Predicting Preference for Art Through Low- and High-Level Features

Post by Leanna Kalinowski

What's the science?

We are surrounded by visual art, from classic paintings in a museum to photographs on social media. While navigating through this art-filled world, we constantly make judgements about whether we like or dislike a particular piece. However, the process by which we perceive art is unclear. Do prior experiences with certain features of the piece of art shape our preferences, or are the visual properties of an image more important? The answer is that both are likely important. Computational methods have previously been applied to tease apart how we develop different preferences. However, in the case of visual art, this process is much more challenging due to the visual complexity and variation of some art. This week in Nature Human Behavior, Iigaya and colleagues developed and tested a computational framework to investigate how preferences for visual art are formed.

How did they do it?

The authors first divided the properties of an image into two categories: ‘low-level’ and ‘high-level’. ‘Low-level’ (i.e., bottom-up) features included those derived from an image’s statistics and visual properties, such as hue and brightness, while ‘high-level’ (i.e., top-down) features included those that require human judgement, such as realism and emotion. Participants were asked to report how much they liked various paintings and photographs on a four-point scale, and the authors used these ratings to determine the extent to which they could predict art preferences. They also applied machine learning: a deep convolutional neural network (DCNN) that had been trained for object recognition to predict the pattern by which these visual features emerge when the brain processes visual images.

What did they find?

By engineering a linear feature summation (LFS) model, the authors first observed that visual preference for art can be predicted through a combination of low- and high-level features. This model predicted preferences for both paintings and photographs, suggesting that the features used for driving visual preferences may be universal across different mediums. They also found that their model may represent a biologically plausible computation, as their DCNN model mirrored the results from the LFS model above. Specifically, when the authors did not specify certain features for the DCNN as they did with the LFS model, they found that the DCNN model could learn to predict all of those features on its own.

What's the impact?

The findings here uncover a mechanism through which art preferences can be predicted, shedding light on how these preferences are formed in the brain. These tools have the potential to influence the arts and media industry by predicting which works of art may be more likely to be preferred, and could be extended to predict judgements and perceptions beyond art.   

Iigaya et al. Aesthetic preference for art can be predicted from a mixture of how- and high-level visual features. Nature Human Behaviour (2021) Access the original scientific publication here.

Attention in the Age of Social Media

Post by Elisa Guma

The advent of the internet

The Internet is the most widespread and rapidly adopted technology in the history of humankind. With the advent of broadband Wi-Fi and smartphone technologies, we have constant access to the internet. This has rapidly changed the way we work, search for and access information, consume media and entertainment, and engage socially. Indeed, we currently live in a media-saturated world, using it not only for entertainment purposes such as listening to music or watching movies but also for communicating with peers. Connecting with family and friends across the globe can help people feel more connected in times of isolation, such as in the current global pandemic. However, access to this endless stream of communication and connection may be changing the way we think and absorb information and may also impact our mental health.

Attention and the brain

Attention is the behavioural and cognitive process by which we selectively concentrate on a discrete aspect of information while ignoring other information. Focusing our attention recruits brain regions such as the prefrontal and visual cortices, thalamic and midbrain nuclei. It can alternatively be thought of as an allocation of limited cognitive processing resources to a particular topic or task. The ability to achieve selective and sustained attention, free from distractions, is critical to our ability to complete tasks, learn new information, and engage socially with others. Once attention is engaged, we remain focused until some external environmental or internal state change occurs that triggers a shift. The constant flow of information and notifications the internet brings may interfere with our ability to maintain sustained concentration on other tasks. Social media is designed to be highly engaging in an attempt to keep us browsing for as long as possible. Furthermore, content that fails to gain our attention is quickly drowned out in a sea of incoming information, while information that does capture our attention is amplified or proliferated.

How does social media impact our attention?

One of the first studies investigating the effect of social media on attention found that heavy social media use may increase people’s susceptibility to distraction from irrelevant stimuli. Neuroimaging studies have shown that those who engage in heavy media multitasking perform poorer in distracted attention tasks while exhibiting greater activity in prefrontal regions during those tasks. These findings suggest that these individuals may require higher cognitive effort to maintain concentration when faced with distractor stimuli. Similarly, heavy internet usage and multitasking have been associated with decreased grey matter volume in brain regions involved in decision-making. Comorbidities between internet use disorders and attention deficit hyperactivity disorders have also been reported, suggesting that there may be strong links between excessive media usage and disorders of inattention.

Although research in this area is growing, the findings are still mixed. Some studies have confirmed these negative effects on attention, whereas others report that increased media multitasking may even be linked to increased performance in some aspects of cognition. It is possible that the internet allows for “cognitive off-loading” of certain cognitively demanding tasks, such as semantic memory retrieval, which may free up our cognitive resources for use in other tasks. It is difficult to disentangle whether heavy social media use leads to higher distractibility, or whether pre-existing differences in neural activity make some individuals more susceptible to distraction. What we do know, is that social media and technology offer easy-to-reach distractions, which may interfere with our ability to focus.

Social media use and mental health

Engaging with social media apps taps into more than just our brain’s attention network. It requires social reward processing, emotion-based processing, regulation, and thinking about the thoughts and feelings of others. Numerous studies have reported that positive attention on social media in the form of likes on Instagram, Twitter, and Facebook may cause our brains to release dopamine and activate reward circuits in the brain. Furthermore, reduced grey matter volume in regions involved in emotional regulation and social cognition, such as the amygdala and ventral striatum have also been associated with excessive social media use. Given the tight link between social media use and the brain’s reward system, there is potential for abuse or dependence.

Heavy social media use may also have important implications for psychological well-being. While social media use may provide an opportunity for social integration with similar interest groups, access to support groups, and motivation for a healthy lifestyle, it may also have more toxic effects on users’ mental health. Increased feelings of depression, anxiety, poor body image, and loneliness have all been reported following social media use.

Why are adolescents more susceptible?

Adolescence is a developmental stage in which the brain is undergoing extensive structural and functional remodeling. Impulse and cognitive control, as well as social reward and emotional processing, are not yet developed. This can lead adolescents to engage in more reward-seeking or risk-taking behaviours, and be more susceptible to distracting highly engaging social media content. As discussed above though, it is unclear whether social media use may influence our long-term ability to sustain attention, or whether it is merely a source of temporary distraction. Of greater importance for this age group may be the effects of social media on mental health. Adolescence is a sensitive developmental window in which neuropsychiatric disorders are most likely to emerge. Parental influence decreases, while the influence of peers and the need for peer acceptance increases. Managing social media use may be one helpful way to avoid overuse and some potential negative outcomes. Setting boundaries with social media use, such as reducing time spent on social networks, and establishing some no-phone zones in the home, or no-phone times (e.g. before bed) can be an effective way to prevent overuse. Gaining a better understanding of how adolescents process media content and peers’ feedback will be of critical importance for understanding how best to avoid negative impacts on mental health. 

What’s the takeaway?

With social media becoming a more and more prominent part of our everyday lives, there are many risks to be aware of, including social media overuse. Furthermore, heavy social media use may have an impact on how our brain functions. Although the extent to which social media use impacts our cognition and attention is still unclear, it certainly provides an additional source of distraction. Of greater concern, however, are the effects it may have on our mental health, particularly in more vulnerable age groups, such as adolescents. More research will be needed to better understand the impact that social media has in our lives, and how we can navigate its use in the future. 

References

Crone EA, Konijn EA. Media use and brain development during adolescence. Nature Communications (2018) 9(588). https://doi.org/10.1038/s41467-018-03126-x

Frith JA, Torous J, Frith J. Exploring the impact of internet use on memory and attention processes. International Journal of Environmental Research and Public Health (2020). 17 (9481); doi:10.3390/ijerph17249481

Baumgartner SE, van der Schuur WE, Lemmens JS, & Poel F.  The Relationship Between Media Multitasking and Attention Problems in Adolescents: Results of Two Longitudinal Studies. Human Communication Research (2017). 44 (1), 3-30. https://academic.oup.com/hcr/article-abstract/44/1/3/4760433

Ra CK, Cho J, Stone MD, De La Cedra J, Goldenson NI, Moroney E, Tung I, Lee SS, Leventhal AM. Association of Digital Media Use With Subsequent Symptoms of Attention-Deficit/Hyperactivity Disorder Among Adolescents. JAMA (2018). 320(3):255-263. doi:10.1001/jama.2018.8931

Cohen R.A. (2014) Neural Mechanisms of Attention. In: The Neuropsychology of Attention. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-72639-7_10

The Role of Self-Talk in Sports

Post by Shireen Parimoo

What is self-talk?

Self-talk refers to our inner dialogue, consisting of statements we say to ourselves, either in our mind or out loud. Most of us use self-talk in our lives, like giving ourselves a pep talk before a job interview or a date. This practice helps us appraise and regulate our thoughts and emotions and can help reduce stress and anxiety in certain situations. Athletes also engage in self-talk during training and in competition, saying things like, “keep going” and “focus on form”, or repeating mantras like, “I’m feeling strong”. In sports, self-talk can serve two functions:

1. Boosting an athlete’s motivation and encouraging them to put in more effort.

2. Directing attention to the relevant actions that the athlete must execute (“pass the ball”, “go faster”) to improve the quality of their movement or performance. This is thought to be more beneficial for sports requiring fine motor control, such as basketball, rather than gross motor control, such as running. 

Types of self-talk

Self-talk varies along many dimensions. For example, self-talk can be positive (“I’m ready”, “I feel good”), negative (“I’m too tired to continue”), verbally articulated, internal, a statement (“I’m a winner”), or a question (“Who’s a winner?”), to name a few. 

There are three broad categories of self-talk:

1. Self-expression: self-talk can often be a spontaneous expression of our thoughts and feelings in the moment (“this is so exciting!” or “it is so hot”). 

2. Interpretive: we can use our inner voice to explicitly think through emotion or experience (“I’m so nervous, but I always feel this way before a game” or “I’m so nervous, maybe I shouldn’t have signed up for another race.”). This is important because negative thoughts can be evaluated differently by different people and therefore have a different impact on performance.

3. Self-regulatory: this is often used intentionally to guide behavior (“check your form”) or self-motivate (“Keep going, don’t stop now”).

The type of self-talk that someone uses depends on traits like motivation, self-esteem, skill level, as well as on the context, like competition level (e.g., self-talk during practice vs during a game), the type of sport, and its culture (individual or team-based), prior experience (e.g., have they ever won a game vs have they consistently won in the past?), and the audience or where the sport is played (e.g., home vs away game). 

Dual process theory and self-talk

Dual process theory proposes that two systems – System 1 and System 2 – underlie many thoughts and behaviors. Where System 1 is engaged in a rapid, automatic, and effortless manner, System 2 is slower, more effortful, intentional, and conscious in nature. 

Under the dual process framework, System 1 might give rise to the spontaneous, self-expressive form of self-talk, making the athlete more aware of their feelings in the moment. System 2 might then be engaged to interpret the content of their self-talk based on any of the several factors identified above, such as their self-esteem and context. In addition, since self-talk arising from System 2 processing is more intentional, it can be used to regulate subsequent behavior. 

Is self-talk effective?

A large body of research, as well as individual experiences of athletes and coaches, shows that self-talk is effective for improving athletic performance. The effectiveness of self-talk on performance depends on situational factors, the athlete, and the features of self-talk itself. For instance, some researchers suggest that instructional self-talk might be more beneficial during training because it helps the athlete finesse their skill, whereas motivational self-talk might boost performance in a competitive setting. Self-talk may primarily act by reducing performance-related anxiety among athletes, particularly when it is positive. Moreover, self-talk has been linked to greater enjoyment, self-confidence, and higher perceived self-competence. 

There is an active area of research geared toward identifying the most effective forms of self-talk. Though research shows that positive self-talk is most effective for performance, some individuals might improve more than others through negative self-talk due to individual differences in motivation and self-esteem. Additionally, in situations where the content of self-talk conflicts with the context or with an individual’s beliefs about themselves, the self-talk might have no effect or even negatively impact performance. For example, a runner might have a positive mantra that they repeat, like “I’ve got this”. However, if they are neck and neck with another runner, they might begin to doubt whether they trained adequately enough to outcompete them. If this doubt begins to conflict with their mantra, they might start to fall behind, and rather than boosting motivation to keep going, the mantra is rendered ineffective. An athlete who does not start doubting their training might instead use the same mantra to push themselves harder to win the race. 

Many self-talk intervention studies train athletes to use self-talk that engages System 2, the slower but more intentional type of self-talk. As described above, some forms of self-talk might rely more on System 2, but it may be difficult for someone to interpret or regulate their self-talk if this slower, more intentional system is maximally engaged by other thoughts. For example, a runner who is tired and doubting their training during a critical moment in a race might start to over-analyze their training and what they could have done better leading up to the race, leaving few cognitive resources to re-appraise the current situation. As a result, they might not be able to engage in motivational self-talk that would otherwise help push through the fatigue. Thus, a number of factors determine whether practicing self-talk has a beneficial effect on performance in any given situation. 

References

Hardy, J. (2006). Speaking clearly: A critical review of the self-talk literature. Psychology of Sport and Exercise, 7(1), 81-97. https://doi.org/10.1016/j.psychsport.2005.04.002

Hatzigeorgiadis, A., Zourbanos, N., Galanis, E., & Theodorakis, Y. (2011). Self-talk and sports performance: A meta-analysis. Perspectives on Psychological Science, 6(4), 348-356. https://doi.org/10.1177/1745691611413136

Hatzigeorgiadis, A., Zourbanos, N., Galanis, E., & Theodorakis, Y. (2014). Self-talk and competitive sport performance. Journal of Applied Sport Psychology, 26(1), 82-95. https://doi.org/10.1080/10413200.2013.790095

McCormick, A., Meijen, C., & Marcora, S. (2017). Effects of a motivational self-talk intervention for endurance athletes completing an ultramarathon. The Sport Psychologist, 32(1), 42-50. https://doi.org/10.1123/tsp.2017-0018

Park, S-H., Lim, B-S., & Lim, S-T. (2020). The effects of self-talk on shooting athletes’ motivation. Journal of Sports Science and Medicine, 19(3), 517-521. PMID: 32874104

Van Raalte, J. L. & Vincent, A. (2017, March 29). Self-Talk in Sport and Performance. Oxford Research Encyclopedia of Psychology. https://doi.org/10.1093/acrefore/9780190236557.013.157

Van Raalte, J. L., Vincent, A., & Brewer, B. W. (2016a). Self-talk interventions for athletes: A theoretically grounded approach. Journal of Sport Psychology in Action, 8(3), 141-151. https://doi.org/10.1080/21520704.2016.1233921

Van Raalte, J. L., Vincent, A., & Brewer, B. W. (2016b). Self-talk: Review and sport-specific model. Psychology of Sport and Exercise, 22, 139-148. https://doi.org/10.1016/j.psychsport.2015.08.004

Walter, N., Nikoleizig, L., & Alfermann, D. (2019). Effects of self-talk training on competitive anxiety, self-efficacy, volitional skills, and performance: An intervention study with junior sub-elite athletes. Sports, 7(6), 148. https://doi.org/10.3390/sports7060148