Music · Cognition · Computation
Amaç Erdem
My work began with music.
At seventeen, I began exploring piano on a computer keyboard.
That interest led me to study composition and write for instruments and electronics.
What is music?
This question has stayed with me through composing and listening.
Music theory, art, and philosophy offer different starting points.
How does the brain process music?
My modelling work draws on research in cognition and neuroscience.
The papers and books below are work by other researchers, included here as references.
A theory of cortical responses
A free-energy account of perception as approximate Bayesian inference, using prediction errors to describe cortical belief updating.
Anatomically distinct dopamine release during anticipation and experience of peak emotion to music
[11C]raclopride PET study of dopamine release during music-evoked pleasure, distinguishing anticipation from peak emotional experience.
Dopamine modulates the reward experiences elicited by music
A pharmacological study examining dopamine's role in musical reward, using pleasure ratings and physiological responses.
Music in the brain
A review of music perception through predictive processing, spanning melodic, harmonic, and rhythmic listening.
Pleasurable music activates cerebral μ-opioid receptors
A [11C]carfentanil PET-fMRI study of μ-opioid activity during pleasurable music listening.
Tonal consonance and critical bandwidth
Local consonance and the relationship between timbre and scale
A model of consonance based on interactions between spectral components, relating timbre to tuning and scale.
Timbral effects on consonance disentangle psychoacoustic mechanisms
A study of how timbre changes consonance judgments, comparing responses to pure and complex tones.
Uncertainty and surprise jointly predict musical pleasure
A study of how uncertainty and surprise jointly relate to musical pleasure.
Auditory expectation: the information dynamics of music perception
Brain correlates of music-evoked emotions
A review of brain systems involved in music-evoked emotion, including reward, memory, and arousal.
When the brain plays music: auditory-motor interactions in music perception and production
The rewards of music listening: response and physiological connectivity of the mesolimbic system
An fMRI study of mesolimbic activity and connectivity during pleasurable music listening.
Interactions between the nucleus accumbens and auditory cortices predict music reward value
On Repeat: How Music Plays the Mind
A monograph on repetition, expectation, and the experience of listening to music.
Music, Language, and the Brain
A comparison of music and language, including rhythm, syntax, and shared processing resources.
Indifference to dissonance in native Amazonians reveals cultural variation in music perception
A cross-cultural study of consonance preferences among Tsimané and comparison groups, examining cultural variation in music perception.
Subcortical sources dominate the neuroelectric auditory frequency-following response
The statistical structure of human speech sounds predicts musical universals
Simultaneous consonance in music perception and composition
A study of simultaneous consonance and its relevance to music perception and composition.
Musical sound features & auditory cortical processing
From reading to modelling.
A computational approach to music.
I began developing Musical Intelligence in 2025, building on my work in composition and bringing together audio analysis, cognition, and computation.
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Computation & Architecture
- Python tools for audio analysis and modelling
- Modular interfaces, dataclasses, and type hints
- Separate layers for audio, temporal context, and cognition
- Git versioning and reproducible runs
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Signal Processing & Psychoacoustics
- DSP — STFT, MFCC, spectral flux, F0 estimation
- Critical bandwidth models (Plomp-Levelt, Sethares)
- Roughness, dissonance, harmonic-product spectrum
- Audio feature vectors for perceptual modelling
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Cognition & Modelling
- Literature-informed models of perception and learning
- Bayesian belief updates and model assumptions
- Predictive processing in music (Vuust et al., 2022)
- Model variables distinguished from measured brain activity
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Neurochemical Modelling
- Literature on dopamine, noradrenaline, opioids, and serotonin
- Anticipation and reward in music (Salimpoor et al., 2011)
- Pharmacological research as a reference (Ferreri et al., 2019)
- Computational representations of neurochemical processes
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Bayesian Statistics & Evaluation
- Precision-weighted Bayesian belief update
- FDR correction for multiple comparisons
- Permutation tests and null comparisons
- Dataset comparisons and reproducibility checks
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Research Methods
- Explicit modelling assumptions and source references
- Documented hypotheses and evaluation criteria
- Testable predictions and model limitations
- Distinguishing interpretation from evidence
Musical Intelligence — exploring musical listening.
Musical Intelligence brings together audio analysis, cognitive models, and computation in an interactive system. It brings together work in music, cognition, and computation. Its outputs are model estimates, not direct measurements of brain activity or neurochemistry.
Background
I studied Composition & Conducting at Dokuz Eylül University, attended Berklee College of Music as a special student, and completed an M.M. in Composition at Boston University as a Fulbright Scholar. In 2024, I completed a professional certificate in AI and deep learning through MIT xPRO. Musical Intelligence builds on this background in music and computation.
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2014–2019
B.A. — Composition & Conducting
Dokuz Eylül University · İzmir State Conservatory
Theory, orchestration, and contemporary composition.
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2022–2024
M.M. — Composition
Boston University · College of Fine Arts
Advisor: Prof. Joshua Fineberg · spectral & contemporary composition.
Fulbright Scholar -
2024
Professional Certificate — AI & Deep Learning
MIT xPRO
Professional training in neural networks and deep learning.
I welcome conversations and collaborations in composition, cognition, audio analysis, and computation, with room for shared questions and different approaches.