Why do you like the music that you like?
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May 10th, 2022

The Science Behind Dopr

Why do you like the music that you like? This is the question we set out to answer in our music neuroscience research. The results show that Familiar Surprise plays a big role.

We have spent years investigating what it is about music that leads to a pleasurable dopamine response in the brain. A major effect that we have found that contributes to the formation of music preference is what we call Familiar Surprise. This effect is robust, and importantly, it can be quantitatively measured and modeled.

High-performing popular music songs feature increased levels of harmonic surprise (the use of statistically rare chords) in sections preceding choruses, and lower levels of harmonic surprise as tension is released in the choruses themselves. We coined a term for this effect between song sections: 'contrastive surprise'.
High-performing popular music songs feature increased levels of harmonic surprise (the use of statistically rare chords) in sections preceding choruses, and lower levels of harmonic surprise as tension is released in the choruses themselves. We coined a term for this effect between song sections: 'contrastive surprise'.

Creativity can be understood in terms of the ideal interaction between order and chaos. All great works of art seem to have found that sweet spot between tradition and innovation, between appropriateness and novelty, between established structure and risk-taking... between familiarity and surpriseFamiliar Surprise, by the definition of its two components, is a metric that optimizes for pushing boundaries and breaking the rules in innovative ways, but within a context that makes it work.

How Music Leads to a Pleasure Response in the Brain

In our award-winning 2017 publication, we were the first to model and quantify optimal patterns of tension and release in music, and we showed that they predict the dopamine pleasure response of music enjoyment.

When you listen to music, you are perceiving it through bottom-up, unconscious (implicit) and top-down, conscious (explicit) brain systems, which are shaped by your culture and experiences.

Through statistical regularities in your environment and culture, you develop unconscious, rigid expectations about how sounds go together. Although there is some debate, music cognition experts generally agree that your music expectations are set at a fairly young age (by the time you turn 7 years old). When patterns within music deviate from these rigid expectations, your brain attends to this ‘surprise’, and it works to learn from this new information, updating a prior schema. Dopamine is not only the pleasure molecule. It is also the learning molecule, and when done right, optimal surprise leads to music preference and increased dopamine release.

Not all surprises are equal, though. Through information theory, we have been able to calculate and model the magnitude of surprise, and found ideal ranges in specific contexts.

When you develop expectations, and when you get surprised as they are deviated, this process happens largely under the surface. It’s the chaos, the innovation, the novelty, the risk-taking... all processed without conscious thought. And because the expectations you developed as a child are so rigid, the effect of surprise leading to pleasure works even if you’ve heard the song many times before. In fact, it can be even more pronounced with familiar songs. How is this possible?

Music can be understood quite simply as an auditory stream of information transmitted through sound waves. It has a unique power, however, to tap into your emotional core. Music is a universal language that can emote and express at a deeply primal level of your consciousness. The emotions music conjures are often explicitly linked to memories of significant events, people, adventures, revelations, triumphs, or tragedies in your life.

Music is an associational cue for some of your strongest memories. Nostalgia and familiarity with specific songs can also lead to music preference and increased dopamine release. Unlike surprise, your processing of familiarity happens consciously. It’s the order, the tradition, the appropriateness, the established structure... all processed in full awareness.

You tend to get the most pleasure from music when it is statistically surprising, yet explicitly familiar. After discovering this effect in our research, we came up with a name for this neuroscientific principle of music enjoyment: Familiar Surprise.

How can Music be Surprising and Familiar?

Familiar Surprise seems like an oxymoron. It makes sense, however, when considering that two distinct types of information are processed by two separate memory systems in your brain:

System 1 - Bottom-up, implicit. Procedural memory. Responds unconsciously to novel or unusual deviations from rigid, set patterns you’ve learned across many examples. (Surprise)

System 2 - Top-Down, explicit. Declarative memory. Responds consciously to things you explicitly recognize and have experienced before. (Familiar)

The implicit system responds to 'surprise'. The explicit system responds to 'familiarity'.
The implicit system responds to 'surprise'. The explicit system responds to 'familiarity'.

Familiarity and surprise are not, despite how it might seem, on opposite ends of a spectrum. They are actually on two different spectrums.

This is because they are processed by the two different memory systems of your brain. These systems are related to different modes of cognition, perception, and decision-making, as detailed by Nobel-prize winning cognitive scientist Daniel Kahneman and his colleague Amos Tversky, in their groundbreaking research on decision making in economics.

Kahneman's book, Thinking, Fast and Slow, summarizes a large body of work covering two distinct cognitive modes and systems. System 1 (unconscious, implicit, fast) and System 2 (conscious, explicit, slow) are the same memory systems that underlie music preference.
Kahneman's book, Thinking, Fast and Slow, summarizes a large body of work covering two distinct cognitive modes and systems. System 1 (unconscious, implicit, fast) and System 2 (conscious, explicit, slow) are the same memory systems that underlie music preference.

At Dopr, our ongoing investigations into music preference formation build upon and leverage previous research from the fields of decision-making, aesthetics, music cognition and psychology, music information retrieval, and neuroscience. This interdisciplinary approach allows us to have a firm understanding of precisely how Familiar Surprise is perceived in the brain and to quantify how it leads to music enjoyment.

The brain likes explicit familiarity - you know it. (System 2)

You tend to like songs that are explicitly familiar to you. This might explain why, at a concert, people head to the bathroom or to get a drink when the lead singer says, “This next song is from the new album we’re working on…” You want to hear what you've already heard before.

This is the 'familiar' part of Familiar Surprise at work. The explicit, conscious memory system in your brain responds favorably to songs you've explicitly heard before.

The brain likes implicit surprise - it's interesting (System 1)

Even among the songs that are explicitly familiar, you tend to like songs that feature statistically improbable, or surprising, elements that violate the rigid rules you learned as a child. Clear examples of this is would be a key change when the third chorus drops after the bridge, or a false cadence that refuses to resolve how you expect it to end. You want to hear something implicitly novel.

This is the 'surprise' part of Familiar Surprise at work. The implicit, unconscious memory system in your brain responds favorably to statistically unusual features in a song.

How Familiar Surprise Leads to Dopamine

Dopamine is released when you know something interesting, because it's like you've learned something valuable.

The combination of optimal familiarity and surprise in music, as processed by your two different memory systems, plays a little biological trick on your brain, a sort of 'hijacking of evolution'.

Your brain rewards you when you know something interesting, because it is as if you have successfully learned something valuable. Like with Pavlov’s dog salivating when hearing the bell, music featuring optimal levels of Familiar Surprise triggers the 'learning molecule' - dopamine - to rush through your brain.

Who hasn't salivated during a peak music experience? When you’re listening to music, you’re not so different from Pavlov's dogs. Both humans and canines implicitly learn associations between stimulus (bell/tension) and response (food/release - leading to pleasure). This is classical conditioning.
Who hasn't salivated during a peak music experience? When you’re listening to music, you’re not so different from Pavlov's dogs. Both humans and canines implicitly learn associations between stimulus (bell/tension) and response (food/release - leading to pleasure). This is classical conditioning.

In fact, to take things to a 'meta level', if you already intuitively knew about this interesting effect before reading the above explanation, you might be experiencing a little dopamine rush right now!

Modeling Optimal Familiar Surprise in Music

By measuring and modeling Familiar Surprise—the robust effect that reliably leads to music enjoyment—it is possible to predict the future enjoyment of a given song for a given audience.

How is this possible?

So it’s pretty straightforward to measure familiarity. You're familiar with songs that you've heard before. Previous research has shown that dopamine reward centers of the brain are activated more when listeners are presented with a song they have heard several times before, than with a song they have never heard.

But what’s the best way to measure surprise? And what is the best way to determine the optimal level of surprise for music enjoyment? Our patented methods use information theory to quantify surprise and model it within music. In one of our earlier neuroscience studies, we analyzed harmonic surprise using this approach, focusing on chord rarity / entropy in Billboard-charting songs (from 1958 to 1991). This study established that harmonic surprise predicts music preference.

The original methods are presented in our 2017 Frontiers in Human Neuroscience publication, “A Statistical Analysis of the Relationship between Harmonic Surprise and Preference in Popular Music”.

This paper presenting our early research was voted by Frontiers in Neuroscience to be a 'Top 10 paper of 2017' out of 3,350 articles.
This paper presenting our early research was voted by Frontiers in Neuroscience to be a 'Top 10 paper of 2017' out of 3,350 articles.

The optimal level of Familiar Surprise is not constant. Since that initial study, we have published work on the over-time effects of harmonic surprise on music preference (Miles et al., 2021). In that study, Dopr analyzed the chords of over 6,000 Billboard-charting songs released between 2000 and 2019. Not only did the effect of surprise predicting preference hold on more recent music, but over time, there was actually an increase in the gap between surprise in top-performing songs and bottom-performing songs. This ‘inflationary-surprise effect' is key to understanding and modeling what future successful music might look like in terms of how it will take advantage of Familiar Surprise.

“Baseline harmonic expectations that were developed through listening to the music of “yesterday” are violated in the music of “today,” leading to preference. Then, once the music of “today” provides the baseline expectations for the music of “tomorrow,” more pronounced violations—and with them, higher harmonic surprise values—become associated with preference formation. We call this phenomenon the Inflationary-Surprise Hypothesis.” (Miles, et al., 2021)

Over 20 years, top-performing songs were shown to increase their level of harmonic surprise at a faster rate than bottom-performing songs.
Over 20 years, top-performing songs were shown to increase their level of harmonic surprise at a faster rate than bottom-performing songs.

The desensitization to reward from drug use or gambling, over time, is a relevant analogy here. Many pleasurable sensations from drugs and gambling operate on the same dopaminergic brain systems as music pleasure. Similar to tolerance built up in the case of drug use and gambling, your brain can require more and more surprise in music over time to elicit the same amount of reward.

In these previous publications, our focus has been on Harmonic Familiar Surprise, focusing exclusively on the chord structure of popular music. At Dopr, we have worked to expand our models to include Familiar Surprise data for other features of music as well:

  • Melody
  • Rhythm
  • Timbre
  • Texture
  • Dynamics

Dopr’s analyses have gone far beyond those documented in these neuroscience publications. Dopr has analyzed millions of songs. We have gained countless insights into how genre, artist size, and other factors influence which Familiar Surprise features are most predictive of preference for a given track.

How does Dopr use neuroscience?

At Dopr, our music NFT predictive analytics are fueled by our founders’ findings of how music preference is formed in the brain, by incorporating Familiar Surprise data into our forecasting models. Our patented methods and resulting data help Dopr accurately appraise the future value of music, and allow for models that dynamically evolve and learn over time, aligning with ever-changing musical tastes.

Alongside many other data points, Familiar Surprise allows Dopr to accurately forecast the future performance of released and unreleased music, at scale.

Dopr's models incorporate our patented neuro-derived data along with many other inputs, which are constantly updating.
Dopr's models incorporate our patented neuro-derived data along with many other inputs, which are constantly updating.

In order to onboard the next 100 million music NFT collectors, there need to be better valuation and discovery tools to make M-NFT collecting easy.

Dopr is a sector-focused and platform-agnostic aggregator of the music NFT landscape - a single access point for collectors to discover, track, and value their music NFTs.

In an earlier article, we detailed the process of ’Forecasting a Music NFT Drop’. We believe it is important for music NFT collectors to have a better understanding of the underlying financial value of the NFTs they are purchasing. If song royalties are to be a core utility of certain NFTs, there needs to be more transparency and information available about the corresponding streaming economics for a given drop. Dopr provides these predictive analytics. We offer a 5-year stream and earnings forecast for music NFTs, as well as a tokenized recoupment calculator for token holders.

In Dopr’s closed beta, some NFT forecasts showed high recoupment times, sometimes in excess of 100 years in our best estimation.

Although financial incentives are only one of many layers of value for music NFTs, the current high valuations are problematic and restrict the growth of the space. Collectors are telling us these forecasts have altered their purchasing decisions. Pricing being driven by hype, speculation, and short-term trends in secondary markets is not sustainable. Everything is so new. There is a lot of excitement by those of us who have already bought in. Unfortunately, many people remain skeptical and associate NFTs with potential scams, rather than acknowledging their potential to support artists.

High NFT valuations and the lack of price discovery tools are major points of friction for new collectors.

Dopr provides 5-year stream and earnings forecasts, as well as a recoupment calculator for music NFTs.
Dopr provides 5-year stream and earnings forecasts, as well as a recoupment calculator for music NFTs.

Dopr will make it easy for collectors to find creators and NFTs that interest them. To start, we provide collectors with performance forecasts for any royalty-bearing music NFT. These drops have been sporadic so far, but drop rates have been increasing on platforms like Royal and Decent over the past few weeks. In the coming years, we expect millions of music creators to offer royalty-bearing NFTs to fans and collectors when uploading their music via distributors, like Distrokid, Tunecore, Ditto (who started Opulous), and others. As the space grows, there will be an even greater need for predictive analytics and automated valuation tools because the sheer volume of music will be unwieldy, as it is in web2.

To achieve the best version of music’s web3 future, Dopr will shine a light on the hidden gems in the long tail of music that would otherwise go unnoticed. Using insights from our understanding of how the brain forms music preference, we will make discovery far better in web3 than it ever was in web2, connecting artists and collectors in a way that drives real, tangible support to artists.

In web3, we can harness findings from neuroscience, and leverage technology to have the most direct and tangible impact on artists’ careers, by helping bring about an economic paradigm shift in the music industry.

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