Friday, November 12, 2021

Musical tastes fluctuate throughout the day: "By integrating an artificial neural network with Spotify’s API, we show a general awareness of diurnal preference in playlists"

Diurnal fluctuations in musical preference. Ole Adrian Heggli, Jan Stupacher and Peter Vuust. Royal Society Open Science, November 10 2021.

Abstract: The rhythm of human life is governed by diurnal cycles, as a result of endogenous circadian processes evolved to maximize biological fitness. Even complex aspects of daily life, such as affective states, exhibit systematic diurnal patterns which in turn influence behaviour. As a result, previous research has identified population-level diurnal patterns in affective preference for music. By analysing audio features from over two billion music streaming events on Spotify, we find that the music people listen to divides into five distinct time blocks corresponding to morning, afternoon, evening, night and late night/early morning. By integrating an artificial neural network with Spotify's API, we show a general awareness of diurnal preference in playlists, which is not present to the same extent for individual tracks. Our results demonstrate how music intertwines with our daily lives and highlight how even something as individual as musical preference is influenced by underlying diurnal patterns.

Statement of relevance: Today, most music listening happens on online streaming services allowing us to listen to what we want when we want it. By analysing audio features from over two billion music streaming events, we find that the music people listen to can be divided into five different time blocks corresponding to morning, afternoon, evening, night and late night/early morning. These blocks follow the same order throughout the week, but differ in length and starting time when comparing workdays and weekends. This study provides an extremely robust and detailed understanding of our daily listening habits. It illustrates how circadian rhythms and 7-day cycles of Western life influence fluctuations in musical preference on an individual as well as population level.

3. Discussion

In this work, we have shown that the rhythms of daily life are accompanied by fluctuations in musical preference. We show that the diurnal patterns of audio features in music can be treated as five distinct subdivisions of the day, with the musically meaningful distinction between them found in the range and distribution of the musical audio features. Our follow-up studies indicate that individuals hold a general awareness and agreement of diurnal musical preference in playlists consisting of multiple tracks, but that single tracks do not necessarily elicit the same diurnal associations. Taken together, this points to the circadian rhythms governing life being reflected in the highly individualized and often subjective preference for music.

The next step in this line of research would be to examine the degree to which the diurnal patterns documented herein reflect universal psychological phenomena in music perception. As previously discussed, some types of music often occur at a specific time of the day and often with a clear link to activities, with perhaps lullabies being a prime example. As lullabies are intended to ease falling asleep, they tend to occur at night and have been found to have partly universal features such as reduced tempo [4042]. If similar time-dependent songs could be collected into a database, it would then be highly interesting to investigate if the audio features of such songs match up with the features that drive the time-of-day preferences uncovered herein. Here, the Spotify API's ability to search user-made playlists for name and description is a highly productive approach, as shown in a recent study uncovering a large amount of variation in sleep music [43].

While the diurnal patterns in musical audio features uncovered in this work are robust and consistent with previous research, there are nonetheless limitations to highlight. In particular, our analysis has not addressed demographical and geographical influence on the results. In part, this is due to the lack of both demographical and individual-level information in the MSSD, and due to our data being based on Spotify, biasing the findings towards the population with access to the service. This means that our results are inherently biased towards Western culture, and we are unable to investigate factors such as age and occupation which have previously been found to impact listening behaviour [44,45]. We would encourage future research to work on combining datasets from multiple providers, such as QQ Music, Gaana and Boomplay, to ensure a wider geographical and cultural representation. Collating such datasets would require collaboration with the music streaming industry and work on harmonizing the many approaches to calculating musically meaningful audio features [46,47]. In addition, the audio features may miss out on nuances in high-level understanding of musical behaviour such as the behavioural functions of the music, and aspects of emotional content [48,49].

As a final note, we would highlight that this project has been carried out using open-source software and publicly available data, with all analysis and programming performed on laptop computers, and that the data collection processes in this work were undertaken without incurring any direct costs. This shows how the availability of digital traces from online activity can be used to investigate human behaviour by scientists both affiliated and independent alike [50].

Microbiome differences in autism spectrum disorder may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD

Autism-related dietary preferences mediate autism-gut microbiome associations. Chloe X. Yap et al. Cell, Nov 11 2021.


• Limited autism-microbiome associations from stool metagenomics of n = 247 children

• Romboutsia timonensis was the only taxa associated with autism diagnosis

• Autistic traits such as restricted interests are associated with less-diverse diet

• Less-diverse diet, in turn, is associated with lower microbiome alpha-diversity

Summary: There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.


In this large ASD stool metagenomics study in which confounders were carefully considered, we found negligible evidence for direct associations between the stool microbiome and ASD diagnostic status, which was also the case for other neurodevelopmental traits (e.g., IQ-DQ, sleep problems). For ASD, there was limited evidence for associations with taxonomic diversity or microbiome-association index (b2Figure 2), and only one differentially abundant species was robustly identified (Figure 3). These results were striking when compared to strong associations of microbiome composition with age, diet, and stool consistency (Figure 2). Importantly, we failed to replicate previously reported ASD-microbiome associations from human studies. Instead, we found evidence linking behaviors associated with the autism spectrum (e.g., repetitive-restricted behaviors or interests, sensory preferences, and social affect) to reduced dietary diversity, which, in turn, was associated with reduced microbiome diversity and looser stool consistency (Figure 4J). This putative model challenges suggestions from animal studies that the microbiome may be causally related to ASD-related traits (). Our findings also stand at odds to the proliferation of experimental interventions and early clinical trials that propose to “treat” ASD by targeting the microbiome ().
In contrast to measures of microbiome composition, ASD was robustly and significantly linked to dietary variables, irrespective of covariates (Table S3). We found (1) that significant variance in ASD diagnosis was associated with diet but not the microbiome in the b2 analysis (Figure 2), (2) reduced meat intake in the ASD group (Figure S5), and (3) reduced dietary diversity in the ASD group despite significantly higher variance in dietary diversity (Figure 4A), which is consistent with the dietetics literature () and some smaller ASD microbiome studies with dietary data ().
One rationale for the interest in the ASD microbiome is the frequent co-occurrence of gastrointestinal complaints (). In the absence of complete gastrointestinal complaint reporting, we analyzed stool consistency scores, with the caveat that it is unclear how this single-time point data reflects chronic conditions. Stool consistency appeared to be more proximal to taxonomic than dietary diversity, although we acknowledge that it is difficult to distinguish between a top-down (i.e., dietary and taxonomic diversity influencing downstream stool consistency) versus bottom-up (i.e., stool consistency being an upstream proxy) relationship. For the former, dietary restrictedness could plausibly affect gut ecology and taxonomic diversity, which in turn affects stool consistency. In relation to a bottom-up model, looser stool may indicate underlying food allergies or intolerances, which may be associated with deliberate (parental) dietary restriction to identify causative agents. Additionally, looser stool consistency reflects reduced gastrointestinal transit time and reduced colonic water reuptake (), which affects taxonomic diversity. As the narrow-sense heritability of gastrointestinal conditions that affect stool consistency (e.g., irritable bowel syndrome) are small (), environmental contributions likely predominate over genetics ().
Our results have important implications for understanding the role of the gut microbiome in ASD and other psychiatric traits. First, in relation to medical care, food selectivity among children on the autism spectrum is an important clinical concern. Food selectivity is related to avoidant/restrictive food intake disorder (ARFID; which is likely underdiagnosed despite affecting over 20% of autistic children []) and can cause nutritional deficiencies among autistic children () to the extent that hospitalization and invasive measures such as enteral feeding are required (). Our results also suggest that dietary quality is poorer in children on the spectrum (Methods S1). Given that elevated microbial diversity is robustly associated with improved health outcomes (), the association of ASD with poorer dietary quality and reduced dietary and taxonomic diversity underlines the importance of dietary and nutritional interventions in this population. Second, our results have implications for the interpretation of cause and effect in relation to diet in microbiome analyses in psychiatric conditions. There is growing interest in the contribution of diet and the microbiome to psychiatric traits (e.g., depression []), but our results emphasize the need to consider the (arguably more intuitive) impact of behavior on the microbiome (). These results add to other reports of diet driving microbiome associations with health ().
For future microbiome studies, we emphasize the importance of collecting detailed dietary data (recent examples []), particularly for ASD and other neuropsychiatric traits with plausible co-variation of diet with diagnosis or treatment. We advocate for larger sample sizes to ensure that results are robust to sampling effects and to identify subtler microbiome associations. We also recommend higher-resolution metagenomics technology and expanded databases since more granular taxonomic measures of microbiome composition were more sensitive (Table S1), gene-level ORMs explained more variance for some traits (Table S1), power to detect associations was weaker with the MetaPhlAn2/NCBI pipeline (Methods S1), and because taxonomic and functional datasets may capture complementary aspects of the microbiome (Figures S1 and S3).
In conclusion, we found negligible direct associations between ASD and the gut microbiome in contrast to strong associations with other phenotypes such as age, dietary variables, and stool consistency. Instead, we find evidence that restricted dietary diversity and poorer quality—which is associated with specific ASD features such as restrictive-repetitive behaviors—is a significant mediator of taxonomic diversity, and in turn, stool consistency. Our results are consistent with an upstream role for ASD-related behaviors and dietary preferences on the gut microbiome and are contrary to claims of the microbiome having a major (or causal) role in ASD.

 Limitations of the study

First, this study did not have a longitudinal design, so we cannot rule out microbiome contributions prior to ASD diagnosis. Second, although this is to our knowledge the largest metagenomics study of the ASD stool microbiome to date, there may nonetheless be sampling biases that require larger studies to overcome (). Third, this study used stool samples as a gut microbiome proxy, which may not accurately represent the more difficult-to-access mucosal microbiome (). Fourth, data on antibiotic intake in this cohort were not systematically collected and so could not be rigorously accounted for other than through exclusion in sensitivity analyses. Fifth, the gold-standard differential abundance analysis relied on per-feature tests that do not reflect the interactions and non-independence that occurs within an ecological or metabolic context. Finally, we await the emergence of datasets with comparable study design, consideration of confounders, and depth of phenotypic and metagenomics data for replication of these results.

Individuals with higher self-esteem had more lifelike and accurate images of themselves in their mind's eye

The Self in the Mind’s Eye: Revealing How We Truly See Ourselves Through Reverse Correlation. Lara Maister et al. Psychological Science, November 11, 2021.

Abstract: Is there a way to visually depict the image people “see” of themselves in their minds’ eyes? And if so, what can these mental images tell us about ourselves? We used a computational reverse-correlation technique to explore individuals’ mental “self-portraits” of their faces and body shapes in an unbiased, data-driven way (total N = 116 adults). Self-portraits were similar to individuals’ real faces but, importantly, also contained clues to each person’s self-reported personality traits, which were reliably detected by external observers. Furthermore, people with higher social self-esteem produced more true-to-life self-portraits. Unlike face portraits, body portraits had negligible relationships with individuals’ actual body shape, but as with faces, they were influenced by people’s beliefs and emotions. We show how psychological beliefs and attitudes about oneself bias the perceptual representation of one’s appearance and provide a unique window into the internal mental self-representation—findings that have important implications for mental health and visual culture.

Keywords: self-representation, body, appearance, reverse correlation, personality, self-face, open data

We investigated how we see ourselves in our mind’s eye by creating visual images of individual participants’ mental representations of both their faces and their body shapes in a data-driven, unconstrained way, minimizing participant biases and experimenter assumptions. This technique produced rich, holistic, and multidimensional visual representations of the face and body, which we found not only carried accurate information about physical appearance but also provided novel insights into the way in which participants’ thoughts and feelings about themselves can color their self-image.

We observed clear interactions between the physical and psychological aspects of the self: Self-portraits of both the face and the body were significantly related to higher level, more abstract self-beliefs and attitudes. In Experiment 1, representations of one’s facial appearance were influenced by beliefs regarding one’s personality traits; for example, if a participant believed that they were highly extraverted, they also held an internal representation of their face that had exaggerated stereotypically extraverted facial features compared with their true appearance. In Experiment 2, we demonstrated similar results for perceptual representations of body shape: Participants with negative attitudes toward their bodies also held visual representations of their body’s physical appearance as wider and typical peers as slimmer, compared with participants who had more positive attitudes.

Until now, there has been little investigation of the interaction between physical and psychological selves, and most of the work that has been done has focused on the bottom-up effects of multisensory and sensorimotor contingencies on higher-level psychological self-representations (Preston & Ehrsson, 2014). Our work uniquely focuses on self-representations stored in long-term memory to point to a close, interactive relationship between physical and psychological representations of the self, consistent with an interactive hierarchical model of self-representation (as proposed by Sugiura, 2013). Higher level self-beliefs and attitudes may influence the perceptual quality of the self-portraits (via a top-down modulation during the reconstruction of these images; see Kosslyn, 2005), but conversely, the perceptual features of the physical self-representation might also lead to congruent inferences about one’s self-beliefs and attitudes. Indeed, evidence from studies on social perception supports a bidirectional causal relationship for our representations of others (Dotsch et al., 2008; Todorov et al., 2015); therefore, a similar bidirectional relationship with regard to self-representations may also be likely.

Although the results with regard to the relationship between physical and psychological self-representations were similar for faces and bodies, there were interesting differences. Participants’ representations of their facial appearance were clearly related to their real facial characteristics, showing a significant level of self-specificity. Classification studies, both using human participants and simulated using a face-recognition algorithm, confirmed that identity could be correctly classified from the self-portraits at well-above-chance levels. In contrast, participants’ perceptual representations of their bodies were less related to real body characteristics (e.g., actual body size) and were more strongly influenced by affective attitudes toward the self. This is consistent with previous evidence using single-dimension measures of body parts (Ben-Tovim et al., 1990) and brings into question the wide literature attempting to characterize perceptual body representations in eating disorders in terms of overestimation or underestimation biases (for a review, see Mölbert et al., 2017). However, it will be important to replicate the findings of both experiments using larger samples of more diverse participants before drawing conclusions. The generalizability of the present study may be limited. In Experiment 1, only young Caucasian adults were tested, and therefore it is necessary to follow up with studies using a wider range of ethnicities. Furthermore, in Experiment 2, only young adult women were tested, and their body size may have been relatively homogeneous compared with the general population.

Interestingly, individual differences in objective accuracy of the facial self-portraits were correlated with self-esteem, specifically with regard to social confidence. The higher an individual’s social self-esteem, the more objectively accurate their self-portrait was. This raises interesting considerations regarding the causal role of social interaction in the development and maintenance of self-representations. Social interactions are an important source of information about our appearance, via feedback on our appearance and via social comparisons (Cash et al., 1983). Therefore, individuals with higher social self-esteem may have engaged in more frequent, close social interactions and thus received more social input about their appearance, leading to more accurate self-perception. Alternatively, individuals with more accurate perception of their appearance may also have smoother, more reciprocal, and more predictable social relationships, leading to greater social confidence. For example, having an accurate perception of one’s own attractiveness may lead to more successful romantic interactions and a lower chance of being rebuffed by someone poorly matched (see Le Lec et al., 2017), leading to higher social self-esteem. Both of these potential explanations appeal to a long-term relationship between self-esteem and the development of an accurate self-face representation. However, it is important to note that in our study, we assessed state self-esteem rather than trait self-esteem. Although it is likely that state and trait self-esteem measures are highly correlated (e.g., see Heatherton & Polivy, 1991), future research may explore whether this finding holds for more stable aspects of self-esteem.

Our results are consistent with the findings of a very recent study, which also used the reverse-correlation technique to create visual self-face representations (Moon et al., 2020). In this study, links were found between the valence of the self-face representations generated, as rated by external observers, and various self-reported traits. Self-esteem, explicit self-evaluation, and extraversion were found to be linked to more positive or pleasant-appearing self-portraits, and social anxiety was related to more negative or unpleasant-appearing self-portraits. The authors concluded that the valence of self-face representations created in this manner was able to reflect the attitude toward self. In the present study, consistent with Moon et al.’s findings, our results also showed a significant association between self-reported psychological traits and the physical features of the self-face representation. However, our results further refine our understanding of this relationship by demonstrating that self-reported personality traits were not merely linked with the perceptual valence of self-face representations, as in Moon et al.’s study, but that individual personality traits were linked to specific facial configurations in the self-portraits that were recognizable as such by independent raters.

Our study further extends existing knowledge in several key ways. First, although Moon et al. (2020) measured participants’ perceptions of self-similarity with their own self-portraits, no work has yet been done to explore the actual accuracy of self-representations or to provide a well-controlled, unbiased assessment of their links to self-beliefs and attitudes. Here, we confirmed the validity of the reverse-correlation method in self-face representation research, demonstrating that the resulting images contain enough visual information to support recognition using subjective ratings from an independent sample of raters as well as objectively using simulated experiments implementing a face-recognition algorithm. Furthermore, when exploring whether these self-face representations are influenced by higher level self-processing, we controlled for real facial features, which is crucial to avoid confounds and to provide a valid, strict test of our hypothesis. Finally, we extended our investigation to consider not only face representations but also body shapes, which enriched and generalized our findings to lend support to a broader mechanism whereby beliefs and attitudes influence perceptual body representations.

In this study, we used a combination of objective, algorithm-based techniques and subjective personality ratings from human observers to analyze both the self-portraits and real photographs. It is possible that the human ratings of the real photographs may have been informed by superficial features of the faces, such as makeup, facial hair, and grooming habits, despite the participants providing the ratings being instructed to ignore such features. However, it is important to note that the effects of this potential source of information could not explain the key results reported here. Such effects would serve only to increase the correlation found between the personality ratings of participants’ real faces and their self-reported personalities. Importantly, it could not alter the relationship between the personality ratings of the self-portraits and the self-reported personality ratings, which is key for our hypothesis, because superficial features such as facial hair and makeup were not represented in the reverse-correlation images. This issue further reiterates the importance of carefully controlling for participants’ real facial ratings, which we ensured was done in each key analysis.

Both the approach we used to produce the self-portraits and our findings are highly relevant to our understanding of clinical disorders of body image, such as anorexia nervosa and body dysmorphia. Previous studies into these disorders have normally focused on online perception of the body or have used distorted images of the patients’ own bodies as stimuli, which did not allow for unbiased measurement (Smeets et al., 1999). Our approach could be used as a unique, direct method of assessing distortions in visual memory in these patients, allowing us to reveal whether they stem from higher level self-beliefs and attitudes or even a disorder in the link between these attitudes and the physical self-representation. This approach will also allow us to compare the effects of different treatments (e.g., those targeting perceptual distortions and those targeting emotional or cognitive aspects of the disorder) as well as assess the effects of treatment across time.

In conclusion, we present a novel way to visually depict how people see themselves in their mind’s eye and, in doing so, revealed visual clues to people’s deeply held self-beliefs and attitudes. Our mental images of our own appearance are fundamental to our understanding of some of the most severe mental disorders that are clustered under the term of body-image disorders. In addition, at a time when our culture is powered by images at an unprecedented level, and our obsession with our own image is evidenced in our social media use (Storr, 2018), our approach and the novel insights presented here pave the way for future explorations, in a data-driven, unconstrained, and richly detailed way, of how we mentally see ourselves.