Does working memory capacity predict cross-modally induced failures of awareness?

Conscious Cogn

Institute of Cognitive and Team/Racket Sport Research, German Sport University Cologne, Cologne, Germany. Electronic address:

Published: January 2016

People often fail to notice unexpected stimuli when they are focusing attention on another task. Most studies of this phenomenon address visual failures induced by visual attention tasks (inattentional blindness). Yet, such failures also occur within audition (inattentional deafness), and people can even miss unexpected events in one sensory modality when focusing attention on tasks in another modality. Such cross-modal failures are revealing because they suggest the existence of a common, central resource limitation. And, such central limits might be predicted from individual differences in cognitive capacity. We replicated earlier evidence, establishing substantial rates of inattentional deafness during a visual task and inattentional blindness during an auditory task. However, neither individual working memory capacity nor the ability to perform the primary task predicted noticing in either modality. Thus, individual differences in cognitive capacity did not predict failures of awareness even though the failures presumably resulted from central resource limitations.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.concog.2015.11.010DOI Listing

Publication Analysis

Top Keywords

working memory
8
memory capacity
8
capacity predict
8
focusing attention
8
attention tasks
8
inattentional blindness
8
inattentional deafness
8
central resource
8
individual differences
8
differences cognitive
8

Similar Publications

Humans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental questions in robotics concerns this characteristic: How can linguistic compositionality be developed concomitantly with sensorimotor skills through associative learning, particularly when individuals only learn partial linguistic compositions and their corresponding sensorimotor patterns? To address this question, we propose a brain-inspired neural network model that integrates vision, proprioception, and language into a framework of predictive coding and active inference on the basis of the free-energy principle.

View Article and Find Full Text PDF

Recent research has revealed the widespread effects of emotion on cognitive functions and memory. However, the influence of emotional valence on verbal short-term memory remains largely unexplored, especially in children. This study measured the effect of emotional valence on word immediate serial recall in 4-6-year-old French children ( = 124).

View Article and Find Full Text PDF

Introduction: , a protein kinase located on human chromosome 21, plays a role in postembryonic neuronal development and degeneration. Alterations to have been consistently associated with cognitive functioning and neurodevelopmental disorders (e.g.

View Article and Find Full Text PDF

Objective: To describe the relationship between executive functions (EF) and symptom's severity, behavioral problems, and adaptive functioning in autistic preschoolers.

Method: Seventy-six autistic preschoolers (age-range: 37-72 months; SD: 8.67 months) without intellectual disability were assessed.

View Article and Find Full Text PDF

Objective: To predict the areas of snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of snail spread, so as to provide insights into investigating the trends in areas of snail spread.

Methods: Data pertaining to snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination ().

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!