Auditory Learning | Music research track
v3.0 · LISTENING HISTORY & PLEASURE
RESEARCH PROPOSAL · BEHAVIOURAL EXPERIMENT AND MODEL TEST
How does learning change the pleasure
of the same musical event?
Amaç Erdem
Independent research proposal · Listening history, controlled learning and model validation
Abstract
The same musical event may feel different after different listening histories. We will combine participant-provided listening
records with Musical Intelligence (MI) features from verified recordings to estimate individual exposure profiles. Recognition and
continuation tasks will test these estimates. A controlled experiment will then manipulate learned expectations while keeping test
audio unchanged and measure pleasure. Separate comparisons will test whether history-informed MI adds predictive value
beyond self-report, listening frequency and acoustic baselines. Exposure is treated as evidence about listening experience, not a
direct measurement of memory.
1. The unresolved question
Research connects musical pleasure to surprise and
uncertainty, but reports different directions of their
interaction.1,2 Learning can also create preferences for
unfamiliar musical structures.3 We ask how measured listening
experience and experimentally learned context relate to the
response to an unchanged musical event.
We will manipulate the probability of two target continuations
and the uncertainty of the cue that precedes them. Cue
meanings will be counterbalanced across listeners. Thus, an
identical cue–target recording can have different learned
meanings without changing its waveform.
2. Two distinct tests
H1. Contextual uncertainty changes the difference in pleasure
between less- and more-expected targets. The primary
contrast is a difference of differences, tested in both
directions.
H2 · Secondary. MI-derived exposure profiles improve held-
out prediction of familiarity, expectation and pleasure beyond
self-report and listening-history baselines. H1 tests
experimentally assigned learning; H2 tests prediction from
observed histories. Neither establishes a unique neural
mechanism.
3. What is held constant?
Within each musical family, target A is the same audio file
wherever it occurs; the same applies to B. Each target is heard
equally often overall. Four cues assign A and B probabilities of
either 1/2 or 1/8, under low or high uncertainty. Three other
continuations preserve these distributions.
This separates conditional predictability from simple target
familiarity. Cue acoustics and target identities are
counterbalanced, with sequence and repetition effects
included in analysis. Specific cue–target pair familiarity
remains linked to probability and limits interpretation.
4. Scope and participants
The planning sample is 192 adult non-musicians, plus a
separate pilot of 24. Twelve new musical families will be
tested; each listener learns one. English comprehension,
headphone screening6 and task practice are required.
Musical experience and reward sensitivity will be measured.4,5
History donation is optional; its absence does not exclude a
listener from H1. H2 uses the consenting subset with usable
records, with coverage and selection reported. Neither
dopamine nor brain activity is measured.
Figure 1. The critical comparison holds the cue–target waveform constant across assigned histories.
Different learned histories
Identical test audio
Measure pleasure
P(A | cue X) = 1/2
P(A | cue X) = 1/8
Cue X
Target A
Does experience change
the response?
Assigned mappings · schematic illustration · no results shown
7 October 2026 · Proposed design · Not preregistered · No new results
1


Auditory Learning | Music research track
v3.0 · LISTENING HISTORY & PLEASURE
5. Learning, listening and reporting
Before testing, consenting listeners provide Spotify, Apple Music or equivalent track records. Names and artists locate
candidate recordings; versions are verified before MI analysis. Frequency and recency are used only when present. A title list
supports a repertoire profile, not a timed exposure history.
An added recognition and continuation session validates the frozen profile, using separate calibration and held-out items. The controlled
experiment retains its planned 50–60 minutes; the pilot will determine the added history-task burden. Pleasure and later secondary
responses remain in separate blocks.
Check
Consent · sound · practice
→
Learn
128 listening trials
→
Test
128 pleasure trials
→
Follow up
Resolution · replay
Figure 2. Probability and uncertainty are separated by design. A and B are the focal targets; C–E are fillers. Across four cues, A and B each occur 40 times in both
exposure and test. Each test yields 80 target trials: 32 more-expected and 8 less-expected trials at each uncertainty level. Test frequencies preserve training
probabilities; cells are not artificially balanced by overplaying rare targets. Cue-to-distribution assignments vary across listeners.
Figure 3. An event-aligned outcome. Pleasure is sampled against the audio clock at 20 Hz. The fixed response window allows for reporting delay; it does not
identify the exact onset of an internal feeling. Interrupted trials and uninitialised responses are flagged.
Continuous pleasure · primary
“How much pleasure do you feel right now?”
Only this response is requested during the primary test. A stationary,
initialised slider is a valid response.
Predicted continuation · learning check
“Out of 100 occasions, how often would each ending follow this cue?”
A
B
C
D
E
Σ = 100
Four cue-specific allocations before exposure, after exposure and after
the primary test. No outcome feedback.
Perceived resolution · secondary
“Press when the musical idea feels settled.”
◇
R · mark a moment
Sixteen later trials. Closure is kept distinct from pleasure; non-response
is not automatically failed attention.
Replay preference · secondary
“Which sequence would you like to hear again?”
▷ Sequence 1
▷ Sequence 2
Eight pairs; one choice is implemented at the end. Curiosity and
pleasure may make different predictions.
a
What each cue predicts
b
The four target conditions
ASSIGNED DISTRIBUTIONS
CROSSED WITHIN EACH LISTENER
Cue
A
B
C
D
E
L1
low H
50%
12.5%
37.5%
0%
0%
L2
low H
12.5%
50%
0%
37.5%
0%
H1 high H
50%
12.5%
12.5%
12.5%
12.5%
H2 high H
12.5%
50%
12.5%
12.5%
12.5%
More expected
p = 1/2
Less expected
p = 1/8
Low uncertainty
1.41 bits
1 bit
32 target trials
3 bits
8 target trials
High uncertainty
2.00 bits
1 bit
32 target trials
3 bits
8 target trials
Cell values: target surprisal, −log₂ p
Matched marginal exposure
A = 40
B = 40
Rare targets remain rare in the test.
a
One trial · one continuous report
PLEASURE ONLY
Musical cue
Target A or B
Silent response interval
0 s
2 s
3 s
4.5 s
7.5 s
9 s
Baseline
Delayed response window
−1 to 0 s from onset
1.5 to 4.5 s from onset
ΔPleasure = mean response −mean baseline
Audio-clock alignment · 20 Hz sampling
0 · none
100 · very strong
7 October 2026 · Proposed design · Not preregistered · No new results
2


Auditory Learning | Music research track
v3.0 · LISTENING HISTORY & PLEASURE
6. What Musical Intelligence can explain
The original audio architecture is retained. R³/T³ descriptors and six existing C³ outputs form candidate representations. A separate,
proposed listener-profile layer combines these measurements with observed listening history; it is not an implemented memory
mechanism.
Full manuscript map
Shared selected inputs
F2 Prediction
F5 Emotion
F6 Reward
SPH  Spatiotemporal Prediction Hierarchy
PWUP  Precision-Weighted Uncertainty Processing
WMED  Working Memory-Entrainment Dissociation
UDP  Uncertainty-Driven Pleasure
SRP  Striatal Reward Prediction
RPEM  Reward Prediction Error in Music
Figure 4. The implemented routes used in the model comparison. Coloured routes use the manuscript's F2/F5/F6 palette; unused routes remain gray. Dashed lines
read R³ directly; solid lines pass through T³ or upstream mechanisms. The selected paths use 17 R³ features and 49 temporal keys. PWUP and WMED supply internal
values. The 28 September source audit is historical, static evidence. Counts are declarations; archived artwork values are not study results. Regional and chemical
outputs are outside the experiment.
Table 1.
Prespecified MI candidates. Incremental prediction is tested jointly; named-mechanism interpretations remain exploratory.
SPH.P1.prediction_error
Audio-derived prediction-error candidate
UDP.P0.context_assessment
Audio-derived context candidate
UDP.P2.reward_computation
Pleasure-related candidate
SRP.P0.wanting
Approach-related candidate
SRP.P1.liking
Pleasure-related candidate
RPEM.current_rpe
Reward-error candidate, not measured RPE
Audio representation + listener history. Frozen MI returns the same scores for the same recording. The proposed profile layer adds track identity,
repeat exposure, recency and feature similarity where available. Its aggregation rules are fitted in development data and frozen before held-out testing.
Profile scores are hypotheses about familiarity, not memories or autobiographical diagnoses. The current MI comparison remains offline.
G
J
K
L0
L0
L2
L2
L1
L1
SPH
SPH
PWUP
PWUP WMED
WMED
UDP
UDP
SRP
SRP
RPEM
RPEM
S1
S1
S2
S2
S3
S3
S4
S4
S5
S5
S6
S6
A
B
C
D
F
H
Audio
R³
T³
C³
9 groups · 97 features
Model readouts
Model input
44.1 kHz
mono
Consonance
7
Energy
5
Timbre
9
Change
4
Pitch/chroma
16
Rhythm/groove
10
Harmony
12
Timbre extended
20
Modulation
14
0
0.5
1
Excerpt mean
32 horizons
5.8 ms
Micro
Meso
Macro
Ultra
981 s
L0
L2
L1
24 operators
3 temporal rules
Features / cell
1
30
Past
Centred
Forward
89 mechanisms
941 outputs
131 beliefs
R
E
A
I
H
Core
App
Ant
F1 Sensory
F2 Prediction
F3 Attention
F4 Memory
F5 Emotion
F6 Reward
F7 Motor
F8 Learning
26 regions
4 proxies
A1/HG
STG
STS
IFG
dlPFC
vmPFC
OFC
ACC
SMA
PMC
AG
TP
VTA
NAcc
Caud
Amyg
Hipp
Put
MGB
Hypo
Ins
IC
AN
CN
SOC
PAG
DA
NE
OPI
5HT
7 October 2026 · Proposed design · Not preregistered · No new results
3


Auditory Learning | Music research track
v3.0 · LISTENING HISTORY & PLEASURE
7. Analysis, decisions and next steps
7.1 One primary behavioural test
For each target trial, we calculate the change in pleasure
specified in Figure 3. A mixed-effects model estimates
probability, uncertainty and their interaction, accounting for
listeners, musical families, recordings and trial order. H1 tests
the interaction at two-sided α = .05; effect sizes and 95%
intervals are primary reporting units.
Probability allocations test whether learning occurred. Main-
study listeners are not excluded for weak learning or low
pleasure. Failed learning limits interpretation of a null H1.
Resolution and replay are secondary; neither establishes causal
mediation.
7.2 Controlled-learning comparison
Four models predict the same pleasure outcome: A, R³/T³
acoustics and timing; B, A plus six MI scores; C, A plus a
sequential cue–continuation learner; D, C plus the MI scores.
B–A tests MI's audio representation; D–C tests its contribution
beyond an explicit learning account (Figure 5, figure atlas).
The learning model receives the actual ordered exposure
history. All models share matched acoustic access and fitting
budgets. Entire musical families and their listeners are withheld
together; tuning uses development folds only. The primary
predictive metric is held-out mean absolute error. Predictive
gains do not validate a named brain mechanism.
7.3 Pilot and registration gate
The pilot uses separate musical families to check learning,
discrimination, timing and burden. N = 192 is a planning figure,
not a power result. Simulation must support detection of a four-
point interaction on the 0–100 pleasure scale before the final
sample is registered. Stimuli, randomisation, exclusions,
windows, code and model versions are frozen before
recruitment.
7.4 Listening-history validation
Compare self-report alone, self-report plus available listening
counts/recency and artist/genre baselines, then the same
model plus an MI-derived exposure profile. Profile ablations use
R³, R³ + T³, and R³ + T³ + the six C³ candidates, with matched
fitting budgets. Freeze histories before testing; evaluation
items never tune the profile.
Primary history validation is recognition discrimination on held-
out familiar candidates and newly created, matched foils;
continuation prediction and pleasure are secondary outcomes.
"Absent from the export" does not mean unheard. Hold out
listeners and recording families; report matching coverage and
sensitivity to omitted tracks. Predictive improvement is not a
causal effect of past listening.
7.5 Recording identity and retention
Match title, artist, recording version, duration and identifiers
where available. Catalogues and public pages, including
YouTube, may locate candidates; uncertain, live, cover or
remastered substitutes are flagged. Extraction requires an
authorised audio source and applicable-use review; academic
purpose and deletion alone do not establish permission.
No audio is redistributed. Temporary analysis audio is deleted
after extraction and quality checks, within a planned seven-day
maximum. Retain only necessary pseudonymous features,
recording identifiers, checksums, match decisions and
extraction/deletion logs under the consented retention policy.
Experimental playback has separate source and retention
requirements.
7.6 Readiness
The history layer is proposed. Record import, matching,
permitted audio access, extraction, behavioral calibration and
its separate precision analysis remain to be completed. The app
illustrates the design and collects no history or responses.
Ethics approval and registration precede recruitment; neither
memory diagnosis nor autobiographical inference is claimed.
Selected references
1. Cheung et al. (2019). Uncertainty and surprise jointly predict musical pleasure. 
Current Biology. 10.1016/j.cub.2019.09.067.
2. Mas-Herrero & Marco-Pallarés (2025). Predictive processes shape individual
musical preferences. PNAS. 10.1073/pnas.2500494122.
3. Kathios et al. (2024; online 2023). Generating new musical preferences from
multilevel mapping of predictions to reward. Psychological Science. 
10.1177/09567976231214185.
4. Müllensiefen et al. (2014). The musicality of non-musicians. PLOS ONE. 10.1371/
journal.pone.0089642. 5. Mas-Herrero et al. (2013). Individual differences in music
reward experiences. 10.1525/mp.2013.31.2.118.
6. Woods et al. (2017). Headphone screening to facilitate web-based auditory
experiments. 10.3758/s13414-017-1361-2. 7. Albury et al. (2026). Stimulus
predictability and liking enhance auditory–motor encoding and memory for melodies.
10.1111/nyas.70310.
Model provenance. MI Systems Neuroscience manuscript, 22 September 2026;
static source audit, 28 September 2026. The original map and channels are
unchanged. The v3 listening-history specification distinguishes proposed additions
from implemented audio routes. No engine or listener-profile validation is claimed.
7 October 2026 · Proposed design · Not preregistered · No new results
4
