A critical review of mechanisms of adaptation to trauma: Implications for early interventions for posttraumatic stress disorder

A critical review of mechanisms of adaptation to trauma: Implications for early interventions for posttraumatic stress disorder. Richard A. Bryant. Clinical Psychology Review, January 24 2021, 101981. https://doi.org/10.1016/j.cpr.2021.101981

Highlights

• Current early intervention strategies to limit PTSD after trauma exposure have achieved limited success.

• Longitudinal studies indicate that trauma survivors follow distinct trajectories of stress response.

• There are diverse acute predictors of PTSD, encompassing a range of biological and cognitive factors.

• The different posttrauma trajectories indicates that early intervention strategies need a more flexible approach.

• The diversity of acute risk factors for PTSD indicates that early intervention strategies need a more flexible approach that accommodate the different mechanisms.

Abstract: Although many attempts have been made to limit development of posttraumatic stress disorder (PTSD) by early intervention after trauma exposure, these attempts have achieved only modest success. This review critiques the biological and cognitive strategies used for early intervention and outlines the extent to which they have prevented PTSD. The major predictors of PTSD are reviewed, with an emphasis on potential mechanisms that may underpin the transition from acute stress reaction to development of PTSD. This review highlights that there is a wide range of biological and cognitive factors that have been shown to predict PTSD. Despite this, the major attempts at early intervention have focused on strategies that attempt to augment extinction processes or alter appraisals in the acute period. The documented predictors of PTSD indicate that a broader range of potential strategies could be explored to limit PTSD. The evidence that people follow different trajectories of stress response following trauma and there is a wide array of acute predictors of PTSD indicates that a flexible and tailored approach needs to be investigated to evaluate more effective early intervention strategies.

Keywords: Posttraumatic stress disorderEarly interventionAcute stressTreatment

13. Implications for early intervention

Although acute symptoms are statistically related to subsequent PTSD, the relationships are modest at best. Sufficient prediction requires balancing

sensitivity (the probability that someone who eventually develops PTSD satisfied the acute predictor), specificity (the probability that someone who does not develop PTSD did not satisfy the acute predictor), as well as positive and negative predictive power (the probability that a person who does/does not satisfies the acute predictor subsequently does/does not develop PTSD). Our modest ability to predict subsequent PTSD points to several important implications in early intervention for PTSD. First, the modest predictive power of any acute markers suggests that identifying people who will require early intervention has a long way to go before we can confidently rely on predictive tools to identify those at risk of PTSD. Second, the conclusion that not all people follow the same trajectory of posttraumatic stress indicates that no single early intervention strategy may be efficacious for all people. Third, the array of acute markers that have been identified as being predictive of later PTSD points to the need for a broad approach to encompass potential intervention targets that may address these different mechanisms. On the basis of these conclusions, and the review conducted above, the following considerations are offered regarding future directions for developing a better science and practice for early intervention to limit PTSD.

13.1. Timing of early intervention

Although some models of fear learning and extinction may suggest that intervening very early in the period after trauma exposure may reduce consolidation of trauma memories (Norrholm et al., 2008; Rothbaum et al., 2012), there is also considerable evidence that the later one attempts to classify people who are at risk of subsequent PTSD the more accurate the prediction may be. For example, acute symptoms that persist in the weeks after trauma are more predictive than those assessed in the very initial period (Briere, Scott, & Weathers, 2005; Panasetis & Bryant, 2003; Solomon et al., 1988). This suggests that attempts to provide early intervention to those identified as being at risk of later PTSD are more likely to be unnecessary because the early identification of risk will yield more false positive identifications. Perusal of trajectory studies of PTSD point to the same conclusion because they suggest that a proportion of people will develop PTSD over time as their condition worsens, and these people may not be identified if the initial assessment of risk is conducted too early (Galatzer-Levy et al., 2018). It appears that there is a balance between offering intervention early because some evidence suggests this may yield a more potent effect (Carpenter et al., 2018) and the risk of providing the intervention to people who may naturally remit. This issue is underscored by evidence from one trial that found that early intervention with TF-CBT facilitated recovery but it did not necessarily lead to greater longer-term outcomes than people's natural recovery trajectories (Shalev et al., 2016). At a practical level, many early intervention studies have attempted to identify risk very soon after trauma exposure, often within the first week, whereas delaying this identification process several weeks after trauma exposure may yield more accurate classification of people at risk of later PTSD and at the same time not comprise the benefits of treatment.

13.2. The benefit of identifying resilient individuals

Trajectory studies of the course of posttraumatic stress have robustly indicated that approximately three-quarters of trauma survivors do not experience PTSD over repeated assessments (Galatzer-Levy et al., 2018). This strongly suggests that these individuals are not in need of mental health assistance for PTSD. Traditionally attempts to focus on trauma survivors who may benefit from early intervention have focused on those who are deemed at risk of PTSD development. Another approach would be to identify the resilient people and focus limited mental health resources on those trauma survivors who are not resilient. This strategy would reduce the problem of identifying people in the acute phase who may not be very distressed but who may worsen over time. Most trajectory studies indicate that approximately 75% of trauma survivors can be classified as resilient, and so excluding these people from consideration would allow mental health resources to be allocated to those in need. Such an approach could involve early intervention and/or ongoing monitoring of the person's condition and intervention could be offered when the condition deteriorates. This strategy would be consistent with the evidence that PTSD is a fluctuating condition that often surfaces following recent stressors (Bonanno, Kennedy, et al., 2012; Bryant et al., 2017; Pietrzak, Van Ness, et al., 2013), which could be identified with regular monitoring of this group of trauma survivors.

13.3. Focus on novel mechanisms related to predictors

This review has highlighted that the early predictors of PTSD encompass a range of factors that are present in the acute period after trauma that appear to represent mechanisms that contribute to PTSD development. Despite the breadth of predictive factors, most early interventions have focused on either extinction-based mechanisms or processes involving adjustment of maladaptive appraisals. These mechanisms form the basis of most TF-CBT programs, as well as novel pharmacological and direct stimulation techniques. However, there are other options that could be used that would address some of the factors identified as early predictors of PTSD. For example, we have noted that overgeneral autobiographical memory retrieval in the acute phase is predictive of later PTSD (Harvey et al., 1998)(Kleim & Ehlers, 2008). In recent years this deficit has been the focus of memory specificity training, which coaches people on retrieving specific memories and has been shown to have a small to moderate effect in reducing depression (Hitchcock, Werner-Seidler, Blackwell, & Dalgleish, 2017). One pilot study has found that this training can also reduce PTSD symptoms (Moradi et al., 2014). This approach could be trialled in the acute posttrauma period to determine its efficacy in limiting PTSD.

We also noted that rumination is a strong predictor of later PTSD. Several treatment options have promise for managing rumination, including metacognitive therapy (Wells & King, 2006), rumination-focused cognitive behaviour therapy (Watkins et al., 2011), and mindfulness-based therapy (Kuyken et al., 2008). There is initial evidence that these interventions can be applied to PTSD (Polusny et al., 2015; Wells & Colbear, 2012). Considering the strong predictive role of rumination in PTSD development, potentially adapting metacognitive and mindfulness-based approaches in the early period may be adaptive for recently traumatized people who present with marked rumination.

Elevated distress and deficient emotion regulation strategies in the acute period have been linked to later PTSD (Bardeen et al., 2013). There are numerous strategies available to improve emotion regulation that have proven capacity in managing PTSD symptoms. Programs that teach skills in identification of emotional states, strategies to reduce distress, addressing emotional avoidance, and tolerating bodily sensations of emotions can effectively limit PTSD or associated anxiety symptoms (Barlow et al., 2017; Bryant et al., 2013; Bryant, McGrath, & Felmingham, 2013; Cloitre et al., 2010). Teaching acutely traumatized people who present with emotion dysregulation skills to identify and manage emotional reactions may also be useful in limiting subsequent PTSD.

The predictive role of depression and negative affect in the acute phase points to the possibility of targeting this initial symptom as a potential early intervention candidate. There has been renewed interest in treatments of anhedonia and negative affect by training people to develop greater awareness of and exposure to positive experiences (Craske, Meuret, Ritz, Treanor, & Dour, 2016). This approach emphasizes enhancement of reward processes, including broadening exposure to positive stimuli, rehearsing appreciation of the pleasurable experiences associated with positive events, and deep learning techniques to enhance the sense of pleasure. This strategy is indicated because of evidence that positive affect can impede fear reacquisition after extinction (Zbozinek & Craske, 2017) and that positive affect can serve as a buffer against the adverse mental health effects of chronic stress (Sewart et al., 2019). Importantly, one controlled trial found that training patients with severe depression or anxiety reported greater reductions in negative affect and improvements in positive affect than those who treatment targeted negative affect (Craske et al., 2019). Considering the role of negative affect in the acute period, this intervention offers an opportunity target this risk factor in the acute posttrauma period.

There is also potential is adapting some of the agents used to augment exposure therapy for chronic PTSD to determine if this can facilitate early interventions. Most attempts at augmenting exposure therapy for PTSD have relied on targeting extinction mechanisms by modulating neural processes implicated in the associative learning process, including pharmaocological agents such as d-cycloserine (de Kleine, Hendriks, Kusters, Broekman, & van Minnen, 2012; Difede et al., 2014; Rothbaum et al., 2014), yohimbine (Tuerk et al., 2018), and MDMA (3,4-methylenedioxymethamphetamine (Barone, Beck, Mitsunaga-Whitten, & Perl, 2019; Mithoefer et al., 2018; Ot'alora et al., 2018). Although these attempts have only been marginally beneficial and are yet to be definitively shown to enhance exposure therapy (Lebois, Seligowski, Wolff, Hill, & Ressler, 2019; Weisman & Rodebaugh, 2018), they are yet to be properly tested in early intervention frameworks. There is also potential in extending extinction procedures beyond those traditionally studied. For example, there is evidence that PTSD is characterized by greater generalization of fear, reflected in both the common symptoms of fear of stimuli that are reminiscent of the trauma and hypervigilance to a range of potential threats and also experimental evidence that people with PTSD demonstrate greater fear overgeneralized fear conditioning (Kaczkurkin et al., 2017). This raises the possibility that exposure therapy in the acute phase may be augmented by targeting exposure to situations or stimuli that approximate the traumatic event as well as trauma-specific stimuli. Based on studies that the cholinergic system appears to contribute to poor discrimination between danger and related stimuli (Thiel, Bentley, & Dolan, 2002; Weinberger, 2007), anticholinergic agencies given at the time of exposure therapy may augment early intervention treatment with those displaying early symptoms of acute posttraumatic stress.

In noting these potential mechanisms, however, there is a need to be cautious about the strength of evidence these factors are significant in fact the mechanisms of change underpinning successful adaptation to trauma. Kazdin (2007) outlined several key criteria for how mechanisms of change should be defined; these include (a) the strength of the associations between an intervention, the purported mediator, and the outcome; (b) specificity of the change mechanism such that the change is not occurring as a result of many other, possibly related, constructs; (c) reliability of the change mechanism across samples and studies; (d) causal proof as shown by experimental manipulation of the proposed mechanism; (e) proof of the temporal sequence of the causes and mediators on the target outcome; (f) a dosage effect such that there is an association between the extent to which the mechanism has been activated and the strength of the outcome; and (g) theoretical plausibility given broader body of knowledge. It should be recognized that our current evidence base for the potential mechanisms that influence successful adaptation to trauma does not currently meet most of the criteria, and so we should be very tentative about claims of how these mechanisms can be utilised in the clinical context until further evidence is obtained.

13.4. Developing more sophisticated prediction models

The capacity for more precise prediction of who will develop PTSD may open new opportunities for targeted early intervention strategies. Although many studies have identified a range of acute symptom, cognitive, and biological factors that can predict later PTSD, this evidence base is traditionally limited by understanding the individual predictive role of these factors rather than considering them in association with each other. There are two advantages in understanding how predictive factors are associated with each other. First, by considering the relationships between predictive factors there is the potential to identify more specifically the mechanisms that should be targeted in early intervention because we can isolate factors that are more causally related to later PTSD by determining their influence over and above related factors. An example of this approach is a recent study that has focused on the role of cognitive factors that we have reviewed above have been shown many times to predict later PTSD. In one multifactorial analysis, this study found that as hypothesized by prevailing cognitive models (Ehlers & Clark, 2000), negative appraisals of the trauma and its sequelae predicted later PTSD both directly and also via maladaptive behavioural and cognitive coping strategies, and that processing of the trauma at the time of the event impacts later PTSD via its effects on later appraisals about oneself, one's environment, and the fragmentation of one's trauma memories; interestingly, including acute symptoms in this model did not improve prediction of later PTSD (Beierl, Bollinghaus, Clark, Glucksman, & Ehlers, 2020). This finding does suggest that early intervention strategies that address negative appraisals, cohesion of the trauma memory, improving coping strategies may be important for facilitating better posttraumatic adjustment.

The second benefit of considering the interplay between acute posttrauma factors that can predict later PTSD is the capacity for more sophisticated statistical modelling to use all available information to derive predictive algorithms that can be used in common acute trauma settings. One of the most commonly studied contexts for acute traumatic stress is emergency rooms, which also collect much routine data on biological indicators that can also be used as markers of acute stress. One recent study used a machine learning approach and tested a predictive model encompassing a range of psychological and biological measures collected in the emergency room, then validated this in a separate large sample, and found that the resulting algorithm achieved 87% accuracy in identifying patients who developed PTSD 12 months after presentation to the emergency room (Schultebraucks et al., 2020). This study found that prediction was best found with a combination of 20 variables that included acute psychological stress reports, combined with biomarkers such as white blood cell count and lymphocytes. Although this approach requires further validation in different settings, it holds the promise of being able to identify people who may benefit from early intervention or at least from ongoing monitoring to determine the trajectory of subsequent psychological health.

13.5. The need for a precision medicine approach

The evidence that there are multiple pathways to developing PTSD highlights that not one form of early intervention will be optimal for all trauma survivors. To date all attempts to limit PTSD by early intervention have focused on a uniform protocol administered to all participants identified as being at risk of PTSD development. There has been considerable attention in recent years on precision psychiatry as a more sophisticated approach to targeting treatments to patients with specific clinical presentations or profiles. Precision medicine is defined as “an emerging approach for treatment and prevention that takes into account each person's variability in genes, environment, and lifestyle” (National Research Council Committee on a Framework for Developing a New Taxonomy of Disease, 2011). Although not a new notion of understanding diseases in terms of the variability between individuals, it has only received focused attention in more recent times. The speed at which precision approaches have been adopted has been much faster in other areas of medicine, such as oncology, and psychiatry is only now embracing a precision approach (Fernandes et al., 2017). In this regard, most work has been done in psychosis and mood disorders (Salazar de Pablo et al., 2020), with most attention focusing on genetic, molecular, and neuroimaging techniques to aid diagnosis and treatment prediction (Williams, 2016).

The precision psychiatry approach can be equally applied to PTSD. This work is in its infancy, with the initial work focusing on genetic (gene PTSD) and neuroimaging (me) approaches to identify people with stress conditions and subtypes of PTSD. The evidence reviewed above indicates that people follow distinct trajectories, and this suggests that a precision psychiatry approach is appropriate for more nuanced screening and early intervention strategies for people shortly after trauma exposure. The study of early intervention for posttraumatic stress has not adequately addressed precision psychiatry approaches and continues to focus on one-size-fits-all programs for screening and interventions. This approach is contrary to the evidence. If early interventions after trauma exposure are to be advanced in a significant way, there is a need for research paradigms to understand the different phenotypes that are present in the acute and chronic phases (e.g. dissocative, hyperaroused, dysphoric), as well as the genetic, neurobiological, cognitive, behavioural, social, and emotional mechanisms that underpin development of the genesis of PTSD following the acute phase. This approach represents an ambitious research agenda but other domains of medicine and psychiatry have shown that it can yield very promising results.

13.6. The role of process therapy

Another approach is to adopt a process therapy approach which does not apply a uniform protocol to all patients but rather tailors the intervention to specific clinical needs (Hofmann & Hayes, 2019). This strategy would not identify trauma survivors simply in terms of the ASD or early PTSD symptoms categorization but would rather identify key acute symptoms or early markers of risk and target them with evidence-based strategies. For example, trauma survivors with re-experiencing symptoms may be provided with emotional processing strategies (e.g. imaginal exposure), avoidance with in vivo exposure, depressive symptoms with behavioural activation, rumination with mindfulness, overgeneral memory with memory specificity training, and anhedonia with positive affect training. The development of precision treatments in mental health have not kept pace with many areas of medicine, and there is much we have to learn about how to match specific treatments with distinct clinical presentations. Nonetheless, matching trauma survivors with primary presenting difficulties with the strategies that optimally meet their needs may provide a new opportunity for greater success in early intervention.

Exceptionality effect is the phenomenon that people associate stronger negative affect with a negative outcome when it is a result of an exception (abnormal behaviour) compared to when it is a result of routine (normal behaviour)

Impact of past behaviour normality: meta-analysis of exceptionality effect. Adrien Fillon, Lucas Kutscher & Gilad Feldman. Cognition and Emotion, Sep 13 2020. https://doi.org/10.1080/02699931.2020.1816910

Abstract: Exceptionality effect is the phenomenon that people associate stronger negative affect with a negative outcome when it is a result of an exception (abnormal behaviour) compared to when it is a result of routine (normal behaviour). In this pre-registered meta-analysis, we examined exceptionality effect in 48 studies (N = 4212). An analysis of 35 experimental studies (n = 3332) showed medium to strong effect (g = 0.60, 95% confidence intervals (CI) [0.41, 0.79]) for past behaviour across several measures (regret/affect: g = 0.66, counterfactual thought: g = 0.39, self-blame: g = 0.44, victim compensation: g = 0.39, offender punishment: g = 0.51). An analysis of 13 one-sample studies presenting a comparison of exceptional and routine behaviours simultaneously (n = 1217) revealed a very strong exceptionality effect (converted g = 1.98, CI [1.57, 2.38]). We tested several theoretical moderators: norm strength, event controllability, outcome rarity, action versus inaction, and status quo. We found that exceptionality effect was stronger when the routine was aligned with the status quo option and with action rather than for inaction. All materials are available on: https://osf.io/542c7/

KEYWORDS: Norm theorynormalityregretpast behaviourexception routinemeta-analysisexceptionality effect

Increasing the minimum wage could result in low loss of headcounts, but a noticeable reduction of lower-pay hours worked

RCTs, Epistemology, and the Minimum Wage: Can experimental evidence clear up the debate on the Minimum wage? Maxwell Tabarrok. Jan 22 2021. https://virginica.substack.com/p/rcts-epistemology-and-the-minimum

The Results

There are four main results: “(1) the wages of hired workers increases, (2) at a sufficiently high minimum wage, the probability of hiring goes down, (3) hours-worked decreases at much lower levels of the minimum wage, and (4) the size of the reductions in hours-worked can be parsimoniously explained in part by the substantial substitution of higher productivity workers for lower productivity workers.”

The significant reductions in hours worked come from two sources according to Horton’s analysis. First, firms are economizing on now more expensive labor; the labor demand curve slopes downward. Second, the substitution of higher productivity workers meant that jobs were completed faster, so the total hours worked went down. Both of these responses to the minimum wage hurt low productivity workers: “I find that workers that had been working for less than the new platform minimum wage raised their wage bids after the platform-wide minimum wage was imposed. These same workers experienced a substantial decrease in their probability of being hired.”

Interestingly, these results are consistent with finding little to no dis-employment effect in an observational study that only measures wages and headcounts (which is what the vast majority of the most popular studies do). This is because almost all of the effects of the minimum wage came from substitution of higher productivity for lower productivity ones, which wouldn’t show up in headcounts, and reduction in hours worked, which is not measured in most conventional data sets.

This finding is also consistent with the predictions of the supply-demand model. Contrary to common understanding, the supply-demand model does not actually predict a dis-employment effect from a minimum wage at all, as explained in Brian Albrecht’s great post. The model does predict a mismatch between how much labor is demanded versus how much is supplied, with deadweight loss resulting from the prevention of positive sum trades. This need not result in unemployment, however, because the supply-demand model describes a market for hours of labor, not for jobs. In our world with large fixed costs for firing and hiring, changing the hours of labor is the natural margin for employers to act on in the face of a minimum wage.

Ultimately, the minimum wage serves as a transfer payment from low productivity workers to high productivity ones. Hardly what supporters of the policy have in mind when they ‘fight for fifteen.’

From 2017... Sexual Differentiation of the Brain: A Fresh Look at Mode, Mechanisms, and Meaning

Margaret M. McCarthy, Geert J. De Vries, Nancy G. Forger, 5.01 - Sexual Differentiation of the Brain: A Fresh Look at Mode, Mechanisms, and Meaning, Editor(s): Donald W. Pfaff, Marian Joëls, Hormones, Brain and Behavior (Third Edition), Academic Press, 2017, Pages 3-32, https://doi.org/10.1016/B978-0-12-803592-4.00091-2

Abstract: This is an exciting time to study sex differences in the brain. Fifty-plus years of building on the foundations established by the organizational/activational hypothesis proposed by Phoenix and colleagues to explain steroid hormone action on the brain has provided an increasingly complex and nuanced view of how the brain develops differently in males and females. In this chapter we first discuss the things we know; there are sex differences in physiology and behavior, in susceptibility to diseases of the nervous system including mental health disorders, and in neuroanatomical and neurochemical measures. These sex differences depend on androgens, estrogens, and sometimes sex chromosomal complement (XX vs XY) acting during development as well as in adulthood, and yet the manifestation of these sex differences may be context dependent. There are four key cellular processes that could potentially underlie sexual differentiation of the brain: cell birth, cell death, cell migration, and cell differentiation, and we discuss the evidence for each in detail. Lastly, we review what we consider major emerging areas and unanswered questions in the field, including the function of sex differences, why they persist, and what they mean.

Keywords: Androgen; Anteroventral periventricular nucleus; Astrocyte; Bed nucleus of the stria terminalis; Bulbocavernosus; Cell death; Dendritic spine; Differentiation; Epigenetic; Estrogen; Hypothalamus; Partner preference; Preoptic area; Sex chromosome; Sex difference; Spinal cord; Vasopressin

5.01.4.5 Is Partner Preference Sexually Dimorphic?

Sexual orientation, also referred to as sexual partner preference,

is defined by the sex of the individuals that are arousing or attractive

to the reference individual, whether it be an individual of the

opposite sex (heterosexual), the same sex (homosexual), or both

sexes (bisexual). The estimated frequency of homosexuality in

humans ranges from 2% to 10%, suggesting that the large

majority of males are sexually oriented toward females and the

majority of females are sexually oriented towardmales. The overwhelming

prevalence of one sex preferring the other is a constant

across all vertebrate species, as would quite naturally be expected

from the point of view of reproductive success. Nonetheless,

what draws the majority of attention is the much less frequent

phenotype of same-sex preference. Notably, the biological basis

of sexual orientation is a matter of impassioned debate only

when it involves discussion of the etiology of homosexuality.

Few seem to question whether opposite-sex orientation is biological.

But actually we understand little about opposite-sex

attraction, and it can be argued that understanding the biological

basis of same-sex orientation would be greatly advanced by

understanding opposite-sex orientation. Thus, a fundamental

question is whether sexual orientation per se is sexually

differentiated.

The answer to this may depend on how you pose the question.

If we state that the majority of males prefer females as

sexual partners and the majority of females prefer males, then

this sounds like a profoundly sexually dimorphic and presumably

differentiated response. Antecedent to this view would be

the assumption that distinct biological processes drive the

neural substrate of partner preference to either a male bias or

a female bias. The existence of distinct processes for a male preference

versus a female preference provides a ready explanation

for why some females prefer other females, some males prefer

males, and why some individuals have no preference.

However, if we take the view that the majority of animals prefer

the opposite sex as partners, then there is no sex difference as

the same drive exists in males and females but it is manifest

differently as a function of one’s own sex. This means that

a component of the neural response is computation of one’s

own sex, which then determines the response to others’ sex.

Given the intensity and early onset of both internal and

external influences of sex on brain development, this is not

outside the realm of possibility. In humans, we are unlikely

to ever be able to definitively separate the impact of nature

from nurture, and our best alternative is the study of naturally

occurring or experimentally manipulated variation in sexual

preference in animals.

The current state of the art of partner preference research is

found on several fronts. These include studies of the programming

effects of gonadal steroids and early experience on partner

preference, the neuroanatomical loci controlling partner preference,

and the study of naturally occurring variation in partner

preference in animal models. Consistent evidence supports

the view that partner preference is organized by gonadal

steroids, such that perinatal androgens, with aromatization to

estrogens in rodents, direct the formation of preference for

a female sexual partner (Brand et al., 1991; Vega Matuszczyk

et al., 1988). In many mammals, odors are the primary signal

indicating sex. Preference can be assessed by determining the

amount of time a test subject prefers to spend with male versus

female stimulus animals or by the amount of time spent investigating

odors generated by stimulus animals. Male- versus

female-specific odors can induce a differential brain response

in the same animal, and likewise, animals of opposite sex

will respond to the same odor differently (Bakker et al.,

1996; Woodley and Baum, 2004). The latter speaks to the

sexual differentiation of partner preference and suggests that

the olfactory system may be the initiation point for subsequent

behavioral responses. In many species, if olfaction is blocked,

there is no partner preference to measure.

Olfaction is important to humans as well, but visual stimuli

are far more potent and the arousal potential of same-sex versus

opposite-sex images depends on the partner preference of the

observer (see for review Baum, 2006). Zebra finches are also

heavily dependent upon vision for expressing partner preference,

and steroids influence partner preference in this species as well

(Adkins-Regan and Leung, 2006). The effect is context dependent,

however, because early experience, i.e., being raised in an

environment with a skewed sex ratio, can also strongly influence

adult partner preference in zebra finches.

The neuroanatomical substrate of partner preference begins

with that portion of the brain detecting and decoding the

sex-specific sensory signals originating from the stimulus

animal, be they olfactory, visual, or auditory. But from there,

all signals appear to converge on the POA, and in particular

an SDN within the POA (see Baum, 2006). An SDN is present

in the POA not only in rats, but also in sheep, gerbils, ferrets,

hamsters, and humans. Lesions of the SDN and its surround

in rats and ferrets either eliminate or reverse sexual preference

(for review, see Baum, 2006). In humans, the third interstitial

nucleus of the hypothalamus (INAH3) may be homologous

to the SDN-POA of rats and is larger in men than women

(Allen et al., 1989). Levay (1991) found that INAH3 is

smaller in homosexual men than in heterosexual men, and

a second study found a mean difference in the same direction

that did not, however, reach statistical significance (Byne

et al., 2001). Thus, the size of INAH3 may be a marker of

partner preference in men, although this conclusion is not

without its detractors.

Another approach is the use of biomarkers to determine if

an individual was exposed to an endocrine environment

in utero that varies from the norm for that sex. These biomarkers

include long bone length, hand digit ratio, and the detection of

small noises made by the inner ear. In general, these studies

support the conclusion that the prenatal hormonal milieu

contributes to the propensity to show same-sex orientation

(Balthazart, 2016). But in many human and animal studies,

a major and unavoidable confound is either the use of surgical

manipulations, such as lesions, or the health status of the

affected individuals, such as the number of HIV-infected

subjects in the homosexual group in human studies. Neither

of these criticisms applies to the study of a naturally occurring

variant of homosexuality, the male-preferring domestic ram.

In at least two different study populations, approximately 8%

of rams prefer to mount other male rams. The frequency of

the phenotype is similar to that observed in humans, and there

are no clear external markers of male-preferring rams. Analyses

of the brain reveal that the SDN of male-preferring rams is

smaller than that of rams that prefer ewes, and it contains fewer

aromatase-expressing neurons. This suggests reduced neuronal

exposure to estradiol developmentally and in adulthood may

be a critical variable in the establishment of same-sex preference

in this species (Roselli et al., 2004).

Thus, on balance, we can conclude that partner preference

is sexually differentiated and that there is an important role for

gonadal steroid exposure in the organization of partner preference,

but early experience may also be important. The primary

detection of the sensory stimulus emanating from a potential

partner is a critical initiating step but the integration and

response to the stimulus appear to be encoded within the

POA. While these are important advances, there remains

much to be learned. Work on the genetics of partner preference

generated a great deal of interest in the early 1990s

(Hammer et al., 1993), but there has been little progress on

that front. There is a continuing interest in the role of birth

order and correlations with handedness, particularly for

male homosexuality, and the proposal of the maternal

immune hypothesis (Blanchard et al., 2006). But again, we

know far less than we should. Moreover, the preponderance

of information is weighted toward understanding male, as

opposed to female partner preferences, although this may be

defensible given the health implications for male versus

female homosexuality. Regardless, progress on both is likely

to remain slow given the paucity of researchers and resources

currently dedicated to this topic.

5.01.4.6 Is the Human Brain Sexual Differentiated?

To some an affirmative answer to this question is self-evident:

how could the human brain not be subject to the same process

that occurs in the majority if not all other mammals as well as

many birds, reptiles, and amphibians? Even invertebrates have

brains that differ in males and females. But others argue, ‘not so

fast, humans are exceptional in many ways.’ We are the only

species with complex computational abilities and a sophisticated

language that includes generation of an historical record.

We also have rich cultural and societal rituals and expectations

that include prescriptions of the appropriate behavior for boys

and girls, men and women. These rules and expectations are

imposed on children even before they are born with the choice

of a gender-typical name and continue with modes of dress and

even the manner in which adults interact with an infant of one

sex versus the other. Thus as we have mentioned many times in

this chapter, it is impossible to parse out the influence of environment

and experience from biology when considering the

brain and behavior of humans. Nonetheless, it is worth a try.

One powerful approach is the study of children at a young

age. While the influence of environment and experience cannot

be eliminated, it is at least lessened compared to that of a fullgrown

adult. Toy choice reflects an interest in different types of

objects, and this varies on average between boys and girls. The

data are messy, with many boys willing to play with girls’ toys

on occasion, and vice versa. One of the strongest influences is

whether a child has a sibling of the opposite sex, demonstrating

the importance both of exposure and of modeling the behavior

of other children. Nonetheless, on average, boys spend more

time with certain types of toys and girls with others. Melissa

Hines has spent most of her career studying this phenomenon

and whether prenatal exposure to androgens in girls can influence

toy choice. She and others have consistently found that

androgen-exposed girls shift their toy preference to that of

boys (reviewed in Hines, 2006). So does this mean there is

a ‘toy’ nucleus in the brain that directs boys to like trucks and

girls to like dolls? That seems unlikely. Hines and colleagues

recently added a new dimension to the sexual differentiation

of play with the observation that girls prefer to mimic the

behavior of other girls or women and that it is this aspect of

their brain development that is influenced by hormones, not

the desire to play with a particular object or in a particular

way (Hines et al., 2016). In girls exposed to androgens in utero,

the desire to mimic other females is lost, and for reasons not

well understood, their preference shifts to toys normally

preferred by boys.

The work of Hines and others speaks to the behavior of

humans, but, as noted above, brain and behavior are not

always closely aligned. Many neuroanatomical sex differences

have been reported in the human brain, but the majority of

these relied on postmortem tissue. Because of this, the majority

also involved the brains of adults although one remarkable

study looked at a nucleus in the hypothalamus from many

different individuals ranging from birth to old age and found

a sex difference that did not appear until late adolescence

and then waned again in older adults (Swaab et al., 2003).

More recently, researchers have taken advantage of noninvasive

imaging techniques that allow for longitudinal analyses of the

same individual as they mature. The magnitude and direction

of sex differences varies with the mode of data acquisition and

analyses (i.e., correcting for total brain volume or not), but

differences in the developmental trajectory of the peak of

cortical gray matter are reliably found and modulated by

androgen receptor allelic variation as well as androgen levels.

White matter increases more rapidly in male brains as development

proceeds and combined with differences in gray matter

amplify the magnitude of sex differences across the life span

(reviewed in Giedd et al., 2012).

More recently advances in imaging have allowed formeasurement

of functional connectivity. Images from almost 1000

humans revealed a profound sex difference in the ‘connectome,’

with females showing strong interhemispheric connectivity and

males the opposite, strong intrahemispheric connections

(Ingalhalikar et al., 2014). The authors interpreted their findings

as supportive of the view that females engage multiple tasks at

once and are highly social whilemales are focused systematizers.

This stereotypical view generated a firestorm of criticism. In

response the authors went on to use a computerized battery of

neurocognitive tasks in combination with imaging and largely

supported their original conclusions (Tunc et al., 2016), but the

controversy here is certainly not resolved.

On the opposite end of the spectrum and equally controversial

was a recent report that reexamined several studies

involving imaging and gender-typical activities. Here the

authors concluded that there was no clear predictability of

sex based on the mean responses (Joel et al., 2015). Instead,

they concluded, every brain is a mosaic of male-like, femalelike,

and neutral features, and therefore there is no such thing

as a ‘male brain’ or a ‘female brain.’ In some ways this conclusion

is intuitively obvious and consistent with the high degree

of regionally specific mechanisms establishing sex differences

in the brain as determined in rodent models. But the finding

was largely misinterpreted by the lay media as demonstrating

there are no sex differences in the brain, which was not the

case even in the Joel et al. study.

This serves as a fitting conclusion to our long treatise on the

modes, mechanisms, and meanings of sex differences in the

brain as it so aptly demonstrates how much we still have to

learn. In some ways, the topic of sex differences in the brain

remains as controversial today as when the first reports were

made in the late 1960s early 1970s. With the changing policies

at major granting agencies, there is likely to be more, not fewer

reports of brain and behavior sex differences. This makes even

more salient the admonishment that scientists bear the burden

of assuring their work is not used or interpreted inappropriately

(Maney, 2016). It is essential that we ‘get it right’ as the implications

of sex differences research reach far beyond the laboratory

to medical, educational, and public health policies that

impact the daily lives of all members of society.

Evidence of cross-cultural variation in assessments of age, and even more of attractiveness, and health, indicating plasticity in perception of female facial appearance across cultures

Cross-cultural perception of female facial appearance: A multi-ethnic and multi-centre study. Rainer Voegeli, Rotraut Schoop, Elodie Prestat-Marquis, Anthony V. Rawlings, Todd K. Shackelford, Bernhard Fink. PLoS One, January 22, 2021. https://doi.org/10.1371/journal.pone.0245998

h/t David Schmitt (20) David Schmitt on Twitter: ""cross-cultural variation in assessments of age, even more of attractiveness/health, indicating plasticity in perception of female facial appearance across cultures, though the decline in attractiveness and health assessments with age is universally found" https://t.co/ZZOWx0oReV" / Twitter

Abstract: Humans extract and use information from the face in assessments of physical appearance. Previous research indicates high agreement about facial attractiveness within and between cultures. However, the use of a narrow age range for facial stimuli, limitations due to unidirectional cross-cultural comparisons, and technical challenges have prevented definitive conclusions about the universality of face perception. In the present study, we imaged the faces of women aged 20 to 69 years in five locations (China, France, India, Japan, and South Africa) and secured age, attractiveness, and health assessments on continuous scales (0–100) from female and male raters (20–66 years) within and across ethnicity. In total, 180 images (36 of each ethnicity) were assessed by 600 raters (120 of each ethnicity), recruited in study centres in the five locations. Linear mixed model analysis revealed main and interaction effects of assessor ethnicity, assessor gender, and photographed participant (“face”) ethnicity on age, attractiveness, and health assessments. Thus, differences in judgments of female facial appearance depend on the ethnicity of the photographed person, the ethnicity of the assessor, and whether the assessor is female or male. Facial age assessments correlated negatively with attractiveness and health assessments. Collectively, these findings provide evidence of cross-cultural variation in assessments of age, and even more of attractiveness, and health, indicating plasticity in perception of female facial appearance across cultures, although the decline in attractiveness and health assessments with age is universally found.

Discussion

Previous research suggested strong agreement in attractiveness assessments, both within and across ethnicities [23, 40, 50], especially for female attractiveness [29, 51, 52]. The present study used a simultaneous multi-centre, multi-ethnic approach to secure assessments of female facial age, attractiveness, and health and identified both similarities and differences in assessments across ethnicities. Perhaps most importantly, there were (three-way) interaction effects of assessor ethnicity and gender, and participant (“face”) ethnicity for attractiveness and health (but not for age). This suggests that differences in female facial attractiveness and health judgments depend on who judges the face (i.e. assessor ethnicity), which face is assessed (i.e. target ethnicity), and whether the assessor is female or male. There is stronger agreement in facial age assessments than in attractiveness and health assessments.

Intra-class correlations (ICCs) corroborate the findings of diversity in cross-cultural face assessments; the ICC for age assessments was higher than for attractiveness and health assessments, suggesting greater agreement for the former than the latter assessments. Inter-correlations of female facial age, attractiveness, and health assessments were large and in the direction predicted by evolutionary approaches to female appearance (see for review Grammer et al. [9], Rhodes [4], and Thornhill and Gangestad [12]), suggesting a strong relationship of attractiveness with health, and a decline in these qualities with age [3, 17, 53]. Collectively, the findings of the present study suggest greater cross-cultural variation in assessments of female facial appearance than indicated in previous research, especially in attractiveness and health assessments.

Recent research reported disagreement among individual facial attractiveness judgements, highlighting the importance of determining how these preferences vary among individuals [54, 55]. Perhaps most relevant for cross-cultural comparisons is the assumed importance of certain facial characteristics in a given society as derived from the study of another society. Facial characteristics investigated in previous studies (e.g., symmetry, averageness, sex-typical features) may not contribute substantially to judgements of facial attractiveness [56–58] or health [59], but even if they do, the contribution of these features may vary across societies depending on environmental conditions [60, 61] or sociocultural settings [62, 63]. Zhang et al. [57] in a data-driven (as opposed to theory-driven) approach detected cross-cultural differences in face preferences not apparent in studies using theory-driven approaches, leading to the conclusion that Chinese and British “White” participants used face information in different ways (i.e. they focused on different features) (see also Kleisner et al. [64]). Similar conclusions were derived from the findings of eye-movement patterns of Western and East Asian participants, suggesting that cultural background shapes visual environment affordance [35]. Coetzee et al. [65] investigated attractiveness assessments of White Scottish and Black S. African students for own- and other ethnicity faces. Black S. African raters relied more heavily on colour cues in their assessments of Black African female attractiveness, whereas White Scottish judges relied more heavily on shape cues in their assessments. The researchers concluded that although there was evidence for the universality of facial attractiveness assessments, the ethnicity of the target face moderated this agreement, i.e. agreement on European faces was higher than on African faces (possibly due to a difference in familiarity with other-ethnicity faces).

In the present study, the female participants (imaged women) were recruited in major cities. We might assume that contact with other ethnicities is considerable. Coetzee et al. [65] stated for S. Africans, for example, there is variation across samples in terms of familiarity with other ethnicities’ facial appearance. However, this alone cannot explain the variation in the facial assessments across ethnicities in our findings. The patterns of age assessments are similar across ethnicities, for both face ethnicity and assessor ethnicity. If assessors of one ethnicity were unable to accurately assess facial appearance of other ethnicities because of unfamiliarity with the variation in morphology, the patterns of age assessments across ethnicities should be more diverse than was the case (although there were differences in mean age assessments). Age-related changes in facial morphology (in terms of shape) and visible skin condition both play a role in age assessments. Yet the relative contribution of these features to age perception may be different across ethnicities depending, for example, on the visibility of skin colouration cues. In lightly pigmented skin, unevenness may be more detectable than in darkly pigmented skin. In the present study, our focus was on the investigation of cross-cultural differences (or similarities) in perceptions of female facial appearance. Thus, we did not quantify facial morphology and/or skin condition. As such, the possibility of cross-cultural variation in the relative importance of these components for age assessments remains to be investigated.

Attractiveness and health assessments showed greater variation across ethnicities, with some large differences associated with face and assessor ethnicity, in addition to gender differences. Perhaps most conspicuous in the pattern of cross-cultural variation in facial attractiveness and health is the low assessments of S. African (and Indian) women (and the absence of a gender difference) made by Indian assessors. This may reflect the influence of socio-cultural factors, namely “colourism” (i.e. a preference for lighter skin colour, possibly dating to colonialism) [66] (but see Wagatsuma, 1967 [67]), on face perception, as darkly pigmented skin in India is perceived negatively, partially due to the hierarchical caste system [68, 69]. Similar “colourism” has been reported for S. Africa where lighter-skinned migrants have been treated more positively than darker-skinned migrants [70]. In the present study, S. African assessors judged French faces lowest and Indian faces highest on attractiveness.

Many additional factors might contribute to cross-cultural differences in attractiveness assessments, including environmental settings [29, 71–73] and measures of national health [28, 39], along with variation within- and between assessors (e.g., hormonal fluctuations), which have been reviewed elsewhere [4, 12, 30, 74, 75] (but see Jones et al. and Marcinkowska et al. [76, 77]). There is consensus that certain facial cues relate to female age and health, both of which correlate with female fecundity and reproductive potential [9, 13, 15, 78]. From an evolutionary perspective, one might assume that these relationships are found universally, and the evidence from industrialized and pre-industrialized societies suggests that this is the case. However, this universality does not preclude variation in the strength of associations across ethnicities. Our findings of cross-cultural variation in perceptions of female facial appearance do not challenge the evidence that certain facial cues provide information about an individual’s mating-related quality. We document negative correlations between age and attractiveness/health, and a positive correlation between attractiveness and health for every combination of face ethnicity and assessor ethnicity. The relative size of effects and the mean assessments may differ across cultures because of differences in environmental conditions, socio-cultural factors, and other variables that contribute to individual differences (see for a review, Pisanski and Feinberg [79]). Nevertheless, the biological blueprint nature uses to convey certain information about an individual’s quality may be the same for all humans [9].

Many studies investigating human physical attractiveness include a statement on the stability of attractiveness ratings across ethnicities (“strong cross-cultural agreement”). However, there is concern about the validity of this statement [54, 55, 80, 81]. The findings of the present study corroborate the presence of differences in the assessment of female facial appearance, depending on the ethnicity of the face and the ethnicity and gender of the assessor. These cross-cultural differences in face assessments are evident especially in attractiveness and health ratings, at least in samples of industrialized and industrializing countries. Previous research reporting differences in face preferences of industrialized vs. pre-industrialized societies [82, 83] suggested that visual experience with facial cues may account for the effect (but see Danel et al. [80]). We suggest that visual experience with faces of other ethnicities alone cannot explain our findings. Rather, our findings may be explained through a combination of ethnocentrism [84, 85] and other effects that emerge from different socio-cultural settings. However, the variation in patterns of assessments of female facial appearance may also reflect evolved preferences expressed in response to environmental settings that contributed to the development of plasticity in the perception of female facial appearance across cultures. Future studies should i) quantify cross-cultural variation in facial morphology and visual skin condition, and disentangle the relative impact of these components on face ratings, and ii) consider the influence of ethnocentrism and stereotyping in cross-cultural (facial) assessment, in addition to effects motivated by human sexual psychology. For example, face research has successfully applied geometric morphometrics in the assessment of facial shape variation in samples of industrialized and non-industrialized societies in relation to physical capacity and/or perception (e.g., Butovskaya et al., Fink et al., Schaefer et al., and Kleisner et al. [86–89]). Similarly, objective measures of skin color and the evenness of skin tone correlate with assessments of facial age, attractiveness, and health [90, 91]. The application of these technologies in the current multi-ethnic and multi-centre study would take the study findings to the next level by investigating features that predict cross-cultural variation in face assessments.

Although the high level of standardization of facial imaging and assessment protocols is a strength of the current study, we contend that it could be realized only in cooperation with local study centres in major cities. The collection of similar stimuli and information from members of small-scale societies in anthropological fieldwork remains challenging. Therefore, evidence from studies that have investigated face assessments cross-culturally should be considered with caution regarding the comparability of study findings. This includes questions about influences from (Western) media shaped face perception, which can be assumed to be present in all population samples of the current study. France, for example, is a global leader in the cosmetics business, and French cosmetic products are highly regarded especially in China and Japan, possibly leading to stereotypic and higher assessments of French women compared to women of other ethnicities. We suggest that studies investigating cross-cultural agreement in face perception and the reasons for geographical variation need to quantify socio-cultural stereotypes (e.g., Choi et al. [92] in inter-population perception in addition to securing objective measures of biological variation in facial appearance.

Equality for (almost) all: Egalitarian advocacy predicts lower endorsement of sexism and racism, but not ageism

Martin, A. E., & North, M. S. (2021). Equality for (almost) all: Egalitarian advocacy predicts lower endorsement of sexism and racism, but not ageism. Journal of Personality and Social Psychology, Jan 2021. https://doi.org/10.1037/pspi0000262

h/t David Schmitt (20) David Schmitt on Twitter: ""egalitarian advocacy predicts greater likelihood to support “Succession”-based ageism, which prescribes that older adults step aside to free up coveted opportunities...equality for all may only mean equality for some" https://t.co/PTYtfpLNKF" / Twitter

Abstract: Past research has assumed that social egalitarians reject group-based hierarchies and advocate for equal treatment of all groups. However, contrary to popular belief, we argue that egalitarian advocacy predicts greater likelihood to support “Succession”-based ageism, which prescribes that older adults step aside to free up coveted opportunities (e.g., by retiring). Although facing their own forms of discrimination, older individuals are perceived as blocking younger people, and other unrepresented groups, from opportunities—that in turn, motivates egalitarian advocates to actively discriminate against older adults. In 9 separate studies (N = 3,277), we demonstrate that egalitarian advocates endorse less prejudice toward, and show more support for, women and racial minorities, but harbor more prejudice toward (Studies 1 and 2), and show less advocacy for (Studies 3–6), older individuals. We demonstrate downstream consequences of this effect, such as support for, and resource allocation to, diversity initiatives (Studies 3–6). Further, we isolate perceived opportunity blocking as a critical mediator, demonstrating that egalitarian advocates believe that older individuals actively obstruct more deserving groups from receiving necessary resources and support to get ahead (Studies 4–6). Finally, we explore the intersectional nature of this effect (Study 7). Together this research suggests that when it comes to egalitarianism, equality for all may only mean equality for some.

Investors who trade in months of less attention are more experienced, engage more in complex trading, have less of a home bias tendency, are wealthier, & have a higher income than those who trade during the highest attention-grabbing months

Net Buyers of Attention-Grabbing Stocks? Who Exactly Are They? Liron Reiter Gavish, Mahmoud Qadan & Joseph Yagil. Journal of Behavioral Finance, Volume 22, 2021 - Issue 1, Pages 26-45, Feb 6 2020. https://doi.org/10.1080/15427560.2020.1716360

Rolf Degen's take: (20) Rolf Degen on Twitter: "Attention-grabbing stocks light inexperienced traders' fire. https://t.co/9KTgBj5Szs https://t.co/JtWRVWP64v"

Abstract: The literature has established that retail investors are “net buyers” of attention-grabbing stocks. In this study, the authors utilize a unique dataset of actual information about 290,000 household investment accounts and track their “net buying” decisions with a focus on their economic and demographic characteristics. Unlike previous research, the authors focus not only on net buyers of attention-grabbing stocks, but also on net sellers of such stocks. They find that factors such as financial experience, wealth, consulting with advisors, and other individual characteristics, indicative of investors’ sophistication, account for the differences in the net buying decision. Specifically, the authors find that more trading experience and a lower tendency for home bias are associated with net selling during months when stocks attract a great deal of attention, and with net buying during months when they are paid less attention. The authors document that investors who trade in months of less attention are more experienced, engage more in complex trading, have less of a home bias tendency, are wealthier, and have a higher income than those who trade during the highest attention-grabbing months. Finally, the use of financial advice varies not only between households, but also between months in which stocks receive a great deal or little attention.

Keywords: Household financeInvestor attentionInvestor literacyInvestor sophisticationTrading bias