#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).

In this new paper led by @drcharlotte.bsky.social and myself, we explored phonological representations in monolingual and bilingual neural networks trained on speech recognition: doi.org/10.1073/pnas...

We are hosting a blood drive at 400 St Croix, St Laurent. Giving blood can save lives. Parking is available. See the link below to choose a time in the appointment schedule. An appointment is required for administrative purposes. docs.google.com/forms/d/1w47...

Re-advertising a tool we created some time ago for rating, sorting and comparing audio samples in the browser. It can be used as a jspsych plugin for online behavioral experiments. Check the repository: github.com/pwdonh/audio_tokens 🧵

Zeus Gracia-Tabuenca, Denise Klein Enhanced efficiency in the bilingual brain through the inter-hemispheric cortico- cerebellar pathway in early second language acquisition www.nature.com/articles/s42...

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?

In a new preprint with @kleind.bsky.social, we ask two questions: (Q1) People differ in how they perceive the similarity of stimuli in their environment. How can we model the features underlying similarity judgments in arbitrary domains, while accounting for individual differences? osf.io/agpb5_v1 🧵