A simple model of the attentional blink and its modulation by mental training.
Nadav Amir, Naftali Tishby, Israel Nelken
PLoS Computational Biology August 2022 DOI: 10.1371/journal.pcbi.1010398 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key points | Proposes a dynamical-systems model of the attentional blink in which attentional load is the response of a dynamical system to incoming impulse signals and non-task-related mental activity is additive noise modulated by meditation. The model offers a parsimonious framework relating behavioral performance, evoked brain potentials, and meditation training through the concept of reduced "mental noise." |
Abstract
The attentional blink (AB) effect is the reduced probability of reporting a second target (T2) that appears shortly after a first one (T1) within a rapidly presented sequence of distractors. The AB effect has been shown to be reduced following intensive mental training in the form of mindfulness meditation, with a corresponding reduction in T1-evoked P3b brain potentials. However, the mechanisms underlying these effects remain unknown. We propose a dynamical-systems model of the AB, in which attentional load is described as the response of a dynamical system to incoming impulse signals. Non-task related mental activity is represented by additive noise modulated by meditation. The model provides a parsimonious computational framework relating behavioral performance, evoked brain potentials and training through the concept of reduced mental noise.
Citations in the library
Cites 11
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and 6 more in the library