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? …

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