1. Implicit learning of melodic structure
Martin Rohrmeier, Patrick Rebuschat
University of Cambridge

2. Implicit learning Definition Reber (1965)
Acquisition of a complex, rule-based system
Without intention to learn (incidental learning)
Without awareness of the learning outcome
Characteristics (Reber 1989, Frensch & Rünger 2003)
Abstract
Tacit knowledge
Independent of IQ and age
Unconscious
Root process for interaction with environment

3. Implicit learning Major research paradigms
Artificial Grammar learning & sequence learning
Reber 1965
Nissen & Bullemer 1987
Dienes et al. 1995
Control of complex, dynamic systems
Berry & Broadbent 1984; Broadbent, Fitzgerald & Broadbent 1986
Language acquisition
Rebuschat & Willams 2006
Relevance for music perception

4. Experiment
Investigating musical structure acquisition under implicit learning experimental paradigm
Learning phase with distraction task
Testing phase
Basic framework:
Acquisition of a rule based sequential, monophonic musical system
Modelled as finite state grammar (standard paradigm in implicit learning experiments)

5. Experiment 1 Finite-State Grammar 13 states, 9 tone pairs 33 sequences
(between 8 and 30 tones)
Learning phase:
17 sequences
presented 3 times in randomised order
Testing phase:
33 grammatical sequences (17 old, 16 new)
33 ungrammatical sequences

6. Experiment 1 Ungrammatical stimuli Applying different error types
Error type 1: random order of lexical elements
Error type 2: randomised sequence, bigrams intact
Error type 3: anchor positions correct, middle randomised
Error type 4: upper and lower paths switched
Error type 5: grammatical stimulus with one order exchange

7. Experiment 1 Experimental setup overview Learning phase Testing phase
3 blocks of 17 grammatical stimuli in randomised order total: 51 sequences
Tone counting distraction task
Testing phase
66 stimuli: 33 grammatical
17 old-grammatical (from learning phase)
16 new-grammatical
33 ungrammatical
Forced choice familiarity and confidence judgments

8. Results experimental group control group musicians 72,00% 62,04%
nonmusicians 69,97% 61,16%
average 70,98% 61,59%
n=59 subjects (22 experimental, 37 control group)
Learning effect (both conditions)
No difference between musicians and nonmusicians

9. Results Performance across stimulus types
Experimental group outperforms control group in all conditions
No difference between new-gram and old-gram for control group (confirms control condition)

10. Results Performance across error types
Experimental group above chance for Error types 1-4
Control group above chance for Error type 1 and 2
 Implications for experimental design

11. Results Control group performs significantly above chance
Performance increases over time (displayed at every 6 responses)
Online-learning effect

12. Experiment 2 Learning effect: surface or underlying structure?
Experiment 2: Cross-lexical transfer paradigm
Different lexicon, but same underlying grammar in learning and testing phase
Lexicon B: 9 different diatonic tone pairs
Experimental
Control
Cross-lexical
Learning phase
Lexicon A -
Lexicon B
Testing phase

13. Results N=13 subjects for cross-lexical condition
No sig. difference between cross-lexical and control group for overall performance, across all stimulus and error types

14. Results Learning not enhanced by structural transfer
Cross-lexical group performance replicates control group
Conclusion
Learning may be based on surface features

15. Conclusions Experiment 1: Finite state-grammar, tone sequencees
Evidence for implicit learning found
Strong online learning effect
Experiment 2: Cross-lexical transfer
No effect found
No performance difference for musicians – nonmusicians
Performance varies across error types
Implications for experimental design
Acknowledgements
This study has been carried out under the funding of the AHRC, and the Microsoft European PhD Scholarship Programme.