#PsychSciSky @theneuro.bsky.social

The development of parallel phonological representations varied based on the timing of language exposure, showing how earlier-learned languages shape the acquisition of subsequent ones.

We show that multiple phonological systems are organized through parallel representations, preserving the unique aspects of each language while maintaining shared articulatory features (here e.g. manner of articulation and consonant voicing).

Item-level fitting, on the other hand, provides an estimate of the information present in the data that is not accounted for by prior knowledge and remains to be explained. We can use the fitted models for exploration and hypothesis generation.

We demonstrate the approach on a dataset collected using a speaker odd-one-out task, where we show that people’s first language can shape how they perceive continuous and categorical aspects of accents.

(Q2) However, we show in simulations how to incorporate design matrices in the model fit. This allows us to quantify how well participants' odd-one-out choices can be explained using prior knowledge (here: stimulus categories).

(Q1) In this task, human raters have to choose the odd-one-out in a triplet of 3 stimuli. In this simulated example two raters disagree on 1 triplet. Our approach assumes a common feature space that describes stimuli, but raters can weigh features differently in their choices.

(Q2) Sometimes the features underlying people’s similarity judgments are not obvious. How can we combine prior knowledge about stimulus domains with data-driven approaches to gain new insights?