Chasing the Rainbow: The Non-conscious Nature of Being. David A. Oakley and Peter W. Halligan. Front. Psychol., November 14 2017. https://doi.org/10.3389/fpsyg.2017.01924
Abstract: Despite the compelling subjective experience of executive self-control, we argue that “consciousness” contains no top-down control processes and that “consciousness” involves no executive, causal, or controlling relationship with any of the familiar psychological processes conventionally attributed to it. In our view, psychological processing and psychological products are not under the control of consciousness. In particular, we argue that all “contents of consciousness” are generated by and within non-conscious brain systems in the form of a continuous self-referential personal narrative that is not directed or influenced in any way by the “experience of consciousness.” This continuously updated personal narrative arises from selective “internal broadcasting” of outputs from non-conscious executive systems that have access to all forms of cognitive processing, sensory information, and motor control. The personal narrative provides information for storage in autobiographical memory and is underpinned by constructs of self and agency, also created in non-conscious systems. The experience of consciousness is a passive accompaniment to the non-conscious processes of internal broadcasting and the creation of the personal narrative. In this sense, personal awareness is analogous to the rainbow which accompanies physical processes in the atmosphere but exerts no influence over them. Though it is an end-product created by non-conscious executive systems, the personal narrative serves the powerful evolutionary function of enabling individuals to communicate (externally broadcast) the contents of internal broadcasting. This in turn allows recipients to generate potentially adaptive strategies, such as predicting the behavior of others and underlies the development of social and cultural structures, that promote species survival. Consequently, it is the capacity to communicate to others the contents of the personal narrative that confers an evolutionary advantage—not the experience of consciousness (personal awareness) itself.
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What if consciousness is not what drives the human mind? David A Oakley & Peter Halligan
November 22, 2017 10.08am GMT
https://dailyaccord.com/consciousness-not-drives-human-mind/
[links removed, check the original link at the Daily Accord]
Everyone knows what it feels like to have consciousness: it’s that self-evident sense of personal awareness, which gives us a feeling of ownership and control over the thoughts, emotions and experiences that we have every day.
Most experts think that consciousness can be divided into two parts: the experience of consciousness (or personal awareness), and the contents of consciousness, which include things such as thoughts, beliefs, sensations, perceptions, intentions, memories and emotions.
It’s easy to assume that these contents of consciousness are somehow chosen, caused or controlled by our personal awareness – after all, thoughts don’t exist until until we think them. But in a new research paper in Frontiers of Psychology, we argue that this is a mistake.
We suggest that our personal awareness does not create, cause or choose our beliefs, feelings or perceptions. Instead, the contents of consciousness are generated “behind the scenes” by fast, efficient, non-conscious systems in our brains. All this happens without any interference from our personal awareness, which sits passively in the passenger seat while these processes occur.
Put simply, we don’t consciously choose our thoughts or our feelings – we become aware of them.
Not just a suggestion
If this sounds strange, consider how effortlessly we regain consciousness each morning after losing it the night before; how thoughts and emotions – welcome or otherwise – arrive already formed in our minds; how the colours and shapes we see are constructed into meaningful objects or memorable faces without any effort or input from our conscious mind.
Consider that all the neuropsychological processes responsible for moving your body or using words to form sentences take place without involving your personal awareness. We believe that the processes responsible for generating the contents of consciousness do the same.
Our thinking has been influenced by research into neuropsychological and neuropsychiatric disorders, as well as more recent cognitive neuroscience studies using hypnosis. The studies using hypnosis show that a person’s mood, thoughts and perceptions can be profoundly altered by suggestion.
In such studies, participants go through a hypnosis induction procedure, to help them to enter a mentally focused and absorbed state. Then, suggestions are made to change their perceptions and experiences.
For example, in one study, researchers recorded the brain activity of participants when they raised their arm intentionally, when it was lifted by a pulley, and when it moved in response to a hypnotic suggestion that it was being lifted by a pulley.
Similar areas of the brain were active during the involuntary and the suggested “alien” movement, while brain activity for the intentional action was different. So, hypnotic suggestion can be seen as a means of communicating an idea or belief that, when accepted, has the power to alter a person’s perceptions or behaviour.
The personal narrative
All this may leave one wondering where our thoughts, emotions and perceptions actually come from. We argue that the contents of consciousness are a subset of the experiences, emotions, thoughts and beliefs that are generated by non-conscious processes within our brains.
This subset takes the form of a personal narrative, which is constantly being updated. The personal narrative exists in parallel with our personal awareness, but the latter has no influence over the former.
The personal narrative is important because it provides information to be stored in your autobiographical memory (the story you tell yourself, about yourself), and gives human beings a way of communicating the things we have perceived and experienced to others.
This, in turn, allows us to generate survival strategies; for example, by learning to predict other people’s behaviour. Interpersonal skills like this underpin the development of social and cultural structures, which have promoted the survival of human kind for millennia.
So, we argue that it is the ability to communicate the contents of one’s personal narrative –– and not personal awareness – that gives humans their unique evolutionary advantage.
What’s the point?
If the experience of consciousness does not confer any particular advantage, it’s not clear what its purpose is. But as a passive accompaniment to non-conscious processes, we don’t think that the phenomenon of personal awareness has a purpose, in much the same way that rainbows do not. Rainbows simply result from the reflection, refraction and dispersion of sunlight through water droplets – none of which serves any particular purpose.
Our conclusions also raise questions about the notions of free will and personal responsibility. If our personal awareness does not control the contents of the personal narrative which reflects our thoughts, feelings, emotions, actions and decisions, then perhaps we should not be held responsible for them.
In response to this, we argue that free will and personal responsibility are notions that have been constructed by society. As such, they are built into the way we see and understand ourselves as individuals, and as a species. Because of this, they are represented within the non-conscious processes that create our personal narratives, and in the way we communicate those narratives to others.
Just because consciousness has been placed in the passenger seat, does not mean we need to dispense with important everyday notions such as free will and personal responsibility. In fact, they are embedded in the workings of our non-conscious brain systems. They have a powerful purpose in society and have a deep impact on the way we understand ourselves.
David A Oakley, Emeritus Professor of Psychology, UCL and Peter Halligan, Hon Professor of Neuropsychology, Cardiff University
Bipartisan Alliance, a Society for the Study of the US Constitution, and of Human Nature, where Republicans and Democrats meet.
Monday, January 1, 2018
Despite the compelling subjective experience of executive self-control, we argue that “consciousness” contains no top-down control processes and that “consciousness” involves no executive, causal, or controlling relationship with any of the familiar psychological processes conventionally attributed to it. The experience of consciousness is a passive accompaniment to the non-conscious processes of internal broadcasting and the creation of the personal narrative. Though it is an end-product created by non-conscious executive systems, the personal narrative serves the powerful evolutionary function of enabling individuals to communicate (externally broadcast) the contents of internal broadcasting.
Bayesian Occam's razor: People's judgments penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be “tuned” to fit the data better than comparatively simpler hypotheses
Blanchard, T., Lombrozo, T. and Nichols, S. (2017), Bayesian Occam's Razor Is a Razor of the People. Cogn Sci. doi:10.1111/cogs.12573
Abstract: Occam's razor—the idea that all else being equal, we should pick the simpler hypothesis—plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam's razor (BOR) is that more complex hypotheses tend to be more flexible—they can accommodate a wider range of possible data—and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people's intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In particular, people's judgments are consistent with the two most distinctive characteristics of BOR: They penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be “tuned” to fit the data better than comparatively simpler hypotheses.
Abstract: Occam's razor—the idea that all else being equal, we should pick the simpler hypothesis—plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam's razor (BOR) is that more complex hypotheses tend to be more flexible—they can accommodate a wider range of possible data—and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people's intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In particular, people's judgments are consistent with the two most distinctive characteristics of BOR: They penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be “tuned” to fit the data better than comparatively simpler hypotheses.
Through employing more than 3000 workers, usage of Corporate Social Responsibility increases employee misbehavior — 20% more employees act detrimentally toward our firm by shirking on their primary job duty
When Corporate Social Responsibility Backfires: Theory and Evidence from a Natural Field Experiment. John A. List, Fatemeh Momeni. NBER Working Paper No. 24169. www.nber.org/papers/w24169
Abstract: Corporate Social Responsibility (CSR) has become a cornerstone of modern business practice, developing from a “why” in the 1960s to a “must” today. Early empirical evidence on both the demand and supply sides has largely confirmed CSR's efficacy. This paper combines theory with a large-scale natural field experiment to connect CSR to an important but often neglected behavior: employee misconduct and shirking. Through employing more than 3000 workers, we find that our usage of CSR increases employee misbehavior — 20% more employees act detrimentally toward our firm by shirking on their primary job duty when we introduce CSR. Complementary treatments suggest that “moral licensing” is at work, in that the “doing good” nature of CSR induces workers to misbehave on another dimension that hurts the firm. In this way, our data highlight a potential dark cloud of CSR, and serve to forewarn that such business practices should not be blindly applied.
Abstract: Corporate Social Responsibility (CSR) has become a cornerstone of modern business practice, developing from a “why” in the 1960s to a “must” today. Early empirical evidence on both the demand and supply sides has largely confirmed CSR's efficacy. This paper combines theory with a large-scale natural field experiment to connect CSR to an important but often neglected behavior: employee misconduct and shirking. Through employing more than 3000 workers, we find that our usage of CSR increases employee misbehavior — 20% more employees act detrimentally toward our firm by shirking on their primary job duty when we introduce CSR. Complementary treatments suggest that “moral licensing” is at work, in that the “doing good” nature of CSR induces workers to misbehave on another dimension that hurts the firm. In this way, our data highlight a potential dark cloud of CSR, and serve to forewarn that such business practices should not be blindly applied.
Updated: Income Inequality in the United States: Using Tax Data to Measure Long-term Trends
Income Inequality in the United States: Using Tax Data to Measure Long-term Trends. Gerald Auten, David Splinter. November 12, 2017. http://davidsplinter.com/AutenSplinter-Tax_Data_and_Inequality.pdf
Abstract: Previous studies using U.S. tax return data, such as Piketty and Saez (2003), concluded that top one percent income shares increased substantially since 1960. But tax return based measures are biased by tax base changes and missing income sources. Accounting for these limitations reduces the increase in top one percent income shares by two-thirds. Further, accounting for government transfers reduces the increase over 80 percent. After-tax income results are similar. This shows that unadjusted tax return based measures present a distorted view of inequality because incomes reported on tax returns are sensitive to tax law changes and omit significant income sources.
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Update to Using Tax Data to Measure Long-Term Trends in U.S. Income Inequality. Gerald Auten and David Splinter. Draft Paper, Annual Conference, ASSA Annual Meeting, 2017. http://www.bipartisanalliance.com/2017/09/using-tax-data-to-measure-long-term.html
Abstract: Previous studies using U.S. tax return data, such as Piketty and Saez (2003), concluded that top one percent income shares increased substantially since 1960. But tax return based measures are biased by tax base changes and missing income sources. Accounting for these limitations reduces the increase in top one percent income shares by two-thirds. Further, accounting for government transfers reduces the increase over 80 percent. After-tax income results are similar. This shows that unadjusted tax return based measures present a distorted view of inequality because incomes reported on tax returns are sensitive to tax law changes and omit significant income sources.
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Update to Using Tax Data to Measure Long-Term Trends in U.S. Income Inequality. Gerald Auten and David Splinter. Draft Paper, Annual Conference, ASSA Annual Meeting, 2017. http://www.bipartisanalliance.com/2017/09/using-tax-data-to-measure-long-term.html
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Timnit Gebru et al. Proceedings of the National Academy of Sciences, vol. 114 no. 50. http://www.pnas.org/content/114/50/13108.abstract
Significance: We show that socioeconomic attributes such as income, race, education, and voting patterns can be inferred from cars detected in Google Street View images using deep learning. Our model works by discovering associations between cars and people. For example, if the number of sedans in a city is higher than the number of pickup trucks, that city is likely to vote for a Democrat in the next presidential election (88% chance); if not, then the city is likely to vote for a Republican (82% chance).
Abstract: The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.
Significance: We show that socioeconomic attributes such as income, race, education, and voting patterns can be inferred from cars detected in Google Street View images using deep learning. Our model works by discovering associations between cars and people. For example, if the number of sedans in a city is higher than the number of pickup trucks, that city is likely to vote for a Democrat in the next presidential election (88% chance); if not, then the city is likely to vote for a Republican (82% chance).
Abstract: The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.
Closing Your Eyes to Follow Your Heart: Avoiding Information to Protect a Strong Intuitive Preference
Closing Your Eyes to Follow Your Heart: Avoiding Information to Protect a Strong Intuitive Preference. Woolley, Kaitlin, and Risen, Jane L. Journal of Personality and Social Psychology, Dec 18 , 2017, http://psycnet.apa.org/doiLanding?doi=10.1037%2Fpspa0000100
Abstract: Rationally, people should want to receive information that is costless and relevant for a decision. But people sometimes choose to remain ignorant. The current paper identifies intuitive-deliberative conflict as a driver of information avoidance. Moreover, we examine whether people avoid information not only to protect their feelings or experiences, but also to protect the decision itself. We predict that people avoid information that could encourage a more thoughtful, deliberative decision to make it easier to enact their intuitive preference. In Studies 1 and 2, people avoid learning the calories in a tempting dessert and compensation for a boring task to protect their preferences to eat the dessert and work on a more enjoyable task. The same people who want to avoid the information, however, use it when it is provided. In Studies 3–5, people decide whether to learn how much money they could earn by accepting an intuitively unappealing bet (that a sympathetic student performs poorly or that a hurricane hits a third-world country). Although intuitively unappealing, the bets are financially rational because they only have financial upside. If people avoid information in part to protect their intuitive preference, then avoidance should be greater when an intuitive preference is especially strong and when information could influence the decision. As predicted, avoidance is driven by the strength of the intuitive preference (Study 3) and, ironically, information avoidance is greater before a decision is made, when the information is decision relevant, than after, when the information is irrelevant for the decision (Studies 4 and 5).
Abstract: Rationally, people should want to receive information that is costless and relevant for a decision. But people sometimes choose to remain ignorant. The current paper identifies intuitive-deliberative conflict as a driver of information avoidance. Moreover, we examine whether people avoid information not only to protect their feelings or experiences, but also to protect the decision itself. We predict that people avoid information that could encourage a more thoughtful, deliberative decision to make it easier to enact their intuitive preference. In Studies 1 and 2, people avoid learning the calories in a tempting dessert and compensation for a boring task to protect their preferences to eat the dessert and work on a more enjoyable task. The same people who want to avoid the information, however, use it when it is provided. In Studies 3–5, people decide whether to learn how much money they could earn by accepting an intuitively unappealing bet (that a sympathetic student performs poorly or that a hurricane hits a third-world country). Although intuitively unappealing, the bets are financially rational because they only have financial upside. If people avoid information in part to protect their intuitive preference, then avoidance should be greater when an intuitive preference is especially strong and when information could influence the decision. As predicted, avoidance is driven by the strength of the intuitive preference (Study 3) and, ironically, information avoidance is greater before a decision is made, when the information is decision relevant, than after, when the information is irrelevant for the decision (Studies 4 and 5).
Personality, IQ, and Lifetime Earnings: The payoffs to personality traits display a concave life-cycle pattern, with the largest effects between the ages of 40 and 60
Personality, IQ, and Lifetime Earnings. Miriam Gensowski. Labour Economics, https://doi.org/10.1016/j.labeco.2017.12.004
Highlights
• This paper estimates the effects of personality traits and IQ on lifetime earnings, both as a sum and individually by age.
• The payoffs to personality traits display a concave life-cycle pattern, with the largest effects between the ages of 40 and 60.
• The largest effects on earnings are found for Conscientiousness, Extraversion, and Agreeableness (negative).
• An interaction of traits with education reveals that personality matters most for highly educated men.
• The overall effect of Conscientiousness operates partly through education, which also has significant returns.
Abstract: This paper estimates the effects of personality traits and IQ on lifetime earnings of the men and women of the Terman study, a high-IQ U.S. sample. Age-by-age earnings profiles allow a study of when personality traits affect earnings most, and for whom the effects are strongest. I document a concave life-cycle pattern in the payoffs to personality traits, with the largest effects between the ages of 40 and 60. An interaction of traits with education reveals that personality matters most for highly educated men. The largest effects are found for Conscientiousness, Extraversion, and Agreeableness (negative), where Conscientiousness operates partly through education, which also has significant returns.
Keywords: Personality traits; Socio-emotional skills; Cognitive skills; Returns to education; Lifetime earnings; Big Five; Human capital; Factor analysis
Highlights
• This paper estimates the effects of personality traits and IQ on lifetime earnings, both as a sum and individually by age.
• The payoffs to personality traits display a concave life-cycle pattern, with the largest effects between the ages of 40 and 60.
• The largest effects on earnings are found for Conscientiousness, Extraversion, and Agreeableness (negative).
• An interaction of traits with education reveals that personality matters most for highly educated men.
• The overall effect of Conscientiousness operates partly through education, which also has significant returns.
Abstract: This paper estimates the effects of personality traits and IQ on lifetime earnings of the men and women of the Terman study, a high-IQ U.S. sample. Age-by-age earnings profiles allow a study of when personality traits affect earnings most, and for whom the effects are strongest. I document a concave life-cycle pattern in the payoffs to personality traits, with the largest effects between the ages of 40 and 60. An interaction of traits with education reveals that personality matters most for highly educated men. The largest effects are found for Conscientiousness, Extraversion, and Agreeableness (negative), where Conscientiousness operates partly through education, which also has significant returns.
Keywords: Personality traits; Socio-emotional skills; Cognitive skills; Returns to education; Lifetime earnings; Big Five; Human capital; Factor analysis
Liars failed to simulate the truthtellers' pattern of forgetting & reported similar amounts of detail when interviewed without or after a delay, demonstrating a stability bias in reporting
A stability bias effect among deceivers. Harvey, Adam Charles, Vrij, Aldert, Hope, Lorraine, Leal, Sharon, and Mann, Samantha. Law and Human Behavior, Vol 41(6), Dec 2017, 519-529. http://psycnet.apa.org/doiLanding?doi=10.1037%2Flhb0000258
Abstract: Research examining how truth tellers’ and liars’ verbal behavior is attenuated as a function of delay is largely absent from the literature, despite its important applied value. We examined this factor across 2 studies in which we examined the effects of a hypothetical delay (Experiment 1) or actual delay (Experiment 2) on liars’ accounts. In Experiment 1—an insurance claim interview setting—claimants either genuinely experienced a (staged) loss of a tablet device (n = 40) or pretended to have experienced the same loss (n = 40). Truth tellers were interviewed either immediately after the loss (n = 20) or 3 weeks after the loss (n = 20), whereas liars had to either pretend the loss occurred either immediately before (n = 20) or 3 weeks before (n = 20) the interview (i.e., hypothetical delay for liars). In Experiment 2—a Human Intelligence gathering setting—sources had to either lie (n = 50) or tell the truth (n = 50) about a secret video they had seen concerning the placing of a spy device. Half of the truth tellers and liars where interviewed immediately after watching the video (n = 50), and half where interviewed 3-weeks later (n = 50; i.e., real delay for liars). Across both experiments, truth tellers interviewed after a delay reported fewer details than truth tellers interviewed immediately after the to-be-remembered event. In both studies, liars failed to simulate this pattern of forgetting and reported similar amounts of detail when interviewed without or after a delay, demonstrating a stability bias in reporting.
Abstract: Research examining how truth tellers’ and liars’ verbal behavior is attenuated as a function of delay is largely absent from the literature, despite its important applied value. We examined this factor across 2 studies in which we examined the effects of a hypothetical delay (Experiment 1) or actual delay (Experiment 2) on liars’ accounts. In Experiment 1—an insurance claim interview setting—claimants either genuinely experienced a (staged) loss of a tablet device (n = 40) or pretended to have experienced the same loss (n = 40). Truth tellers were interviewed either immediately after the loss (n = 20) or 3 weeks after the loss (n = 20), whereas liars had to either pretend the loss occurred either immediately before (n = 20) or 3 weeks before (n = 20) the interview (i.e., hypothetical delay for liars). In Experiment 2—a Human Intelligence gathering setting—sources had to either lie (n = 50) or tell the truth (n = 50) about a secret video they had seen concerning the placing of a spy device. Half of the truth tellers and liars where interviewed immediately after watching the video (n = 50), and half where interviewed 3-weeks later (n = 50; i.e., real delay for liars). Across both experiments, truth tellers interviewed after a delay reported fewer details than truth tellers interviewed immediately after the to-be-remembered event. In both studies, liars failed to simulate this pattern of forgetting and reported similar amounts of detail when interviewed without or after a delay, demonstrating a stability bias in reporting.
Public Response to a Near-Miss Nuclear Accident Scenario Varying in Causal Attributions and Outcome Uncertainty
Cui, J., Rosoff, H. and John, R. S. (2017), Public Response to a Near-Miss Nuclear Accident Scenario Varying in Causal Attributions and Outcome Uncertainty. Risk Analysis. doi:10.1111/risa.12920
Abstract: Many studies have investigated public reactions to nuclear accidents. However, few studies focused on more common events when a serious accident could have happened but did not. This study evaluated public response (emotional, cognitive, and behavioral) over three phases of a near-miss nuclear accident. Simulating a loss-of-coolant accident (LOCA) scenario, we manipulated (1) attribution for the initial cause of the incident (software failure vs. cyber terrorist attack vs. earthquake), (2) attribution for halting the incident (fail-safe system design vs. an intervention by an individual expert vs. a chance coincidence), and (3) level of uncertainty (certain vs. uncertain) about risk of a future radiation leak after the LOCA is halted. A total of 773 respondents were sampled using a 3 × 3 × 2 between-subjects design. Results from both MANCOVA and structural equation modeling (SEM) indicate that respondents experienced more negative affect, perceived more risk, and expressed more avoidance behavioral intention when the near-miss event was initiated by an external attributed source (e.g., earthquake) compared to an internally attributed source (e.g., software failure). Similarly, respondents also indicated greater negative affect, perceived risk, and avoidance behavioral intentions when the future impact of the near-miss incident on people and the environment remained uncertain. Results from SEM analyses also suggested that negative affect predicted risk perception, and both predicted avoidance behavior. Affect, risk perception, and avoidance behavior demonstrated high stability (i.e., reliability) from one phase to the next.
KEYWORDS: Causal attribution; near-miss; nuclear power; risk perception; structural equation modeling
Abstract: Many studies have investigated public reactions to nuclear accidents. However, few studies focused on more common events when a serious accident could have happened but did not. This study evaluated public response (emotional, cognitive, and behavioral) over three phases of a near-miss nuclear accident. Simulating a loss-of-coolant accident (LOCA) scenario, we manipulated (1) attribution for the initial cause of the incident (software failure vs. cyber terrorist attack vs. earthquake), (2) attribution for halting the incident (fail-safe system design vs. an intervention by an individual expert vs. a chance coincidence), and (3) level of uncertainty (certain vs. uncertain) about risk of a future radiation leak after the LOCA is halted. A total of 773 respondents were sampled using a 3 × 3 × 2 between-subjects design. Results from both MANCOVA and structural equation modeling (SEM) indicate that respondents experienced more negative affect, perceived more risk, and expressed more avoidance behavioral intention when the near-miss event was initiated by an external attributed source (e.g., earthquake) compared to an internally attributed source (e.g., software failure). Similarly, respondents also indicated greater negative affect, perceived risk, and avoidance behavioral intentions when the future impact of the near-miss incident on people and the environment remained uncertain. Results from SEM analyses also suggested that negative affect predicted risk perception, and both predicted avoidance behavior. Affect, risk perception, and avoidance behavior demonstrated high stability (i.e., reliability) from one phase to the next.
KEYWORDS: Causal attribution; near-miss; nuclear power; risk perception; structural equation modeling
Overconfidence Among Beginners: Is a Little Learning a Dangerous Thing?
Overconfidence Among Beginners: Is a Little Learning a Dangerous Thing? Sanchez, Carmen, and Dunning, David. Journal of Personality and Social Psychology, Nov 02 , 2017, http://psycnet.apa.org/doiLanding?doi=10.1037%2Fpspa0000102
Abstract: Across 6 studies we investigated the development of overconfidence among beginners. In 4 of the studies, participants completed multicue probabilistic learning tasks (e.g., learning to diagnose “zombie diseases” from physical symptoms). Although beginners did not start out overconfident in their judgments, they rapidly surged to a “beginner’s bubble” of overconfidence. This bubble was traced to exuberant and error-filled theorizing about how to approach the task formed after just a few learning experiences. Later trials challenged and refined those theories, leading to a temporary leveling off of confidence while performance incrementally improved, although confidence began to rise again after this pause. In 2 additional studies we found a real-world echo of this pattern of overconfidence across the life course. Self-ratings of financial literacy surged among young adults, then leveled off among older respondents until late adulthood, where it begins to rise again, with actual financial knowledge all the while rising more slowly, consistently, and incrementally throughout adulthood. Hence, when it comes to overconfident judgment, a little learning does appear to be a dangerous thing. Although beginners start with humble self-perceptions, with just a little experience their confidence races ahead of their actual performance.
Abstract: Across 6 studies we investigated the development of overconfidence among beginners. In 4 of the studies, participants completed multicue probabilistic learning tasks (e.g., learning to diagnose “zombie diseases” from physical symptoms). Although beginners did not start out overconfident in their judgments, they rapidly surged to a “beginner’s bubble” of overconfidence. This bubble was traced to exuberant and error-filled theorizing about how to approach the task formed after just a few learning experiences. Later trials challenged and refined those theories, leading to a temporary leveling off of confidence while performance incrementally improved, although confidence began to rise again after this pause. In 2 additional studies we found a real-world echo of this pattern of overconfidence across the life course. Self-ratings of financial literacy surged among young adults, then leveled off among older respondents until late adulthood, where it begins to rise again, with actual financial knowledge all the while rising more slowly, consistently, and incrementally throughout adulthood. Hence, when it comes to overconfident judgment, a little learning does appear to be a dangerous thing. Although beginners start with humble self-perceptions, with just a little experience their confidence races ahead of their actual performance.