- Better measuring individual capacities in "Statistical learning" (hereafter SL): Standard test for measuring have under-examined psychometric problems and are not well suited for measuring individual's SL capacities. Together with Dr. Noam Siegelman I devised and evaluated novel improved tests in both the auditory and visual modality. We discovered an important problem with standard tasks that use verbal syllable material, which is that the prior knowledge participants have from their own language has a substantial impact on how they perform our tasks, leading to unreliable measures. We designed a task with nonverbal material that does not suffer from the same problem and has desirable psychometric characteristics.
- Online measures of regularity learning: We investigated the scope of the functionality of self-paced learning paradigms in capturing learning as it takes place. In these tasks participants advance a steam of stimuli by themselves and their reaction times provide a measure of learning. We found that self-paced paradigms are well suited also for neuroimaging work. Results of the self-paced task with embedded quadruplet patterns revealed important insights regarding the exact computations of SL. An outstanding controversy is whether learners extract global clusters or whether they are tuned to simple local transitions between elements. We provided evidence for inter-individual mixture, with different reliance on the two types of computations across individuals.
- Neuroimaging work: Using electro-encephalography we were able to demonstrate significantly increased power for frequencies in the beta range in the window leading up to presentations of unpredictable shapes at statistical pattern transitions relative to that same window prior to predictable shapes (i.e. shapes within a statistical pattern). Looking at the evolution of this spectral signature we found that it emerges with increased exposure. These findings hold the promise of converging on an online brain signature we aimed for and provide important insights regarding the role of prediction in SL.
- Subcomponents of SL: One working hypothesis we started the project with splits the variance in SL performance into 1) variance related to efficiency in encoding the representations of inputs, and 2) variance related to computational efficiency of detecting regularities. Processing and learning were seen as independent steps. Our data challenged this hypothesis and we entertained the novel hypothesis that one single computational principle, rate of information, can explain SL performance. We now have several datasets in support for this hypothesis, showing that rate of information does a very good job in accounting for SL outcomes.
- Work that examined the link between individual capacities in regularity learning and literacy skills: in a first line of work we tested the impact of time constraints in encoding and increasingly difficult regularities (affecting the learning component) on the performance on subjects with reading difficulties and control subjects with good literacy skills. We found overall lower performance for the group with reading difficulty but no specific impairment in one of the components. A second line of work, which I was invited to collaborate on with Dr. Elpida Pavlidou, explored SL capacities in young typical readers across three different modalities. The obtained data revealed a positive correlation between visual SL performance and phonological awareness but no significant correlations between SL and reading measures.
- Preliminary results of a training study reveal that the sensitivity to regularities might be trainable, we were however faced with methodological challenges that prevent clear conclusions and demand further investigation of this issue.
Results have been disseminated in 3 published manuscripts and the following presentations at the following places/conferences/workshops:
Hamburg University, September 2019, Hamburg, Germany
Interdisciplinary Advances in Statistical Learning, June 27-29 2019, San Sebastian
McDonnell Foundation Workshop: The Future of Statistical Learning, August 2018, Québec city
Annual conference of the Society for the Neurobiology of Language, August 2018, Québec city
Xiamen University, July 10th 2018, Xiamen
First international Workshop on Predictive Processing, June 2018, San Sebastian
Psychonomic Society annual meeting, Nov 2017, Vancouver
McDonnell Foundation Workshop: The Future of Statistical Learning, November 2017, New Haven
Conference of the European Society for Cognitive Psychology, Sept 2017,Potsdam
