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Rudolf Horns
Rudolf Horns

8 Faster Than Fear __HOT__

A fast, subcortical pathway to the amygdala is thought to have evolved to enable rapid detection of threat. This pathway's existence is fundamental for understanding nonconscious emotional responses, but has been challenged as a result of a lack of evidence for short-latency fear-related responses in primate amygdala, including humans. We recorded human intracranial electrophysiological data and found fast amygdala responses, beginning 74-ms post-stimulus onset, to fearful, but not neutral or happy, facial expressions. These responses had considerably shorter latency than fear responses that we observed in visual cortex. Notably, fast amygdala responses were limited to low spatial frequency components of fearful faces, as predicted by magnocellular inputs to amygdala. Furthermore, fast amygdala responses were not evoked by photographs of arousing scenes, which is indicative of selective early reactivity to socially relevant visual information conveyed by fearful faces. These data therefore support the existence of a phylogenetically old subcortical pathway providing fast, but coarse, threat-related signals to human amygdala.

8 Faster Than Fear

Separate sets of study participants were then tasked with classifying and distinguishing between the different scream types. In one task, 33 volunteers were asked to listen to screams and given three seconds to categorize them into one of the seven different screams. In another task, 35 different volunteers were presented with two screams, one at a time, and were asked to categorize the screams as quickly as possible while still trying to make an accurate decision about what type of scream it was, either alarming screams of pain, anger or fear or non-alarming screams of pleasure, sadness or joy. It took longer for participants to complete the task when it involved fear and other alarming screams, and those screams were not as easily recognizable as non-alarming screams like joy, the researchers report online April 13 in PLOS Biology.

Quelling fear-driven stigmatization and discrimination during the SARS outbreak required tailored intervention strategies carried out by the SARS Community Outreach Team. These activities complemented traditional risk communication for the general public. To be effective, behavioral intervention approaches, messages, and materials had to be salient for the affected population, in this case Asian-American communities within the United States. Further, these interventions aimed at promoting an accurate understanding of the epidemic both in the general population and within the affected community, that is, the dynamic nature of the outbreak and its cause, treatment options, and prevention strategies. Through interpersonal connections, the team members worked to promote reassurance and enhance community resiliency.

We thank the following CDC staff members who volunteered their time to translate critical information into multiple languages during the SARS outbreak: Feng Chai, Rachanee Cheingsong, Feng Xiang Gao, Wenlin Huang, Han Li, Wenkai Li, Xiaofang Li, Timothy Lim, Gang Liu, Yuko Mizuno, Christine Huong Montgomery, Xuanthao Ngo, Doan Quang, Yang Xia, and Yingtao Zhou.

There is often a mismatch between levels of crime and the fear of becoming a victim of crime. It is not uncommon to find individuals who suffer little or no crime but yet who are still fearful of some future crime. Alternatively, a place or region might see an increase in crime over time while the fear of crime remains unchanged. Building on a model that previously considered the fear of crime as an opinion shared by simulated individuals, here the impact that different distributions of crime have on the fear experienced by the population is analysed. Simulating the dynamics of the fear of individuals, along with changes of the distribution of crime, leads to results which show that fear is sensitive to the distribution of crime and that there is a phase transition for high levels of concentration of crime. A policy may be oriented to reduce crime, so that the population effectively suffers less crime, but if the victimisation is displaced to other individuals, then the perception of insecurity may not decrease, with fear becoming more widespread.

Fear of crime depends on many factors, perhaps the most obvious being actual crime: at a micro level, it might be expected that people who suffer more crime also experience more fear and, at a macro level, that regions with a higher number of crimes are also considered to be less secure. However, this is rarely the case. At an individual scale, significant levels of fear are often reported by people who enjoy low levels of victimisation and, in general, many more people are fearful than are actually victimised (Skogan, 1987). Also, at a regional scale, places with less crime might be perceived as being less secure, and furthermore, fluctuations observed in the number of crimes suffered do not lead to increases or decreases in the general concerns of crime within a region (Prieto Curiel and Bishop, 2016b). Thus, observing the mismatch between crime and its fear and the relevance it has at a social and political level, warrants further investigations of the aspects which might affect the personal perception of crime, for instance, demographic factors (such as age or gender), regional factors (if it is a dark or crowded street) and others, such as the amount and style of media coverage of crime.

Previous analysis of the fear of crime has already produced important outcomes. For instance, women and older people tend to feel more insecure (Carro et al., 2010; Borooah and Carcach, 1997), ethnic minorities tend to be more fearful (Brunton-Smith and Sturgis, 2011) as well as poor people (Pantazis, 2000), and that having some familiarity with the area reduces concerns about suffering a crime (Gilchrist et al., 1998). One of the most frequently considered causes of the fear of crime is media. However, the audience of different media channels is self-selective (Lane and Meeker, 2003) and messages often depend on the interpretation of the consumer (Heath and Gilbert, 1996; Ditton et al., 2004), also, crime reported on the media is not a reflection of reality, with media placing more emphasis on violent crime (Chadee and Ditton, 2005), therefore, the impact of the media on the fear of crime is unclear (Hollis et al., 2017).

In terms of fear of crime, the most obvious cause is actually suffering a crime. There have been some quantitative results which show that past victimisation more than doubles the odds ratio of having fear of crime (Hale et al., 1994; Tseloni, 2007), while different types of crime have a different impact on fear (Skogan and Maxfield, 1981). However, since the majority of the population does not, in fact, suffer any crime (Prieto Curiel and Bishop, 2016b) fear of crime is thus the result of a more complex social dynamics which does involve the victims of crime but also involves other social aspects. Crime is, relatively speaking, a rare event (Tseloni et al., 2010) tending to be highly concentrated so that, unfortunately, a particular person, a business, or a street may suffer a much higher number of crimes than the others (Farrell and Pease, 1993; Farrell, 2015; Pease, 1998; Brantingham and Brantingham, 2010; Johnson, 2010). But the fact that crime is rare and highly concentrated also means that fear of crime is more frequent than crime itself (Grogger and Weatherford, 1995).

How then should the mismatch between crime and its fear be explained and how does the fear of crime emerge as a social phenomenon? Furthermore, how can policies be designed to tackle this complex social issue if it is not clearly understood? Having data or observations to validate the analysis would be ideal, however, at an individual scale, it is almost impossible to measure the impact that suffering a crime has compared to the impact of other aspects of fear (for instance, hearing that a neighbour suffered a crime as opposed to being the actual victim of that crime). Observations at an individual scale are typically based on victimisation surveys, which frequently do not track the fears of the same individuals over time. This said, there has been a study that does consider two cohorts of the same interviewees to determine changes in attitudes and concerns before and after suffering a crime (Skogan, 1987). But, for this type of study, only a small number of those interviewed were the victims of crime (5% of personal crime and only 6 elderly people who suffered recent victimisation) since crime is infrequent. To generalise any result or pattern based on the experiences of only a few individuals is not sensible and, at individual scale, detecting any of the factors related to fear of crime is highly complicated, particularly if the person does not suffer any crime but is nonetheless fearful.

At a regional scale, having observed a mismatch between crime and its fear which is not clearly explained by aspects at individual scale, means that the emergence of a complex pattern can be observed, that arises as a result not just of crime, but also, due to the interactions of the individuals, with feedbacks and nonlinear effects in the process that need to be considered.

Having a model, which simplifies interactions of individuals and assumes certain properties and distributions, that is capable of reproducing the complex observed behaviour, gives us the ability to then have an idea of the impact that quantitative changes (such as a reduction of the crime rates) and qualitative changes (such as a different distribution of the same amount of crime) would have on the fear of crime.

A regional measure of fear (the fear from a city, a district or a specific group of individuals) is constructed, given simply by the average fear of the individuals \(\overline s\)(t), which might then be used to compare between two different regions or the same region between different time periods.

Although other elements could also be relevant, for instance, the influence of the media with crime-related news reported on the radio or the television or even social media, the impact that media has on the fear of crime is not clear and so other elements are not considered in the model. Also, the model considers all crimes to be the same whereas the victim of kidnap might have a different level of fear than a person whose wallet is stolen. Furthermore, the way in which fear is quantified, reducing a complex issue into a single number does provide useful answers in terms of why a mismatch between crime and fear of crime is observed. What is more, the model enables us to explain the reasons why a decrease in crime might not improve the perception of security of a region and the impact that different distributions and degrees of concentration of crime have on the generalised fear. Combining these three effects gives a reasonable description of the dynamics of the fear of crime. 041b061a72


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