Work from Home, IT Professionals: Total hours worked increased by roughly 30%, including a rise of 18% in working after normal business hours; average output did not significantly change, so productivity fell by about 20%

Work from Home & Productivity: Evidence from Personnel & Analytics Data on IT Professionals. Michael Gibbs, Friederike Mengel, Christoph Siemroth. Becker Friedman Institute, Working Paper 2021-56, May 6 2021. https://bfi.uchicago.edu/working-paper/2021-56

Using personnel and analytics data from over 10,000 skilled professionals at a large Asian IT services company, we compare productivity before and during the work from home [WFH] period of the Covid-19 pandemic. Total hours worked increased by roughly 30%, including a rise of 18% in working after normal business hours. Average output did not significantly change. Therefore, productivity fell by about 20%. Time spent on coordination activities and meetings increased, but uninterrupted work hours shrank considerably. Employees also spent less time networking, and received less coaching and 1:1 meetings with supervisors. These findings suggest that communication and coordination costs increased substantially during WFH, and constituted an important source of the decline in productivity. Employees with children living at home increased hours worked more than those without children at home, and suffered a bigger decline in productivity than those without children.

What is your earliest memory? It depends.

What is your earliest memory? It depends. Carole Peterson. Memory, May 6 2021. https://doi.org/10.1080/09658211.2021.1918174

Abstract: This article is a selective review of the literature on childhood amnesia, followed by new analyses of both published and unpublished data that has been collected in my laboratory over two decades. Analyses point to the fluidity of people’s earliest memories; furthermore, methodological variation leads to individuals recalling memories from substantially earlier in their lives. How early one’s “earliest” memory is depends on whether you have multiple interviews, how many early memories were requested within an interview, the type of interview, participation in prior tasks, etc. As well, people often provide chronologically earlier memories within the same interview in which they later identify a chronologically older memory as their “earliest”. There may also be systematic mis-dating to older ages of very early memories. Overall, people may have a lot more memories from their preschool years than is widely believed, and be able to recall events from earlier in their lives than has been historically documented.

KEYWORDS: Childhood amnesiainfantile amnesiaearly memoriesfirst memoriesautobiographical memory

Discussion

It is clear that very young children indeed form memories, and many of these can be verbally described (see Bauer et al., 2019, for an overview of types of relevant evidence). The question of “when do personal memories start” has been an often-asked question in the childhood amnesia literature, and answers to this question have influenced theory construction about early memory. However, recent research has shown that access to early memories is often shaped by a range of both cognitive and social factors that interact (see Wang & Gülgöz, 2019, for a number of articles that address this in a special issue on childhood memory, as well as the edited volume by Gülgöz & Sahin-Acar, 2020, on autobiographical memory development).

Theoretical implications

In the current article, I have reviewed relevant literature on childhood amnesia and then re-examined data collected from a range of research studies that has been conducted in my laboratory over a number of years as well as included new data that have not been previously published. These analyses have several theoretical implications.

First, an answer to the question of when one’s earliest memory occurs is a moving target rather than being a single static memory. Thus, what many people provide when asked for their earliest memory is not a boundary or watershed beginning, before which there are no memories. Rather, there seems to be a pool of potential memories from which both adults and children sample. Table 1 demonstrates considerable movement in the identification of their earliest memory, even though the memory they had described in an earlier interview was not forgotten. Moreover, almost half the time they retrieved a new and yet-earlier “first” memory when interviewed 2 years later. Some prior reports have emphasised the important role of forgetting (Cleveland & Reese, 2008; Van Abbema & Bauer, 2005), but Table 1 suggests that although forgetting is occurring and cannot be theoretically “forgotten”, as Bauer (2015) reminds us, it is but a partial explanation for changes in what is identified as the “earliest memory”.

Secondly, what is provided as a so-called “earliest memory” is highly malleable. Prior research has shown that it can be experimentally manipulated (Kingo et al., 2013b; Peterson et al., 2009b; Wessel et al., 2019). However, as Table 2 shows, one does not need external prompts; simply recalling one memory seems to internally cue others from that early period of life, and many of these later-mentioned memories are chronologically much earlier, on average a full year and a half earlier in our data. This self-cueing is also demonstrated in Table 3 when one compares the date of individuals’ identified earliest memory and their chronologically earliest memory (i.e., comparing the top panel to the bottom panel). Thus, providing an early memory often results in self-cueing to additional and yet-earlier memories. This mechanism of self-cueing is likely also responsible for participants who had a prior Memory Fluency Task subsequently providing earlier memories in the Earliest Memory Task (compare the left and right panels in Table 3).

Thirdly, when recalling multiple memories from the same life period, people do not seem to situate them on a continuous timeline as the memories are recalled. Prior research has suggested that the memories themselves and dating of those memories are independent; Table 4 suggests that memory dates are also independent of each other. How else can one explain the phenomenon of people providing memories from specific dates and a few minutes later identifying different and later-dated memories as their very first one? A mental timeline of memories does not seem to be constructed during recollection.

Limitations

In all of the analyses presented above, participants were providing their own dating of their very early memories. Yet people are notoriously poor at memory dating, as a host of other research studies have shown. The telescoping errors described above are only one example of dating error, and few other studies focus on the accuracy of the dating for people’s very early memories. What is needed in childhood amnesia research are independently confirmed or documented external dates against which personally derived dates can be compared. These are not found in the research cited above on telescoping errors since parental dating was used there for comparison with child dates, and parents too are likely to make dating errors. Such research using verified dating is currently ongoing, both in my laboratory and elsewhere.

Secondly, there are statistical limitations to the analyses presented above. Most of the analyses are post-hoc rather than pre-planned, and as such, are tentative. They can be seen as patterns that require further targeted research, and suggest avenues for additional exploration.

Structural, Functional, & Metabolic Brain Differences as a Function of Identity or Orientation: It is possible that more differences too subtle to measure with available tools yet contributing to identity & orientation could be found

Structural, Functional, and Metabolic Brain Differences as a Function of Gender Identity or Sexual Orientation: A Systematic Review of the Human Neuroimaging Literature. Alberto Frigerio, Lucia Ballerini & Maria Valdés Hernández. Archives of Sexual Behavior, May 6 2021. https://rd.springer.com/article/10.1007/s10508-021-02005-9

Abstract: This review systematically explored structural, functional, and metabolic features of the cisgender brain compared with the transgender brain before hormonal treatment and the heterosexual brain compared to the homosexual brain from the analysis of the neuroimaging literature up to 2018, and identified and discussed subsequent studies published up to March 2021. Our main aim was to help identifying neuroradiological brain features that have been related to human sexuality to contribute to the understanding of the biological elements involved in gender identity and sexual orientation. We analyzed 39 studies on gender identity and 24 on sexual orientation. Our results suggest that some neuroanatomical, neurophysiological, and neurometabolic features in transgender individuals resemble those of their experienced gender despite the majority resembling those from their natal sex. In homosexual individuals the majority resemble those of their same-sex heterosexual population rather than their opposite-sex heterosexual population. However, it is always difficult to interpret findings with noninvasive neuroimaging. Given the gross nature of these measures, it is possible that more differences too subtle to measure with available tools yet contributing to gender identity and sexual orientation could be found. Conflicting results contributed to the difficulty of identifying specific brain features which consistently differ between cisgender and transgender or between heterosexual and homosexual groups. The small number of studies, the small-to-moderate sample size of each study, and the heterogeneity of the investigations made it impossible to meta-analyze all the data extracted. Further studies are necessary to increase the understanding of the neurological substrates of human sexuality.

Discussion

Main Findings

The results from our systematic review and meta-analyses do not allow us to conclude on the specific brain phenotypes differential for each of the groups covered by this review. Although functional MRI studies (i.e., involving either fMRI or rs-MRI) on gender identity seem to indicate that fronto-parietal and cingulo-opercular brain regions are differentially relevant in transgenderism, a clear pattern accompanied by consistent structural changes is still to be found. Studies on gender identity with moderate-to-larger samples which included individuals with different sexual orientation in their control groups (Baldinger-Melich et al., 2020; Manzouri & Savic, 2019), exposed the complexities underlying both gender identity and sexual orientation. The data extracted may suggest that before hormonal treatment the majority of transgenders’ brain features covered by the studies reviewed could be similar to those of their natal sex, but certainly some brain parameters differ resembling those of their experienced gender. Also, although homosexual’s neuroanatomy, neurophysiology, and neurometabolism may tend to resemble those of heterosexual individuals of their same sex, some brain features differ and are similar to those of heterosexual individuals of the opposite sex in some of the studies analyzed.

The compilation of the data from the studies included shows neural differences between the groups studied. However, brain functions are mediated by different brain areas and their interactions, rather than by single structures. The correlation or association between a certain brain function, volumetric change or activation, with a certain activity and/or behavior does not establish whether (or not) that structure/function is causally important for that activity/behavior (Koob et al., 2013; Maney, 2016). It merely shows a possible involvement or apparent trend. Complex human behaviors (and few simple behaviors) cannot be entirely explained by phenomena occurring only in a single brain region. Therefore, the idea that brain sexual differences cause behavioral sexual differences, rather than being an assumption, still constitutes a hypothesis to verify.

Studies on cisgender and heterosexual samples have reported sex differences in brain anatomy on a global scale, regarding absolute volumes (Kurth et al., 2016). Studies have also reported sexual dimorphism in the relative sizes and shapes of regional brain structures, with the direction of the sex effect varying between regions, including the Broca’s region (Kurth et al., 2016), corpus callosum (Prendergast et al., 2015), amygdala and hippocampus (Giedd et al., 1996). These findings reflect on the selectivity of the brain regions analyzed by the studies included in this review. However, research investigating differences at the level of regional tissue volumes is highly contradictory. A large study that analyzed MRI data of 1400 cisgender heterosexual individuals from four different datasets (Joel et al., 2015) found substantial overlap in the distribution of anatomical traits between males and females in all brain regions and connections examined, undermining attempts to clearly distinguish between “male” and “female” forms of specific brain features. They arrived at the idea that human brains cannot in fact, be distinctly categorized into two distinct classes but rather, that male and female brains are comprised of “unique mosaics” of features, some of which are more common in one sex than the other and some that are common in both.

Some authors refer to an early programming of gender and sexual inclination driven by sexual differentiation in the brain, proposing that the latter influences the development of the brain areas modulating body perception (i.e., related to gender identity) or sexual arousal (i.e., related to sexual orientation) (Burke et al., 2017; Manzouri & Savic, 2019). Others underline the interaction between brain, culture and behavior, arguing that structural and functional brain changes in transgender individuals may be consequence of culture and behavior (Mohammadi & Khalegi, 2018). The etiology and drivers of differences in gender identity and sexual orientation is out of the scope of this review, and caution must be exercised to drive conclusions from the neuroscience literature alone, as human behavior, ultimately, is not reducible to biological nor to cultural factors, but is a consequence of their interaction. As such, human sexuality is a multilevel complex, and the challenge is to investigate how biological, historical and cultural elements interact with each other.

Regions of Interest Analysis

The lack of data did not allow us to meta-analyze the information obtained from the studies that conducted ROI analyses. From extracting and summarizing all the information available, differences were found between cisgender and transgender people in white matter microstructure, volumetric analyses, cortical thickness, and corpus callosum shape. Differences between heterosexual and homosexual people were found in cortical thickness, subcortical volumes, and cerebral hemisphere, but not in white matter tracts. The studies included, in the rest of the ROIs analyzed, either did not find significant differences between cisgender and transgender brains nor between heterosexual and homosexual; or found significant differences just between transgenders and opposite sex cisgenders, and between homosexuals and opposite sex heterosexuals (see Tables 3 and 4). Our findings on gender identity are consistent with previous studies that also attempted to summarize the literature findings on this topic, according to which gross morphology in transgenders is more similar to cisgender people of their natal sex than to cisgender people of their experienced gender (Guillamon et al., 2016; Kreukels & Guillamon, 2016; Mueller et al., 2017; Smith et al., 2015), even though white matter microstructure (Kreukels & Guillamon, 2016; Mueller et al., 2017; Smith et al., 2015), cortical thickness (Guillamon et al., 2016; Smith et al., 2015), and subcortical volumes (Mueller et al., 2017) may deviate from the biological sex towards values of experienced gender.

Stereotaxic Coordinates Analysis

Occipital brain regions, involved in visual processing, are the ones that most frequently were found to have a different activation in cisgenders compared to transgenders, followed by some fronto-temporal foci. This is not surprising given that, in general, most fMRI studies involved in both analyses involved visual stimulation. In addition, specifically the BA 23 had different activations for heterosexuals with respect to homosexuals. Our meta-analysis found different brain activations between different groups scattered across the whole brain, but overall with low frequency (see Tables 5 and 6). Our results on gender identity are consistent with some of the previous studies mentioned above, according to which in certain brain areas transgenders’ activation is closer to those of their experienced gender (Guillamon et al., 2016; Smith et al., 2015). While there is still concensus that a clear picture has yet to emerge (Mueller et al., 2017), recent advances in artificial intelligence confirm the observations above, by indicating that some fronto-parietal and cingulo-opercular areas may be of relevance for predicting hormonal therapy outcomes (Moody et al., 2021).

Metabolic Analysis

In transgenders and homosexuals, some metabolic features seem to differ slightly from cisgenders of their natal sex and from heterosexuals of their natal sex respectively. However, given the reduced number of studies included that conducted these analyses, these findings cannot be generalized. This is in line with what the scientific literature on gender identity up to date has concluded in this respect (Smith et al., 2015).

Strengths and Limitations

To the best of our knowledge, this is the first systematic review and meta-analysis of the neuroimaging literature on structural, functional, and metabolic differences as a function of both gender identity (before the hormonal treatment) and sexual orientation. In addition, we carefully extracted and processed all data from all studies considered for meta-analyses and made them publicly available to facilitate further research in this important area.

Several limitations regarding the small sample size of the meta-analysis and the heterogeneity of the investigations must be acknowledged. The analyses of our systematic search up to 2018 included 51 studies (i.e., 30 on gender identity and 21 on sexual orientation) all with relatively small samples, conducted with different neuroimaging techniques (1 SPECT, 3 PET, 6 fMRI, 8 rs-fMRI, and 13 MRI on gender identity; 4 PET, 5 MRI, 3 rs-fMRI, and 11 fMRI on sexual orientation). Different studies conducted with MRI investigated different brain structures (cortex, subcortical volumes, white matter, CSF, and ventricles in gender identity; cortex, subcortical volumes, and white matter in sexual orientation). fMRI was conducted under different stimulations (1 smelling, 1 vocal stimulation, 1 mental rotation task, 1 verbal fluency test, and 2 visual in gender identity; 10 visual stimulation and 1 emotional judgment task in sexual orientation). Metabolic analysis investigated different brain areas (hypothalamic network, serotonin transport distribution in different ROI, and rCBF in gender identity studies; hypothalamic activation and functional connectivity in sexual orientation studies) using different neuroimaging techniques (PET and SPECT in gender identity research; PET in sexual orientation research). As a result, it was not possible to meta-analyze the results from all studies that fit our inclusion/exclusion criteria, and the main contribution of our work, therefore, is limited to the scientific compilation and synthesis of the data available. An update on the primary search conducted in one database, added 12 papers to the analyses which, although enriched the data presented, was rather confirmatory of our main findings and added heterogeneity to the results.

Moreover, some studies had some limitations regarding the presentation of their data. First, some studies did not report statistical parameters and just reported whether or not there were significant differences between cisgenders and transgenders and between heterosexuals and homosexuals. Second, other studies reported statistical parameters only in case of significant differences between groups, and omitted reporting negative results (i.e., when no differences were found) (gender identity investigation: Burke et al., 2014; Kranz et al., 2014b, 2015; Ku et al., 2013; Lin et al., 2014; Luders et al., 2009; Nota et al., 2017; Pol et al., 2006; Santarnecchi et al., 2012; Soleman et al., 2013; Spies et al., 2016; Yokota et al., 2005; Zubiaurre-Elorza et al., 2013; sexual orientation investigation: Hu et al., 2008; Ponseti et al., 2007; Savic and Lindström, 2008; Sylva, 2013; Zeki & Romaya, 2010; for more detailed information, please see analysis of bias in Appendix 5 and 6). A complete presentation of scientific data, including negative results, is important to precisely evaluate scientific investigations on a certain topic (Matosin et al., 2014).

Information on the biological sex of the studies’ participants is part of the scientific data we collected and made available. The data presented show MtF and FtM transgender individuals do not have mirror images of brain differences. However, the heterogeneity of the design of the studies involved, despite enriching the scope of this review, due to the limited number of studies included and their sample sizes, made it impossible to draw conclusions on specific biological sex differences for the groups covered in this review. For example, some papers compared MtF with MC, others MtF with FC, others FtM with MC, and others FtM with FC. The studies included in this review on transgenderism did not provide information on early-onset or late-onset transgenderism. Therefore, analysis and information of this important point is lacking.

Finally, as Guillamon et al. (2016) noted, some studies conducted on gender identity did not report the sexual orientation of the individuals that constituted their sample. Gender identity and sexual orientation are conceptually different, i.e., both cisgender and transgender people are either heterosexual or homosexual (Burke et al., 2017; Moser, 2010), and there are more gender identities other than cis-/transgender(ism) (such as genderqueer or non-binary) and other sexual orientations other than hetero-/homosexual(ism) (such as bi-, pan-, and asexual). Sexual orientation could be associated with brain structural specific features regardless and independently from gender identity as some recent studies suggest (Baldinger-Melich et al., 2020; Manzouri & Savic, 2019). Thus, meaning that the structural, functional, and metabolic variations found in homosexual transgenders with respect to heterosexual cisgenders may be related to their sexual orientation rather than to their gender identity (Blanchard et al., 1987). A recent study identified brain regions where both sexual orientation and gender identity seemingly interact (Wang, Han, et al., 2020).

The self-reported quality of the father-son relationship did not predict hegemonic masculinity; adverse childhood experiences, mother-son relationship quality, & family support also failed to predict hegemonic masculinity

Daddy issues: Friends rather than fathers influence adult men's hegemonic masculinity. George Van Doorn, Jacob Dye, Ma Regina de Gracia. Personality and Individual Differences, Volume 171, March 2021, 110467. https://doi.org/10.1016/j.paid.2020.110467

Abstract: Hegemonic masculinity often refers to negative and socially aversive traits and behaviours associated with idealised masculine norms. Extant literature suggests that several social determinants might influence men's conformity to negative and socially-averse masculine norms, but studies are limited. The present study examined whether the quality of the father-son relationship in childhood impacts hegemonic masculinity in adulthood. In addition, we also determined whether adverse childhood experiences, mother-son relationship quality, as well as familial and peer support explained any additional variance in hegemonic masculinity after controlling for the quality of a man's relationship with his father. A sample of 188 men (aged 18 to 62 years) completed an online survey that included the K-Short Form-42, the Adverse Childhood Experiences scale, and the Conformity to Masculine Norms Inventory-29. Results from a hierarchical regression analysis showed that the self-reported quality of the father-son relationship did not predict hegemonic masculinity. Adverse childhood experiences, mother-son relationship quality, and family support also failed to predict hegemonic masculinity. However, a decrease in support from friends was associated with an increase in hegemonic masculinity, even after controlling for all other variables. The importance of peer relationships in developing and maintaining a man's adherence to traditional masculine norms is underscored.