Biology subjects

Bornkessel-Schlesewsky, I.

Publications and source records attributed to Bornkessel-Schlesewsky, I..

3 recordsLinked to original sources

bioRxiv2017

Sleep-Dependent Memory Consolidation and Incremental Sentence Comprehension: Computational Dependencies during Language Learning as Revealed by Neuronal Oscillations

We hypothesise a beneficial influence of sleep on the consolidation of the combinatorial mechanisms underlying incremental sentence comprehension. These predictions are grounded in recent work examining the effect of sleep on the consolidation of linguistic information, which demonstrate that sleep-dependent neurophysiological activity consolidates the meaning of novel words and simple grammatical rules. However, the sleep-dependent consolidation of sentence-level combinatorics has not been studied to date. Here, we propose that dissociable aspects of sleep neurophysiology consolidate two different types of combinatory mechanisms in human language: sequence-based (order-sensitive) and dependency-based (order-insensitive) combinatorics. The distinction between the two types of combinatorics is motivated both by cross-linguistic considerations and the neurobiological underpinnings of human language. Unifying this perspective with principles of sleep-dependent memory consolidation, we posit that a function of sleep is to optimise the consolidation of sequence-based knowledge (the when) and the establishment of semantic schemas of unordered items (the what) that underpin cross-linguistic variations in sentence comprehension. This hypothesis builds on the proposal that sleep is involved in the construction of predictive codes, a unified principle of brain function that supports incremental sentence comprehension. Finally, we discuss neurophysiological measures (EEG/MEG) that could be used to test these claims, such as the quantification of neuronal oscillations, which reflect basic mechanisms of information processing in the brain.

neuroscience↗

bioRxiv2017

Towards a reliable, automated method of individual alpha frequency (IAF) quantification

Individual alpha frequency (IAF) is a promising electrophysiological marker of interindividual differences in cognitive function. IAF has been linked with trait-like differences in information processing and general intelligence, and provides an empirical basis for the definition of individualised frequency bands. Despite its widespread application, however, there is little consensus on the optimal method for estimating IAF, and many common approaches are prone to bias and inconsistency. Here, we describe an automated strategy for deriving two of the most prevalent IAF estimators in the literature: peak alpha frequency (PAF) and centre of gravity (CoG). These indices are calculated from resting-state power spectra that have been smoothed using a Savitzky-Golay filter (SGF). We evaluate the performance characteristics of this analysis procedure in both empirical and simulated EEG datasets. Applying the SGF technique to resting-state data from n = 63 healthy adults furnished 61 PAF, and 62 CoG estimates. The statistical properties of these estimates were consistent with previous reports. Simulation analyses revealed that the SGF routine was able to reliably extract target alpha components, even under relatively noisy spectral conditions. The routine consistently outperformed a simpler method of automated peak detection that did not involve spectral smoothing. The SGF technique is fast, open-source, and available in two popular programming languages (MATLAB and Python), and thus can easily be integrated within the most popular M/EEG toolsets (EEGLAB, FieldTrip and MNE-Python). As such, it affords a convenient tool for improving the reliability and replicability of future IAF-related research.

bioinformatics↗

bioRxiv2017

Commentary on Sanborn and Chater: Posterior Modes are Attractor Basins

Sanborn and Chater [1] propose an interesting theory of cognitive and brain function based on Bayesian sampling instead of asymptotic Bayesian inference. Their proposal unifies many current observations and models and, in spite of focusing primarily on cognitive phenomena, their work provides a springboard for unifying several proposed theories of brain function. It has the potential to serve as a bridge between three influential overarching current theories of cognitive and brain function: Bayesian models, Fristons [2-4] theory of cortical responses based on the free-energy principle, and attractor-basin dynamics [5,6]. Specifically, their proposal suggests a high-level perspective on Fristons theory, which in turn proposes a sampling procedure including appropriate handling of autocorrelation as well as a plausible neurobiological implementation. In turn, these two theories together link into attractor-basin dynamics at the level of networks (via Friston) as well at the level of behavior (via the relationship between the modes of prior and posterior distributions, as discussed by Sanborn and Chater). We will argue here that, by linking Sanborn and Chaters approach to neurobiological models based on the free-energy principle on the one hand and attractor-basin dynamics on the other, the scope of their proposal can be broadened considerably. Moreover, a unified perspective along these lines provides an elegant solution to several of Sanborn and Chaters Outstanding Questions relating to the neural implementation of sampling.

neuroscience↗