Scope Neglect

Fortunately, there are many reasons to believe that we can take advantage of scope insensitivity because people have already discovered ways to make the most of other forms of non-extensibility. Scale neglect or scope insensitivity is a cognitive bias that occurs when an assessment of a problem is not assessed using a multiplicative relationship with its dimension. After all, if we didn’t neglect scale, we would be more rational and therefore perhaps happier and healthier, living in a world where everyone has more of what they want, because without scale insensitivity there would be no it’s so difficult to convince people to help those who are far away, who need more than those close to them, who need less. Here I will look at one such use case, namely the use of scope insensitivity to prepare for high-risk situations in low-risk situations. [Sources: 2, 3]

The more anxious, depressed, or generally frustrated you are, the more likely you are to treat low-stakes situations as high, and thus, you will have even more opportunities to practice judo numbness than people who are calmer and are fair. Indeed, studies of neglect of scale, in which the quantitative variation is large enough to elicit any sensitivity, show a small linear increase in willingness to pay, corresponding to an exponential increase in volume. When you notice any of these situations, consider if this is really a high rate or if you think it is simply due to the viewfinder’s insensitivity. Expansion neglect [a] is a type of cognitive error that occurs when sample size is ignored when evaluating a study in which the sample size is logically significant. [Sources: 2, 5, 6]

Two other hypotheses to explain domain neglect include purchase of moral gratification (Kahneman and Knutch, 1992) and just cause burial (Harrison, 1992). The most widely accepted explanation for scale neglect is the affect heuristic. This can cause their reaction to problems to be disproportionate to the size of the problem. [Sources: 4, 5]

Baron and Green (1996) found no effect of a tenfold change in the number of lives saved. Kahneman, Daniel, Barbara Fredrickson, Charles Schreiber and Don Redelmeier. Wilson, Thomas, Christopher Houston, Catherine Etling and Nancy Brecke. [Sources: 0, 5]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://link.springer.com/article/10.1023/A:1007835629236

[1]: https://medium.com/@shravanshetty/scope-neglect-e76bfc623286

[2]: https://mapandterritory.org/scope-insensitivity-judo-a07f9166f165

[3]: http://zims-en.kiwix.campusafrica.gos.orange.com/wikipedia_en_all_nopic/A/Scope_neglect

[4]: https://www.thehindu.com/opinion/op-ed/what-is-scope-neglect-in-psychology/article24463617.ece

[5]: https://www.briangwilliams.us/global-catastrophic-risks/scope-neglect.html

[6]: https://en.wikipedia.org/wiki/Extension_neglect

Neglect Of Probability

Probability neglect, a type of cognitive bias, is the tendency to ignore probability when making decisions under uncertainty, and is an easy way for people to regularly break normative rules when making decisions. Probability neglect, a type of cognitive bias, is the tendency to completely ignore probability when making decisions under uncertainty, and is an easy way for people to regularly break normative rules when making decisions. When probability is neglected, people focus on the “worst case” and ignore the question of whether the worst case is possible — an approach that can also lead to overregulation. There are many related ways that people violate the normative rules for making decisions about likelihood, including hindsight bias, disregard for the influence of previous base bets, and player error. [Sources: 0, 1, 4, 5]

Cass Sunstein, a senior adviser to Barack Obama, says that people show a likelihood of rejection when faced with vivid images of terrorism, so that when their emotions are intensely involved, people’s attention is focused on the worst outcome. is unlikely to happen. This bias can lead subjects to decisively violate expected utility theory when making decisions, especially when a decision has to be made when the possible outcome is of much lower or higher utility, but with little likelihood (for example, [Sources: 3, 5]

However, this bias is different in that the actor does not abuse the probability, but completely ignores it. In a 2001 article, Sunstein addressed the question of how the law should respond to the rejection of probability. Again, the subject ignores probability when making a decision, considering every possible outcome to be equal in his reasoning. [Sources: 0, 4]

They assume that the likelihood is more likely to be overlooked if the results evoke emotion. In this respect, rejecting the probability bias is similar to rejecting the effect of previous base rates. Subadditivity effect. The tendency to rate the likelihood as wholly less than the probabilities of the parties. [Sources: 2, 4, 5]

While government policy on potential hazards should focus on statistics and probabilities, government efforts to raise awareness of these hazards must focus on worst-case scenarios to be most effective. He noted that there are methods available, such as Monte Carlo analysis, to study probability, but all too often “the probability continuum is ignored. Dobelly described the US Food Act of 1958 as a “classic example” of the rejection of probability. [Sources: 4]

Blind Spot Bias The tendency to believe that one’s own bias is less than that of others, or the ability to recognize others’ cognitive biases is greater than one’s own. connection error. Tend to assume that certain conditions are more likely than general conditions. Basic bet error or basic bet lost. Tend to ignore basic speed information (general and general information) and focus on specific information (information only relevant to a specific situation). [Sources: 2]

The player’s illusion. Tend to think that future probabilities will be distorted by past events when they have not really changed. All these biases indicate a tendency to focus on irrelevant information when making a decision. Berkson’s paradox. The tendency to misunderstand statistical experiments using conditional probability. [Sources: 2]

Irrational escalation. A phenomenon in which people justify an increase in investment in a solution based on previous cumulative investment despite new evidence that the decision was likely wrong. Choice bias. The tendency to remember your choice as better than it really was. [Sources: 2]

When national security is at stake, cost-benefit analysis is much less promising because it is usually impossible to assess the likelihood of an attack. The availability heuristic, widely used by ordinary people, can lead to highly exaggerated perceptions of risk, as serious incidents lead citizens to think the risk is much greater than it actually is. Civil libertarians overlook this point, believing that the meaning of the Constitution does not change in the face of intense public fear. [Sources: 1]

Deformation Professionalnelle is a French term for the tendency to look at things from the point of view of one’s profession rather than from a broader point of view. [Sources: 0]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://www.linkedin.com/pulse/cognitive-biases-every-risk-manager-must-know-part-2-sidorenko-crmp

[1]: https://muse.jhu.edu/article/527368/summary

[2]: https://behavioralgrooves.com/behavioral-science-glossary-of-terms/

[3]: https://www.cambridge.org/core/books/risk/quantifying-uncertainty/B41C7A211929DBA2B5CB4CEA4E3A66A1

[4]: https://en.wikipedia.org/wiki/Neglect_of_probability

[5]: https://nlpnotes.com/2014/03/22/neglect-of-probability/

Less-Is-Better Effect

Four studies related to real reward support the effect of uncertainty in motivation. Thus, this study highlighted the hedonistic aspects of resource allocation methods and determined when accepting one’s destiny hedonically is better than fighting for the best. [Sources: 2]

This study examines repetition decisions, that is, whether to repeat a behavior (such as a purchase) after receiving an incentive (such as a discount). This study documents the motivation-uncertainty effect and indicates when this effect occurs. [Sources: 2]

This effect occurs only when people focus on the process of seeking a reward, not when they focus on the result (the reward itself). Because people are excited to know what they can actually accomplish, working for an indefinite reward makes the whole situation more like a game than a job. [Sources: 2, 6]

They found that more people ran out of water in order to receive an undefined amount of money. This effect did not disappear in four consecutive rounds of testing. To find out if this accelerating effect persists in the context of real-world behavior, Shen and Hsi conducted their experiment in the gym. This acceleration effect occurred regardless of the absolute value or absolute speed of the number, and even when the number was not tied to any particular award. [Sources: 2, 5, 6]

This behavior tracking helps to stimulate further action, and new research shows that even small scores can act as effective motivators if these scores rise. For example, my coauthors and I studied when people work and earn too much (preparing for publication in Psychological Sciences), when free competition makes people unhappy (preparing for publication in OBHDP), why idleness is bad, and how to keep people busy and happy. (2010). in Psychological Science), and which factors have an absolute influence on happiness, and which factors have only a relative influence on happiness (2009 in the Journal of Marketing Research). [Sources: 4, 5]

Christopher K. Hsee and Reed Hastie of the University of Chicago pointed out four main reasons why we do not follow the decisions that make us happy (Hsee & Hastie, 2006). We like that our decision-making process looks reasonable; unfortunately, seemingly rational decisions can make us less happy. Studies have shown that people prefer to receive beetle chocolate as a gift compared to heart-shaped chocolate, even if they know they prefer heart-shaped chocolate. [Sources: 3]

It makes more sense to choose the most expensive gift, but it makes people less happy. Therefore, “if givers want the recipients of gifts to perceive them as generous, it is best for them to present a valuable item from the low value category (for example, Thaler (1980) called this model this model – the fact that people often demand a lot more in order to give up item than they would be willing to pay to acquire it: the endowment effect. [Sources: 3, 8, 11]

However, these effects only apply to products that are unfamiliar to buyers and do not have observable target prices, and can be mitigated if sellers are encouraged to mimic a single pricing decision. The point is, the human brain doesn’t like to think about cost or prices in isolation. They look for benchmarks – 40-piece crockery sets or 10-ounce cups – and think about relative value. [Sources: 1, 2]

As with Hsees items, people are looking at a 40-piece cookware set with 9 broken pieces, I see a 5-piece play set with 2 games that I already have. This “3 out of 5” comparison would lower my rating for the package. He explains this “less is better” phenomenon by the fact that in a separate evaluation mode, we compare options – clothing, video game kits, dinnerware sets – with a benchmark for that category. [Sources: 1]

Evidence has shown that this only happens when options are individually assessed; it disappears when they are evaluated together. Fischbach, Hsi, and Shen explain this effect by postulating that making the unknown known — that is, figuring out what is in the wrapped package, or figuring out what reward it got — is a positive experience. The conventional wisdom is that people will feel happier with more favorable assumptions (such as higher income) than less favorable assumptions. [Sources: 2, 6, 11]

The downside to larger effect is a type of preference inversion that occurs when a smaller or smaller alternative to a sentence is preferred, when evaluated separately but not evaluated together. The smaller, the more the effect has been demonstrated in several studies leading up to the 1998 Hsees experiment. [Sources: 11]

Based on existing theory, Shen and Hsi suggested that it would be difficult for people to measure the rate of change in ratings (speed), and this figure is difficult to assess without another rating for comparison. This acceleration may seem like they are getting better and better, even if they know the score is not related to actual performance. [Sources: 5]

In three related experiments, the researchers asked participants to type in as many target words as possible within 3 minutes. One group of participants was asked to estimate the cost of 8 ounces of ice cream in a 10-ounce cup; the other was to estimate 7 ounces in a 5-ounce cup; and the third was to compare the two. One group only saw group A but never saw group B, and the other group did the same with group B. [Sources: 1, 5, 11]

People who saw a set with fewer items were willing to pay more than those who saw a set with more items. People were willing to pay a little more for the extra undamaged cups and saucers from Set A. [Sources: 1]

On separate evaluation, preference was given to the newer book; the oldest book was selected in the joint evaluation. A 1996 study by HSEE asked participants to rate two used music vocabularies, one containing 20,000 entries with a torn cover, and the other containing 10,000 entries and looking brand new. [Sources: 11]

Journal of Consumer Research, 27 (3) 279-290 2002 Robin Leboff Choice Based on Identity and Inconsistency of Preferences 2004 Joseph Johnson Johnson, J.G. and Busemeyer, J.R. (2005). [Sources: 0]

A dynamic stochastic computational model of the preference inversion phenomenon. Communication structures and receptivity to information relevant to the solution of logical and statistical problems. Multi-Attribute Linear Ballistic Battery Model of Context Effects in Multi-Choice. The impact of other people’s decision making on regulatory focus and choice overload. [Sources: 0]

Dee Adam Arenson (Cambridge, Massachusetts, Harvard University Press, 2011) 340 pages. Curated by Jeff Horne, Leonard N. Rosenband and Merritt Rowe Smith (Cambridge, Massachusetts, MIT Press, 2010) 362 pages. Di Nuala Zahedieh (New York, Cambridge University Press, 2010) 329 pages. Dee Helen Lefkowitz Horowitz (New York, Oxford University Press, 2010) 251 pages. [Sources: 9]

Every year SJDM awards the Hillel Einhorn Prize for Best Young Detective Paper. The winner is announced at the annual meeting and invited to present the winning entry. The winner is determined by a committee appointed by the SJDM Board of Directors. The John Castellan SJDM Service Award is named after the first editor of the company’s newsletter. [Sources: 0]

The most amazing and memorable research experience happened when I was not doing research, but was on the bus many years ago. Research shows that knowing about these types of biases and mistakes can help us combat them. The acceleration effect can even last a whole day. [Sources: 3, 4, 5]

 

— Slimane Zouggari

 

##### Sources #####

[0]: http://www.sjdm.org/history.html

[1]: https://www.psychologyofgames.com/2013/10/less-humble-bundles-are-more/

[2]: http://www.luxishen.com/research

[3]: https://www.spring.org.uk/2008/06/4-ways-we-fail-to-choose-happiness.php

[4]: https://indecisionblog.com/tag/hsee/

[5]: https://www.psychologicalscience.org/news/releases/meaningless-accelerating-scores-yield-better-performance.html

[6]: https://www.eurekalert.org/pub_releases/2014-10/uocb-urm101314.php

[7]: https://www.alleydog.com/glossary/definition.php?term=Less-Is-Better+Effect

[8]: https://pubs.aeaweb.org/doi/abs/10.1257/jep.5.1.193

[9]: https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/why-do-humans-reason-arguments-for-an-argumentative-theory/53E3F3180014E80E8BE9FB7A2DD44049

[10]: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=930083

[11]: https://en.wikipedia.org/wiki/Less-is-better_effect

Insensitivity To Sample Size

Sample size neglect is a cognitive bias that Amos Tversky and Daniel Kahneman have studied well. Key Findings Sample size neglect is a cognitive bias explored by Amos Tversky and Daniel Kahneman. [Sources: 3]

Regarding the hospital issue, most of the participants in Tversky and Kahnemans rated more than 60 percent of boys to be equally likely to be admitted to small and large hospitals, presumably because these events are described by the same statistics and are therefore equally representative of the general population (Tversky et al. Kahneman calls this “sample size insensitivity”). Tversky and Kahneman explained that these results are due to the representativeness heuristic, in which people intuitively evaluate samples as having properties similar to their collection, without taking other considerations into account. Sample size insensitivity is a cognitive error that occurs when people estimate the likelihood of obtaining statistical information from a sample regardless of the sample size. [Sources: 0, 4]

In this article, we empirically explore the psychometric properties of some of the best-known statistical and logical cognitive illusions of Daniel Kahneman and Amos Tversky’s Heuristics and Bias research program, which nearly 50 years ago presented fascinating puzzles such as the famous Linda problem, Wason’s paper selection problem, and so-called Bayesian reasoning problems (eg mammography problem). The cognitive illusions they then presented provided empirical evidence that human reasoning abilities defy the laws of logic and probability. [Sources: 4]

Ignoring the sample size refers to the inability to consider the role of the sample size in determining the reliability of statistical statements, while ignoring the benchmark rate means that people tend to ignore existing knowledge of a phenomenon when evaluating new information. Insensitive to sample size People do not understand the significant difference in sample size for any probability calculation. In this article, we studied the famous statistical and logical cognitive illusions in the heuristic and bias research project of Daniel Kahneman and Amos Tversky from the perspective of psychometrics. This is called the sample size insensitivity bias, or, if you will, the law of decimals. [Sources: 1, 2, 3, 4]

This occurs when users of statistical information make false conclusions by ignoring the sample size of the data in question. Thus, it is very important to determine whether the sample size used to obtain a given statistic is large enough to allow meaningful conclusions to be drawn. So it struck me that you can argue that all of these books and articles on cognitive errors are pretty unscientific in their own way, or lack the proper sample size, because they only focus on where heuristics lead to errors. (and furthermore, these same errors are measured under highly unrealistic conditions in psychological laboratories using highly unrepresentative samples of college students). [Sources: 2, 3]

This performance sampling works like everything else: the larger the sample size, the more uncertainty is reduced, and the more likely you are to make the right decision. However, the frequency composition, attenuation characteristics and other factors of earthquake ground motion vary greatly. Therefore, the precedent for a single earthquake is that the sample size is very small. Before drawing conclusions from information about a limited number of events (samples), it is important to choose from a large number of events (populations) and to understand some information about sample statistics. [Sources: 1]

Therefore, the last part of the Tversky and ​​Kahnemans paper-about subjective probability distribution-is not covered in other books because it is expressed in such dense mathematical terms that it is almost incomprehensible, and because they criticized the idea of ​​decision theory in 1970 -x is too far away from most people’s daily concerns. In other words, variation is more likely to occur in smaller samples, but people may not expect it. According to the so-called “law of decimals”, we often use small samples of information to speak on behalf of or on behalf of a wider group of people. [Sources: 0, 2, 5]

Exaggerated trust in little champions is just one example of a more general illusion: we place more emphasis on the content of messages than on information about their reliability, and the result is a simpler, more consistent view of the world around us. what the data justify. Of course, if the champion was extreme, say 6 people, you would doubt it. The most common form of delusion is the tendency to assume that small samples should be representative of their parent population, with player delusion being a special case of this phenomenon. [Sources: 1, 6]

Bias due to the presence of the search set. Imagine you are choosing words from random text. Performance records are generated by a combination of core capabilities and sample variation. Heuristics are mental shortcuts that our brains use to help us make quick decisions. We often select past experiences that we believe should be similar to future events or that we believe should reflect an ideal outcome. [Sources: 1, 2, 5]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://en.wikipedia.org/wiki/Insensitivity_to_sample_size

[1]: https://fs.blog/mental-model-bias-from-insensitivity-to-sample-size/

[2]: https://astrofella.wordpress.com/tag/insensitivity-to-sample-size/

[3]: https://www.investopedia.com/terms/s/sample-size-neglect.asp

[4]: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.584689/full

[5]: https://thedecisionlab.com/biases/gamblers-fallacy/

[6]: https://www.oxfordreference.com/view/10.1093/oi/authority.20110803100439475

[7]: https://hyperleap.com/topic/Insensitivity_to_sample_size

Hyperbolic Discounting

The phenomenon of hyperbolic discounting is implicit in Richard Hernstein’s Law of Conformity, the discovery that most subjects allocate their time or effort between two continuous and non-exclusive sources of reward (concurrently variable interval patterns) in direct proportion to the rate and size of reward. rewards from two sources, and inversely proportional to their delays. The phenomenon of hyperbolic discounting is implicit in Richard Hernstein’s Law of Conformity, the discovery that most subjects allocate their time or effort between two continuous and non-exclusive sources of reward (concurrently variable interval patterns) in direct proportion to the rate and size of reward. rewards from two sources, and inversely proportional to their delays. In behavioral economics, hyperbolic discounting refers to the empirical finding that people tend to prefer smaller and faster gains over larger, later ones, when smaller gains are inevitable; but when the same achievements are distant in time, people tend to prefer more, even if the delay from smallest to largest is the same as before. [Sources: 1, 3]

Indeed, multiple studies have used hyperbolic discounting measures to find that addicts do not account for delayed outcomes more than corresponding independent controls, suggesting that extreme discounting with delay is a fundamental behavioral process in addiction. After reporting this effect in the case of delay (Chung and Herrnstein, 1967), George Ainslie pointed out that in the only choice between more later reward and less reward, the inverse proportionality of the delay would be described earlier. a delay diagram that was hyperbolic in shape, and that this shape should lead to a change in preference from more and then to a lower reward, initially only because the delays in the two rewards were reduced. [Sources: 0, 3]

People who use hyperbolic discounting have a strong tendency to make inconsistent choices over time: they make choices today that their future selves would rather not make, despite knowing the same information. In addition, the hyperbolic shape of the curves from single and discrete selection is only a manifestation of Herrnstein’s law of correspondence, which describes the same inverse proportionality of cost to delay in parallel and concurrent programs of unpredictable rewards (parallel VI-VI; Chung and Herrnstein 1967), where subadditivity would not be a factor. They retain much of the analytic controllability of the exponential discount, while at the same time reflecting the key qualitative characteristic of the discount using true hyperbolas. In cases where both alternatives are reasonably certain that they will occur, if chosen, this discounting model is dynamically incompatible and therefore incompatible with standard rational choice models, since the discount rate between times “t” and “t + 1” will be low at time “t-1”, when “t” is the near future, but high at time “t”, when “t” is the present, and time “t + 1” is the near future. [Sources: 0, 1, 2, 3]

Since then, many psychological studies have shown deviations in instinctual preferences from a constant discount rate adopted at an exponential discount. The most important consequence of hyperbolic discounting is that it creates time preferences for small rewards that occur earlier than larger and later ones. A standard experiment used to elicit a hyperbolic discounting curve for subjects is to compare short-term preferences with long-term preferences. According to hyperbolic discounting, ratings fall relatively quickly for earlier lag periods (for example, from now to a week), but then decline more slowly during longer lag periods (for example, more than a few days). [Sources: 0]

According to this hypothesis, the value of forthcoming awards will always increase, and the smoothing of the curves into an exponential shape that reflects sequential selection is what needs to be explained. The economist Robert Strots (1956) pointed out that people can recognize non-exponential discount curves in themselves and therefore expect their current plans to change in a predictable way over time; and behavioral psychologists Shin-Ho Chang and Richard Hernstein (1967) reported that pigeons working for food on two non-exclusive and unpredictable schedules (simultaneous VI VI) distribute their pecks in proportion to the inverse average food delivery delay, proving this. that the Herrnsteins comply with the law applies to delay. This likely requires that discount curves be hyperbolic and appetite a reward seeking process. Hyperbolic temporary actualization in drinking people and problem drinkers. [Sources: 1, 2]

Altered functional relationship associated with the actualization of time in chronic pain. Let the Music Play is an experimental study of background music and timing preferences. Ferrari, Linda Cavalier, Alessia De Marchi, Eliza and Bunterle, Alessandro, 2019. Joseph, Walter J. Cuccolo, Nicholas G. Chow, Ian Moroni, Elizabeth A. & Bierce, Emily H. 2020. [Sources: 4]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://en.wikipedia.org/wiki/Hyperbolic_discounting

[1]: https://en-academic.com/dic.nsf/enwiki/452991

[2]: https://www.picoeconomics.org/HTarticles/HDvCF/HDvCF2.html

[3]: http://taggedwiki.zubiaga.org/new_content/76d4ba3172db6cfb52b26251ead58091

[4]: https://www.cambridge.org/core/journals/science-in-context/article/models-of-temporal-discounting-19372000-an-interdisciplinary-exchange-between-economics-and-psychology/993C3C0EF0ED87BB493C966B3F6C012B

Duration Neglect

Duration abandonment is the psychological recognition that people’s judgments about unpleasant experiences are very little dependent on the duration of those experiences (Kahneman and Fredrickson, 1993). Impact research by Kahneman, Fredrickson, Schreiber, and Redelmeyers (1993) has also provided key evidence for the end-of-the-peak rule, especially with regard to our memories of painful experiences. A rational and impartial retrospective assessment will consist of additional pain assessments over time; instead, assessments of our hedonic past often do not take into account the duration of the experience and are more influenced by peak and end levels of discomfort (Fredrickson & Kahneman, 1993). When assessing our hedonic past, the duration of the experience is often overlooked and is more influenced by peak and terminal levels of discomfort (Fredrickson & Kahneman, 1993). [Sources: 0, 1]

Kahneman and Tversky studied the peak rule in 1999, and they concluded that people remember their experience mainly based on their feelings at the peak and the end, not their shared experience. The peak and end rules were explored for the first time in a study in which participants were shown short, undrawn shots (Fredrickson & Kahneman, 1993). Duration neglect is a psychological observation that people’s unpleasant judgments about painful experiences hardly depend on the duration of these experiences. Evidence from research on the usefulness of positive experiences in memory shows that the best way to end pleasant experiences is also important (Diener, Wirtz & Oishi, 2001; Do, Rupert & Wolford, 2008; Fredrickson and Kahneman, 1993). [Sources: 0, 1, 5]

But a preference for a more moderate final list may arise from people simply averaging subjective moments that occur throughout the experience (Fredrickson and Kahneman, 1993; Diener et al., 2001; see also Anderson, 1981; 1965 for the mean contrast with additive processing during impression formation). The Peak Rule is a psychological term coined by Barbara Fredrickson and Daniel Kahneman. When assessing past experiences, people focus on the peak and end, while other parts of the experience are ignored. [Sources: 0, 6]

In their study (1993), participants watched a series of videos of varying degrees of enjoyment and length. In one study, Daniel Kahneman and Barbara Fredrickson showed that subjects are filmed with pleasure or with trouble. [Sources: 1, 5]

This controversial finding stems from the fact that people tend to use the so-called peak rule instead of judging experience based on total or average pain over time. [Sources: 7]

Schreiber and Kahneman (2000) have shown that evaluating uncomfortably loud sounds demonstrates clear end-of-peak effects. The results of the cold water immersion study have also been confirmed in an experiment using unpleasant noises (Schreiber and Kahneman, 2000). Second, research has shown that the impact of the law of peaks on daytime experiences is small. Adding a good ending encourages people to relive the event a second time. [Sources: 1, 6]

This study argued that people rate the experience at the end of the event. In another demonstration, Kahneman and Fredrickson and others asked subjects to dip their hands in painfully cold water. Our feelings at the climax and at the end of an event determine how people usually think about the experience. [Sources: 1, 5, 6]

Conversely, the negative ending spoils the memory of the experience, even if everything was perfect before. If a person wants him or others to do less, adding negative ending to the experience helps. Consequently, the participants in Group B found the experience less unpleasant, although in terms of both the duration and the amount of unpleasantness, Group A <Group B. were used to evaluate the procedure. [Sources: 6]

Two experiments show that the structure of learning fragments affects students’ assessment of their learning experience. High-profile ending experiences tend to be remembered in a more positive way. There are many ways to actively focus on experiences with a good ending. [Sources: 0, 4]

Researchers at University College London have developed a sort of corollary to Kahneman’s Self-Recollection. They showed that positive expectations affect a person’s overall happiness in the same way as actual experience. It is therefore not surprising that numerous studies show that the emotional component of the customer experience (how customers feel) is a better indicator of loyalty than the cognitive component (functional aspects such as efficiency and ease). However, in 2013, only 8% of companies included in the Forrester Index received excellent customer service ratings. [Sources: 8]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2970645/

[1]: https://positivepsychology.com/what-is-peak-end-theory/

[2]: https://www.semanticscholar.org/paper/Duration-neglect-in-retrospective-evaluations-of-Fredrickson-Kahneman/85c233d9075c32a1eafebb14360c320e74b7ef5b

[3]: https://journals.sagepub.com/doi/10.1111/j.1467-9280.1993.tb00589.x

[4]: https://thedecisionlab.com/biases/peak-end-rule/

[5]: https://en.wikipedia.org/wiki/Duration_neglect

[6]: https://kids.kiddle.co/Peak-end_rule

[7]: https://debiasme.com/biases/duration-neglect/

[8]: https://lippincott.com/insight/happiness/

Conjunction Fallacy

One explanation for why we make a conjunction error in cases like Linda, the bank teller, is that we misuse what Tversky and Kahneman call a representative heuristic. The most cited example of this error came from Amos Tversky and Daniel Kahneman. Conjunction error (also known as Linda’s problem) is a formal error that occurs when it is assumed that certain conditions are more likely than one common one. The original report by Tversky and Kahneman [2] (later republished as a chapter in book [3]) describes four problems causing the conjunction error, including Linda’s problem. [Sources: 0, 5]

Amos Tversky and Daniel Kahneman are known for their work on a large number of cognitive errors that we all tend to make over and over again. Although Linda’s problem is the most famous example, researchers have developed dozens of problems that reliably detect conjunction error. This is known as conjunction error or Linda’s problem and is a source of behavioral bias in decision making. While representativeness bias occurs when we ignore low base rates, conjunction error occurs when we attribute a higher probability to an event with a higher specificity. [Sources: 0, 2, 3, 5]

Tversky and ​​Kahneman (1983) asked participants to solve the following problems. Tversky and ​​Kahneman have explored many variants of the Linda problem formula. The two found many logical errors, and we often make these errors when we are faced with information that seems a bit familiar. [Sources: 0, 2, 5]

As discussed in our article on storytelling errors, Kahneman and Tversky’s most famous and most controversial experiments involved a fictional woman named Linda. Assessing the connection of two events as more likely than just one of the events is an example of a connection error; the human tendency to do this is commonly known as cumulative error. Likewise, conjunction error also occurs when people are asked to bet with real money [16], as well as when solving intuitive physics problems in various projects. In his book Thinking Fast and Slow, which summarizes his life and Tversky’s work, Kahneman introduces biases that stem from misalignment — the false belief that a combination of two events is more likely than one event alone. [Sources: 0, 2]

The error of representativity and conjunction arises from the fact that we mentally shorten the path from the supposed plausibility of the scenario to its probability. But perhaps Tversky and Kahneman are wrong about the Linda case. Conjunction bias is a common reasoning error in which we believe that two events occurring together are more likely than one of those events to happen on their own. [Sources: 2, 5]

The so-called cognitive defect, which the study participants operated on in the Linda case, is also associated with the formulation of the problem. However, there are studies that have observed indistinguishable rates of conjunctive errors with formalized stimuli in terms of likelihood and frequency. The representativeness heuristic was invented by Daniel Kahneman and Amos Tversky, two of the most influential figures in behavioral economics. [Sources: 0, 1, 5]

With this way of stating the problem, if you attach great importance to the background information about Linda’s student days, we would expect study participants to attribute equal probability to 3 (a) and 3 (b). However, the probability that two events will occur together is always less than or equal to the probability that one will happen alone. [Sources: 5]

Daniel Kahneman and Amos Tversky have spent decades in psychological research to unravel the patterns in the mistakes of human thinking. So, if business is done right, the answer is that Linda is more likely to be a bank teller (rather than a bank teller and a feminist). [Sources: 2, 5]

The problem with the representativeness heuristic is that representativeness has nothing to do with probability, but we value it more than relevant information. Based on this description, subjects are asked which of the following statements is more likely. Prototypes guide our assumptions about probability, as in the example above about Steve and his profession. [Sources: 1, 4]

This term refers to the tendency to think that a combination of two events is more likely than either of these events to occur separately. When we try to make decisions about unknown things or people, we refer to this environment – the prototype – as a representative example of the entire category. According to a categorization theory known as prototype theory, people use unconscious mental statistics to understand what the “average” member of a category looks like. However, there is another main reason why the representativeness heuristic arises. [Sources: 1, 3]

However, the first option is a shorter letter than the other two and is more likely. This means that we often rely on labels to quickly judge the world. [Sources: 1, 3]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://en.wikipedia.org/wiki/Conjunction_fallacy

[1]: https://thedecisionlab.com/biases/representativeness-heuristic/

[2]: https://fs.blog/bias-conjunction-fallacy/

[3]: https://econowmics.com/the-conjunction-fallacy/

[4]: https://en.shortcogs.com/bias/conjunction-fallacy

[5]: https://www.psychologytoday.com/us/blog/the-superhuman-mind/201611/linda-the-bank-teller-case-revisited

Compassion Fade

Extinction of compassion is a cognitive bias that refers to a decrease in compassion that is shown to people in need as the number of victims increases. This may be the result of the mental numbness first proposed by Robert Jay Lifton. [Sources: 9]

Psychologist Paul Slovic came up with this phrase after observing that as suffering increases, people’s compassion decreases. Our sympathy for suffering and loss is rapidly falling as more and more victims are presented to us. As the number of victims of the tragedy increases, our sympathy, our willingness to help, reliably decreases. [Sources: 0, 2, 6]

Fading away compassion is the tendency for empathy to diminish as more people need help. Another way to explain the decline in the concept of compassion is to view it as a tendency for empathy to decrease as more people need help. According to psychologist and researcher Paul Slovic, the extinction of compassion is the idea that the more people are affected by tragedy, the less empathy is. This erosion of compassion can significantly impede individual and collective (eg, political) responses to urgent large-scale crises such as genocide, mass famine [5] or severe environmental degradation [32]. [Sources: 4, 5, 11]

The main tenet of this study is that compassion, and therefore concern for society, is often diminished rather than increased in the face of more serious threats. The main purpose of this article is to understand the psychological basis of this perverse phenomenon. In this article, we explore how attention-focused affective feelings may underlie the findings that when it comes to awakening compassion, an individual with a face and name usually elicits a stronger reaction than a group. [Sources: 5]

These results support the idea that the extinction of compassion is an affective phenomenon in which feelings are more pronounced towards individuals or groups perceived as separate units. These discoveries not only expand our understanding of the psychology of compassion, but also offer ways to deal with the loss of feelings as the need increases. The first evidence of this comes from research showing that compassion for victims decreases as the number of people needing help increases [30], the identifiability of victims decreases [31] and the percentage of victims who received assistance decreases [7]. [Sources: 1]

The people who have the most empathy and also tend to experience the most empathic discomfort are actually more likely to experience a collapse of compassion than people who are less important. As I continued to explore the extinction of compassion, I found that people tend to ignore feelings because they are trying to avoid depression or emotional distress. Perhaps fading compassion is partly a way for people to vaccinate themselves so they don’t look deeper into any guilt or shame they may feel because of their privilege and / or contribution to the problem of this wholeness. And this is not an accident of human psychology, it is a real barrier to compassion that can prevent people from doing things that might matter. [Sources: 2, 11]

Compassion collapses not because our ability to empathize or care is so limited, but because we can’t find a way to get to that part of compassion that includes feeling like we have the resources to do something important. which will change the situation. … This is because it has stretched over time, and our common human problem – to understand numbers, to comprehend these numbers – may be one of the reasons why compassion begins to fade. This clarifies the reason why we don’t feel that other people deserve compassion. The statement that “people expect the needs of large groups to be potentially overwhelming” suggests that we consciously consider what this attachment might entail and move away from it, or that we are aware that we are reaching an endpoint of compassion and are beginning to deliberately change. ..classification of the accident from personnel to statistics. [Sources: 2, 10, 11]

The affect heuristic forces people to make decisions based on emotional attachment to a stimulus … It is this emotional element of System 1 that leads us to the effects of compassion disappear when we make decisions based on attachment and the feelings of others. emotion that goes beyond the facts of the situation. [Sources: 4]

As noted below, compassion can also be viewed as having many different feelings and behaviors depending on the context. For example, one of the roots of compassion is caring parenting behavior. As a social attitude, compassion has a flow as we can be compassionate towards others, be open to compassion for others, and be compassionate towards ourselves. Compassion is caused by an awareness of the special suffering and pain of others. [Sources: 3, 7]

Here we experience the absolute spontaneity of compassion that arises beyond all differences and differences, attachments and conceptual structures. We can see boundless compassion that is itself spontaneous, not fabricated, free of concepts or visions of any kind. Usually our most direct experience of compassion is triggered by the awareness of suffering itself. [Sources: 3]

When it seems to be most needed, compassion is felt the least. Affective feelings such as empathy, empathy, sadness and compassion are often viewed as important in motivating help [9], [10]. [Sources: 1, 12]

Since psychotherapy focuses on mental distress, the development of motives and skills of compassion for self and others may be the focus of psychotherapy. In Buddhism, however, compassion is understood as something much more extensive than a simple feeling or emotion, with all the guiding qualities that these words imply. [Sources: 3, 7]

Human compassion has a hard limit. It is one of the most powerful psychological forces that shape human events. The answer—whether it is an overseas refugee crisis or a family health debate—is often related to Paul Slovic. Vocabulary research shows that the human mind is not good at thinking and sympathizing with millions of people. Our sympathy for the plight of strangers can be reduced to a number comparable to the number of people with whom we can be friends—a number with whom we unconsciously associate. [Sources: 0, 10]

We use the term “fading out of compassion” to mean 1) decrease in behavior and 2) influence as the number of people in need increases. Thus, “fading out of compassion,” as used here, means a decrease in positive affect, leading to a decrease in donation as the number of those in need increases. [Sources: 1]

The authors concluded that the largest donations to an individual victim were most likely due to the stronger emotional distress caused by those victims. The findings are consistent with both the psychophysical function of the lost frames and a possible decrease or extinction of compassion as the number of victims identified as risk groups increases. [Sources: 1, 5]

The number that causes the “collapse of compassion” may differ from person to person, but I think it may start to disintegrate along the correlated continuum of Dunbar 150. Think of the collapse of compassion on a grid where compassion is represented on the y-axis and the number of victims on the axis X. This lesson will explore why our compassion sometimes collapses under the weight of great suffering, and what we can do about it. support compassion and change the world for the better. [Sources: 2, 10]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://www.vox.com/explainers/2017/7/19/15925506/psychic-numbing-paul-slovic-apathy

[1]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4062481/

[2]: https://www.linkedin.com/learning/self-compassion-when-compassion-is-difficult/understanding-compassion-collapse

[3]: https://lithub.com/on-the-uses-of-compassion/

[4]: https://redefineschool.com/compassion-fade/

[5]: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100115

[6]: https://www.nytimes.com/2015/12/06/opinion/the-arithmetic-of-compassion.html

[7]: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.586161/full

[8]: https://www.researchgate.net/figure/A-model-depicting-psychic-numbing-compassion-fade-when-valuing-the-saving-of-lives_fig9_263289454

[9]: http://econowmics.com/compassion-fade/

[10]: https://bigthink.com/the-present/why-compassion-fades/

[11]: https://podcast.wellevatr.com/why-compassion-fades

[12]: https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780190464684.001.0001/oxfordhb-9780190464684-e-20

Base Rate Fallacy

As a minimum definition, we can say that we make a base rate error when we make a statistical inference that somehow ignores the base rate (or prior probability) of the interest rate. Base bet error occurs when we make judgments too quickly, ignoring base bets or odds in favor of new information. Baseline velocity bias is a common cognitive error that distorts decision-making, with the result that information about the occurrence of some common characteristics in a given population is ignored or of little importance in the decision-making process. [Sources: 5, 9, 11]

When assessing the probability of an uncertain event, the tendency to ignore the baseline rate, underestimate the baseline rate or the current information of previous information, which determines the probability of an uncertain event, is the main distortion of a person’s probability conclusion (1, 2). It highlights the long-term research program of experimental psychology that uses ideal Bayesian inference as a model of human behavior and attempts to understand how people estimate probability by examining the systematic deviation of probability estimates from the ideal model predictions (3). [Sources: 7]

These results indicate that bias in probabilistic inference, widely observed in human probabilistic judgments, arise from calculations of information weighting and relative sensitivity to information variability in the brain. These results indicate that, by combining precedent and likelihood, sensitivity to information variability and the calculation of subjective weights critically affect the individual heterogeneity of base rate neglect. Neglecting the base rate — a serious error in assessing the likelihood of uncertain events — describes a person’s tendency to underestimate the base (previous) rate compared to the discovery of information (probability). [Sources: 7]

However, although predictive information is easily used, even when the actual reliability of the predictors is questionable, the information inherent in the baseline event rate criteria is often underutilized (Tversky & Kahneman, 1982). For example, when the predictor is a witness that a suspicious car was blue, human participants tend to believe it was indeed blue, even when faced with evidence that the base frequency of blue cars is low at that particular location. When presenting information related to the base rate (i.e. general information about prevalence) and specific information (i.e. information that is specific to a specific case), people tend to ignore the base rate in favor of identifying information instead of correctly integrate them. … [Sources: 4, 14]

The error arises from confusing the nature of the two different failure rates. Consequently, 100% of all cases when the alarm sounds for non-terrorists, but it is impossible to even calculate a false negative indicator. [Sources: 4]

If there were as many terrorists as there are non-terrorists in the city, and the number of false positives and the number of false negatives were almost equal, then the probability of false identification would be about the same as the number of false positives of the device. In this case, the number of false positives per positive test will be almost equal to the number of false positives per non-pregnant woman. Even a very low rate of false positives will result in so many false positives that such a system is practically useless. [Sources: 1]

In the latter case, it is impossible to derive the posterior probability p (drunkenness | positive result) by comparing the number of drunk drivers and positive results with the total number of people who received a positive breathalyzer test, since information on the basis of the fare is not saved and should be explicitly re-introduced using Bayes’ theorem. In situations where our test is very accurate (for example> 95%), but the previous probability or base rate of the percentage characteristic is low (say only 1/125), then when the first (hence the proportion of positive cases) positively classified) will be high ( 95%, in our case, just for the accuracy of the test), the latter (therefore, the proportion of positive cases classified as positive) can be very low (because the vast majority of cases classified positively are actually false positives due to the fact that the number of those who passes the test does not have the characteristic of interest, ultimately due to the low base rate). The likelihood of a positive test result is determined not only by the accuracy of the test, but also by the characteristics of the sample. [Sources: 4, 9]

We tend to make judgments based on known specific numbers and percentages, and ignore necessary general statistics. Therefore, when making statistical conclusions, it is very important to be wary of our tendency to fall into such traps. If you cannot draw a conclusion, then you are approaching Bayesian probability, and you are not alone. When providing data on the likelihood of breast cancer in women who have a positive mammogram, an alarming 80% of doctors are wrong. [Sources: 2, 9, 13]

Clinical profilingists are likely to resist the idea of ​​adding a percentage figure to their predictions – this seems to contradict clinical intuition or judgment. It always happens that way; People unfamiliar with the technical rules of prior probability usually ignore previous statistics because they don’t seem relevant. There is still a lot to think about before calculating the bounce rate. The profiler needs to communicate more clearly by entering a personal prediction percentage (e.g. 30%) so that researchers can judge how strongly the profiler thinks an event will happen. [Sources: 2, 5, 8]

When predicting criterion events by predictors in probabilistic parameters, it is normatively advisable to take into account two types of information: the global base frequency of criterion events and the values ​​of predictors for a particular case. Moreover, even when the predictors did not contain any useful statistical information, there was a bias in the selection of criterion events that outwardly resembled predictors due to the lack of conjugation with event criteria (Goodie & Fantino, 1996). [Sources: 14]

They argued that many judgments of probability or cause and effect are based on how representative one object is to another or a category. They argued that many judgments of probability or cause and effect are based on how representative one object is to another or a category. One of the main theories postulates that this is a matter of relevance, so we ignore the base rate information because we classify it as irrelevant and therefore think it should be ignored. [Sources: 1, 4, 6]

Representativeness heuristics may lead to basic rate errors, because we can treat events or objects as highly representative and make probabilistic judgments based solely on this, without stopping to consider the basic rate value. This is a common psychological bias associated with representative heuristics. They are influenced by personal stories, for example, a story about a smoker who lived to be 95 years old. [Sources: 6, 12]

It also happens when the profiler feels he is better equipped to deal with the problem based on previous experience. For example, a profiler might focus on a specific criminal, obscuring useful information about a group of criminals with similar characteristics. Subjective judgments about likelihood based on personal belief profiles, such as that the perpetrator will commit the crime again, or that a particular suspect is the prime suspect, or that the perpetrator lives in a specific area. The mechanical application of Bayess’s Theorem to identify performance errors is inadequate when (1) key model assumptions are unchecked or severely violated, and (2) no attempt is made to define the goals, values, and assumptions that are the responsibility of decision-makers. [Sources: 0, 5]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/base-rate-fallacy-reconsidered-descriptive-normative-and-methodological-challenges/5C0138815B364140B87110364055683B

[1]: https://psychology.fandom.com/wiki/Base_rate_fallacy

[2]: https://www.statisticshowto.com/base-rates-base-rate-fallacy/

[3]: https://www.investopedia.com/terms/b/base-rate-fallacy.asp

[4]: https://en.wikipedia.org/wiki/Base_rate_fallacy

[5]: https://ifioque.com/social-psychology/base-rate-fallacy

[6]: https://thedecisionlab.com/biases/base-rate-fallacy/

[7]: https://www.pnas.org/content/117/29/16908

[8]: https://www.adcocksolutions.com/post/no-8-of-86-base-rate-fallacy

[9]: https://www.capgemini.com/gb-en/2020/10/the-base-rate-fallacy-what-is-it-and-why-does-it-matter/

[10]: https://www.oxfordreference.com/view/10.1093/oi/authority.20110803095449924

[11]: https://tactics.convertize.com/definitions/base-rate-fallacy

[12]: https://fs.blog/mental-model-bias-from-insensitivity-to-base-rates/

[13]: https://www.wheelofpersuasion.com/technique/base-rate-neglect-base-rate-fallacy/

[14]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2441578/

Restraint Bias

Nordgren concluded that “we tend to overestimate our ability to control our impulses,” a phenomenon known as the “self-control illusion,” which often leads us to make very bad decisions in life. The illusion of self-control is the tendency to overestimate our ability to control impulsive behavior. Moderation bias is the tendency for people to overestimate their ability to control impulsive behavior. Projection bias causes people to overestimate their ability to resist the temptations around them, thereby undermining their attempts to exercise self-control. [Sources: 4, 8, 11]

Now that you know your projection bias, it can help you get rid of overconfidence in your ability to control temptation. Projection bias can lead non-cigarette users to underestimate the strength and disadvantages of addiction. Thinking that you are rational despite the obviousness of the irrationality of others is also known as the blind spot bias. [Sources: 2, 8]

Every cognitive distortion exists for a reason, primarily to save time or energy in our brains. Cognitive biases are just tools that are useful in certain contexts and harmful to others. Some things that we recall later make all of the above systems more biased and more detrimental to our mental processes. [Sources: 6, 7]

With these four problems and their four implications in mind, the accessibility heuristic (and in particular the Baader-Meinhof phenomenon) ensures that we notice our biases more often. If you visit this page to refresh your memory from time to time, the spacing effect will help you highlight some of these thought patterns to control blind spot bias and naive realism. Nothing we do can make the four problems disappear (until we have a way to expand the processing power and memory of our minds to match those in the universe), but if we admit that we are constantly biased and that there is room for improvement: Confirmation bias will continue to help us find corroborating evidence that will ultimately lead us to a better understanding of ourselves. Nothing we do can make the 4 problems go away (until we have a way to expand the processing power of our minds and storage of memory to match those in the universe), but if we admit that we are constantly biased, but that there is room for improvement, confirming bias will continue to help us find corroborating evidence that will ultimately lead us to a better understanding of ourselves. [Sources: 6, 7]

Minimizing the strength of constraint bias means more accurate perception of our impulse control and, accordingly, making better decisions. First, we can take an inventory of the areas of our life that we think are most affected by impulsivity or incontinence. Attention has a lot to do with prejudice, self-control, and impulses in our environment. [Sources: 3, 4]

Herding Behavior This effect is evident when people do what others do, instead of using their own information or making independent decisions. This tells us that impulsiveness and selfishness are only two halves of the same coin, as are their opposites – moderation and compassion. [Sources: 5, 9]

This may be why people with dark personality traits like psychopathy and sadism have a low compassion score but a high impulsivity. A gap between hot and cold empathy occurs when people underestimate the impact of visceral states (for example, this is also called an “empathy gap”, when people underestimate the impact of visceral states (for example, projection bias is a tendency to project current preferences into the future). As if future tastes will be the same as the current ones (Loewenstein, ODonoghue & Rabin, 2003). [Sources: 5, 8, 9]

Prediction Bias In behavioral economics, prediction bias is related to the assumption that people’s tastes or preferences remain constant over time (Loewenstein et al., 2003). Optimism bias People tend to overestimate the possibility of positive events and underestimate the possibility of negative events in the future (Sharot, 2011). The tendency to confidently assume that other people have the same mentality, opinions, and beliefs as us is called projection bias. A related effect, called false consent bias, makes us think that other people also agree with our views, thereby furthering this trend. [Sources: 8, 9]

Believing that we can control ourselves and everything around us makes us feel safe. In practice, we find it difficult to imagine the strength that inner impulses and emotions can display and the strength they have to break our willpower and our self-control. Levenshtein explains that we have limited memory for intuitive experience, which means we can remember the impulsive state, but we cannot recreate the feeling of the impulsive state, which causes us to repeat the same mistake over and over, which leads to a fall. the illusion of self-control. [Sources: 11]

Self-control Self-control in psychology is a cognitive process that serves to curb certain behaviors and emotions aimed at temptations and impulses. Control premium In behavioral economics, the control premium refers to the willingness of people to give up potential rewards in order to control (avoid delegating) their income. Inflated beliefs in impulse control cause people to overly succumb to temptation, thereby contributing to impulsive behavior. [Sources: 0, 9]

What’s more, Suchek showed that the degree of their bias – their inability to leave their own head – predicted how impulsive and selfish they were in the first experiment. There is a division between cold and hot empathies, which states that when people are in a cold state, for example, do not feel hungry, they tend to underestimate these influences in a hot state. [Sources: 4, 5]

If this area is exposed to an electric current, people are better able to perceive someone else’s point of view. If the neurons inside it are better connected (and well connected to other parts of the brain), people will exhibit less bias towards their own groups. But new research by Alexander Saucek of the University of Zurich suggests that self-control is also influenced by another area of ​​the brain, which puts this ability in a different light. License effect. The licensing effect, also known as self-expression or moral license, occurs when people allow themselves to do something bad (for example, [Sources: 5, 9]

McGonigal also suggests creating obstacles for yourself and making a commitment to be more responsible for your impulses. Another way to avoid getting this page in the future is to use the Privacy Pass. But despite trying to assimilate the information on this page many times over the years, there seems to be very little left. [Sources: 3, 6, 10]

Objectives and methods. Here, we selectively examined s / fMRI studies of ADHD and DBD to identify disorder-specific and common aberrant neural mechanisms of AI and RI. Results. In ADHD, deviating functional activity of the prefrontal and lumbar parts of the lower back was associated with an increase in IS. [Sources: 1]

The “problem” was that some people were told before screening that they had a high level of self-control, while others were told that they could not control their impulses. With the ability to evaluate interrupted intent, they instead began looking for results. [Sources: 5, 11]

 

— Slimane Zouggari

 

##### Sources #####

[0]: https://journals.sagepub.com/doi/abs/10.1111/j.1467-9280.2009.02468.x

[1]: https://www.sciencedirect.com/science/article/pii/S0149763418300162

[2]: https://www.businessinsider.com/cognitive-biases-2015-10

[3]: https://thedecisionlab.com/biases/restraint-bias/

[4]: https://en.wikipedia.org/wiki/Restraint_bias

[5]: https://www.theatlantic.com/science/archive/2016/12/self-control-is-just-empathy-with-a-future-you/509726/

[6]: https://qz.com/776168/a-comprehensive-guide-to-cognitive-biases/

[7]: https://betterhumans.pub/cognitive-bias-cheat-sheet-55a472476b18

[8]: https://uxdesign.cc/projection-bias-how-it-affects-us-in-our-daily-lives-influence-our-design-decisions-933baa3a3084

[9]: https://www.behavioraleconomics.com/resources/mini-encyclopedia-of-be/

[10]: https://www.researchgate.net/publication/38061630_The_Restraint_Bias_How_the_Illusion_of_Self-Restraint_Promotes_Impulsive_Behavior

[11]: https://psychology-spot.com/illusion-of-self-control-hot-cold-empathy-gap/