A Compensatory Effect on Mate Selection? Importance of Auditory, Olfactory, and Tactile Cues in Partner Choice among Blind and Sighted Individuals. Agnieszka Sorokowska, Anna Oleszkiewicz, Piotr Sorokowski. Archives of Sexual Behavior, April 2018, Volume 47, Issue 3, pp 597–603. https://link.springer.com/article/10.1007/s10508-018-1156-0
Abstract: Human attractiveness is a potent social variable, and people assess their potential partners based on input from a range of sensory modalities. Among all sensory cues, visual signals are typically considered to be the most important and most salient source of information. However, it remains unclear how people without sight assess others. In the current study, we explored the relative importance of sensory modalities other than vision (smell, touch, and audition) in the assessment of same- and opposite-sex strangers. We specifically focused on possible sensory compensation in mate selection, defined as enhanced importance of modalities other than vision among blind individuals in their choice of potential partners. Data were obtained from a total of 119 participants, of whom 78 were blind people aged between 16 and 65 years (M = 42.4, SD = 12.6; 38 females) and a control sample of 41 sighted people aged between 20 and 64. As hypothesized, we observed a compensatory effect of blindness on auditory perception. Our data indicate that visual impairment increases the importance of audition in different types of social assessments for both sexes and in mate choice for blind men.
Wednesday, March 21, 2018
Punishing injustices is more pleasureable (reward system of the brain) than compensating the victims
Neurobiological Mechanisms of Responding to Injustice. Mirre Stallen, Filippo Rossi, Amber Heijne, Ale Smidts, Carsten K.W. De Dreu and Alan G. Sanfey. Journal of Neuroscience, March 21 2018, 38 (12) 2944-2954; https://doi.org/10.1523/JNEUROSCI.1242-17.2018
Abstract: People are particularly sensitive to injustice. Accordingly, deeper knowledge regarding the processes that underlie the perception of injustice, and the subsequent decisions to either punish transgressors or compensate victims, is of important social value. By combining a novel decision-making paradigm with functional neuroimaging, we identified specific brain networks that are involved with both the perception of, and response to, social injustice, with reward-related regions preferentially involved in punishment compared with compensation. Developing a computational model of punishment allowed for disentangling the neural mechanisms and psychological motives underlying decisions of whether to punish and, subsequently, of how severely to punish. Results show that the neural mechanisms underlying punishment differ depending on whether one is directly affected by the injustice, or whether one is a third-party observer of a violation occurring to another. Specifically, the anterior insula was involved in decisions to punish following harm, whereas, in third-party scenarios, we found amygdala activity associated with punishment severity. Additionally, we used a pharmacological intervention using oxytocin, and found that oxytocin influenced participants' fairness expectations, and in particular enhanced the frequency of low punishments. Together, these results not only provide more insight into the fundamental brain mechanisms underlying punishment and compensation, but also illustrate the importance of taking an explorative, multimethod approach when unraveling the complex components of everyday decision-making.
SIGNIFICANCE STATEMENT The perception of injustice is a fundamental precursor to many disagreements, from small struggles at the dinner table to wasteful conflict between cultures and countries. Despite its clear importance, relatively little is known about how the brain processes these violations. Taking an interdisciplinary approach, we combine methods from neuroscience, psychology, and economics to explore the neurobiological mechanisms involved in both the perception of injustice as well as the punishment and compensation decisions that follow. Using a novel behavioral paradigm, we identified specific brain networks, developed a computational model of punishment, and found that administrating the neuropeptide oxytocin increases the administration of low punishments of norm violations in particular. Results provide valuable insights into the fundamental neurobiological mechanisms underlying social injustice.
Check also: Preschool children and chimpanzees incur costs to watch punishment of antisocial others. Natacha Mendes, Nikolaus Steinbeis, Nereida Bueno-Guerra, Josep Call & Tania Singer. Nature Human Behaviour (2017). http://www.bipartisanalliance.com/2017/12/preschool-children-and-chimpanzees.html
Abstract: People are particularly sensitive to injustice. Accordingly, deeper knowledge regarding the processes that underlie the perception of injustice, and the subsequent decisions to either punish transgressors or compensate victims, is of important social value. By combining a novel decision-making paradigm with functional neuroimaging, we identified specific brain networks that are involved with both the perception of, and response to, social injustice, with reward-related regions preferentially involved in punishment compared with compensation. Developing a computational model of punishment allowed for disentangling the neural mechanisms and psychological motives underlying decisions of whether to punish and, subsequently, of how severely to punish. Results show that the neural mechanisms underlying punishment differ depending on whether one is directly affected by the injustice, or whether one is a third-party observer of a violation occurring to another. Specifically, the anterior insula was involved in decisions to punish following harm, whereas, in third-party scenarios, we found amygdala activity associated with punishment severity. Additionally, we used a pharmacological intervention using oxytocin, and found that oxytocin influenced participants' fairness expectations, and in particular enhanced the frequency of low punishments. Together, these results not only provide more insight into the fundamental brain mechanisms underlying punishment and compensation, but also illustrate the importance of taking an explorative, multimethod approach when unraveling the complex components of everyday decision-making.
SIGNIFICANCE STATEMENT The perception of injustice is a fundamental precursor to many disagreements, from small struggles at the dinner table to wasteful conflict between cultures and countries. Despite its clear importance, relatively little is known about how the brain processes these violations. Taking an interdisciplinary approach, we combine methods from neuroscience, psychology, and economics to explore the neurobiological mechanisms involved in both the perception of injustice as well as the punishment and compensation decisions that follow. Using a novel behavioral paradigm, we identified specific brain networks, developed a computational model of punishment, and found that administrating the neuropeptide oxytocin increases the administration of low punishments of norm violations in particular. Results provide valuable insights into the fundamental neurobiological mechanisms underlying social injustice.
Check also: Preschool children and chimpanzees incur costs to watch punishment of antisocial others. Natacha Mendes, Nikolaus Steinbeis, Nereida Bueno-Guerra, Josep Call & Tania Singer. Nature Human Behaviour (2017). http://www.bipartisanalliance.com/2017/12/preschool-children-and-chimpanzees.html
How many hours does it take to make a friend?
How many hours does it take to make a friend? Jeffrey A. Hall. Journal of Social and Personal Relationships, https://doi.org/10.1177/0265407518761225
Abstract: The question of this investigation is, how many hours does it take to make a new friend? Drawing from Dunbar’s social brain hypothesis and Communicate Bond Belong theory, friendship status was examined as a function of hours together, shared activities, and everyday talk. In Study 1, MTurk participants (N = 355) who had recently relocated estimated time spent with a new acquaintance. Hours together was associated with closer friendships. Time spent engaging in leisure activities also predicted closeness. In Study 2, first-year students (N = 112) reported the number of hours spent with two new acquaintances three times over 9 weeks. Hours together was associated changes in closeness between waves. Two types of everyday talk predicted changes in closeness.
Abstract: The question of this investigation is, how many hours does it take to make a new friend? Drawing from Dunbar’s social brain hypothesis and Communicate Bond Belong theory, friendship status was examined as a function of hours together, shared activities, and everyday talk. In Study 1, MTurk participants (N = 355) who had recently relocated estimated time spent with a new acquaintance. Hours together was associated with closer friendships. Time spent engaging in leisure activities also predicted closeness. In Study 2, first-year students (N = 112) reported the number of hours spent with two new acquaintances three times over 9 weeks. Hours together was associated changes in closeness between waves. Two types of everyday talk predicted changes in closeness.
Although we find that Democrats/liberals are somewhat more analytic than Republicans/conservatives overall, political moderates and non-voters are the least analytic whereas Libertarians are the most analytic
Cognitive Reflection and the 2016 US Presidential Election. Gordon Pennycook, David G Rand. February 2018, DOI 10.13140/RG.2.2.21167.64162
Description: It has often been claimed that conservatives tend to rely more on their intuitions and gut feelings than liberals. However, support for this claim is often indirect and inconsistent. Moreover, it is unclear how analytic thinking and political ideology interact to influence political behavior. Here we investigate the relationship between individual differences in analytic thinking (using the Cognitive Reflection Test) and political affiliation, ideology, and voting in the 2016 Presidential Election using a large online sample (N = 15,001). We find that individuals who voted for Donald Trump are less analytic than those who voted for Hillary Clinton or a 3rd party candidate. However, this difference was driven most by Democrats who chose Trump over Hillary Clinton (and, to a lesser degree, Independents). Among Republicans, in contrast, Clinton and Trump voters were similarly analytic, whereas those who voted for a third-party candidate showed more analytic thinking. Furthermore, although we find that Democrats/liberals are somewhat more analytic than Republicans/conservatives overall, political moderates and non-voters are the least analytic whereas Libertarians are the most analytic. Our results suggest that, in addition to the previously theorized positive relationship between analytic thinking and liberalism, there are three additional ways in which intuitive versus analytic thinking is relevant for political cognition: 1) Facilitating political apathy versus engagement, 2) Supporting the adoption of orthodox versus heterodox political positions and behavior, and 3) Drawing individuals toward political candidates who share an intuitive versus analytic cognitive style, and towards policy proposals which are intuitively versus analytically compelling.
Description: It has often been claimed that conservatives tend to rely more on their intuitions and gut feelings than liberals. However, support for this claim is often indirect and inconsistent. Moreover, it is unclear how analytic thinking and political ideology interact to influence political behavior. Here we investigate the relationship between individual differences in analytic thinking (using the Cognitive Reflection Test) and political affiliation, ideology, and voting in the 2016 Presidential Election using a large online sample (N = 15,001). We find that individuals who voted for Donald Trump are less analytic than those who voted for Hillary Clinton or a 3rd party candidate. However, this difference was driven most by Democrats who chose Trump over Hillary Clinton (and, to a lesser degree, Independents). Among Republicans, in contrast, Clinton and Trump voters were similarly analytic, whereas those who voted for a third-party candidate showed more analytic thinking. Furthermore, although we find that Democrats/liberals are somewhat more analytic than Republicans/conservatives overall, political moderates and non-voters are the least analytic whereas Libertarians are the most analytic. Our results suggest that, in addition to the previously theorized positive relationship between analytic thinking and liberalism, there are three additional ways in which intuitive versus analytic thinking is relevant for political cognition: 1) Facilitating political apathy versus engagement, 2) Supporting the adoption of orthodox versus heterodox political positions and behavior, and 3) Drawing individuals toward political candidates who share an intuitive versus analytic cognitive style, and towards policy proposals which are intuitively versus analytically compelling.
Downward comparison (comparing to worse‐off others) and upward comparison (comparing to better‐off others) constitute two types of social comparisons that produce different neuropsychological consequences - Functional brain imaging studies on the downward and upward comparisons
Social comparison in the brain: A coordinate‐based meta‐analysis of functional brain imaging studies on the downward and upward comparisons. Yi Luo et al. Hum Brain Mapp 39:440–458, 2018. https://doi.org/10.1002/hbm.23854
Abstract: Social comparison is ubiquitous across human societies with dramatic influence on people's well‐being and decision making. Downward comparison (comparing to worse‐off others) and upward comparison (comparing to better‐off others) constitute two types of social comparisons that produce different neuropsychological consequences. Based on studies exploring neural signatures associated with downward and upward comparisons, the current study utilized a coordinate‐based meta‐analysis to provide a refinement of understanding about the underlying neural architecture of social comparison. We identified consistent involvement of the ventral striatum and ventromedial prefrontal cortex in downward comparison and consistent involvement of the anterior insula and dorsal anterior cingulate cortex in upward comparison. These findings fit well with the “common‐currency” hypothesis that neural representations of social gain or loss resemble those for non‐social reward or loss processing. Accordingly, we discussed our findings in the framework of general reinforcement learning (RL) hypothesis, arguing how social gain/loss induced by social comparisons could be encoded by the brain as a domain‐general signal (i.e., prediction errors) serving to adjust people's decisions in social settings. Although the RL account may serve as a heuristic framework for the future research, other plausible accounts on the neuropsychological mechanism of social comparison were also acknowledged.
Abstract: Social comparison is ubiquitous across human societies with dramatic influence on people's well‐being and decision making. Downward comparison (comparing to worse‐off others) and upward comparison (comparing to better‐off others) constitute two types of social comparisons that produce different neuropsychological consequences. Based on studies exploring neural signatures associated with downward and upward comparisons, the current study utilized a coordinate‐based meta‐analysis to provide a refinement of understanding about the underlying neural architecture of social comparison. We identified consistent involvement of the ventral striatum and ventromedial prefrontal cortex in downward comparison and consistent involvement of the anterior insula and dorsal anterior cingulate cortex in upward comparison. These findings fit well with the “common‐currency” hypothesis that neural representations of social gain or loss resemble those for non‐social reward or loss processing. Accordingly, we discussed our findings in the framework of general reinforcement learning (RL) hypothesis, arguing how social gain/loss induced by social comparisons could be encoded by the brain as a domain‐general signal (i.e., prediction errors) serving to adjust people's decisions in social settings. Although the RL account may serve as a heuristic framework for the future research, other plausible accounts on the neuropsychological mechanism of social comparison were also acknowledged.
Asleep at the automated wheel—Sleepiness and fatigue during highly automated driving
Asleep at the automated wheel—Sleepiness and fatigue during highly automated driving. Tobias Vogelpohl et al. Accident Analysis & Prevention, https://doi.org/10.1016/j.aap.2018.03.013
Abstract
Due to the lack of active involvement in the driving situation and due to monotonous driving environments drivers with automation may be prone to become fatigued faster than manual drivers (e.g. Schömig et al., 2015). However, little is known about the progression of fatigue during automated driving and its effects on the ability to take back manual control after a take-over request. In this driving simulator study with Nö=ö60 drivers we used a three factorial 2ö×ö2ö×ö12 mixed design to analyze the progression (12ö×ö5ömin; within subjects) of driver fatigue in drivers with automation compared to manual drivers (between subjects). Driver fatigue was induced as either mainly sleep related or mainly task related fatigue (between subjects). Additionally, we investigated the drivers’ reactions to a take-over request in a critical driving scenario to gain insights into the ability of fatigued drivers to regain manual control and situation awareness after automated driving.
Drivers in the automated driving condition exhibited facial indicators of fatigue after 15 to 35ömin of driving. Manual drivers only showed similar indicators of fatigue if they suffered from a lack of sleep and then only after a longer period of driving (approx. 40ömin). Several drivers in the automated condition closed their eyes for extended periods of time. In the driving with automation condition mean automation deactivation times after a take-over request were slower for a certain percentage (about 30%) of the drivers with a lack of sleep (Mö=ö3.2; SDö=ö2.1ös) compared to the reaction times after a long drive (Mö=ö2.4; SDö=ö0.9ös). Drivers with automation also took longer than manual drivers to first glance at the speed display after a take-over request and were more likely to stay behind a braking lead vehicle instead of overtaking it.
Drivers are unable to stay alert during extended periods of automated driving without non-driving related tasks. Fatigued drivers could pose a serious hazard in complex take-over situations where situation awareness is required to prepare for threats. Driver fatigue monitoring or controllable distraction through non-driving tasks could be necessary to ensure alertness and availability during highly automated driving.
Keywords: Fatigue; Sleep; Automated driving; Transition to manual; Take-over request
Abstract
Due to the lack of active involvement in the driving situation and due to monotonous driving environments drivers with automation may be prone to become fatigued faster than manual drivers (e.g. Schömig et al., 2015). However, little is known about the progression of fatigue during automated driving and its effects on the ability to take back manual control after a take-over request. In this driving simulator study with Nö=ö60 drivers we used a three factorial 2ö×ö2ö×ö12 mixed design to analyze the progression (12ö×ö5ömin; within subjects) of driver fatigue in drivers with automation compared to manual drivers (between subjects). Driver fatigue was induced as either mainly sleep related or mainly task related fatigue (between subjects). Additionally, we investigated the drivers’ reactions to a take-over request in a critical driving scenario to gain insights into the ability of fatigued drivers to regain manual control and situation awareness after automated driving.
Drivers in the automated driving condition exhibited facial indicators of fatigue after 15 to 35ömin of driving. Manual drivers only showed similar indicators of fatigue if they suffered from a lack of sleep and then only after a longer period of driving (approx. 40ömin). Several drivers in the automated condition closed their eyes for extended periods of time. In the driving with automation condition mean automation deactivation times after a take-over request were slower for a certain percentage (about 30%) of the drivers with a lack of sleep (Mö=ö3.2; SDö=ö2.1ös) compared to the reaction times after a long drive (Mö=ö2.4; SDö=ö0.9ös). Drivers with automation also took longer than manual drivers to first glance at the speed display after a take-over request and were more likely to stay behind a braking lead vehicle instead of overtaking it.
Drivers are unable to stay alert during extended periods of automated driving without non-driving related tasks. Fatigued drivers could pose a serious hazard in complex take-over situations where situation awareness is required to prepare for threats. Driver fatigue monitoring or controllable distraction through non-driving tasks could be necessary to ensure alertness and availability during highly automated driving.
Keywords: Fatigue; Sleep; Automated driving; Transition to manual; Take-over request
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