The science of emotion - now at least 27 categories of emotional states.

So...I guess we knew emotions are complicated. There has been intense debate on how to describe them in semantic and geometric dimensions such as valence and arousal. Cowan and Keltner use a natural history approach to gather and analyze self descriptions of emotional states elicited by 2,185 emotionally evocative short videos (check out the geometrical space of their results in the link below):

Significance

Claims about how reported emotional experiences are geometrically organized within a semantic space have shaped the study of emotion. Using statistical methods to analyze reports of emotional states elicited by 2,185 emotionally evocative short videos with richly varying situational content, we uncovered 27 varieties of reported emotional experience. Reported experience is better captured by categories such as “amusement” than by ratings of widely measured affective dimensions such as valence and arousal. Although categories are found to organize dimensional appraisals in a coherent and powerful fashion, many categories are linked by smooth gradients, contrary to discrete theories. Our results comprise an approximation of a geometric structure of reported emotional experience.

Abstract

Emotions are centered in subjective experiences that people represent, in part, with hundreds, if not thousands, of semantic terms. Claims about the distribution of reported emotional states and the boundaries between emotion categories—that is, the geometric organization of the semantic space of emotion—have sparked intense debate. Here we introduce a conceptual framework to analyze reported emotional states elicited by 2,185 short videos, examining the richest array of reported emotional experiences studied to date and the extent to which reported experiences of emotion are structured by discrete and dimensional geometries. Across self-report methods, we find that the videos reliably elicit 27 distinct varieties of reported emotional experience. Further analyses revealed that categorical labels such as amusement better capture reports of subjective experience than commonly measured affective dimensions (e.g., valence and arousal). Although reported emotional experiences are represented within a semantic space best captured by categorical labels, the boundaries between categories of emotion are fuzzy rather than discrete. By analyzing the distribution of reported emotional states we uncover gradients of emotion—from anxiety to fear to horror to disgust, calmness to aesthetic appreciation to awe, and others—that correspond to smooth variation in affective dimensions such as valence and dominance. Reported emotional states occupy a complex, high-dimensional categorical space. In addition, our library of videos and an interactive map of the emotional states they elicit (https://s3-us-west-1.amazonaws.com/emogifs/map.html) are made available to advance the science of emotion.

Will you be above or below “the API” in the emerging economy?

An application programming interface (API) is a set of subroutine definitions, protocols, and tools that are building blocks for application software...software of the sort that Uber uses to connect taxi drivers to customers without other human intervention. From Anthony Wing Kosner:

Customers use an app interface to enter their data into the system. The app sends a request that includes account data, pickup and dropoff locations via API to Uber's servers that poll available drivers nearby and dispatches one to the customer to fulfill the request. The only two humans involved are the customer and the driver. Danny DeVito has been furloughed!

From Peter Reinhardt:

Drivers are opting into a dichotomous workforce: the worker bees below the software layer have no opportunity for on-the-job training that advances their career, and compassionate social connections don’t pierce the software layer either. The skills they develop in driving are not an investment in their future. Once you introduce the software layer between ‘management’ (Uber’s full-time employees building the app and computer systems) and the human workers below the software layer (Uber’s drivers, Instacart’s delivery people), there’s no obvious path upwards. In fact, there’s a massive gap and no systems in place to bridge it.

Kosner notes some of the longer term implication of such software:

Uber drivers, Amazon Mechanical Turk workers, 99design contestants, TaskRabbit taskers and HomeJoy cleaners are all targets for further automation...Yes, self-driving cars on the way, and it is likely that automated taxi fleets will be the first commercial application of this technology... Uberization of work may soon be coming to your chosen profession, affecting not just cab drivers and house cleaners, but extending to lawyers, doctors and even (some day) venture capitalists.

Color naming across languages reflects color use

Gibson et al. do a study showing that warm colors are communicated more efficiently than cool colors, and that this cross-linguistic pattern reflects the color statistics of the world:

Significance

The number of color terms varies drastically across languages. Yet despite these differences, certain terms (e.g., red) are prevalent, which has been attributed to perceptual salience. This work provides evidence for an alternative hypothesis: The use of color terms depends on communicative needs. Across languages, from the hunter-gatherer Tsimane' people of the Amazon to students in Boston, warm colors are communicated more efficiently than cool colors. This cross-linguistic pattern reflects the color statistics of the world: Objects (what we talk about) are typically warm-colored, and backgrounds are cool-colored. Communicative needs also explain why the number of color terms varies across languages: Cultures vary in how useful color is. Industrialization, which creates objects distinguishable solely based on color, increases color usefulness.

Abstract

What determines how languages categorize colors? We analyzed results of the World Color Survey (WCS) of 110 languages to show that despite gross differences across languages, communication of chromatic chips is always better for warm colors (yellows/reds) than cool colors (blues/greens). We present an analysis of color statistics in a large databank of natural images curated by human observers for salient objects and show that objects tend to have warm rather than cool colors. These results suggest that the cross-linguistic similarity in color-naming efficiency reflects colors of universal usefulness and provide an account of a principle (color use) that governs how color categories come about. We show that potential methodological issues with the WCS do not corrupt information-theoretic analyses, by collecting original data using two extreme versions of the color-naming task, in three groups: the Tsimane', a remote Amazonian hunter-gatherer isolate; Bolivian-Spanish speakers; and English speakers. These data also enabled us to test another prediction of the color-usefulness hypothesis: that differences in color categorization between languages are caused by differences in overall usefulness of color to a culture. In support, we found that color naming among Tsimane' had relatively low communicative efficiency, and the Tsimane' were less likely to use color terms when describing familiar objects. Color-naming among Tsimane' was boosted when naming artificially colored objects compared with natural objects, suggesting that industrialization promotes color usefulness.

Separate prefrontal areas code desirability versus availability.

When we make a decision, we calculate its “expected value,” by multiplying the value of something (how much we want or need it) with the probability that we might be able to obtain it, a concept first introduced by 17th-century mathematician Blaise Pascal. Rudebeck et al. show that this value determination involves two separate prefrontal areas:

Advantageous foraging choices benefit from an estimation of two aspects of a resource’s value: its current desirability and availability. Both orbitofrontal and ventrolateral prefrontal areas contribute to updating these valuations, but their precise roles remain unclear. To explore their specializations, we trained macaque monkeys on two tasks: one required updating representations of a predicted outcome’s desirability, as adjusted by selective satiation, and the other required updating representations of an outcome’s availability, as indexed by its probability. We evaluated performance on both tasks in three groups of monkeys: unoperated controls and those with selective, fiber-sparing lesions of either the OFC or VLPFC. Representations that depend on the VLPFC but not the OFC play a necessary role in choices based on outcome availability; in contrast, representations that depend on the OFC but not the VLPFC play a necessary role in choices based on outcome desirability.

Both OFC and VLPFC send connections to the ventromedial prefrontal cortex (VMPFC), and functional magnetic resonance imaging suggests that the VMPFC may be where choices ultimately get made.

How we see what we expect to see.

Kok et al. show that expectations can induce the preactivation of stimulus templates in our brain that resemble the neural signals actually generated when the stimuls is presented:

Significance

The way that we perceive the world is partly shaped by what we expect to see at any given moment. However, it is unclear how this process is neurally implemented. Recently, it has been proposed that the brain generates stimulus templates in sensory cortex to preempt expected inputs. Here, we provide evidence that a representation of the expected stimulus is present in the neural signal shortly before it is presented, showing that expectations can indeed induce the preactivation of stimulus templates. Importantly, these expectation signals resembled the neural signal evoked by an actually presented stimulus, suggesting that expectations induce similar patterns of activations in visual cortex as sensory stimuli.

Abstract

Perception can be described as a process of inference, integrating bottom-up sensory inputs and top-down expectations. However, it is unclear how this process is neurally implemented. It has been proposed that expectations lead to prestimulus baseline increases in sensory neurons tuned to the expected stimulus, which in turn, affect the processing of subsequent stimuli. Recent fMRI studies have revealed stimulus-specific patterns of activation in sensory cortex as a result of expectation, but this method lacks the temporal resolution necessary to distinguish pre- from poststimulus processes. Here, we combined human magnetoencephalography (MEG) with multivariate decoding techniques to probe the representational content of neural signals in a time-resolved manner. We observed a representation of expected stimuli in the neural signal shortly before they were presented, showing that expectations indeed induce a preactivation of stimulus templates. The strength of these prestimulus expectation templates correlated with participants’ behavioral improvement when the expected feature was task-relevant. These results suggest a mechanism for how predictive perception can be neurally implemented.

Computer design cues taken from human brains

Metz does an interesting article on the waning of do-it-all chips, central processing units of the sort that are running my MacBook Air as I type this, in favor distributed systems that offload specialized tasks, like hearing and seeing, to A.I. (artificial intelligence) chips specialized for those tasks, much as the human brain stem oversees the system and sends different jobs to different specialized parts of the surrounding cortex (auditory, visual, somatosensory, motor, executive, motivational, etc.):

...machines that spread computations across vast numbers of tiny, low-power chips can operate more like the human brain, which efficiently uses the energy at its disposal.

…the leading internet companies are now training their neural networks with help from another type of chip called a graphics processing unit, or G.P.U. These low-power chips — usually made by Nvidia — were originally designed to render images for games and other software, and they worked hand-in-hand with the chip — usually made by Intel — at the center of a computer. G.P.U.s can process the math required by neural networks far more efficiently than C.P.U.s.

G.P.U.s are the primary vehicles that companies use to teach their neural networks a particular task, but that is only part of the process. Once a neural network is trained for a task, it must perform it, and that requires a different kind of computing power.

After training a speech-recognition algorithm, for example, Microsoft offers it up as an online service, and it actually starts identifying commands that people speak into their smartphones. G.P.U.s are not quite as efficient during this stage of the process. So, many companies are now building chips specifically to do what the other chips have learned.

Google built its own specialty chip, a Tensor Processing Unit, or T.P.U. Nvidia is building a similar chip. And Microsoft has reprogrammed specialized chips from Altera, which was acquired by Intel, so that it too can run neural networks more easily.

The hope is that this new breed of mobile chip can help devices handle more, and more complex, tasks on their own, without calling back to distant data centers: phones recognizing spoken commands without accessing the internet; driverless cars recognizing the world around them with a speed and accuracy that is not possible now.

In other words, a driverless car needs cameras and radar and lasers. But it also needs a brain.

It’s all about tribes - not ideas, morals, or principles.

Thomas Edsall does another excellent piece on what is happening in our politics. I suggest you read it...here are a few clips:

Since the advent of Trump,

...white evangelicals went from being the least likely to the most likely group to agree that a candidate’s personal immorality has no bearing on his performance in public office.

Christopher Achen and Larry Bartels, political scientists at Princeton and Vanderbilt:

In the conventional view, democracy begins with the voters. Ordinary people have preferences about what their government should do. They choose leaders who will do those things, or they enact their preferences directly in referendums. In either case, what the majority wants becomes government policy ..... the more realistic view is that Citizens’ perceptions of parties’ policy stands and their own policy views are significantly colored by their party preferences. Even on purely factual questions with clear right answers, citizens are sometimes willing to believe the opposite if it makes them feel better about their partisanship and vote choices....group and partisan loyalties, not policy preferences or ideologies, are fundamental in democratic politics.

Edsall cites further work showing that those with strongest Republican identification are most likely to embrace Trump's swings in political stance to either the right or the left.

The intractability of racial discrimination

Sobering findings from Quillian et al.:

Significance

Many scholars have argued that discrimination in American society has decreased over time, while others point to persisting race and ethnic gaps and subtle forms of prejudice. The question has remained unsettled due to the indirect methods often used to assess levels of discrimination. We assess trends in hiring discrimination against African Americans and Latinos over time by analyzing callback rates from all available field experiments of hiring, capitalizing on the direct measure of discrimination and strong causal validity of these studies. We find no change in the levels of discrimination against African Americans since 1989, although we do find some indication of declining discrimination against Latinos. The results document a striking persistence of racial discrimination in US labor markets.

Abstract

This study investigates change over time in the level of hiring discrimination in US labor markets. We perform a meta-analysis of every available field experiment of hiring discrimination against African Americans or Latinos (n = 28). Together, these studies represent 55,842 applications submitted for 26,326 positions. We focus on trends since 1989 (n = 24 studies), when field experiments became more common and improved methodologically. Since 1989, whites receive on average 36% more callbacks than African Americans, and 24% more callbacks than Latinos. We observe no change in the level of hiring discrimination against African Americans over the past 25 years, although we find modest evidence of a decline in discrimination against Latinos. Accounting for applicant education, applicant gender, study method, occupational groups, and local labor market conditions does little to alter this result. Contrary to claims of declining discrimination in American society, our estimates suggest that levels of discrimination remain largely unchanged, at least at the point of hire.

Neuroforecasting crowd funding outcomes

Genevsky et al. find that directly measuring brain activities in the nucleus accumbens of individuals while they decide whether to fund proposed projects described on an Internet crowdfunding website proves to be a better predictor of crowding funding outcomes (weeks later) than direct behavioral measurements on the same individuals:

Abstract

Although traditional economic and psychological theories imply that individual choice best scales to aggregate choice, primary components of choice reflected in neural activity may support even more generalizable forecasts. Crowdfunding represents a significant and growing platform for funding new and unique projects, causes, and products. To test whether neural activity could forecast market-level crowdfunding outcomes weeks later, 30 human subjects (14 female) decided whether to fund proposed projects described on an Internet crowdfunding website while undergoing scanning with functional magnetic resonance imaging. Although activity in both the nucleus accumbens (NAcc) and medial prefrontal cortex predicted individual choices to fund on a trial-to-trial basis in the neuroimaging sample, only NAcc activity generalized to forecast market funding outcomes weeks later on the Internet. Behavioral measures from the neuroimaging sample, however, did not forecast market funding outcomes. This pattern of associations was replicated in a second study. These findings demonstrate that a subset of the neural predictors of individual choice can generalize to forecast market-level crowdfunding outcomes—even better than choice itself.

SIGNIFICANCE

Forecasting aggregate behavior with individual neural data has proven elusive; even when successful, neural forecasts have not historically supplanted behavioral forecasts. In the current research, we find that neural responses can forecast market-level choice and outperform behavioral measures in a novel Internet crowdfunding context. Targeted as well as model-free analyses convergently indicated that nucleus accumbens activity can support aggregate forecasts. Beyond providing initial evidence for neuropsychological processes implicated in crowdfunding choices, these findings highlight the ability of neural features to forecast aggregate choice, which could inform applications relevant to business and policy.

Do Americans care about rising inequality?

Interesting ideas from McCall et al.:

Significance

Although rising economic inequality in the United States has alarmed many, research across the social sciences repeatedly concludes that Americans are largely unconcerned about it. We argue that this conclusion may be premature. Here, we present the results of three experiments that test a different perspective—the opportunity model of beliefs about inequality. Tempering the conclusions of past work, the findings suggest that perceptions of rising economic inequality spark skepticism about the existence of economic opportunity in society that, in turn, may motivate support for equity-enhancing policies. Hence, this work calls for new theoretical and methodological approaches to the study of rising economic inequality, especially those that bridge disciplinary boundaries, as well as the largely separate experimental and correlational liter

Abstract

Economic inequality has been on the rise in the United States since the 1980s and by some measures stands at levels not seen since before the Great Depression. Although the strikingly high and rising level of economic inequality in the nation has alarmed scholars, pundits, and elected officials alike, research across the social sciences repeatedly concludes that Americans are largely unconcerned about it. Considerable research has documented, for instance, the important role of psychological processes, such as system justification and American Dream ideology, in engendering Americans’ relative insensitivity to economic inequality. The present work offers, and reports experimental tests of, a different perspective—the opportunity model of beliefs about economic inequality. Specifically, two convenience samples (study 1, n = 480; and study 2, n = 1,305) and one representative sample (study 3, n = 1,501) of American adults were exposed to information about rising economic inequality in the United States (or control information) and then asked about their beliefs regarding the roles of structural (e.g., being born wealthy) and individual (e.g., hard work) factors in getting ahead in society (i.e., opportunity beliefs). They then responded to policy questions regarding the roles of business and government actors in reducing economic inequality. Rather than revealing insensitivity to rising inequality, the results suggest that rising economic inequality in contemporary society can spark skepticism about the existence of economic opportunity in society that, in turn, may motivate support for policies designed to redress economic inequality.

How to regulate Artificial Intelligence

As a postscript to MindBlog's Aug. 23 post on Artificial Intelligence (AI), I pass on chunks from Oren Etzioni's more recent piece on how to artificial intelligence might be regulated in an effort to respond to apocolytic fears being voiced by Elon Musk, Stephen Hawking, and others. While caution is in order, he doesn't think progress in AI should be slowed down over concerns over it will run Amok, because competition with China for primacy is intense. He suggests amending:

...the “three laws of robotics” that the writer Isaac Asimov introduced in 1942: A robot may not injure a human being or, through inaction, allow a human being to come to harm; a robot must obey the orders given it by human beings, except when such orders would conflict with the previous law; and a robot must protect its own existence as long as such protection does not conflict with the previous two laws.

Pointing out their ambiguity, he suggests an alternative set of rules, as a starting point for further discussion:

1...an A.I. system must be subject to the full gamut of laws that apply to its human operator. This rule would cover private, corporate and government systems. We don’t want A.I. to engage in cyberbullying, stock manipulation or terrorist threats; we don’t want the F.B.I. to release A.I. systems that entrap people into committing crimes. We don’t want autonomous vehicles that drive through red lights, or worse, A.I. weapons that violate international treaties.

2...an A.I. system must clearly disclose that it is not human. As we have seen in the case of bots — computer programs that can engage in increasingly sophisticated dialogue with real people — society needs assurances that A.I. systems are clearly labeled as such. In 2016, a bot known as Jill Watson, which served as a teaching assistant for an online course at Georgia Tech, fooled students into thinking it was human. A more serious example is the widespread use of pro-Trump political bots on social media in the days leading up to the 2016 elections, according to researchers at Oxford.

3...an A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information. Because of their exceptional ability to automatically elicit, record and analyze information, A.I. systems are in a prime position to acquire confidential information. Think of all the conversations that Amazon Echo — a “smart speaker” present in an increasing number of homes — is privy to, or the information that your child may inadvertently divulge to a toy such as an A.I. Barbie.

How “ought” exceeds but implies “can”

From John Turri:

This paper tests a theory about the relationship between two important topics in moral philosophy and psychology. One topic is the function of normative language, specifically claims that one “ought” to do something. Do these claims function to describe moral responsibilities, encourage specific behavior, or both? The other topic is the relationship between saying that one “ought” to do something and one’s ability to do it. In what respect, if any, does what one “ought” to do exceed what one “can” do? The theory tested here has two parts: (1) “ought” claims function to both describe responsibilities and encourage people to fulfill them (the dual-function hypothesis); (2) the two functions relate differently to ability, because the encouragement function is limited by the person’s ability, but the descriptive function is not (the interaction hypothesis). If this theory is correct, then in one respect “ought implies can” is false because people have responsibilities that exceed their abilities. But in another respect “ought implies can” is legitimate because it is not worthwhile to encourage people to do things that exceed their ability. Results from two behavioral experiments support the theory that “ought” exceeds but implies “can.” Results from a third experiment provide further evidence regarding an “ought” claim’s primary function and how contextual features can affect the interpretation of its functions.

Debate over a scientific wellness study.

Ryan Cross discusses reactions to a new "Scientific Wellness" pilot study set up by distinguished biologist Lee Hood and their recent report on the effort in Nature Biotechnology:

Leroy “Lee” Hood is one of biology's living legends. Now 78 years old, he played an influential role in the development of the first automated DNA sequencer, pioneered systems biology, and still leads an institute devoted to it in Seattle, Washington. But his latest venture may not burnish his reputation: a company promoting “scientific wellness,” the notion that intensive, costly monitoring and coaching of apparently healthy people can head off disease.

In a pilot study of the concept, Hood and colleagues compiled what he calls “personal, dense, dynamic data clouds” for 108 people: full genome sequences; blood, saliva, urine, and stool samples taken three times at 3-month intervals and analyzed for 643 metabolites and 262 proteins; and physical activity and sleep monitoring. The team reports in the August issue of Nature Biotechnology that dozens of the participants turned out to have undiscovered health risks, including prediabetes and low vitamin D, which the coaching helped them address...nearly every participant had something to worry about: Ninety-five had low vitamin D levels, 81 had high mercury levels, and 52 were considered prediabetic. One person had high blood levels of the iron-containing protein ferritin and a genetic risk for developing hemochromatosis

Hood says the findings justify commercializing the monitoring, in a service costing thousands of dollars a year. But some colleagues disagree. The effort takes health monitoring “to new heights, or depths, depending on how you look at it,” says Eric Topol, director of the Scripps Translational Science Institute in San Diego, California....many of the problems the monitoring uncovered could be detected with simpler and cheaper tests, he adds.

Clayton Lewis, one of the subjects in the first study,

...joined with study leaders Nathan Price and Hood to launch the new company, called Arivale, with Lewis as CEO. Now 2 years old, the Seattle-based company has already enrolled 2500 people. They pay a first-year $3499 subscription fee for tracking and analysis similar to the pilot study, and nearly all have opted to let their data be used in research by Hood's Institute of Systems Biology.

From Jonathan Berg, a physician scientist who studies cancer and genetics at the University of North Carolina School of Medicine in Chapel Hill:

The problem is that we don't have any idea at all how this information should be used clinically. Topol agrees, noting that he had comparable concerns about a similar barrage of tests on presumably healthy people, including genome sequencing and a full-body MRI scan, from a company launched by another genome legend, J. Craig Venter.

Oxytocin reduces xenophobic outgroup rejection.

This work from Marsh et al. suggests that it might be useful to spritz a bit of oxytocin up the noses of alt-right, alt-left, fascist, and antifa partisans:

Significance

In the midst of rapid globalization, the peaceful coexistence of cultures requires a deeper understanding of the forces that compel prosocial behavior and thwart xenophobia. Yet, the conditions promoting such outgroup-directed altruism have not been determined. Here we report the results of a double-blind, placebo-controlled experiment showing that enhanced activity of the oxytocin system paired with charitable social cues can help counter the effects of xenophobia by fostering altruism toward refugees. These findings suggest that the combination of oxytocin and peer-derived altruistic norms reduces outgroup rejection even in the most selfish and xenophobic individuals, and thereby would be expected to increase the ease by which people adapt to rapidly changing social ecosystems.

Abstract

Never before have individuals had to adapt to social environments defined by such magnitudes of ethnic diversity and cultural differentiation. However, neurobiological evidence informing about strategies to reduce xenophobic sentiment and foster altruistic cooperation with outsiders is scarce. In a series of experiments settled in the context of the current refugee crisis, we tested the propensity of 183 Caucasian participants to make donations to people in need, half of whom were refugees (outgroup) and half of whom were natives (ingroup). Participants scoring low on xenophobic attitudes exhibited an altruistic preference for the outgroup, which further increased after nasal delivery of the neuropeptide oxytocin. In contrast, participants with higher levels of xenophobia generally failed to exhibit enhanced altruism toward the outgroup. This tendency was only countered by pairing oxytocin with peer-derived altruistic norms, resulting in a 74% increase in refugee-directed donations. Collectively, these findings reveal the underlying sociobiological conditions associated with outgroup-directed altruism by showing that charitable social cues co-occurring with enhanced activity of the oxytocin system reduce the effects of xenophobia by facilitating prosocial behavior toward refugees.

Evaluation of brain training programs.

By now there is a consensus that most claims about brain training making improving mental agility have no scientific basis. Most brain training only makes you better at the exercises themselves, and doesn't carry those gains over to your real-world concentration, productivity, or mental acuity. An article by Grothaus suggests that a single exception may be BrainHQ and Cognifit exercises that focus on improving visual processing speed. I've done the BrainHQ 'double decision' exercise, in which

You see an image in the center of your vision–for example, either a car or a truck–and at the same time, you see another image way off in your peripheral vision. The images are only on the screen for a brief period of time–well under a second. You then have to say whether you saw the car or the truck in the center of your vision, and then you have to show where you saw the image in your peripheral vision. This challenges the speed and the accuracy of your visual system. And as you get faster and more accurate, the speed increases and the peripheral vision task gets more demanding–pushing your brain further.

I have noticed that doing this exercise for about 10 min a day over a period of days enhances my attention to and awareness of peripheral visual details while I am driving. The effect wears off if the exercises are stopped.

Grothaus ends his article with the usual advice to those who aren't inclined towards computer games: engage novelty, be physically active, eat right, etc.

American Nirvana

I've been meaning to point to an engaging article by polymath Adam Gopnik reviewing Robert Wright's recent book "Why Buddhism Is True." (Wright has written popular books on evolutionary psychology.) Gopnik briefly reviews the major appearances of Buddhism in late 19th century New England and then in the nineteen-fifties, spurred by writings of Suzuki and others. A few clips from the review, which I encourage you to read in its entirety:

Wright, like his Bay Area and Boston predecessors, is delighted to announce the ways in which Buddhism intersects with our own recent ideas. His new version of an American Buddhism is not only self-consciously secularized but aggressively “scientized.” He believes that Buddhist doctrine and practice anticipate and affirm the “modular” view of the mind favored by much contemporary cognitive science. Instead of there being a single, consistent Cartesian self that monitors the world and makes decisions, we live in a kind of nineties-era Liberia of the mind, populated by warring independent armies implanted by evolution, representing themselves as a unified nation but unable to reconcile their differences, and, as one after another wins a brief battle for the capital, providing only the temporary illusion of control and decision. By accepting that the fixed self is an illusion imprinted by experience and reinforced by appetite, meditation parachutes in a kind of peacekeeping mission that, if it cannot demobilize the armies, lets us see their nature and temporarily disarms their still juvenile soldiers.

Meditation, in Wright’s view, is not a metaphysical route toward a higher plane. It is a cognitive probe for self-exploration that underlines what contemporary psychology already knows to be true about the mind.

Meditation shows us how anything can be emptied of the story we tell about it: he tells us about an enlightened man who tastes wine without the contextual tales about vintage, varietal, region. It tastes . . . less emotional. “All the states of equanimity come through the realization that things aren’t what we thought they were,” Wright quotes a guru as saying. What Wright calls “the perception of emptiness” dampens the affect, but it also settles the mind. If it isn’t there, you don’t overreact to it.

Simply to sit and breathe for twenty-five minutes, if only to hear cars and buses go by on a city avenue—listening to the world rather than to the frantic non sequiturs of one’s “monkey mind,” fragmented thoughts and querulous moods racing each other around—can intimate the possibility of a quiet grace in the midst of noise.

A faith practice with an authoritarian structure sooner or later becomes a horror; a faith practice without an authoritarian structure sooner or later becomes a hobby. The dwindling down of Buddhism into another life-style choice will doubtless irritate many, and Wright will likely be sneered at for reducing Buddhism to another bourgeois amenity, like yoga or green juice...Yet what Wright is doing seems an honorable, even a sublime, achievement. Basically, he says that meditation has made him somewhat less irritable. Being somewhat less irritable is not the kind of achievement that people usually look to religion for, but it may be as good an achievement as we ought to expect. (If Donald Trump became somewhat less irritable, the world would be a less dangerous place.)

Washing away your sins in the brain.

From Tang et al.

The association between moral purity and physical cleanliness has been widely discussed recently. Studies found that moral threat initiates the need of physical cleanliness, but actual physical cleaning and priming of cleaning have inconsistent effects on subsequent attitudes and behaviors. Here, we used resting-state functional magnetic resonance imaging to explore the underlying neural mechanism of actual physical cleaning and priming of cleaning. After recalling moral transgression with strong feelings of guilt and shame, participants either actually cleaned their faces with a wipe or were primed with cleanliness through viewing its pictures. Results showed that actual physical cleaning reduced the spontaneous brain activities in the right insula and MPFC, regions that involved in embodied moral emotion processing, while priming of cleaning decreased activities in the right superior frontal gyrus and middle frontal gyrus, regions that participated in executive control processing. Additionally, actual physical cleaning also changed functional connectivity between insula/MPFC and emotion related regions, whereas priming of cleaning modified connectivity within both moral and sensorimotor areas. These findings revealed that actual physical cleaning and priming of cleaning led to changes in different brain regions and networks, providing neural evidence for the inconsistent effects of cleanliness on subsequent attitudes and behaviors.

Mindfulness management of stress and inflammation

I pass on a description from the Univ. of Wisconsin Center for Healthy Minds of research suggesting that mindfulness meditation may be an effective way to manage inflammation the the expression of disease. Their text:

In one study in the journal Brain, Behavior, and Immunity, the group compared people with asthma that had high versus low levels of chronic stress. Both groups were exposed to an acute stressor. During exposure to the stressor, the increase in activity in the mid-insula – a part of the brain involved in bi-directional influence with the state of the body – was associated with greater stress reactivity and predicted subsequent airway inflammation after the stressor. The findings provide support for the idea that psychological stressors result in detrimental outcomes in inflammatory disease expression, particularly in people experiencing chronic life stress.

In another study, Rosenkranz and scientists measured inflammatory responses in experienced meditators and people with no or little meditation experience. By examining participants’ responses to an acute stressor through their levels of cortisol – a stress hormone – in saliva samples and inflammatory response to a topical capsaicin cream, the team found that experienced meditators showed lower reactivity, suggesting that meditation practices may be helpful in mitigating inflammatory responses brought about by psychological stress.

With roughly 10 percent of the U.S. population living with asthma, and inflammation being a contributor to many other chronic conditions such as cancer, heart disease and Alzheimer’s disease, Rosenkranz says the findings are important in challenging the medical community to look beyond pharmaceutical approaches to address these physical manifestations of disease and to also consider strategies that harness the influence of the mind on the body.

How to make time slow down.

Many days I feel by 5 p.m. like my day has evaporated without my noticing it. I recall that when I was 20-40 years old my days seems to stretch out much longer. Cooper does a piece on the interesting science of time perception that explains how this has a lot to do with my being in my 76th year. Put most simply, when we are younger we are attending to more new information, it takes our brains a while to process it all, and the longer this processing takes, the longer that period of time feels. When we are older we typically are taking in information we've processed before ("I've see it all."), the brain doesn't work so hard, so it processes time faster.

Our ‘sense’ of time is unlike our other senses—i.e. taste, touch, smell, sight and hearing. With time, we don’t so much sense it as perceive it...our brains take a whole bunch of information from our senses and organize it in a way that makes sense to us, before we ever perceive it. So what we think is our sense of time is actually just a whole bunch of information presented to us in a particular way, as determined by our brains.

When our brains receive new information, it doesn’t necessarily come in the proper order. This information needs to be reorganized and presented to us in a form we understand. When familiar information is processed, this doesn’t take much time at all. New information, however, is a bit slower and makes time feel elongated...it isn’t just a single area of the brain that controls our time perception—it’s done by a whole bunch of brain areas, unlike our common five senses, which can each be pinpointed to specific area.

So, here's the self-helpy message: How do we make our days last longer? We can feed our brains more new information - keep learning, visit new places, meet new people, try new activities, be spontaneous. The extra processing time required will make us feel like time is moving more slowly! 

[[By the way, sharp readers will have noted a conflict of the above with yesterday's blog post, namely in the statement above with "Our ‘sense’ of time is unlike our other senses—i.e. taste, touch, smell, sight and hearing. With time, we don’t so much sense it as perceive it..." While the basic message above is still OK, yesterday's post points out that we don't directly 'sense it', i.e.  directly taste, touch, smell, see, and hear... the function of that sensory input is to test and tweak our top-down ongoing model of tasting, touching, smelling, seeing. That model, like our perception of time, is a derivative perception, which can also be altered in various ways.]]

An essay on the real problem of consciousness.

For those of you who are consciousness mavens, I would recommend having a glance at Anil Seth’s essay, which does a clear headed description of some current ideas about what consciousness is. He summarizes the model of consciousness as an ensemble of predictive perceptions. Clips from his essay:

The classical view of perception is that the brain processes sensory information in a bottom-up or ‘outside-in’ direction: sensory signals enter through receptors (for example, the retina) and then progress deeper into the brain, with each stage recruiting increasingly sophisticated and abstract processing. In this view, the perceptual ‘heavy-lifting’ is done by these bottom-up connections. The Helmholtzian view inverts this framework, proposing that signals flowing into the brain from the outside world convey only prediction errors – the differences between what the brain expects and what it receives. Perceptual content is carried by perceptual predictions flowing in the opposite (top-down) direction, from deep inside the brain out towards the sensory surfaces. Perception involves the minimisation of prediction error simultaneously across many levels of processing within the brain’s sensory systems, by continuously updating the brain’s predictions. In this view, which is often called ‘predictive coding’ or ‘predictive processing’, perception is a controlled hallucination, in which the brain’s hypotheses are continually reined in by sensory signals arriving from the world and the body. ‘A fantasy that coincides with reality,’ as the psychologist Chris Frith eloquently put it in Making Up the Mind (2007).

...instead of asking which brain regions correlate with conscious (versus unconscious) perception, we can ask: which aspects of predictive perception go along with consciousness? A number of experiments are now indicating that consciousness depends more on perceptual predictions, than on prediction errors. In 2001, Alvaro Pascual-Leone and Vincent Walsh at Harvard Medical School asked people to report the perceived direction of movement of clouds of drifting dots (so-called ‘random dot kinematograms’). They used TMS to specifically interrupt top-down signalling across the visual cortex, and they found that this abolished conscious perception of the motion, even though bottom-up signals were left intact.

More recently, in my lab, we’ve been probing the predictive mechanisms of conscious perception in more detail. In several experiments...we’ve found that people consciously see what they expect, rather than what violates their expectations. We’ve also discovered that the brain imposes its perceptual predictions at preferred points (or phases) within the so-called ‘alpha rhythm’, which is an oscillation in the EEG signal at about 10 Hz that is especially prominent over the visual areas of the brain. This is exciting because it gives us a glimpse of how the brain might actually implement something like predictive perception, and because it sheds new light on a well-known phenomenon of brain activity, the alpha rhythm, whose function so far has remained elusive.