The World Happiness Report 2018 has been published today (but no e-copy is available yet), so I will wait for the e-copy to became available. Meanwhile, as I was anticipating the report and was in an analytical mood, I reread the World Happiness Report 2017 and want to share some of my thoughts and observation based around that while we get ready for the new report to take the conversation forward.
GDP (PPP) Per Capita based on 2008 estimates http://www.imf.org/ (Photo credit: Wikipedia)
The World Happiness reports are based around measuring life satisfaction using a Cantril ladder and this is used as a proxy for happiness/ subjective well-being in most of the analysis. Sometimes, positive and negative affect, as experienced the day before, is also used as a measure of experiential happiness.
The world happiness report measures happiness of more than 150 countries, sampling about 1000 respondents in each country and uses data from Gallup World Poll. The Cantril ladder measures national happiness on a scale form 0 to 10 and the top 10 happiest nations have an average national happiness level of about 7.4, while the most miserable, bottom 10 nations had an average national happiness of only about 3.4 , thus there being around 4 point gap of happiness that if bridged can make the world more happier.
The report measures six other correlates viz GDP per capita, social support, healthy life expectancy, social freedom, generosity, and absence of corruption, constructs that are theoretically and empirically linked to well-being. As expected GDP per capita and healthy life expectancy, which are indications of material prosperity, do have an impact on national happiness, but the rest of the four factors that make up the social fabric of the country have a much larger effect.
To illustrate, social support was measured by a yes/no answer to the question as to whether one could count on someone in times of need. If one could move 10 % more people (who reported no) towards yes, then the increase in national happiness is predicted to be of the same amount that would be achieved by doubling the per capita GDP. And of course doubling the per capita GDP is much more difficult than ensuring that 10 % more population have someone they can count to in times of stress.
Similar effect, though of lower magnitude, was present for the rest of the social indicators. Also, other parameters like Gini coefficient which measures income inequality , and well-being inequality itself, were found to be associated with lower national well-being.
The case that economic growth and GDP is not the be all and end all, is aptly illustrated by the case study of China. China raised its GDP five fold between 1990’s and 2015-16, but the Subjective well- being (SWB) actually declined. The SWB during this period was U shaped with a trough in 2000-05, while the GDP was actually increasing and inflation at an all time low. As per this economic trend, SWB should have increased or at least maintained it 1990’s levels.
However, the situation becomes crystal clear when one looks at graphs showing unemployment rate and social fabric/ safety net indices (pension/ health benefits) during the same time which clearly paint a different picture of China’s economy and social method of alleviating misery structure. The unemployment rates peaked in 2000-05 while the safety net showed a trough, and this causally explained the trough in SWB much better, than the GDP story. Further analysis showed that it is those who are at lower rungs of economic ladder who are most affected in such circumstances.
The story of America is similar: per capita GDP growth which has tripled since 1960 has not lead to corresponding gains in happiness; as a matter of fact SWB is declining while GDP is growing in recent years. This is attributed to breakdown in social fabric.
An interesting fact that was highlighted by data from African nations, was that happiness depends on good governance and this can be conceptualized as both the ability to deliver services as well as democratic institutions. It was found that ability to deliver services was much more important, at least in African context, and people of Africa were willing to trade democracy for access to services.
The report also had a section on how we can best alleviate misery and increase happiness for the maximum people; increasing income, increasing years of education, reducing unemployment, ensuring people stay married/ have a partner, preventing physical illness and preventing mental illness (depression and anxiety) were all considered important as each of this predicts happiness. However, it was found that the mots cost effective is by focusing on alleviating mental illness as that impact happiness levels more than anything, including physical illness.
Another analysis showed that emotional health at age 16 was better predictor of adult happiness than academic competence at that age. This makes a strong case for focusing on emotional and behavioral development of children and for positive education.
Another section of the report looked at work determinants of happiness and found that unemployment was again a big no-no, causing a lot of misery directly and indirectly even in those not unemployed. Of course blue collar workers had lower satisfaction levels than white collar workers and the usual factors that affect job and overall satisfaction, like autonomy at work were highlighted.
Overall, I think its a wake up call to policy makers, to focus more on social determinants of happiness and not get obsessed by economic indices like per capita GDP. I’m hoping 2018 report builds on these earlier observation and makes a strong case for policy changes.
English: A diagram to illustrate the layout of a hierarchical organisation. (Photo credit: Wikipedia)
The top level of the hierarchy consists of metatraits of Stability and Plasticity also called Alpha and Beta. Stability is related to the shared variance between Neuroticism, Conscientiousness and Agreeableness while Plasticity consists of Extraversion and Openness/Intellect. Stability refers to the fact that one has consistency of goals, interpretations and strategies; someone with low Stability will easily abandon goals etc due to internal or external disturbances. Plasticity refers to the fact that any dynamical system needs to also explore its environment for new goals, interpretations and strategies. Someone rigid with low Plasticity would lose on opportunities that are present in the uncertain environment out there.
At the intermediate level of hierarchy lie the Big Five each consisting of exactly two aspects. The aspects may be further made up on n number of facets. We will mostly not go beyond the aspects and focus more on the five Big Five traits and the 10 aspects. I have previously too blogged about CB5T and readers may find it interesting to read that post to see how my thoughts have evolved.
Cybernetics thinks in terms of goal driven self-organizing systems and that is the framework that I will be using here. I have blogged previously about there being different types of goals: approach goals, Avoidance goals, learning goals, performance goals and differences in conceptualizing a goal as either approach or avoidance, learning or performance has different implications that are well established.
I think in terms of underlying Psychological / brain based systems and believe that we can decompose the human systems into these subsystems:
The Avoidance System: Goals are conceptualized as avoidance goals i.e a reference state that is to be avoided is on top of the mind. This system is sensitive to cues of threats and punishments and uncertainty. Punishment is something that happens when avoidance is not successful and threat is any impending punishment. The variation in parameters of this system lead to variation in Neuroticism trait in humans. Serotonin system is typically found to be associated with his subsystem. Avoidance can be further be of two types:
BIS (behavioral Inhibition System): Passive avoidance whereby whenever there is a conflict between an avoidance goal and an approach goal, the approach goal/actions are inhibited. This aspect is also called Withdrawal and is associated with depression and Anxiety.
FFFS (Flight-Fight-Freeze-Faint system) : Active avoidance or escape where one exhibits anger or rage or panic but is geared to do something about the situation. This aspect is also called Volatility and is associated with anger disorders.
The Approach system: Goals are conceptualized as approach goals i.e a rewarding stimuli that has to be pursued and achieved. This system is sensitive to cues of reward and associated with the dopamine system. This is further made up of:
BAS (behavioral activation system) : This is the ‘wanting’ system as opposed to the ‘liking’ system, a difference that was first proposed by Berridge. This is more directly related to dopamine and is also called Assertiveness aspect and composed of drive etc.
Pleasure system : This pleasure system is related to opioid systems and is related to the ‘liking’ system or the hedonistic pleasure one feels when consummating a goal. It is related to Enthusiasm aspect and marked by positive emotions, sociability etc.
The Attend (Learning) system: Any dynamic system is sort of torn between whether to learn more about the system to increase it performance in future or to act in such a way as to maximize its performance in present. This system, which is marked by openness/ intellect, is a cognitive exploration system associated with the dopamine system. This is further made up of:
Imagination System: This is related to the openness aspect and related to apophenia or psychosis proneness. The key mechanisms here is finding patterns and correlations between sensory and perceptual inputs.
Intelligence system: This is related to the Intellect aspect and related to things like working memory capacity. The key mechanisms here are finding causal and logical relations between semantic and abstract information.
The Achievement (Performance) system: This system is focused around achieving long term goals by focusing in the here and now and following rule and procedures. This system, marked by Conscientiousness, is further made up of two parts:
The grit system: This is related to Industriousness aspect whereby one overcomes distractions in the service of non-immediate goals. This is probably a top down process based around inhibiting distracting stimuli.
The discipline system: This is related to Orderliness aspect whereby one wants to tend towards perfectionism and following routines and self made or other made rules to achive efficiency.
The Attachment (Interpersonal) system: This system is focused around increasing cooperation and ensuring altruism among man, the social animal. The dysfunctions of this system lead to the Dark Tetrad of personality. This system is marked by Agreeableness trait and is made of two parts:
The caring system: This is related to the bottom up processes of compassion and caring built on the foundations of empathy. The Compassion aspect is relevant here with its opposite pole being callousness. The opposite pole would be characterized by sadism and psychopathy.
The social system: This is related to the top down process of curbing anti-social impulses and tendencies etc by reigning in those baser instincts. The Politeness aspect and its opposite pole that of exploitativeness is relevant here. The opposite pole would be characterized by Machiavellianism and Narcissism.
Overall, this CB5T layout maps well to the ABCDS framework.
Neuroticism, or its opposite emotional stability is related to Affective stability.
Extraversion is related to Behavioral exploration.
Openness/Intellect is related to Cognitive exploration.
Conscientiousness is related to Motivational/ Dynamic stability.
Agreeableness is related to Social stability.
Together , the ABCD(S) model and the CB5T model make immense sense and provide a good way to characterize the personality structure.
IQ is used synonymous and interchangeably with intelligence; however in this paper [pdf] Angela Duckworth et al argue that non-cognitive factors like test motivation also affect the IQ scores and have differential predictive validity.
Raven’s Progressive Matrices Example (Photo credit: Wikipedia)
Intelligence, which is the ability to flexibly adapt to complex situations, is usually measured using IQ scores on intelligence tests. IQ scores however do not measure juts the raw intelligence; they also measure how motivated someone is to take the test and achieve a high score.
Intelligence tests, that lead to IQ Scores, are supposed to measure the maximal intelligence ability that a person has and not the typical intelligence that he/she uses. In all intelligence testing it is assumed that the person will devote his entire attention and exert the maximum effort possible so as to achieve the highest score possible.
While the assumption that IQ measures maximal intelligence may be true in high-stake testing situations, where the IQ results would be used for academic admissions, job placement or promotions; in normal measurement of IQ, say in a typical school setting, the stakes are quite low (there are no real/tangible repercussions of doing bad or well on the test) and hence IQ does not typically measure the maximal intelligence, but is confounded by test motivation.
Test motivation refers to the fact that some people will be less motivated to take the test or continue with it and may display behaviors that indicate low motivation. While others may be highly motivated to take the IQ test. Thus, there would be individual differences at trait level on test motivation.
Test motivation is also a state variable that can be manipulated by incentivizing getting high scores on the tests. When such incentives are in place, the IQ score should increase from the baseline level or when the test was given under non-incentivized conditions.
Intelligence, as measured by IQ, has been associated with a number of good outcomes. Non cognitive factors as measured by test motivation are also theoretically linked to important life outcomes. For the purposes of this paper, two academic outcomes (years of education and academic achievement) and two non-academic outcomes (employment and criminal conviction) were measured and analyzed.
The study 1 performed a meta-analysis of various independent samples where a comparison was made between the IQ scores received in standardized conditions vis-a-vis under incentivized conditions. For analysis the sample was divided in high IQ (those with IQ greater than 100) and low IQ (those with IQ less than 100). The main results were that incentives did result in higher IQ scores, the effect was stringer for low IQ group and there was dose-response effect with larger incentives leading to greater IQ points gains.
Thus, for low IQ group, the lower IQ scores in standardized conditions could be due to lower intelligence or lower test motivation. If you increase the test motivation, you could bump up the IQ score of some of them. High IQ group, on the other hand had higher scores because they had both higher intelligence and higher test motivation.
In study 2, a thin-slice video of children giving the intelligence test was behaviorally rated for signs of low test motivation. This was a longitudinal study and the IQ scores, test motivation and four types of outcomes were analyzed to find the differential impact of IQ/intelligence and test-motivation/ non-cognitive factors on life outcomes.
The main finding was that test motivation had a significant impact, independent of IQ, on important life outcomes. This was specially pronounced for nonacademic outcomes like employment and criminal convictions. Intelligence as measured by IQ still had significant effect on all adult outcomes. They also found that test motivation predicted IQ scores, thus IQ score measures both intelligence and test motivation.
This is an important paper [pdf] that shows that IQ scores need to be interpreted with caution, and that both cognitive and non-cognitive factors are important for life outcomes.
Today’s research summary builds on the work of Gabrielle Oettingen on WOOP/mental contrasting with implementation intentions. The paper [pdf] is co-authored by Angela Duckworth et al and successfully demonstrates the utility and incremental benefit of mental contrasting over mere positive thinking in achieving desired outcomes.
The Power of Positive Thinking (EP) (Photo credit: Wikipedia)
When one wants to achieve goals, then the first step is to clearly articulate the desired goal. It has been shown that merely having a goal vs not having a clear goal is instrumental in goal achievement. Another process that is usually implicated in successful goal achievement is positive thinking, where you clearly visualize the positive outcomes from having achieved the goal.
The exercise ‘Best possible future selves’ is predicated on the same premise that visualizing a better future self leads to increase in hope and optimism and positive striving to achieve the goal.
In popular parlance though, positive thinking is equated with not thinking about any negatives at all, including the possible obstacles that may lie in the path. This obsession with just the positive aspects of future, to the ignoring of the current reality, may have detrimental effects as one’s commitment to the goal may not change with mere positive future visualizing.
Mental contrasting is a technique whereby a positive future outcome visualization is contrasted with current reality and the client encouraged to think about internal obstacles within them that may hamper the goal achievement.
Goal commitment is hypothesized to be made of two components: Goal desirability( which apparently does not change with either mental contrasting or positive future visualizing) and Goal feasibility (goal commitment increases in mental contrasting if the goal is considered of high feasibility as the current reality/obstacles become surmountable in one’s mind’s eyes; on the other hand if goal feasibility is low then goal commitment becomes less as the obstacles seem insurmountable and the goal is disengaged from while doing mental contrasting)
While the exact mechanism of how mental contrasting works in not known, it is believed to work by increasing efforts (towards overcoming surmountable obstacles) , by using better strategies ( for example to remain focused and not get distracted) or by seeking help from others.
The current studies consisted of making the class 2, 3 or 5 grade students learn foreign language words, and this learning was incentivized by promises of candy bag or small monetary reward (5 $). The gap between learning and recall varied from 2 weeks to 4 days. There were two conditions: in the positive future condition, the students filled out a section in which they listed the best possible outcomes from having mastered the foreign vocabulary words. In the mental contrasting condition, the students besides writing the best possible outcome, also reflected and wrote, what within them may prevent their achieving the goal of mastering the foreign language vocabulary.
The foreign language vocabulary task was something that was within the capability of the students and was thus considered a task with high goal feasibility and thus should have led to greater goal commitment in the mental contrasting condition.
Across two studies they found that indeed there was significant difference in recall of foreign language words between the two conditions, with mental contrasting leading to better learning/ recall.
One big limitation of the study , which is acknowledged by authors in the limitations section, is that they did not include a neutral control condition in which neither positive future visualization nor mental contrasting was used. It would have been interesting to know how big an impact positive visualization has and how big an impact mental contrasting has over and above that.
This paper is of immense practical utility as it showed that mental contrasting can also be used in group settings and is effective with minimum instructions and for a common goal. This enables tools like WOOP which build on this research to be extended to group settings. I myself use WOOP in my work with school children and have found it very useful.
Overall it is a pretty decent paper [pdf] that shows the benefits of mental contrasting over mere positive future visualization.
Today’s research summary is based on a shortish paper [pdf] by Angela Duckworth et al (Walter Mischel of Marshmallow effect fame is a co-author!) which focuses on how viewing oneself from a distance, or from a third person perspective, a previous emotional experience, can lead to better and more adaptive outcomes.
Out of body experience (Photo credit: Wikipedia)
Bad stuff happens. And we make it worse by brooding about it. There is some research that shows that thinking or ruminating about negative experiences can lead to bad outcomes in the present like compromised health or impeded cardiovascular recovery following exercise etc. Ruminative thinking style is known as a precursor and risk factor for depression.
On the other hand there is a rich tradition of expressive writing (for e.g. Pennebaker’s work) in which people write about their negative experiences and traumas and seem to benefit (boosts in long term mood and well-being) from such an expressive act.
Different sort of mechanisms are hypothesized in both the above cases. In the first case, one may be reliving the negative experience or recounting it and thus get overwhelmed once more in the present by such a recollection. In the second case, one may be reinterpreting the situation and making fresh sense of the events or reconstruing the events. So reflecting in a negative experience per se may not be bad or good but may lead to a good outcome only when reconstruing happens more than recounting.
Putting a distance between oneself or seeing events from a detached third person perspective have been shown to increase one’s self control and control one’s impulses and also helpful in alleviating depression by enabling better cognitions. It has been hypothesized that self-distancing or viewing things form a detached third person perspective will lead to better and more adaptive outcomes while self-reflecting, as one will not recount or relive the experiences but will be better able to reconstrue or make new sense of the experiences.
The current study looked at ~ 100 fifth grade students and asked them to recollect a negative angry outburst/ interaction which was interpersonal in nature. They were then instructed either to feel the event as of it was happening in the present and they were at the center of the action, or that they were watching the event unfold from a distance and observing the distant self. After they had recalled the experience in both conditions, they filled a brief survey measuring their emotional reactivity (how much power the vent still holds over them) and avoidance behavior (do they avoid talking/ thinking about that issue) . They were also asked to write an essay about their reflection and the essay was content analyzed for recounting thoughts, reconstruing thoughts and blame attributions.
The results showed that when you put a distance between self while recollecting a negative experience, then the emotional reactivity is lesser than when you feel as if you are reliving the experience. Thus, if you want to make a negative experiences hold smaller on you recollect it while putting a distance from self. Thus it was clear that self-distancing was a more adaptive outcome.
They also found that those students who had put a distance between their earlier self while reflecting on their angry interaction, had fewer recounting statements in their essays and more reconstruing statements. They also made fewer blame attributions.
They also did a path analysis and found that self-distancing had its impact on more adaptive outcomes (less negative affect and emotional reactivity) via the mediating variables of more reconstruing statements than recounting statements, which in turn led to lesser blame attributions and thus a closure that led to lesser emotional reactivity.
The take home message, children can benefit form self reflective exercises that make them reflect on negative experiences as long as they are supported in putting a distance between themselves and their past self, so that they don’t merely recount the experience but are able to reconstrue the experience.
Overall, a pretty decent paper [pdf] that stresses the importance of self-distancing while reflecting about past negative experiences.
Today’s research summary looks at another paper [pdf] by Angela Duckworth et al this time focusing on whether it makes sense to include personality variables in long national longitudinal surveys/studies like the MIDUS/ Dunedin/ HRS.
Nonconcordant traits (Photo credit: Wikipedia)
Personality differences can be conceptualized to be either differences in ability (like cognitive ability), traits (stable patterns of thinking, feeling, acting) , motives or narratives and this paper focuses on traits to the exclusion of other measures of personality. Even in traits, the traits of concern are the Big Five traits of Neuroticism, Extraversion, Agreeableness, Conscientiousness and Openness.
Personality, in general, and these traits, in particular, are known to predict a range of outcomes like health, achievement, and relationships. The authors believe that large panel surveys should measure these traits to find the correlations with other outcomes being measured. They review research on how traits predict wealth and health and are predicted by underlying genetic polymorphisms or variations.
For elaborating the association between traits and genes they look at candidate gene studies as well as GWAS. Extraversion is associated with polymorphisms in Dopamine subsystem related genes. Nueroticism is primarily associated with serotenergic genes. Agreeableness and Conscientiousness are both affected by polymorphsism in genes related to dopamine as well as serotonin. Openness to COMT variation. Read the paper to get additional nuances.
When it comes to economic outcomes, more introverted and more emotionally stable (less in neuroticism) individuals were more likely to save over the lifetime and borrow less; reverse was found for those high in agreeableness. Emotional stability was the best predictor of earnings; extraversion had a complex relation but overall positively predicted earnings; while agreeableness had a very slight negative impact on earning.
In terms of health, traits like Conscientiousness had a direct effect on health as well as indirect effects mediated by healthy behaviors and educational attainments. In general it is safe to conclude that personality traits do not affect health outcomes directly but by their impact on problematic or protective behaviors. Personality traits have also been linked to mortality.
The authors recommend that personality traits should be measured in large panel studies, and measured as far as consistently, using say BFI, so that they can be used to predict important life outcomes. Moreover they recommend that as personality traits can change , they should be treated as dependent variables too and measured in each subsequent measurement time.
One recommendation they have is to keep such trait measures short and relevant; also they recommend multiple measures using informant reports or cognitive tests like go-no go task. However I ‘m not sure if that may be practical in large surveys.
They also highlight the concerns about ‘flush-right’ responding where some unmotivated participants who are juts going through the motions of filling the survey may keep choosing the extreme right option making the survey results suspect. The instruments should have something inbuilt to detect such responding just like one detects social desirability.
Overall its a pretty decent paper to understand some of the antecedents (genetics) as well as consequents (health and wealth) of Big Five traits and makes a strong case for incorporating big five measures in such large scale studies and surveys. Check the paper here [pdf] .
Today’s research summary is slightly technical. It is based on this paper [pdf] by Angela Duckworth et al that shows a causal relation between self-control and academic achievement.
The Illustrated Sutra of Cause and Effect. 8th century, Japan (Photo credit: Wikipedia)
Some personality variables like self-control predict important life outcomes. It is well know that self-control as measured at age 4 (using the marshmallow test) can predict important life outcomes years later. However, prediction may not imply causality as a third factor may be responsible for causing both the phenomena under consideration.
The test for causality is a) causal variable must precede the effect in time; b) the causal variable and outcome variable should be correlated; and c) any third party confound or variable should be ruled out. This is easy to achieve in double blind randomized placebo controlled experiments, but personality traits like self-control are hard to manipulate as trait variables in experimental settings.
Typically personality traits and their outcomes are studied using a longitudinal study design where changes in say self control at time T1 are correlated with outcomes like academic achievements at a later time T2, of course measure other confounding variables and factoring their effects; thus self-control, along with IQ, may be measured at the beginning of a school session and at the end of session the CGPA obtained will be used to find whether and how much self-control led to academic achievement. This however cannot establish causality in a strict sense as not all variables of interest can be identified and measured. Often the dependent variable (CGPA in our case) is itself controlled for to ensure that a higher CGPA at point T1 does not lead to higher CGPA at time T2 independent of self-control at T1.
To take care of third party confounding variables, Angela et al used growth curve analysis with Hierarchical Linear Modelling (HLM). This involves taking multiple measures of say self -control at different times and also multiple measures of the outcome say CGPA. The independent variable is considered a time varying co-variate and used to figure the within-person relationship between the two variables of interest. Consider a between subjects confound like socio economic status (SES) that could potentially lead to different outcomes (CGPA) – if not controlled for the self control- CGPA relation arrived at by analysis of between subjects data might lead to erroneous conclusions. However, a stable thing like SES (which doesn’t change with time and is constant for an individual) will have no impact on the correlation or causal relation between how changes in self-control affect CGPA over time in the same individual.
The direction of causality can also be ascertained by using HLM with reversed time lagged, time varying co-variates. What this means os that we can try to see of the causal arrow runs in other direction by taking measures of CGPA as predictor and self control as outcome variable.
In this study, self control was measured using self-report, parents and teachers ratings of students for four consecutive academic years (as they moved from fifth grade to eighth grade) using the Brief Self-Control Scale ; CGPA was measured each year as the outcome variable. Self-esteem and IQ was also measured and so was gender, ethnicity etc.
They found that self control measured 6 months earlier predicted CGPA six months later; average self-control predicted the baseline CGPA as well as the slope of CGPA changes (how fast the CGPA increased or decreased over time). Howsoever, the reverse analysis whereby short term CGPA was used to predict self-control gave negative results thus establishing the causal direction.
It was thus established that self-control does indeed cause or lead to higher academic outcomes like higher CGPA. A limitation of the study was that a time varying third variable that increased and decreased in tandem with self-control can still account for the relationship between self control and academic achievement.
I liked the paper, though its more methodological. You can find the full paper here [pdf].
Pathological mental health problems in children and young adults have been classified into externalizing (substance abuse, conduct disorder etc) and internalizing disorders (depression , anxiety etc). Today’s post will try to work out the structure of this internalizing spectrum.
English: An anxious person (Photo credit: Wikipedia)
The first major difference, that is made in say DSM, is between Mood disorders (disturbance in mood) and Anxiety disorders (characterized by anxiety and avoidance behaviors) . However, Watson in this article (pdf) emphasizes that this classification is not proper and in many cases these disorder say depression (say MDD) and Anxiety (say Panic disorder) are co-morbid with each other.
To explain this as well as other genotypical and phenotypical findings, Watson has developed a structure of these ‘distress disorders’ – however the road was long, an intermediate stop was tripartite model of depression/anxiety.
According to this tripartite model (developed by Watson and Clark), both depression (MDD, dysthymia etc) and anxiety disorders (phobia, panic etc) share a common non-specific factor called Negative Affect (NA) which is characterized by things like preponderance of negative emotions like sadness, fear, guilt, anger etc as well as irritability, difficulty concentrating etc.
Depressive disorders meanwhile are specifically characterized by lack of Positive Affect (PA), which means less emotions like happiness, interest etc, but also Anhedonia or inability to derive pleasure from earlier pleasurable experiences. Anxiety disorders, on the other hand, are characterized by physiological hyper arousal (PH) (shortness of breath, dizzyness etc) .
This model however was also found wanting and replaced with an hierarchical integrative model, which posited that there was a generic non-specific factor of NA common to both anxiety and depressive disorders, and a lower order low PA factor characterizing depression and more specific multiple low order factors (instead of one PH hyperarousal factor) associated with the different types of anxiety disorders like panic/ agoraphobia, Phobia-specif stimuli, phobia social etc .
However , Watson further modified the structure and came up with this model shown below: One broad factor of distress/NA; two specific factors of anxious-misery and fear and then further unique factor specific to individual diagnosis.
To summarize and also extending it a a bit,
At top there is an internalizing spectrum and associated with it a non-specific NA factor.
In middle there are four spectrum:- a depressive spectrum , a Fear spectrum and a bipolar spectrum and an Obsessive compulsive spectrum.
each of these can be further divided into discrete diagnosis along two factors/dimensions (I will not eb focusing too much on bipolar or OCD for the purposes of this post) :
Lets focus more closely on Depressive and Fear Spectrum and try to see alignment with ABCD model. MDD/Dysthemia imho are mainly about mood or Affect; GAD/PTSD are more Cognitive (reaming stuck in a thought loop) ; Panic/agorophobia more Physiological or Dynamic in nature and Phobia (both specific and Social) more Behavioral in nature (avoiding people, places and animals).
Each of these in turn splits into four factors; for ex PTSD splits into four factors- Dysphoria (A), Intrusions (C), Hyperarousal(D) and Avoidance (B). Similarly, recent research has shown that MDD is itself heterogeneous made up of four neural subtypes- one way to list those would be – marked primarily by Anhedonia (A), Anxiety (C) , Psychomotor retardation (D) and Fatigue (B) . Similar analysis should be possible for other discrete diagnosis.
For now, we will turn to the structure of Bipolar and OCD spectrum by analogy to dep/anxiety spectrum.
Flight of ideas (Cognitive)
Within this OCD can be seen to be comprising of four factors: Hoarding (A?) , Order and symmetry (C), Obsessions and Checking (D) and Washing and cleaning (B).
Another way to think about the depressive and anxiety spectrum is to say that Depression rgoup 1 is characterized by Low PA, depression group 2 by high PH; Fear group 1 by High PH and Fear group 2 by low PA. What distinguished Fear spectrum from Depression spectrum is the fact that much more variance is explained by High NA for depressive syndromes and only moderate variance explained by NA for Fear syndromes.
What do you think is missing from the above model? Where might it be wrong? where might it be correct? If correct what are the implications?
I recently came across an article titled “More than Resisting Temptation: Beneficial Habits Mediate the Relationship between Self-Control and Positive Life Outcomes” by Brian Galla and Angela Duckworth, which argues that the positive outcomes associated with self-control have more to do with habits for self-regulation, than with in-the-moment exercise of willpower.
Self Control (film) (Photo credit: Wikipedia)
Self-control is defined by APA as the ability to delay gratification and resist short-term temptations for long-term gains. Thus the main challenge while exercising self-control is how to take care of inevitable temptations that happen to cross our path. One approach is to build our willpower or in-the-moment inhibitory self-control that is able to overrule the impulses that drive us to engage with the temptation. This reliance on willpower will typically deplete our cognitive resources each time we use this willpower and leave us drained or ego-depleted and less able to resist temptations in the near term.
The other approach is to structure your day and activities in such a way as to minimize the temptations that you are exposed to. There is a five step process that is recommended to self-regulate. Start with selecting the situation. If you want to study , study in the library where distractions are likely to be minimal. The next is situation modification. If you cant study in library and have to study in living room, turn off the TV and put your remote away to minimize distractions. The next step is selective attention, where if you have TV turned on (due to n number of reasons) and still want to study you selectively attend to your textbook/ study notes and do not attend to the TV noise in the background. If the earlier three stages are not available, or you don’t have an opportunity to use them then comes cognitive re-framing; maybe you can’t turn off the TV and are not able to resist watching it over say studying for math which seems hard and boring. You can re-frame studying as preparing for a better future, which hopefully inspires you; and watching TV as wasting time. The last step in this framework is to rely on brute-force willpower to turn off the TV and go back to studying. This last recourse of using willpower is to be exercised and relied on , only if all else (the earlier steps ) fail.
Thus, its evident that self-regulation is best implemented by having good habits of selecting and modifying situations to minimize temptations etc. Also, its better to use your willpower to create healthy habits like exercising everyday and letting the subconscious take care of executing that on auto-pilot, once the habit has been formed, than to rely on conscious inhibitory self-control.
From this, I propose the following structure for self control:
The main challenge is resisting temptations
One way to do so is by creating habits that minimize exposure to temptations or that test oneself.
Another way to do so is to rely on willpower or brute force in-the-moment inhibitory self-control to resist temptations.
There is now some research available that shows that the self-report self-control we measure, and which is associated with all sorts of positive outcomes (see PDF), primarily measures the habit or auto-pilot self-control rather than the state self-control. The in-the-moment or state self-control is not such a good predictor of future positive outcomes.
Now lets think for a moment about a related but different concept, Grit, which is defined as passion and perseverance for long term goals; and which again has been shown to be a predictor of all sorts of good outcomes.
By analogy I have come up with the following structure for Grit:
The main challenge is persevering despite obstacles/ failures
One way to do so is by minimizing possibility of failure by careful planning (orderliness) and habits to circumvent obstacles or bulldoze through then by working hard (industriousness). Together this can be construed as the trait Conscientiousness.
Another way to do so is to rely on ordinary magic of in-the-moment resilience to bounce back from failures and getting up and restarting after colliding with an obstacle.
By analogy, I believe that the self-report Grit that is associated with all sorts of positive outcomes will correlate more with trait contentiousness rather than the in-the-moment ability to be resilient. And it follows that it is better to create habits of orderliness and industriousness rather than relying on our ability to bounce back and get up after falling.
I should perhaps stop here, but I can also see parallels with what I think is the reverse of having Self-control. Too much not having self-control, or being impulse driven may be associated with the psychological disorders clubbed under addiction.
There has now been a lot of research showing that addiction is not so much about dependence on substances or biologically based but due to lack of satisfying interpersonal and social relationships.
With that in mind, and extending the analogy, here is what I propose to be the structural quality of all types of addiction, whether related to substance abuse or behavioral (internet etc) in nature:
The main challenge to remaining addicted is availability of satisfying relationships.
One way to to remain addicted is habitually prioritizing one activity/ substance to the exclusion of others (salience), so that the joy from other activities like satisfying relationships is not experienced at all.
Another way to remain addicted is to get so much in-the-moment high form indulging in the activity/ using the substance, that it overrules any comparisons with the satisfaction derived from relationships.
If I had to go on a limb, I would say that the former system which relies on habits or prioritizing a particular activity over others is related to the ‘wanting’ system , while the latter system which is related to experiencing in the moment highs is related to the ‘liking’ system. And we all know that the ‘wanting’ system is more powerful than the ‘liking’ system. So most likely addiction is maintained by the former system where a habitual pattern of (mis) use has been formed.
So what are the takeaways? Build good habits and do not rely on in-the-moment strengths or capabilities to tide you over in times of crises. And measures of these good habits are what would drive success and lead to all sorts of positive outcomes.
What goes into the making of a genius? More mundanely, what factors are required for success in any field? Your answer will differ based on what factors you consider to be the most important for success.
Photo of the obverse of a Fields Medal made by Stefan Zachow for the International Mathematical Union (IMU), showing a bas relief of Archimedes (as identified by the Greek text) (Photo credit: Wikipedia)
No one can deny the large role that intelligence and talent play in the making of a genius, or to achieve moderate levels of success compared to peers. We can probably club these two factors together as ability, that is more or less inborn, and is not very easy to increase or amenable to interventions.
Let me be a be it more specific. I consider ability to be made of two components: specific talent in a particular domain, say singing talent or mathematical talent; and fluid intelligence, or the ability to solve problems in real time using cognitive resources like working memory and typically measured by IQ. While talent is domain specific, fluid intelligence is domain general, but both will be required to be successful in a domain. Intelligence (fluid) will enable one to learn at an exceptional rate and also to learn form ones mistakes and improve.
Both talent and intelligence have been shown to explain up to 50 % of variance, in say, academic performance. Thus they are definitely required to achieve extraordinary success/ genius.
However, another stream of research informs us that putting in 10,000 hours or more of deliberate practice is what does the magic. As per research by Andres Ericsson and colleagues to achieve and expertise in any domain you need 10,000 hours or more of focused, deliberate practice. Here two things are important to note: you are not putting in brute force efforts blindly, but following a process of deliberate practice (picking up a weakness, working on it constantly to improve soliciting feedback etc) and the second is that you do put in more than 10,000 hours of such efforts to attain some expertise and then again 10,000 hours more to achieve genius level expertise.
Thus, one can subsume these factors under the common label effort: comprising of a daily ritual of deliberate practice or Riyaaz or smart efforts; and a long term fruits of putting in 10,000 or 20,000 hrs of such efforts in the form of expertise or domain specific crystallized intelligence.
Both indulging in deliberate practice daily and building expertise by putting in the required hours are correlated with genius level expert performance or success. In more mundane terms, if you really want to make contributions to mathematics such that they deserve a Fields Medal, you need to systematically work on which areas of Maths need improving and actually spend hours daily honing your maths skills for a few years before hoping to get one.
and of course as Angela Duckworth says, talent * effort = skill and skill* effort = performance, so effort counts twice and is an important determinant of success.
But this brings us to the question is effort same as grit, another factor that has been shown to predict success/ achievement/ genius?
While to the naive mind they may appear same; to me effort is willingness and ability to work smart and work hard; while grit is more about being passionate about a particular super-ordinate goal and getting back on track and showing persistence in the face of setbacks/ adversity.
And of course another personality factor or character strength that is similarly predictive of success is self-control. Self-control is the ability to resist temptations and forego pleasure-in-the-now for gains-in-the-future. It reliably predicts success in many domains and is domain general trait. Grit however is more domain specific. Also while Self-control works on a shorter time scale, Girt works on a longer time scale.
Both can be subsumed under goal-commitment: a in the moment domain general self control factor and a long term domain specific grit factor.
And this brings us to the final set of factors which are equally important for success: enjoying and being engrossed in what you are doing and being curious/exploratory about the things you don’t know/ haven’t experienced yet. These are emotional-motivational processes that ensure that you actually do put in the efforts required to meet the goal commitments and to actualize your ability.
Recent research has shown that a hungry mind is very important for predicting academic success. This hungry mind is conceptualized as intellectual curiosity. Curiosity as initially defined by Todd Kashadan et al was comprised of Exploration (or Curiosity as they define now per se) and Absorption. Later Todd et al have disowned absorption as a part of curiosity, and they are right to do so, but given the high correlations between absorption and exploration, I think they were on to something. Important for us is to remember that curiosity or the appetitive strivings for novelty, complexity,uncertainty and ambiguity; and Absorption or flow or full engagement in specific activities, taken together are again strong predictors of success/ achievement.
Thus, we have a fourth big factor predicting and causing success, viz Engagement: one sub-factor of which is a domain/ task specific flow or absorption and the other a domain general or task independent curiosity or love of learning or intrinsic motivation.
With that we can probably summarize the ingredients required to make a genius:
Ability, both talent and intelligence
Effort, both daily deliberate practice and 10,000 hours of expertise
Goal commitment, both self control and grit
Engagement, flow as well as curiosity
As an aside, this fits my ABCD model: Engagement or flow/curiosity are Affective in nature; Effort is Behavioral; Ability (intelligence) I consider as Cognitive and goal-commitment as Dynamic/motivational.
So, what are you going to do different to achieve extraordinary performance after having learned this? Will you work on your curiosity, put in more hours of deliberate practice , ensure you are feeling flow and absorption or work or your self-control muscle. There are many paths to greatness, and you can choose to focus on one or more to take you where you need to be!
Rating: 0.0/10 (0 votes cast)
Read Full Article
Read for later
Articles marked as Favorite are saved for later viewing.
Scroll to Top
Separate tags by commas
To access this feature, please upgrade your account.