influenced in ways they had not before recognized.

Though a heightened understanding of the nature of human behavior has the potential to improve certain industries, translation of information about decision making to effective business strategies and governmental policies is in its infancy. So too is consumer education on how to use such information to make more effective choices in day-to-day life.

Over the past few decades, elegant experiments have helped determine the ‘whats’ of our behavior (i.e. what we do in certain situations) and continue to delineate both the factors that affect our behavior and the patterns of the behaviors themselves. As we attempt to operationalize this information, we may be aided by some understanding of the ‘why’ of our behavior.

In the coming months, I’d like to discuss some of these behavioral observations from the perspective of a neuroscientist. I will argue that if we contemplate what may be surprising behavior in light of the operations of our brains, the behaviors are not as irrational as they may seem. I will discuss topics including the framing effect, the status quo bias, and that popular idea that we are risk averse with respect to gains and risk seeking with respect to losses.

My aim is to demonstrate that though these effects refer to behaviors that occur in distinct contexts, they are all manifestations of a single principle: the brain has a limited computational capacity. If we consider potential strategies for dealing with the massive amount of information with which we are constantly bombarded, we may come to view our predictable behavior as the rational result of a system that prioritizes efficiency over precision.

References

Ariely, D. (2008) Predictably Irrational. New York: HarperCollins.

Kahneman, D. (2011) Thinking Fast and Slow. New York: Farrar, Straus, and Giroux

Thaler, R.H. and Sunstein, C.R. (2008) Nudge: Improving Decisions About Health, Wealth, and Happiness. London: Penguin Books

Image via ra2studio / Shutterstock.

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Girls Avoiding STEM – What Neural Sex Differences Can and Cannot Tell Us http://brainblogger.com/2014/06/15/girls-avoiding-stem-what-neural-sex-differences-can-and-cannot-tell-us/ http://brainblogger.com/2014/06/15/girls-avoiding-stem-what-neural-sex-differences-can-and-cannot-tell-us/#respond Sun, 15 Jun 2014 11:00:47 +0000 http://brainblogger.com/?p=16634 Despite the projected rise in job opportunities in science, technology, engineering, and mathematics (STEM) in coming years, students in the United States trail several other nations in their performance in these disciplines. Moreover, the lack of women pursuing careers in these fields has been raising concern across America. Identifying the reasons contributing to the relative absence of women in STEM positions and developing relevant solutions could help bolster the United States’ presence in a world rapidly increasing its reliance on technology.

Though research has consistently demonstrated sex differences in performance in specific cognitive tasks, analysis of standardized test scores from recent years suggests that men and women have a similar understanding of mathematical principles. Accordingly, girls and boys appear to perform comparably in mathematics during their primary school years.

However, functional magnetic resonance imaging (fMRI) studies have shown differences in neural activation patterns in men and women during performance of cognitive tasks in which they perform at equal levels. Such findings are perhaps not surprising given the sex differences in the functional organization of the brain and the observations that males and females use different cognitive strategies during learning.

Whereas the neural differences in men and women may not underlie differences in mathematical ability, they may partly explain the difference in the tendency for men and women to pursue careers in STEM. Certain sex differences appear to be present from birth and are thus not attributable to cultural influences. For example, newborn girls spend more time gazing at human faces, whereas newborn boys spend more time gazing at mechanical objects. These observations are consistent with the enhanced tendency for females to process emotional information and to perform better in cognitive tasks that incorporate emotional and social information. The way such information differentially influences male and female learning is likely a result of evolution. Indeed, it has been demonstrated even in bees that reproductive success, in females specifically, is enhanced by cooperative abilities that rely on perceiving and engaging in complex social behaviors.

Though cultural factors are often cited as deterrents for women’s entrance to STEM careers, the asymmetry in male and female representation in STEM jobs likely results from both cultural and biological influences. That the mechanisms of cognition and learning are different in males and females suggests that academic performance for members of each sex may differ based on how individuals are taught.

Accordingly, the adaptation of STEM courses to facilitate female learning may affect girls’ enthusiasm and perceived competence in STEM. Understanding the nature of biological and social influences on female selection of STEM careers and creating interventions to mitigate these factors are essential for our ability to keep up with other economies.

References

Amdam GV, Csondes A, Fondrk MK, & Page RE Jr (2006). Complex social behaviour derived from maternal reproductive traits. Nature, 439 (7072), 76-8 PMID: 16397498

Bell, E., Willson, M., Wilman, A., Dave, S., & Silverstone, P. (2006). Males and females differ in brain activation during cognitive tasks NeuroImage, 30 (2), 529-538 DOI: 10.1016/j.neuroimage.2005.09.049

Cahill L (2003). Sex- and hemisphere-related influences on the neurobiology of emotionally influenced memory. Progress in neuro-psychopharmacology & biological psychiatry, 27 (8), 1235-41 PMID: 14659478

Connellan, J., Baron-Cohen, S., Wheelwright, S., Batki, A., & Ahluwalia, J. (2000). Sex differences in human neonatal social perception Infant Behavior and Development, 23 (1), 113-118 DOI: 10.1016/S0163-6383(00)00032-1

Frings L, Wagner K, Unterrainer J, Spreer J, Halsband U, & Schulze-Bonhage A (2006). Gender-related differences in lateralization of hippocampal activation and cognitive strategy. Neuroreport, 17 (4), 417-21 PMID: 16514369

Hedges LV, & Nowell A (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science (New York, N.Y.), 269 (5220), 41-5 PMID: 7604277

Huhman, H R. (2012). STEM fields and the gender gap: Where are the women? Forbes.

Jaušovec N (2012). Sex differences in event-related potential components during the solution of complex mental rotation tasks. Neuroreport, 23 (6), 360-3 PMID: 22357397

Kenney-Benson GA, Pomerantz EM, Ryan AM, & Patrick H (2006). Sex differences in math performance: The role of children’s approach to schoolwork. Developmental psychology, 42 (1), 11-26 PMID: 16420115

Kimball, M. (1989). A new perspective on women’s math achievement. Psychological Bulletin, 105 (2), 198-214 DOI: 10.1037//0033-2909.105.2.198

Lachance JA, & Mazzocco MM (2006). A longitudinal analysis of sex differences in math and spatial skills in primary school age children. Learning and individual differences, 16 (3), 195-216 PMID: 20463851

Lindberg SM, Hyde JS, Petersen JL, & Linn MC (2010). New trends in gender and mathematics performance: a meta-analysis. Psychological bulletin, 136 (6), 1123-35 PMID: 21038941

Linn MC, & Petersen AC (1985). Emergence and characterization of sex differences in spatial ability: a meta-analysis. Child development, 56 (6), 1479-98 PMID: 4075870

Pollack, E. (2013). Why are there still so few women in science? New York Times.

Shaywitz, B., Shaywltz, S., Pugh, K., Constable, R., Skudlarski, P., Fulbright, R., Bronen, R., Fletcher, J., Shankweiler, D., Katz, L., & Gore, J. (1995). Sex differences in the functional organization of the brain for language Nature, 373 (6515), 607-609 DOI: 10.1038/373607a0

Swanson, W H. (2014). Meeting the STEM challenge. US News

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Why Can’t We Stop Eating? http://brainblogger.com/2014/04/22/why-cant-we-stop-eating/ http://brainblogger.com/2014/04/22/why-cant-we-stop-eating/#comments Tue, 22 Apr 2014 11:30:35 +0000 http://brainblogger.com/?p=16341 We have long known the simplest recipe for weight loss: eat less and exercise more. Yet despite our understanding of the causes of weight fluctuation and the serious health risks associated with obesity, our collective weight continues to rise.

Researchers have suggested several potential culprits for the ‘obesity epidemic,’ including genetic predisposition, lack of education, and cultural incentives for unhealthy behaviors (such as time and cost savings). However, none of these factors provides a thorough explanation of the problem. Indeed, most of us, regardless of our specific genes, knowledge, and cultures, can relate to the desire to eat too much, and many of us indulge in this temptation, despite our rational understanding of the disadvantages of doing so.

Though there are likely several factors that lead to obesity, it is generally accepted that the most direct cause for obesity is excessive consumption. Thus, the most universal solution for obesity may intervene in the decision making process by modifying feeding choices. A prerequisite for such a solution is an understanding of the neural mechanisms underlying our decisions regarding food.

In response to the complexity of obesity and to the recent controversial declaration by the American Medical Association (AMA) that obesity is itself a disease, Nature featured a comprehensive Outlook issue on the ‘disease’ this month. The collection of articles closed with a discussion of the neural circuits involved in appetite, the elucidation of which has been greatly facilitated in the past few years by the development of optogenetic techniques, which allow for the activation and inactivation of individual neurons using light.

According to Bradford Lowell, a neuroscientist at Beth Israel Deaconess Medical Center in Boston, the hypothalamus, which has long been recognized as the feeding center of the brain, is “a tangle of circuits that look like a Jackson Pollock painting.” The Nature discussion on the physiology of appetite included an explanation of agouti-related peptide (AgRP) neurons and pro-opiomelanocortin (POMC) neurons in the hypothalamus, which stimulate and suppress appetite, respectively. In the 1990s, scientists demonstrated that knocking out the genes for individual appetite stimulator peptides does not affect eating behavior or weight. Further, though full destruction of the AgRP nucleus in mice results in starvation, GABA receptor stimulation of the parabrachial nucleus, which communicates directly with the hypothalamus, reinstates food consumption. It thus appears that mammals have evolved some neural redundancy that increases the chances that we remember to eat.

Nonetheless, the circuitry underlying appetite can be altered such that eating becomes excessive or absent. For example, loss of oxytocin neurons in the hypothalamus produces an insatiable appetite, as observed in Prader-Willi syndrome, whereas stimulation of calcitonin gene-related protein (CGRP) neurons in the parabrachial nucleus of mice leads to starvation. The latter examples illustrates one challenge in dealing with obesity: unlike with drugs of abuse, abstinence from food is not a viable solution to problematic indulgence.

Some researchers believe that our understanding of the molecules that signal hunger and satiety information will facilitate the development of drugs that are effective for eating disorders. However, I believe there are limitations to this approach. First, the sensation of hunger often indicates a physiological need. We can consume many (‘empty’) calories without satiety if the calories do not consist of appropriate amounts of protein. Thus, pharmacologically blocking hunger or inducing satiety may prevent consumption of important nutrients and exacerbate health problems. Second, much of our problematic eating is not actually a response to hunger. In other words, we often eat not to eliminate the aversive sense of hunger, but to reap the rewarding effects of food that are independent of nourishment. Enhancing satiety would likely have little impact on this type of consumption.

Though the integration of reward signals in the hypothalamus is mentioned in the Outlook issue of Nature, the implications of the rewarding effects of food consumption are largely ignored. Nonetheless, the impact of high sugar and high fat foods on reward centers of the brain is reminiscent of that of drugs of abuse. Like drugs, these foods enhance dopamine release in the midbrain, thereby increasing the likelihood that such foods will be consumed again in the future. Drug addicts and compulsive eaters also both display structural abnormalities in the prefrontal cortex (PFC), which is important for executive control. Accordingly, both populations tend to have difficulty controlling their consumption, despite the negative consequences associated with it. Such behavior is consistent with the abundance of research suggesting that changes in PFC render us more vulnerable to reward and habitual behaviors and that those with such changes tend to choose immediate gratification over long-term accomplishments.

Much of society’s food consumption results from reward signals that are not nutritionally informative. Further, behaviors resulting from these reward signals reinforce unhealthy choices. Targeting these signals, rather than hypothalamic signals that indicate nutritional status, may therefore be more effective in reducing problematic eating and promoting overall health.

References

Atasoy, D., Betley, J., Su, H., & Sternson, S. (2012). Deconstruction of a neural circuit for hunger Nature, 488 (7410), 172-177 DOI: 10.1038/nature11270

Carter ME, Soden ME, Zweifel LS, & Palmiter RD (2013). Genetic identification of a neural circuit that suppresses appetite. Nature, 503 (7474), 111-4 PMID: 24121436

Dalley JW, Everitt BJ, & Robbins TW (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron, 69 (4), 680-94 PMID: 21338879

DiLeone RJ, Taylor JR, & Picciotto MR (2012). The drive to eat: comparisons and distinctions between mechanisms of food reward and drug addiction. Nature neuroscience, 15 (10), 1330-5 PMID: 23007187

Elias MF, Elias PK, Sullivan LM, Wolf PA, & D’Agostino RB (2003). Lower cognitive function in the presence of obesity and hypertension: the Framingham heart study. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity, 27 (2), 260-8 PMID: 12587008

Erickson JC, Clegg KE, & Palmiter RD (1996). Sensitivity to leptin and susceptibility to seizures of mice lacking neuropeptide Y. Nature, 381 (6581), 415-21 PMID: 8632796

Guegan, T., Cutando, L., Ayuso, E., Santini, E., Fisone, G., Bosch, F., Martinez, A., Valjent, E., Maldonado, R., & Martin, M. (2013). Operant behavior to obtain palatable food modifies neuronal plasticity in the brain reward circuit European Neuropsychopharmacology, 23 (2), 146-159 DOI: 10.1016/j.euroneuro.2012.04.004

Gunstad J, Paul RH, Cohen RA, Tate DF, Spitznagel MB, & Gordon E (2007). Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Comprehensive psychiatry, 48 (1), 57-61 PMID: 17145283

Maayan L, Hoogendoorn C, Sweat V, & Convit A (2011). Disinhibited eating in obese adolescents is associated with orbitofrontal volume reductions and executive dysfunction. Obesity (Silver Spring, Md.), 19 (7), 1382-7 PMID: 21350433

Ollmann MM, Wilson BD, Yang YK, Kerns JA, Chen Y, Gantz I, & Barsh GS (1997). Antagonism of central melanocortin receptors in vitro and in vivo by agouti-related protein. Science (New York, N.Y.), 278 (5335), 135-8 PMID: 9311920

Piguet O, Petersén A, Yin Ka Lam B, Gabery S, Murphy K, Hodges JR, & Halliday GM (2011). Eating and hypothalamus changes in behavioral-variant frontotemporal dementia. Annals of neurology, 69 (2), 312-9 PMID: 21387376

Scully, T. (2014). Obesity. Nature, 508.

Smith DG, & Robbins TW (2013). The neurobiological underpinnings of obesity and binge eating: a rationale for adopting the food addiction model. Biological psychiatry, 73 (9), 804-10 PMID: 23098895

Trivedi BP (2014). Neuroscience: Dissecting appetite. Nature, 508 (7496) PMID: 24740131

Volkow ND, Wang GJ, Telang F, Fowler JS, Goldstein RZ, Alia-Klein N, Logan J, Wong C, Thanos PK, Ma Y, & Pradhan K (2009). Inverse association between BMI and prefrontal metabolic activity in healthy adults. Obesity (Silver Spring, Md.), 17 (1), 60-5 PMID: 18948965

Vucetic Z, Kimmel J, & Reyes TM (2011). Chronic high-fat diet drives postnatal epigenetic regulation of ?-opioid receptor in the brain. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 36 (6), 1199-206 PMID: 21326195

Williams, S., & Deisseroth, K. (2013). Optogenetics Proceedings of the National Academy of Sciences, 110 (41), 16287-16287 DOI: 10.1073/pnas.1317033110

Wu Q, Boyle MP, & Palmiter RD (2009). Loss of GABAergic signaling by AgRP neurons to the parabrachial nucleus leads to starvation. Cell, 137 (7), 1225-34 PMID: 19563755

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New Insights into Cooperation http://brainblogger.com/2014/03/26/new-insights-into-cooperation/ http://brainblogger.com/2014/03/26/new-insights-into-cooperation/#comments Wed, 26 Mar 2014 11:30:09 +0000 http://brainblogger.com/?p=16181 Game theory has repeatedly confirmed the human tendency to help others, even when helping is costly. The Prisoner’s Dilemma is one of the most popular demonstrations of cooperation. Though behavior in the Prisoner’s Dilemma has long been observed, studies in neuroscience continue to elucidate the brain mechanisms underlying the choices that have puzzled some researchers for decades.

In this game, two players choose whether to cooperate or defect. For each player, the highest payout occurs when the player defects and their partner cooperates. Smaller payouts to both players result from mutual cooperation. However, players who cooperate while their partners defect receive no payout.

If the goal is to receive the largest payout, the best strategy in this game is to defect. If your partner cooperates, defecting guarantees you a bigger payout than if you too cooperated. On the other hand, if your partner defects, you get a payout of zero, regardless of your choice. Those familiar with game theory will likely recognize a scenario wherein both players defect as the Nash Equilibrium for this dilemma. However, in practice, players cooperate about 50% of the time in the Prisoner’s Dilemma.

Scientists and economists have described this cooperative behavior as rational when players repeatedly endure the dilemma with the same partner. In a situation where retaliation will prevent players from subsequent payouts, cooperation is the best long-term strategy. Economists and evolutionary biologists have pointed to potential monetary and reproductive benefits as explanations for this ‘direct reciprocity.’

Nonetheless, humans appear to cooperate in the Prisoner’s Dilemma game, even when they know their interaction will occur only once. This ‘indirect reciprocity,’ which does not provide clear benefits to players, is harder to explain. In a study that will be published this month in Proceedings of the National Academy of Sciences, Naoki Masuda and colleagues describe, for the first time, distinct neural mechanisms underlying two forms of indirect reciprocity: ‘reputation based’ and ‘pay-it-forward.’

Reputation-based reciprocity is relatively simple for scientists to tackle, as a good reputation built from cooperative behavior can lead to the same types of benefits reaped from direct reciprocity. Indeed, in a society, the emergence of which may represent the most recent evolutionary shift, success is often inextricably linked to reputation. However, pay-it-forward reciprocity occurs independent of any impact on reputation. Such reciprocity therefore benefits society but has no obvious advantage for the individual provider. So why are humans, the most intelligent species, much more likely than other species to help those to whom they are not related at their own expense?

The choices we make rely heavily on the information that reaches the striatum, the part of the brain that processes value and executes voluntary behaviors. Accordingly, various forms of cooperation have been shown to engage the striatum, including reputation-based reciprocity. It is therefore perhaps not surprising that Masuda and his colleagues found that both forms of indirect reciprocity enhanced the ability of certain brain areas to communicate with the striatum. However, whereas reputation-based reciprocity enhanced communication from areas of the brain involved in cognition, pay-it-forward reciprocity enhanced communication from areas involved in emotion and empathy. Thus, we appear to deem each form of reciprocity as valuable, but for different reasons.

Why may we have evolved to respond emotionally to the needs of those who cannot be of help to us? With a high level of sophistication comes a need for efficiency. Professors David Rand and Martin Nowak, whose combined expertise spans mathematics, biology, economics, and psychology, suggest that this behavior may represent over-generalization of cooperative strategies that are directly beneficial to the cooperator. In other words, as we have learned that certain forms of cooperation are personally beneficial, we may have adapted a general tendency to cooperate, perhaps so as not to use time and biological resources to assess the value of each cooperative action.

Regardless of the reason, we can all be grateful that, in our species, even strangers are likely looking out for us.

References

Bailey KR, & Mair RG (2006). The role of striatum in initiation and execution of learned action sequences in rats. The Journal of neuroscience : the official journal of the Society for Neuroscience, 26 (3), 1016-25 PMID: 16421321

Cardinal RN, Parkinson JA, Hall J, & Everitt BJ (2002). Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience and biobehavioral reviews, 26 (3), 321-52 PMID: 12034134

Delgado MR, Frank RH, & Phelps EA (2005). Perceptions of moral character modulate the neural systems of reward during the trust game. Nature neuroscience, 8 (11), 1611-8 PMID: 16222226

Fowler JH, & Christakis NA (2010). Cooperative behavior cascades in human social networks. Proceedings of the National Academy of Sciences of the United States of America, 107 (12), 5334-8 PMID: 20212120

King-Casas B, Tomlin D, Anen C, Camerer CF, Quartz SR, & Montague PR (2005). Getting to know you: reputation and trust in a two-person economic exchange. Science (New York, N.Y.), 308 (5718), 78-83 PMID: 15802598

Milinski M, Semmann D, & Krambeck HJ (2002). Reputation helps solve the ‘tragedy of the commons’. Nature, 415 (6870), 424-6 PMID: 11807552

Moll J, Krueger F, Zahn R, Pardini M, de Oliveira-Souza R, & Grafman J (2006). Human fronto-mesolimbic networks guide decisions about charitable donation. Proceedings of the National Academy of Sciences of the United States of America, 103 (42), 15623-8 PMID: 17030808

Nash JF (1950). Equilibrium Points in N-Person Games. Proceedings of the National Academy of Sciences of the United States of America, 36 (1), 48-9 PMID: 16588946

Phan KL, Sripada CS, Angstadt M, & McCabe K (2010). Reputation for reciprocity engages the brain reward center. Proceedings of the National Academy of Sciences of the United States of America, 107 (29), 13099-104 PMID: 20615982

Rand DG, & Nowak MA (2013). Human cooperation. Trends in cognitive sciences, 17 (8), 413-25 PMID: 23856025

Rilling J, Gutman D, Zeh T, Pagnoni G, Berns G, & Kilts C (2002). A neural basis for social cooperation. Neuron, 35 (2), 395-405 PMID: href=”http://www.ncbi.nlm.nih.gov/pubmed/12160756″>12160756

Sanfey AG (2007). Social decision-making: insights from game theory and neuroscience. Science (New York, N.Y.), 318 (5850), 598-602 PMID: 17962552

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Watanabe T, Takezawa M, Nakawake Y, Kunimatsu A, Yamasue H, Nakamura M, Miyashita Y, & Masuda N (2014). Two distinct neural mechanisms underlying indirect reciprocity. Proceedings of the National Academy of Sciences of the United States of America PMID: 24591599

Image via Kheng Guan Toh / Shutterstock.

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Can Current Technology Identify Liars? http://brainblogger.com/2014/03/05/can-current-technology-identify-liars/ http://brainblogger.com/2014/03/05/can-current-technology-identify-liars/#comments Wed, 05 Mar 2014 12:00:08 +0000 http://brainblogger.com/?p=16089 Identifying deception is something humans have attempted to do for centuries. Initial techniques, such as facial expression interpretation, were developed without technology. Later, simple technologies, such as the polygraph, were designed to detect physiological changes consistent with the autonomic arousal that often accompanies the act of lying. More recently, a powerful technique, functional magnetic resonance imaging (fMRI), has gained popularity as a potential lie detector and is sometimes used commercially for this purpose.

fMRI detects changes in blood flow, and because more active brain areas experience enhanced blood flow, fMRI can provide information about neural activity level. Because the technique has been used successfully in neuroscience research focused on mapping functionality, the idea arose that fMRI should be useful in identifying the function of lying. Consistent with this notion, studies have shown that differences in brain activation revealed through fMRI can facilitate the identification of falsehoods. Further, a recent meta-analysis found a high degree of consistency among studies as to the areas of the brain involved in lying. These areas, located largely in the prefrontal cortex, are important for executive functioning.

If we contemplate the cognitive process of lying, the involvement of executive functions is intuitive. Specifically, while voluntarily lying, one must keep track of two sets of circumstances: those consistent with the truth, and those consistent with the lie. This requirement would certainly increase cognitive load and the activation of relevant brain areas. However, individual brain differences can produce inconsistent activity patterns. For example, a recent study showed that when criminals with antisocial personality disorder lied, they lacked the pattern of prefrontal activity that has consistently been observed in healthy volunteers.

Even when considering lying only in those who lack psychiatric illness, detecting lies through fMRI is complicated by several factors. First, whereas lying may necessitate enhanced cognitive processing, such cognitive processing can occur independent of dishonesty. Further, if one intends to lie, the resulting cognitive load is not necessarily eased while that person tells the truth. Accordingly, studies have found that those being dishonest demonstrate more activity in areas of the brain associated with executive functioning both when telling the truth and when lying.

Another complicating factor is that not all lies are equivalent. Though someone committed to telling the truth may be burdened with a smaller cognitive load, liars may be able to make their lies less cognitively laborious so that the activity associated with their dishonesty resembles activity normally associated with honesty. For example, researchers have shown that the patterns of brain activity associated with well rehearsed lies diverge from those associated with spontaneous lies.

As with the commercial use of fMRI in strategic marketing, use of fMRI for lie detection remains controversial and has yet to be deemed admissible in a court of law. Though fMRI is an effective tool for elucidating brain function, much more research is required to determine if this technique will provide a reliable method for revealing dishonesty.

References

Christ SE, Van Essen DC, Watson JM, Brubaker LE, & McDermott KB (2009). The contributions of prefrontal cortex and executive control to deception: evidence from activation likelihood estimate meta-analyses. Cerebral cortex (New York, N.Y. : 1991), 19 (7), 1557-66 PMID: 18980948

Davatzikos C, Ruparel K, Fan Y, Shen DG, Acharyya M, Loughead JW, Gur RC, & Langleben DD (2005). Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage, 28 (3), 663-8 PMID: 16169252

Farah MJ, Hutchinson JB, Phelps EA, & Wagner AD (2014). Functional MRI-based lie detection: scientific and societal challenges. Nature reviews. Neuroscience, 15 (2), 123-31 PMID: 24440904

Ganis G, Kosslyn SM, Stose S, Thompson WL, & Yurgelun-Todd DA (2003). Neural correlates of different types of deception: an fMRI investigation. Cerebral cortex (New York, N.Y. : 1991), 13 (8), 830-6 PMID: 12853369

Greene JD, & Paxton JM (2009). Patterns of neural activity associated with honest and dishonest moral decisions. Proceedings of the National Academy of Sciences of the United States of America, 106 (30), 12506-11 PMID: 19622733

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Jiang W, Liu H, Liao J, Ma X, Rong P, Tang Y, & Wang W (2013). A functional MRI study of deception among offenders with antisocial personality disorders. Neuroscience, 244, 90-8 PMID: 23578713

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Image via Igor Stevanovic / Shutterstock.

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