Wednesday, March 30, 2022

Meta-analysis: Cognitive abilities are not related to the willingness to take financial risks

Cognitive abilities affect decision errors but not risk preferences: A meta-analysis. Tehilla Mechera-Ostrovsky, Steven Heinke, Sandra Andraszewicz & Jörg Rieskamp. Psychonomic Bulletin & Review, Mar 30 2022. https://link.springer.com/article/10.3758/s13423-021-02053-1

Abstract: When making risky decisions, people should evaluate the consequences and the chances of the outcome occurring. We examine the risk-preference hypothesis, which states that people’s cognitive abilities affect their evaluation of choice options and consequently their risk-taking behavior. We compared the risk-preference hypothesis against a parsimonious error hypothesis, which states that lower cognitive abilities increase decision errors. Increased decision errors can be misinterpreted as more risk-seeking behavior because in most risk-taking tasks, random choice behavior is often misclassified as risk-seeking behavior. We tested these two competing hypotheses against each other with a systematic literature review and a Bayesian meta-analysis summarizing the empirical correlations. Results based on 30 studies and 62 effect sizes revealed no credible association between cognitive abilities and risk aversion. Apparent correlations between cognitive abilities and risk aversion can be explained by biased risk-preference-elicitation tasks, where more errors are misinterpreted as specific risk preferences. In sum, the reported associations between cognitive abilities and risk preferences are spurious and mediated by a misinterpretation of erroneous choice behavior. This result also has general implications for any research area in which treatment effects, such as decreased cognitive attention or motivation, could increase decision errors and be misinterpreted as specific preference changes.

Discussion

We conducted a Bayesian meta-analysis with a total of 30 studies and examined whether a potential meta effect size is better explained by the risk-preference hypothesis, which assumes a correlation between cognitive abilities and risk aversion because cognitive abilities affect the evaluation of risky options and, consequently risk-taking behavior, or by the error hypothesis, which assumes that mixed results are the product of a relationship between cognitive abilities and decision errors resulting from a bias of the architectural properties of the risk-preference-elicitation task. Our results show that the correlation between cognitive ability and risk aversion is noncredible. Notably, we find that when studies applied unbalanced choice sets, they reported a stronger negative (or positive) correlation between cognitive abilities and risk aversion depending on the direction of this unbalance. The effect of the RCRT bias was robust across all meta-analytical model specifications and thus provides strong evidence for the error hypothesis. That is, our findings support the claim that previous mixed evidence of a correlation between cognitive abilities and risk aversion is mainly driven by the important interaction between the architecture of the risk-preference-elicitation task and errors in decision making. In addition, we found an effect of task framing, where including losses in risk-preference-elicitation tasks only weakly moderates the relation between cognitive abilities and risk aversion. Note that this effect was not robust across all meta-analytical model specifications and appears to be highly correlated with the RCRT bias of the choice set, where the latter has a higher explanatory power. We found no mediating effects of the type of cognitive ability test applied or of the number of decisions. We conclude that a potential correlation between cognitive abilities and risk aversion is moderated by the link between cognitive abilities and the probability of making unsystematic decision errors.

A recent meta-analysis by Lilleholt (2019) similarly explored the link between cognitive abilities and risk preferences. However, in contrast to our work, Lilleholt’s analysis did not directly test whether the mixed findings regarding the link between cognitive abilities and risk preferences could be explained by the error hypothesis and the bias in the architecture of most risk-preference-elicitation tasks. There are other important differences. First, Lilleholt had a broader literature search scope, leading to a larger set of examined studies. For instance, the author included experience-based risk-preference-elicitation tasks, which we excluded from our analysis. In such tasks, people have no information about the outcomes of gambles and the probabilities with which the outcomes occur but learn this from feedback. Thus, in these tasks, learning plays a major role in how people make their decisions, thereby making the interpretation of a potential link between cognitive abilities and risk preferences more complicated. In general, it has been argued that description-based and experience-based tasks differ in both architecture and interpretation (Frey et al., 2017). Therefore, in contrast to Lilleholt, we have focused on a description-based task that makes it easier to code all relevant task-architecture information precisely.

Since Lilleholt (2019) ran the meta-analysis for each domain separately, we compared Lilleholt’s results with our results by estimating our meta-analytic models on Lilleholt’s merged data set (see Appendix Table 8). In line with Lilleholt’s results, we find a credible metaeffect of −.05 with a 95% BCI ranging from −.07 to −.03 for the loss, gain, and mixed domains. Note that our restricted data set exhibits a comparable effect size of −.03, with a 95% BCI ranging from −.08 to .02. Additionally, the inclusion of losses as outcomes of the choice options had a credible effect on the correlation between cognitive abilities and risk preferences with a mean estimate of .12 and a 95% BCI ranging from .08 to .15 (see Appendix Table 8, Model Mf). The model comparison shows that the model that includes this variable is superior to a model that exclude it (see Appendix Table 8, Models Mf, M2). The effect of the RCRT bias towards risk aversion on the correlation between cognitive abilities and risk preferences was credible across all model specifications (see Appendix Table 8, Models Mf, M1, M2) and exhibited a mean estimate of −.17 and a 95% BCI ranging from −.25 to −.09 (see Appendix Table 8, Model Mf). More importantly, a regression model comparison procedure (see Appendix Table 8) shows that accounting for RCRT bias (Mf vs. M1 BF = 5.9×106) and the inclusion of losses (Mf vs. M2BF = 2.1×1010) improve the model fit substantially for the merged data set of Lilleholt (2019), replicating our results. However, given the larger set of studies in Lilleholt compared with ours, this replication should be interpreted with caution.

Our finding of a moderating effect of an RCRT-biased task architecture on the correlation between cognitive ability and risk aversion contributes to the discussion in the decision sciences and experimental economics literature. For instance, in line with the error hypothesis, Andersson et al. (2016) experimentally demonstrated that the link between cognitive abilities and risk aversion is spurious, as it is moderated by the link between cognitive abilities and random choice behavior (Andersson et al., 2016). In keeping with this result, Olschewski et al. (2018) reported that in risk-taking tasks, cognitive abilities correlated negatively with decision errors. We followed this work and rigorously tested the error hypothesis with a meta-analysis. Our results show that the correlation between cognitive abilities and risk aversion can be explained by the characteristics of the choice set (i.e., task architecture), implying an RCRT bias, a phenomenon that leads to misclassifying random choices as a specific risk preference.

Our findings support the view of the error hypothesis that cognitive abilities are linked to the probability of making unsystematic errors (Burks et al., 2008; Dean & Ortoleva, 2015; Olschewski et al., 2018; Tymula et al., 2013). Additionally, it is plausible to assume that people with lower cognitive abilities apply simpler decision strategies (i.e., heuristics) that reduce information-processing load. However, the use of heuristics does not necessarily imply more or less risk-taking behavior; only the interaction between the applied heuristic and the task architecture leads to a specific observed risk-taking behavior. As we discussed above, some heuristics lead to higher (or lower) observed risk-seeking behavior compared with more complex decision strategies, depending on the choice set. Therefore, one would not necessarily expect a specific correlation between people’s cognitive abilities and the observed risk-taking behavior across the different tasks, but instead expect some heterogeneity in the results. However, the use of specific strategies cannot explain the relationship between the observed average risk preferences in a task and the RCRT bias in the task. Thus, the link between cognitive abilities and the selected decision strategies does not imply a link between cognitive abilities and the latent risk preferences. Crucially, when examining the potential link between cognitive abilities, decision strategies, and risk preferences, it is necessary to first identify the specific strategies people apply in specific environments or task architectures (Olschewski & Rieskamp, 2021; Rieskamp, 2008; Rieskamp & Hoffrage, 19992008; Rieskamp & Otto, 2006). Future work should examine the different heuristics and decision strategies to arrive at a comprehensive understanding of whether and how those shape the correlation between cognitive abilities and risk preferences.

The results of this study also resonate with a recent empirical discourse on the validity of risk-preference-elicitation measures. For instance, Frey et al. (2017) and Pedroni et al. (2017) found behavioral risk-elicitation tasks to be less stable elicitations of risk preferences compared with self-reported measures. Importantly, the difference between behavioral and self-reported measures could disappear once measurement errors are accounted for (Andreoni & Kuhn, 2019) by applying better task architectures.

Our results also have implications for interpreting experimental results in other research domains. For example, when testing for a specific treatment effect it appears important to control for increased decision errors, so that a potential increase in errors is not misinterpreted as a specific treatment effect. Whether such misinterpretation is likely to occur depends on whether the task architecture has a bias, so that random choice behavior leads to a specific psychological interpretation. For instance, a potential effect of increased time pressure on people’s risk preferences could also simply be due to an increase in decision errors under high time pressure (e.g., Olschewski & Rieskamp, 2021). Likewise, the potential effect of cognitive load on people’s risk preferences, intertemporal time preferences, or social preferences could also simply be due to an increase in decision errors under cognitive load manipulations (e.g., Olschewski et al., 2018). Finally, the potential effect of increased monetary incentives on people’s preferences could also be due to lower decision errors with higher monetary incentives (e.g., Holt & Laury, 2002; Smith & Walker, 1993). In general, treatment effects on preferences have been observed in intertemporal discounting (e.g., Deck & Jahedi, 2015; Ebert, 2001; Hinson et al., 2003; Joireman et al., 2008) as well as social preferences (e.g., Cappelletti et al., 2011; Halali et al., 2014; Schulz et al., 2014). Across these domains, it is important to understand how changes in decision errors affect preference measurements. Failure to do so could potentially lead to misinterpretations of observed effects.

Consequently, addressing the issue of decision errors captured by the error hypothesis is of general importance to any research in behavioral economics and psychology with the objective to elicit individual preferences. There are two possible ways to address this matter. First, one can account for random errors ex ante by choosing an experimental design that controls for random errors. At the experimental design stage, researchers could apply a variety of measures to assess people’s preferences. In this way, they could cancel out systematic errors and minimize measurement errors in the associated biased classifications (Frey et al., 2017). For instance, Andersson et al. (2016) suggested choosing a symmetrical choice set when measuring risk preferences. However, this approach may not always be suitable for every preference-elicitation task. Leading to the second approach, one can account for error at the data analysis stage. For example, accounting for potential biases with an explicit structural decision-making model what includes an error theory at the data-analysis stage could be advantageous (Andersson et al., 2020). Recently, behavioral economists Gillen et al. (2015) and Andreoni and Kuhn (2019) proposed an instrumental variable approach to address this problem (see also Gillen et al., 2015).

It is important to note the task architecture determines the context in which a choice option is presented. Consequently, various theories relating to the context effect could also contribute to the fact that people with lower cognitive abilities are more prone to be influenced by the task architecture. For example, Andraszewicz and Rieskamp (2014) and Andraszewicz et al. (2015) demonstrated that pairs of gambles with the same differences in expected values and the same variances (i.e., risk) but various covariances (i.e., similarity) result in more unsystematic choices when the covariance between the two gambles is lower (Andraszewicz et al., 2015; Andraszewicz & Rieskamp, 2014). This effect called the covariance effect results from the fact that pairs of gambles with low covariances are more difficult to be compared with each other. Simonson and Tversky (1992) demonstrated that context effects can result from the available sample of choice options, such that extreme outcomes may appear as extreme in face of the available sample (Simonson & Tversky, 1992). Along the same lines, Ungemacht et al. (2011) demonstrated that people’s preferential choices depend on one’s exposure to hypothetical choice options.

To summarize, this meta-analysis highlights the importance of accounting for choice-set architecture, in particular, its interaction with random decision errors. Our applied methods and results go beyond the current research scope and suggest that neglecting the effect of random decision errors at the experimental design stage or at the data-analysis stage can lead to spurious correlations and the identification of “apparently new” phenomena (Gillen et al., 2019). The findings presented in this meta-analysis offer an important contribution to the scientific communities in judgment and decision making, psychology, experimental finance, and economics. In these fields of studies, measuring risk-taking propensity is particularly important. Therefore, findings of the current meta-analysis are very relevant to all researchers investigating risk-taking behavior using common risk-preference-elicitation methods.

A majority could well imagine undergoing psychotherapy via artificial intelligence, among other things because of the ability to comfortably talk about embarrassing experiences

Attitudes and perspectives towards the preferences for artificial intelligence in psychotherapy. Mehmet Emin Aktan, Zeynep Turhan, İlknur Dolu. Computers in Human Behavior, March 29 2022, 107273. https://doi.org/10.1016/j.chb.2022.107273

Highlights

• We explored the factors of choosing AI-based psychotherapy.

• The less stigma and remote access were found as key in preferring AI-based therapy.

• Trust of the security of data in AI-based therapy were less than human therapists.

• The beliefs about limited ability to empathize in AI-based psychotherapy was found.

Abstract: The use of artificial intelligence (AI) in psychotherapy has been increased in recent years. While these technologies in psychotherapy are growing, the circumstances of accepting artificial tools during psychotherapy need to be explored to improve effective AI tools during the sensitive therapeutic environment. In this study, the factors around the preferences for AI-based psychotherapy were investigated. This cross-sectional study was conducted with a sample of 872 individuals who are highly educated, 18 aged and above. Attitude towards AI-based Psychotherapy, Attitude towards Seeking Professional Psychological Help Scale- Short Form, and Stigma Scale for Receiving Psychological Help Scale were used to examine the factors of participants' preferences for AI-based psychotherapy. While 55% of the sample preferred AI-based psychotherapy, the majority of participants trusted more human psychotherapists than AI-based systems when asked participants’ trust about the security of personal data. However, three important benefits of AI-based psychotherapy were identified as being able to comfortably talk about the embarrassing experiences, having accessibility at any time, and accessing remote communication. Importantly, factors of preferences for AI-based psychotherapy were related to the idea of AI-based psychotherapy systems can improve themselves based on the results from previous therapeutic experiences. Gender and the types of profession related to psychology and technical/engineering were also associated with choosing AI-based psychotherapy. The results suggest that both raising awareness of the benefits and effectiveness of psychotherapy as well as the trust to the artificial intelligence tools can improve the rate of the preferences for AI-based psychotherapy.

Keywords: Artificial intelligencePsychotherapyAccessibilityHelp-seeking behaviorStigma


Serial Sexual Murderers: Criminal paraphilia developed to reinforce positive emotions from sexual fantasies and helped to create a sense of intimacy to avoid being rejected

The Role of Child and Adult Sexual Fantasies and Criminal Paraphilia Involving Serial Sexual Murderers. Heather Brown. Walden University, PhD Dissertation. Mar 2022. https://www.proquest.com/openview/9bca3a21831039db7a1412f44af51536/1?pq-origsite=gscholar&cbl=18750&diss=y

Abstract: Childhood trauma may be a reason a child develops maladaptive coping mechanisms such as sexual fantasies and paraphilia. These coping mechanisms increase in intensity, leading to sexual violence to gain a sense of power and control. Even though researchers have identified that serial sexual killers suffer from child and adult sexual fantasies and criminal paraphilia, details of the sexual fantasies and paraphilia have not been examined. The purpose of this qualitative exploratory case study was to explore the role of child and adult sexual fantasies and criminal paraphilia involving serial sexual murderers. Hickey’s trauma-control model and relational paraphilic attachment theory were used as the theoretical foundations. Data were collected from 12 U.S. male participants identified as serial sexual murderers. Four themes were identified from the thematic analysis and were linked to all 12 case participants. Findings indicated child and adult sexual fantasies began as a maladaptive coping mechanism to avoid feeling abandoned, which escalated to ways of feeling control and revenge. Criminal paraphilia developed to reinforce positive emotions from sexual fantasies and helped to create a sense of intimacy to avoid being rejected. Findings may assist law enforcement, school staff, and mental health professionals to promote positive social change by preventing future risk for behaviors that lead to and are incorporated into the sexual murders committed by serial killers.


Men and women invested equally in improving their appearance if exercising and bodybuilding were included

Sex Differences in Physical Attractiveness Investments: Overlooked Side of Masculinity. Marta Kowal. Int. J. Environ. Res. Public Health 2022, 19(7), 3842; Mar 24 2022. https://doi.org/10.3390/ijerph19073842

Abstract

Background: Public opinion on who performs more beauty-enhancing behaviors (men or women) seems unanimous. Women are often depicted as primarily interested in how they look, opposed to men, who are presumably less focused on their appearance. However, previous studies might have overlooked how masculinity relates to self-modification among men. Methods: We explored this issue in depth by conducting a qualitative Study 1 aimed to establish how men and women enhance their attractiveness (N = 121) and a quantitative Study 2 aimed to test time spent on activities that increase one’s attractiveness in a longitudinal design (with seven repeated measures from 62 participants; N(total) = 367). Results: We observed no sex differences in beauty investments. Although women spent more time on make-up and cosmetics usage, men caught up with women in exercising and bodybuilding. Conclusion: Our study provides evidence that there may not be such wide sex differences in the intensity of enhancing one’s appearance as has been previously thought. We hypothesize that this might partly stem from changes in gender roles regarding masculinity.

Keywords: gender; diary study; enhancing beauty; self-modification; sex comparison


Tuesday, March 29, 2022

The cat parasite Toxoplasma gondii might boost people's sexual attractiveness, possibly thru changes in facial symmetry

Borraz-León JI, Rantala MJ, Krams IA, Cerda-Molina AL, Contreras-Garduño J. 2022. Are Toxoplasma-infected subjects more attractive, symmetrical, or healthier than non-infected ones? Evidence from subjective and objective measurements. PeerJ 10:e13122. Mar 2022. https://doi.org/10.7717/peerj.13122

Abstract

Background: Parasites are among the main factors that negatively impact the health and reproductive success of organisms. However, if parasites diminish a host’s health and attractiveness to such an extent that finding a mate becomes almost impossible, the parasite would decrease its odds of reproducing and passing to the next generation. There is evidence that Toxoplasma gondii (T. gondii) manipulates phenotypic characteristics of its intermediate hosts to increase its spread. However, whether T. gondii manipulates phenotypic characteristics in humans remains poorly studied. Therefore, the present research had two main aims: (1) To compare traits associated with health and parasite resistance in Toxoplasma-infected and non-infected subjects. (2) To investigate whether other people perceive differences in attractiveness and health between Toxoplasma-infected and non-infected subjects of both sexes.

Methods: For the first aim, Toxoplasma-infected (n = 35) and non-infected subjects (n = 178) were compared for self-perceived attractiveness, number of sexual partners, number of minor ailments, body mass index, mate value, handgrip strength, facial fluctuating asymmetry, and facial width-to-height ratio. For the second aim, an independent group of 205 raters (59 men and 146 women) evaluated the attractiveness and perceived health of facial pictures of Toxoplasma-infected and non-infected subjects.

Results: First, we found that infected men had lower facial fluctuating asymmetry whereas infected women had lower body mass, lower body mass index, a tendency for lower facial fluctuating asymmetry, higher self-perceived attractiveness, and a higher number of sexual partners than non-infected ones. Then, we found that infected men and women were rated as more attractive and healthier than non-infected ones.

Conclusions: Our results suggest that some sexually transmitted parasites, such as T. gondii, may produce changes in the appearance and behavior of the human host, either as a by-product of the infection or as the result of the manipulation of the parasite to increase its spread to new hosts. Taken together, these results lay the foundation for future research on the manipulation of the human host by sexually transmitted pathogens and parasites.

Discussion

Limitations and future directions

Results showed that boys as young as age 3 generally valued strength more than girls: boys, on average, said it was more important to be strong than girls did, & were more likely to prefer strength-related occupations than girls

Early Gender Differences in Valuing Strength. May Ling D. Halim, Dylan J. Sakamoto, Lyric N. Russo, Kaelyn N. Echave, Miguel A. Portillo & Sachiko Tawa. Archives of Sexual Behavior, Mar 28 2022. https://link.springer.com/article/10.1007/s10508-021-02185-4

Abstract: Being strong is a prominent male stereotype that children learn early in life; however, it is unknown as to when children start to value being strong and when gender differences in valuing strength might emerge. In the current study, we interviewed an ethnically diverse sample of 168 3–5 year-olds (88 girls, 80 boys) to address this gap in the literature. Results showed that boys as young as age 3 generally valued strength more than girls: (1) boys, on average, said it was more important to be strong than girls did, and (2) boys were more likely to prefer strength-related occupations than girls. Boys were also more likely to select boys than girls as the gender who cares more about physical strength. Additionally, with age, both girls and boys demonstrated knowledge of the stereotype that boys care about physical strength, with girls also being less likely to associate being a girl with being strong. Overall, the results suggest that valuing physical strength starts in early childhood, and gender differences in valuing strength are evident at the eve of gender identity development. Possible implications for boys’ later well-being and health are discussed.


Individuals with higher income may exhibit profiles of adult personality development that more closely resemble aspects of a healthy personality

Income moderates changes in big-five personality traits across eighteen years. Vincent YS Oh, Ismaharif Ismail, Eddie MW Tong. European Journal of Personality, March 28, 2022. https://doi.org/10.1177/08902070221078479

Abstract: The role of income in adult personality change remains poorly understood. Using latent growth modeling, we performed exploratory analyses of how longitudinal trajectories of change in personal income and the Big Five personality traits would be related. We examined 4234 participants (2149 Males, 2085 Females; MT1age = 46.42, SDT1age = 13.36, age range at T1: 20–74 years) across three time points spanning 18 years using data from the Midlife in the United States study. Results indicated that starting levels of income moderated changes in four personality traits. Specifically, income moderated the slopes of openness to experience, extraversion, agreeableness, and neuroticism, such that for high-income individuals, openness to experience, extraversion, and agreeableness were less likely to decline and more likely to either increase or remain stable over time, while neuroticism was less likely to increase and more likely to remain stable over time. Conversely, personality traits were weaker predictors of income change as slopes of income were not moderated by starting levels of any of the personality traits. Moreover, changes in income were not correlated with changes in any of the personality traits. The findings suggest that individual differences in income could potentially explain diverging trajectories of personality change.

Keywords: big five, income, personality development, personality change, socioeconomic status


Women are unhappier than men in anxiety, depression, fearfulness, sadness, loneliness, & anger; are also less satisfied with many aspects of their lives such as democracy, the economy, the state of education and health services

The Female Happiness Paradox. David G. Blanchflower & Alex Bryson. NBER Working Paper 29893.  March 2022. https://www.nber.org/papers/w29893

Abstract: Using data across countries and over time we show that women are unhappier than men in unhappiness and negative affect equations, irrespective of the measure used – anxiety, depression, fearfulness, sadness, loneliness, anger – and they have more days with bad mental health and more restless sleep. Women are also less satisfied with many aspects of their lives such as democracy, the economy, the state of education and health services. They are also less happy in the moment in terms of peace and calm, cheerfulness, feeling active, vigorous, fresh and rested. However, prior evidence on gender differences in global wellbeing metrics – happiness and life satisfaction – is less clear cut. Differences vary over time, location, and with model specification and the inclusion of controls especially marital status. We also show that there are significant variations by month in happiness data regarding whether males are happier than females but find little variation by month in unhappiness data. It matters which months are sampled when measuring positive affect but not with negative affect. These monthly data reveal that women’s happiness was more adversely affected by the COVID shock than men’s, but also that women’s happiness rebounded more quickly suggesting resilience. As a result, we now find strong evidence that males have higher levels of both happiness and life satisfaction in recent years even before the onset of pandemic. As in the past they continue to have lower levels of unhappiness. A detailed analysis of several data files, with various metrics, for the UK confirms that men now are happier than women.


Monday, March 28, 2022

Genetic Link to Fear Memories Found Hiding Within Mice's "Junk DNA"

ADRAM is an experience-dependent long noncoding RNA that drives fear extinction through a direct interaction with the chaperone protein 14-3-3. Wei Wei et al. Cell Reports, Vol 38, Iss 12, Mar 22 2022. https://doi.org/10.1016/j.celrep.2022.110546

Highlights

• Targeted RNA sequencing reveals learning-induced lncRNAs in the adult brain

• ADRAM is critical for the formation of fear extinction memory

• ADRAM coordinates the epigenomic regulation of Nr4a

Summary: Here, we used RNA capture-seq to identify a large population of lncRNAs that are expressed in the infralimbic prefrontal cortex of adult male mice in response to fear-related learning. Combining these data with cell-type-specific ATAC-seq on neurons that had been selectively activated by fear extinction learning, we find inducible 434 lncRNAs that are derived from enhancer regions in the vicinity of protein-coding genes. In particular, we discover an experience-induced lncRNA we call ADRAM (activity-dependent lncRNA associated with memory) that acts as both a scaffold and a combinatorial guide to recruit the brain-enriched chaperone protein 14-3-3 to the promoter of the memory-associated immediate-early gene Nr4a2 and is required fear extinction memory. This study expands the lexicon of experience-dependent lncRNA activity in the brain and highlights enhancer-derived RNAs (eRNAs) as key players in the epigenomic regulation of gene expression associated with the formation of fear extinction memory.


Discussion

Here, we report the discovery of widespread experience-dependent lncRNA activity in the adult ILPFC, and further reveal a significant number of inducible eRNAs that respond selectively to fear extinction learning. This class of lncRNA was first discovered at scale more than a decade ago by the Greenberg group who identified thousands of sites outside known promoter regions in primary cortical neurons stimulated with KCl in vitro, which exhibited features of enhancer elements, including binding of CBP and the deposition of the histone modification H3K4me1 (). Transcriptional activity at these sites showed a positive correlation with downstream mRNA expression, suggesting a context-specific permissive relationship between eRNAs and their proximal mRNA partners.  went on to functionally characterize neuronal enhancers and identify another histone modification, H3K27ac, as a key marker of their active state. An overlay of our lncRNA capture-seq data with learning-induced enhancer signatures in the adult brain (), as well as our cell-type-specific ATAC-seq signatures in learning-activated Arc+ neurons, revealed that there are many experience-dependent lncRNAs in the ILPFC that are endowed with features of activity-inducible eRNAs. Notably, all six of the validated eRNA-associated protein-coding gene candidates have been shown to be involved in plasticity, suggesting that this class of lncRNA is, in general, permissively involved in the regulation of experience-dependent gene expression.
One of the most interesting findings of our study beyond the necessary role of ADRAM in fear extinction is that it binds directly to the Nr4a2 promoter; however, in doing so it does not form an R-loop or promote chromosome looping. trans-Acting lncRNAs are known to form triplex structures on double-stranded DNA using a Hoogsteen base-pairing rule in the DNA target (). These structures are distinct from R-loops and could represent a mechanism by which lncRNAs act in a combinatorial manner to simultaneously serve as both guides and scaffolds. Indeed, examination of the 1 kb upstream promoter sequence of NR4A2 revealed two sites proximal to the TSS, with 25 nucleotide long complementary sequences found within exon III of ADRAM. Notably, these sites overlap with G-quadruplex motifs that are predicted to enable triplex formation. These findings suggest that ADRAM functions as a guide via a direct interaction with the Nr4a2 promoter and may do so via the formation of an RNA:DNA triplex at sites of structural reactivity. Future studies will investigate whether dynamic DNA structure states are the key to how lncRNAs find their genomic targets to regulate gene expression in an experience-dependent manner.
The 14-3-3 family of evolutionarily conserved chaperone proteins is ubiquitously expressed in the brain and highly enriched at the synapse () being involved in a variety of neuronal processes, including synaptic plasticity (). Our discovery of a direct interaction between ADRAM and 14-3-3 extends the capabilities of this class of chaperones to include functional activity as both an RNA-binding protein and a molecule that exerts its influence through protein-protein interactions. This is not without precedent as many proteins are able to interact with RNA, DNA, and other proteins. For example, YY1 interacts with both RNA and DNA, as well as other proteins, to promote its role as a regulator at the Xist locus (). Together with the observation that 14-3-3 is involved in learning and memory (), and our demonstration of how 14-3-3 interacts with eRNA to facilitate gene expression in fear extinction, these findings advance our understanding of the functional importance of this class of chaperones in the brain.
Histone modifications at neuronal enhancers also appear to be a requirement for the induction of activity-dependent genes and are particularly important in the case of rapidly induced immediate-early genes (). We found a broad overlap with H3K27ac, an open chromatin ATAC signature in activated neurons, and the expression of lncRNAs. Previous work has shown that eRNA activity often precedes, and then drives, the expression of immediate-early genes, such as c-Fos, which occurs via a direct interaction with the histone acetyltransferase domain of CBP (). In addition, a large number of eRNAs have been shown to bind to CBP, correlating with the expression of downstream genes that require CBP for their induction (). Our data on the functional relationship between ADRAM, HDAC3, HDAC4, CBP, and Nr4a2 agree with these observations and, importantly, extend the findings to include the ILPFC where they are critically involved in fear extinction. Our conclusion is that ADRAM functions as both a guide and a scaffold to epigenomically regulate extinction learning-induced Nr4a2 expression. There are now many examples of multifunctional lncRNAs. For example, in dopaminergic neurons, antisense Uchl1 regulates the expression of Uchl1 in the nucleus and then shuttles to the cytoplasm where it promotes Uchl1 translation (). Furthermore, owing to its modular domain structure, Neat1 functions in cis to coordinate the deposition of learning-related repressive chromatin modifiers along the genome () and in trans to govern paraspeckle assembly by influencing phase separation ().
In summary, the discovery of an lncRNA that is required for fear extinction deepens our understanding of learning-induced epigenomic mechanisms by integrating the modular function of enhancer-derived lncRNAs with key epigenomic processes involved in memory, and answers the long-standing question of how certain HDACs and CBP coordinate to confer their influence on localized gene regulation with a high degree of state-dependent selectivity. LncRNAs therefore provide a bridge to link dynamic environmental signals with epigenomic mechanisms of gene regulation. Together, these findings broaden the scope of experience-dependent lncRNA activity, and underscore the importance of considering eRNAs in the adult cortex as potential therapeutic targets for fear-related neuropsychiatric disorders.

 Limitations of the study

In this work, we identify an enhancer-derived lncRNA that is necessary for the formation of fear extinction memory in male mice. However, an examination of ADRAM and Nr4a2 mRNA expression in the ILPFC after extinction learning in randomly cycling females revealed no increase in ADRAM expression in the female ILPFC. In contrast, Nr4a2 was induced by training in both males and females (Figure S9). These results indicate that, although Nr4a2 may be generally induced by experience in both sexes, it may not be regulated by ADRAM under conditions where successful extinction does not occur. With respect to sex differences in fear extinction, we have also previously found that male and female mice respond differently to the standard extinction protocol used in our laboratory (

) and that the electrophysiological signature in the prelimbic PFC during fear extinction is very different between males and females (

, 

). Therefore, it is highly plausible that there are female-specific molecular mechanisms that are involved in fear-related learning. Future studies on the role of lncRNAs in fear extinction in females will require the use of a sex-specific learning protocol to reveal brain region-specific molecular mechanisms underlying memory in these mice. In addition, although we have demonstrated that 14-3-3 is a key regulatory protein that interacts with ADRAM, there were other candidates identified by mass spectrometry that have not been validated. At this stage, these data should therefore be considered preliminary until further experiments are carried out. Finally, it is not yet known whether ADRAM is necessary for other forms of learning.