The paper presents the design and construction of a time-stamped multimodal dataset for reading research, including multiple time-aligned temporal signals elicited with four experimental trials of connected text reading by both child and adult readers. We present the experimental protocols, as well as the data acquisition process and the post-processing phase of data annotation/augmentation. To evaluate the potential and usefulness of a time-aligned multimodal dataset for reading research, we present a few statistical analyses showing the correlation and complementarity of multimodal time-series of reading data, as well as some results of modelling adults’ reading data by integrating different modalities. The total dataset size amounts to about 2.5 GByte in compressed format.

The lexicon is the store of words in long-term memory. Any attempt at modelling lexical competence must take issues of string storage seriously. In the present contribution, we discuss a few desiderata that any biologically-inspired computational model of the mental lexicon has to meet, and detail a multi-task evaluation protocol for their assessment. The proposed protocol is applied to a novel computational architecture for lexical storage and acquisition, the """"Topological Temporal Hebbian SOMs"""" (T2HSOMs), which are grids of topologically organised memory nodes with dedicated sensitivity to time-bound sequences of letters. These maps can provide a rigorous and testable conceptual framework within which to provide a comprehensive, multi-task protocol for testing the performance of Hebbian self-organising memories, and a comprehensive picture of the complex dynamics between lexical processing and the acquisition of morphological structure.