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Drugging and Driving: Benzos, opioids and antidepressants and increased risk of driving accidents

May 11, 2011 1 comment

ResearchBlogging.orgThere are many reasons why one might find it preferable not to drive an automobile: For one, it’s expensive (gas, insurance, repairs, and tickets). It pollutes the environment. And its dangerous. Based on data from the Federal Highway Administration, there are over 6 million auto accidents in the United States every year on average. And around 40, 000 of those accidents result in people being killed by people driving under the influence of alcohol.

A new study from Australian researchers provides another reason to hop on the bus or train rather than get behind the wheel. The study looked at the association between driving and taking prescription medications. And the results were not very promising, finding that users of many prescription medications are at increased risk for car accidents.

The researchers performed what is called a meta-study, in which all the research that can be located pertaining to a given topic and meeting certain criteria of validity and reliability are rolled into one mega study in an attempt to achieve maximal statistical power. Two different types of studies were examined:

1. Epidemiological studies. These are studies of patterns of association between prescription drugs and driving accidents based on real-life data coming from a variety of sources. There are several advantages and drawbacks to these kinds of studies and Wikipedia is a good place to get some background. One of the major drawbacks of epidemiological studies is that they are correlational; in other words, it’s difficult to say one thing caused another, but merely that they occurred together. One can’t control for all of the possible confounding variables that could be the true source of the relationship between variables.

2. Experimental Studies. These are controlled studies that allow researchers to explore causal relationships between variables. Again, wiki is a good place to go for a primer. The advantage of experimental studies is that if they’re designed correctly, one can explore causality but the drawback is that they lack “ecological validity”; that is, they may not represent “real world” conditions.

The over all goal of a meta-study is to ascertain whether the data from numerous sources, and from both epidemiological and experimental studies, converge on the same conclusions.

Several classes of prescription drugs were examined:
1. Benzodiazepine: these include drugs such as diazepam, flurazepam, flunitrazepam and nitrazepam. They’re commonly prescribed for generalized anxiety disorder, panic disorder, Insomnia, seizures and alcohol withdrawal.
2. Non benzo hypnotics: Include drugs like pentobarbital. These are frequently prescribed for insomnia.
3. Antidepressants, which can be divided into two classes: SSRIs and TCAs. SSRIs include drugs like Lexapro, Prozac, and Celexa. TCAs, or trycylic antidepressants, include drugs like mipramine (Tofranil) and maprotiline (Ludiomil).
4. Anxiolytics (Anti Anxiety drugs)
5. Opioids

For those interested in the details, please consult the study. I’ll just be presenting a simplified summary of the findings. But before I get there, just a couple of quick thoughts. Meta studies can often be difficult to interpret. Particularly for a topic such as this, where there are so many confounding variables, such as a huge variety of different types of drug, varying range of dose, the problem that those on medication also have depression, anxiety, and other disorders (making it difficult to parse out the effects of the drug alone), tolerance effects, age and gender effects, the possibility that the epidemiological studies only include the worst cases (only accidents that resulted in injury), and so on. It becomes very difficult to make conclusive or generalizable statements about the findings. Some researchers are opposed to meta studies for that very reason. That being said, the evidence here does seem to have reasonably converged toward a handful of conclusions. Keeping the limitations in mind, here they are:

1. Benzodiazepine users show 60-80% increased risk of traffic accidents. Drivers responsible for causing an accident are 40% more likely to be positive for benzos than those who are not responsible. Elderly people show decreased risk (versus non-elderly).

2. Benzodiazepine plus alcohol users show 7.7 fold increase in risk for traffic accidents

The 2- to 3-fold increase in accident risk associated with … long-acting benzodiazepines and zopiclone is equivalent to what has been observed with a blood alcohol concentration of 0.05–0.08 g/dL,[100,101] which is above the legal limits for driving in most countries…

The authors recommend that anyone prescribed diazepam should be urged not to drive for the first four weeks of treatment.

3. Anxiolytics seems to impair drivers independent of the drug’s half life. (A half life is the duration of action of a drug and indicates the period of time required for the drug in the body to be reduced in half.)

4. Impairment caused by hypnotics tends to be related to the drug’s half life.

For hypnotic medication, an option for prescribers is to avoid these hypnotics (flurazepam, flunitrazepam, nitrazepam and zopiclone) if patients are engaged in driving. Relatively safer alternatives would be shorter acting hypnotics, such as triazolam, temazepam, zolpidem and zaleplon, which were not found to cause driving impairment, at least in experimental studies (although there is evidence that some of the drugs are associated with increased accident risk)…

5. As far as antidepressants go, no clear distinction emerged between sedative and non-sedative subclasses (according to epidemiological studies). One major confounding variable in the studies examined is depression itself, as cognitive and psychomotor deficits are associated with depression alone. Furthermore, antidepressants might interact differently depending on stage of treatment, e.g. effects of antidepressants take one to two weeks to appear, so driving may be even more impaired over this time period than depression alone or after drug effects kick in.

Sedative antidepressants probably lead to worse driving for the first 3-4 weeks, and until tolerance to sedative effects increases and depression lifts. This is supported by some experimental evidence. (Patient groups with sedative/non-sedative antidepressants improved their driving skills after a few weeks). Epidemiological studies suffer from the confound of comparing groups on anti-depressants (people with depression) with those not on anti-depressants (people who don’t have depression) and are therefore of limited utility.

6. Opioids – There weren’t enough studies of opioids and driving to make any conclusions.

I wasn’t able to locate data indicating how many people in the US are currently taking the drugs mentioned in this study. What I did find was that antidepressants (many of which are probably sedatives) are the most popular prescription drug for adults aged 20 to 59 in the US. And the most recent annual data (from the CDC) suggests that 48% of Americans took at least one prescription drug in the past month. This suggests the possibility that the number of those driving under the influence of cognitively-impairing prescription drugs is likely to be in the millions country wide. Cause for concern? Perhaps. Prescription drugs are certainly not becoming any less popular, and I’m certainly glad to live in a city that provides alternative transportation options. Then again I often bike in that city, and mildly sedated depressive or anxious drivers might be the least of my worries (cab drivers and delivery trucks being a much larger concern).

Dassanayake T, Michie P, Carter G, & Jones A (2011). Effects of benzodiazepines, antidepressants and opioids on driving: a systematic review and meta-analysis of epidemiological and experimental evidence. Drug safety : an international journal of medical toxicology and drug experience, 34 (2), 125-56 PMID: 21247221

Social cognitive deficits in autism spectrum disorder

May 6, 2011 3 comments

ResearchBlogging.org One of the hallmarks of Autism Spectrum Disorder (ASD) is an impairment in social cognitive skills. This manifests in individuals with ADS having trouble orienting their attention towards people. Accordingly, they also show deficits orienting their attention in response to social cues from others, such as eye gaze, head turns and pointing gestures.

Understanding the social cognitive impairments associated with ASD has been challenging in that studies set in naturalistic settings often reveal the deficit but lab experiments performed on computers don’t.

For example, some naturalistic studies have looked at home movies of infants and found that those later diagnosed with ASD showed less social orienting and were less responsive to cues from others to orient to objects. For example, if their mom was in the room, they would look at her a lot less and they’d also be less likely to respond when their mothers tried to direct their attention to a toy in the room by looking or pointing at it.

However, people with ASD have been shown to respond to non-naturalistic social cues in the lab. Social orienting has been frequently been tested by use of a variation on Michael Posner’s spatial cueing paradigm. This works as follows:

1. Participants are seated in front of a computer
2. A stimulus – a pair of eyes gazing to either side (or straight ahead) or arrows pointing to either side or neither – appears on the screen
3. Shortly after, a stimulus (the target object) appears to one side or the other, either on the side which the eyes or arrows were pointing towards or the opposite side.
4. Participants have to indicate which side the target object appeared on by pressing either a right or left button.
5. Performance on the task is assessed by measuring the amount of time it takes to participants to press the button indicating on which side the target appeared. Most participants, including ASD patients, are as quick with the gaze cue (the eyes) as with the arrow cue.

Posner cue paradigm

(The left side of the above figure shows a single trial (with “directional eyes”), in which participants first see a fixation cross, then one of four directional/non-directional stimuli, after which the target appears either on the same side indicated by the cue or the opposite side. Participants need to indicate which side a target stimulus appeared on by pushing a button. The right side shows the three other trial types (from top to bottom): neutral arrow, directional arrow, neutral eyes)

Past studies have shown that people orient faster to cued (like in the left side of the above figure) versus noncued locations, known as the facilitation effect. Previous studies using this task have produced inconsistent results, but most of them have shown ASD populations performing comparably to non-ASD populations.

In this study, researchers used the above-described cue task to examine the neural mechanisms underlying social orienting in ASD, with the hope that if there were no behavioral differences, neural activity might reveal that ASD individuals are performing the task differently. Other studies have shown that non-ASD populations treat social and non-social cue stimuli differently. It was hoped that neural activity revealed in this study would shed light on the discrepancies in behavioral results for ASD populations in lab versus computer settings.

Results
In terms of behavior, both the control and the ASD group showed quicker responses for gaze and arrow cues with no between group difference, which is consistent with previous lab studies.

However, neural activation patterns showed significant group differences. The control group showed greater activation for social vs. nonsocial cues in many different brain regions, with gaze (eyeball) cues eliciting increased activity in many frontoparietal areas, supporting the idea that neurotypical brains treat social stimuli different from non-social stimuli. The ASD group, on the other hand, showed much less difference in neural activation between social vs. non-social cues. Although these differences in neural activation are too numerous to cover here, one region of interest, superior temporal sulcus (STS), stood out. The STS has been shown to be associated with the perception of eye gaze and other work has suggested the region may be involved in understanding the intentions and mental states of others. In this study, ASD individuals showed decreased STS in the gaze cue condition (versus controls). This data suggests that the STS may not be sensitive toward the social significance of eye gaze in ASD individuals.

Implications
The authors point out that although ASD individuals don’t seem to rely on the same neural circuitry to perceive social cues such as eye gaze, they have found a way to use the low-level perceptual information available in social cues to adapt a strategy that allows them to discern that gaze direction conveys meaning about the environment. That being said, ASD individuals mostly don’t do this very well in more naturalistic environments. So, although this strategy might work in a scanner with “cartoon” eyes and where there are no environmental distractions, it’s unlikely that ASD individuals could adapt this strategy in a naturalistic environment. On the contrary, one could also frame these results from the perspective of the ASD individual; that is, given the non-naturalistic environment of the scanner, and the fact that the task demands were very simple and not dependent on social cognitive processing, why should non-ASD individuals treat the gaze vs. arrow stimuli differently? Why not just rely on low-level information and thus expend less cognitive energy? It’s a good example of the automaticity of social cognitive processes. Give humans a set of cartoon eyeballs to look at and they can’t help but process these as distinct from something non-social.

An additional take away from this paper is that even when one finds no behavioral differences between groups, there might be some interesting differences in neural activity worth exploring via fMRI or EEG.

References

Greene DJ, Colich N, Iacoboni M, Zaidel E, Bookheimer SY, & Dapretto M (2011). Atypical neural networks for social orienting in autism spectrum disorders. NeuroImage, 56 (1), 354-62 PMID: 21334443

Google crosses the web/brain barrier?

May 5, 2011 1 comment

Google, are you reading my mind?

One interesting aspect of having a blog is checking out the search terms that people used to land at one’s site. It’s often difficult to figure out why a particular and seemingly unrelated term might bring someone this way.

But one recent search seems to have transcended the blog and gone straight into my brain-o-sphere, into the existential recess where some of my darker thoughts about grad school are stored:

“PhD meaninglessness”

Google, you know me so well. Now stop it, you’re freaking me out.

Categories: Uncategorized

Mortality among users of marijuana, cocaine, amphetamine, ecstasy and opioids

May 2, 2011 11 comments

This post was chosen as an Editor's Selection for ResearchBlogging.orgWhile Illicit drugs have long been linked to higher mortality rates, the data is wildly variable.

In a paper recently published in the journal Drug and Alcohol Dependence, Danish researchers attempted to establish standard mortality ratios for the drugs cannabis, cocaine, amphetamine, MDMA (ecstasy) and opioids (e.g. heroin)*, while taking into consideration the effects of two intervening variables: drug injection with needles and psychiatric disorders (Is the mortality rate of cocaine users mediated by whether they have, for example, clinical depression?)

(*Individuals’ primary drug of choice)

The population they looked at included 20,581 people treated for drug abuse in Denmark over a 10-year period from 1996-2006. (These data are correlational and, therefore, the possibility of unidentified moderating variables exerting an effect on death rates is high.)

In brief, the results showed the following:

1. Those who injected drugs showed significantly higher mortality rates across the board. (This does conflict with past findings, which found no difference.)

2. Overall, psychiatric illness was not associated with higher mortality rates, with the exception of cocaine/amphetamine users, who, if they presented with psychiatric disorders, did show higher morality rates.

3. Pot smokers showed 5x increase in mortality rates (compared to the general population). Researchers suggest that increased mortality among pot smokers could be related to driving accidents, violent injuries and various other types of accidents. (a personal note: Based on my personal experience, this seems unlikely. Pot smokers tend to drive very conservatively (too slow, if anything!) and are famously not prone to violence.) What seems more likely to explain pot smokers’ higher mortality rate is that they are also using other drugs. Other studies have borne this out.

4. Cocaine and amphetamine users showed 6x death rates of the general population. Previous reports on stimulant abuse related deaths are highly variable. The variability is likely the result of other factors including physical conditions, HIV/AIDS, overdose, cardiovascular problems, injuries accidents, violent deaths and suicides.

5. Opiod users show increased mortality rates. Findings for both stimulants and opioids are in accordance with studies from other countries. Users of Heroin and other opioids showed by far the highest mortality rates of all drugs of abuse.

6. Ecstasy (MDMA) users did not show increased mortality rates. (However, it’s possible that a low number of deaths from MDMA contribute to low statistical power).

Conclusions that can be drawn from this report? Stay away from all drugs if you want to increase your chances of staying alive; but, especially, don’t do intravenous heroin. Psychiatric disorders plus drugs of abuse aren’t associated with increased mortality risks except for in the case of cocaine/amphetamine. Ecstasy is unlikely to kill you on its own, but that’s not to say it won’t do some long-term damage if abused. Although marijuana users showed higher mortality rates, there’s not good reason to believe this is solely the effect of marijuana, but other factors. Finally, the population under study here consisted of people seeking treatment, so it’s unknown if this represents the drug using population as a whole.

I think it’s pretty clear, given the number of questions and unknowns this study presents, that there is a lot more to learn about drug-related mortality risk.

References
Arendt M, Munk-Jørgensen P, Sher L, & Jensen SO (2011). Mortality among individuals with cannabis, cocaine, amphetamine, MDMA, and opioid use disorders: a nationwide follow-up study of Danish substance users in treatment. Drug and alcohol dependence, 114 (2-3), 134-9 PMID: 20971585

Categories: Drugs Tags: , , ,

The Neural Correlates of Romantic Love

May 1, 2011 Leave a comment


ResearchBlogging.org

For the most part, fMRI studies attempt to localize cognitive processes to specific regions in the brain. Popular media often introduce these studies with headlines that tout the discovery of “the brain region” for memory, language, empathy, moral reasoning, loving weiner schnitzel and so on.

These headlines can be terribly misleading, as they’re often misinterpreted to suggest a specific brain region is dedicated to a single function, when, in fact, any given function maps on to a network of regions (forming a circuit), while any given region is part of multiple circuits subserving many functions. Similar faux pas can be found in descriptions of the functions associated with genes, e.g. “The gene for (fill in the blank).”

A few years back, the NY Times ran an infamous piece featuring the work of a neuromarketing company. In a horrible experiment fit for The Onion, participants lay in the scanner while looking at pictures of then presidential candidates. Subjects showed increased amygdala activation to pictures of Mitt Romney, which researchers interpreted as a sign of anxiety.

But after watching Romney speak on video, the amygdala activity died down, which researchers said showed that voters’ anxiety had decreased.

Meanwhile subjects’ anterior cingulates lit up to pictures of Hillary Clinton.

Here’s how researchers interpreted this neural activity:

Emotions about Hillary Clinton are mixed. Voters who rated Mrs. Clinton unfavorably on their questionnaire appeared not entirely comfortable with their assessment. When viewing images of her, these voters exhibited significant activity in the anterior cingulate cortex, an emotional center of the brain that is aroused when a person feels compelled to act in two different ways but must choose one. It looked as if they were battling unacknowledged impulses to like Mrs. Clinton.

The Times article about the “research” was quickly and roundly criticized by prominent neuroscientists, 17 of whom quickly responded with a signed letter to the editor, which the Times ran a couple of days later:

To the Editor:

“This Is Your Brain on Politics” (Op-Ed, Nov. 11) used the results of a brain imaging study to draw conclusions about the current state of the American electorate. The article claimed that it is possible to directly read the minds of potential voters by looking at their brain activity while they viewed presidential candidates.

For example, activity in the amygdala in response to viewing one candidate was argued to reflect “anxiety” about the candidate, whereas activity in other areas was argued to indicate “feeling connected.” While such reasoning appears compelling on its face, it is scientifically unfounded.

As cognitive neuroscientists who use the same brain imaging technology, we know that it is not possible to definitively determine whether a person is anxious or feeling connected simply by looking at activity in a particular brain region. This is so because brain regions are typically engaged by many mental states, and thus a one-to-one mapping between a brain region and a mental state is not possible.As cognitive neuroscientists, we are very excited about the potential use of brain imaging techniques to better understand the psychology of political decisions. But we are distressed by the publication of research in the press that has not undergone peer review, and that uses flawed reasoning to draw unfounded conclusions about topics as important as the presidential election.

Adam Aron, Ph.D., University of California, San Diego
David Badre, Ph.D., Brown University
Matthew Brett, M.D., University of Cambridge
John Cacioppo, Ph.D., University of Chicago
Chris Chambers, Ph.D., University College London
Roshan Cools, Ph.D., Radboud University, Netherlands
Steve Engel, Ph.D., University of Minnesota
Mark D’Esposito, M.D., University of California, Berkeley
Chris Frith, Ph.D., University College London
Eddie Harmon-Jones, Ph.D., Texas A&M University
John Jonides, Ph.D., University of Michigan
Brian Knutson, Ph.D., Stanford University
Liz Phelps, Ph.D., New York University
Russell Poldrack, Ph.D., University of California, Los Angeles
Tor Wager, Ph.D., Columbia University
Anthony Wagner, Ph.D., Stanford University
Piotr Winkielman, Ph.D., University of California, San Diego

Undoubtedly, fewer people saw that letter than saw the original article, which was much more prominently displayed.

(By the above study’s logic, looking at a picture of Donald Trump should elicit activity in the anterior insula, a region often associated with disgust responses)

It’s unfortunate that the study received such a prominent platform for distribution because people, especially non scientists, can be heavily influenced by articles with pictures of brains or technical sounding neuro language. One study, which I’ve written about on the blog, found that people were much more likely to believe a nonsensical article if it had meaningless neuroscience language in it than if it didn’t. As the average lay person doesn’t possess the technical skills to distinguish between valid and invalid fMRI studies, it’s up to the scientific community to police itself, which it does a pretty good job of through the peer review process.

This next study I’ll talk about demonstrates some of the challenges inherent to mapping localized neural activity onto unseen mental processes; in this case, the subjective experience of intense, romantic long-term love.

Aaron and colleagues previously published a study that presented neural correlates of intense romantic love (2005). In brief, the study reported that regions in the reward circuit of the brain were activated in response to pictures of a lover (versus a close friend). In the current study, they wanted to explore if these findings could be extended to long-term married couples (couples together for more than 20+ years who report still being madly in love).

Participants lay in the scanner and were repeatedly presented with pictures from four different categories: their partner, a close friend, and both a highly familiar and low-familiar neutral acquaintance. They were instructed to think about “experiences with each stimulus person (that were) nonsexual in nature.”

The fMRI data was analyzed via subtraction, a common fMRI analysis method in which one condition is compared to another to see if any differences fall out. The contrast of interest was between the partner and the close friend. In a cognitive process sense, the only difference thought to exist between perceiving these two individuals was thought to be the subject’s romantic love for one and not for the other. So if neural activity in the close friend condition is subtracted from activity in the partner condition, whatever is left over should represent the neural substrate of romantic love.

Researchers found activation in the ventral tegmental area, substantia nigra and nucleus accumbens (and the hippocampus, which corresponded with reported sex frequency, but the effect seems to be driven largely by two outliers, one of whom reported they have sex almost every day).

The activity does suggest a classic reward response and replicates previous findings. However, the big question isn’t whether there is a response, but rather what’s driving it?

A valid fMRI study doesn’t only rely on the integrity or analysis of the fMRI data, but, also, and perhaps more importantly, on the experimental design. In order to attribute increased activation in one condition versus the other to a specific cognitive function, one must be confident they have created conditions that have cleanly isolated the independent variable of interest (romantic love). The ventral tegmental area and other regions in the basal ganglia have been repeatedly shown to encode reward value – that is, they respond to things that give us hedonic pleasure, such as food, drugs, sex or receiving money. Past work has shown that intense romantic love is associated with activity in the those regions (Aaron 2005). In the current study, activity in some neural regions previously associated with maternal pair bonding was shown (substantia nigra, for one). The authors hypothesized that neural correlates for romantic long-term love should encompass those associated with both intense romantic love and maternal pair bonding.

But this analysis is dependent on long-term, romantic love being the only difference between conditions that would explain the differences in brain activity. And that may not entirely be the case.

Alternative Explanations
Just to refresh, the major dependent measure of interest was neural activity, especially of reward circuitry, while subjects looked at pictures of their long-term partner versus a close friend. One additional difference between these conditions (beyond romantic love) is that romantic partners are probably more familiar and closer to participants than close friends. This is a shortcoming acknowledged by the authors.

This difference suggests a causal chain of cognitive operations that could offer alternative explanations for some of the data seen in this study. First, It’s been shown that we prefer things that are familiar to us (the “mere exposure” effect, Zajonc, 1968). Second, we’re able to process familiar things (or people) much more fluently compared to the less familiar (Reber). Third, fluency processing has been associated with judgments of aesthetic appreciation such that the more fluently we can process something, the more beautiful or attractive we’re likely to rate it (Alter). Although the objective attractiveness of the photos was controlled for via a group of independent raters, the participants were likely much more subjective in their judgments and perhaps found their partners more attractive than an objective viewer might. Viewing attractive faces has been shown to elicit strong neural activity, particularly in the reward circuitry (NaCC and OFC).

Furthermore, it has been posited that people incorporate close others into their psychological construct of self. Recent studies (deGreck 2008) showed that regions active in a reward task, such as the bilateral ventral striatum, and the ventral tegmental area (VTA), are also involved in differentiating between high and low personal relevance.It seems that we find thinking about ourselves pretty damn rewarding! (We’re all at least a little bit narcissistic). To the extent that someone has been incorporated into our self concept, thinking about that person, or looking at their picture as in this study, could be correlated with responses in reward regions of the brain in part because they activate thoughts of ourselves.

Both the familiarity –> processing fluency –> attractiveness model and self relevant thinking are plausible alternative explanations for at least some of the neural correlates found in this paper.

One other potential area of concern is that there is no way of knowing that participants weren’t thinking about sex with their partners, even though they were told not to. This might be especially difficult to achieve, especially for the two outliers who reported almost daily sex. Regions active during sexual arousal include R. amygdala, hypothalamus, hippocampus, midbrain, mOFC and nucleus accumbens, many of which were found to be active in this study.

The neural activity measured here may very well reflect some aspect of individuals’ love for their partners. But there seem to be other possible explanations for some of the data. I suppose that’s why people call the study of consciousness, of which subjective experiences such as romantic love are a subset, the “hard problem.”

References
Acevedo BP, Aron A, Fisher HE, & Brown LL (2011). Neural correlates of long-term intense romantic love. Social cognitive and affective neuroscience PMID: 21208991

Aron, A., Fisher, H., Mashek, D., Strong, G., Li, H., Brown, L. (2005). Reward, motivation and emotion systems associated with early-stage intense romantic love. Journal of Neurophysiology, 93, 327–37.

DEGRECK, M., ROTTE, M., PAUS, R., MORITZ, D., THIEMANN, R., PROESCH, U., BRUER, U., MOERTH, S., TEMPELMANN, C., & BOGERTS, B. (2008). Is our self based on reward? Self-relatedness recruits neural activity in the reward system NeuroImage, 39 (4), 2066-2075 DOI: 10.1016/j.neuroimage.2007.11.006

Alter, A., & Oppenheimer, D. (2008). Easy on the mind, easy on the wallet: The roles of familiarity and processing fluency in valuation judgments Psychonomic Bulletin & Review, 15 (5), 985-990 DOI: 10.3758/PBR.15.5.985

Peskin, M., & Newell, F. (2004). Familiarity breeds attraction: Effects of exposure on the attractiveness of typical and distinctive faces Perception, 33 (2), 147-157 DOI: 10.1068/p5028

Reber, R., Schwarz, N., & Winkielman, P. (2004). Processing Fluency and Aesthetic Pleasure: Is Beauty in the Perceiver’s Processing Experience? Personality and Social Psychology Review, 8 (4), 364-382 DOI: 10.1207/s15327957pspr0804_3

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