Content uploaded by Sean Noah
Author content
All content in this area was uploaded by Sean Noah on May 06, 2020
Content may be subject to copyright.
Neural Mechanisms of Attention to Objects
Abstract
Poster from virtual presentation – Cognitive Neuroscience Society annual conference 2020
... The main aim of the study was to investigate the effects of oxygen deficiency on the cued shifting of visual attention in space as mirrored in the cue-target time span by attention-orienting ERP deflections, namely the anterior ADAN/CNV and the parieto-occipital LDAP/TP, contralateral to the attention direction shift. Thanks to the use of a modified version of Attention Network Test 15 , we first replicated previous findings showing how ADAN/CNV and LDAP/TP are closely related to visuospatial orienting of attention 25,26,[28][29][30]34,54,55 . In addition, our findings further support the view that ADAN/CNV reflect an integration of neurocognitive processes such as visuospatial selective processing, task preparation, and motor programming and execution. ...
Our study investigated the effects of hypoxia on visuospatial attention processing during preparation for a single/double-choice motor response. ERPs were recorded in two sessions in which participants breathed either ambient-air or oxygen-impoverished air. During each session, participants performed four cue-target attention orienting and/or alerting tasks. Replicating the classic findings of valid visuospatial attentional orienting modulation, ERPs to pre-target cues elicited both an Anterior directing attention negativity (ADAN)/CNV and a posterior Late directing attention positivity (LDAP)/TP, which in ambient air were larger for attention orienting than for alerting. Hypoxia increased the amplitude of both these potentials in the spatial orienting conditions for the upper visual hemifield, while, for the lower hemifield, it increased ADAN/CNV, but decreased LDAP/TP for the same attention conditions. To these ERP changes corresponded compensatory enhanced activation of right anterior cingulate cortex, left superior parietal lobule and frontal gyrus, as well as detrimental effects of hypoxia on behavioral overt performance. Together, these findings reveal for the first time, to our knowledge, that (1) these reversed alterations of the activation patterns during the time between cue and target occur at a larger extent in hypoxia than in air, and (2) acute normobaric hypoxia alters visuospatial attention orienting shifting in space.
Perceptual expertise and attention are two important factors that enable superior object recognition and task performance. While expertise enhances knowledge and provides a holistic understanding of the environment, attention allows us to selectively focus on task-related information and suppress distraction. It has been suggested that attention operates differently in experts and in novices and much remains unknown. This study investigates the relationship between perceptual expertise and attention using convolutional neural networks (CNNs) which are shown to be good models of primate visual pathways. Two CNN models were trained to become experts in either face or scene recognition, and the effect of attention on performance was evaluated in tasks involving complex stimuli, such as merged images containing superimposed faces and scenes. The goal was to explore how feature-based attention (FBA) influences recognition within and outside the models' domain of expertise. Results showed that each model performed better in its area of expertise and that FBA further enhanced task performance only within the domain of expertise, increasing performance by up to 35% in scene recognition and 15% in face recognition. However, attention had reduced or negative effects when applied outside the model's expertise domain. Neural unit-level analysis revealed that expertise leads to stronger neuronal tuning towards category-specific features and sharper tuning curves, reflected in greater representational dissimilarity between targets and distractors, which, according to the biased competition model of attention, reduces competition and leads to enhanced performance. These findings highlight the critical role of neural tuning at the single neuron level as well as at the level of multivariate neural representations in distinguishing the effects of attention in experts and in novices and demonstrate that CNNs can be used fruitfully as computational models for addressing neuroscience questions not practical with the empirical methods.
ResearchGate has not been able to resolve any references for this publication.