1

Central issues

• For both experimental and modeling field it is important to start from theory

• A common theory (and a common lexicon) will help building the interface between modelers and experimentalist– Features mean different things for different people! – Make your research question as precise as possible

• Important step taken in this workshop!

2

Central issues

• Experimentalists– Need more data, more experiments (to test theories)

• Modelers– Need better models which can predict future data

• Ideally, models make predictions, which can be tested experimentally, leading to improved models…

3

Central issues

• Theory of language acquisition– Bottom-up: often aimed for in speech research

• Hard to determine relevant features, categories, etc.• Often unsuccessful: lacking insight into which data/features are

important for the language learner

– Top-down: example Petra Hendriks and colleagues• Many assumption regarding relevant features, categories, etc.

– Or both?– Integration of information from various domains

(Feldman)

• Is ‘good enough’ sufficient?4

Central issues

• Input– Too rich/not rich enough?– How much input is needed?– How are the relevant cues in the input selected

by the learner?– What filters/constraints does the language

learner use?

5

6

Bold claims about early language acquisition

Kuhl et al. (2008)

Recurrent issues

• Experiments– Only trust experimental results that are

replicable!• As a community we are responsible for replicating

studies and informing the field about non-replicable results

• More communication within the field

7

Recurrent issues

• Models need to be built on reliable experimental results/claims– Communication between modelers and

experimentalists is essential

• Models need to make sure that experimentalists know what good models are– Communication between modelers and

experimentalists is essential

8

Central issues

• Development– How can it be described?– What triggers development?– Longitudinal (perception) studies are rare

9

Recurrent issues

• Experiments– Do the dependent measures (often looking time)

reflect underlying knowledge and processes sufficiently?

• Are time course measures more insightful?• Discrimination is not categorization!

– Selection of stimulus materials is crucial• Sufficiently informed by linguistic theory?• Comparison of stimuli materials in similar paradigms to

gain insight into which cues in the materials help/hinder learning

10

Recurrent issues

• Models need to be cognitively plausible• Different types of models:

– Models are not just fancy calculators (although they can be), – but preferably also make predictions and provide insight into

mechanisms of acquisition

• Models give insight into development– Of individual children?– Or language learners in general?

• Urgent need to compare models!• Do modelers of acquisition of syntax encounter the same

issues as those modeling early speech perception?11

Other issues

• Cross-linguistic studies (non-Indoeuropean)• Variation

– In input– Between learners (individual differences)

• Learning in the lab vs. learning in real life– Feedback– Role of social interaction

• Abstraction/Generalization• Relation perception/production?

12

13

Share data and

For language resources and technology infrastructure !

14

Which conferences could be meeting places?

…

15Thanks to all participants! Let’s meet again soon!