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Topic: fronto-parietal network

Seminar
2 seminars
ePoster
1 ePoster

In Cognition and Neuroscience

Seminar · Brain Imaging

Disentangling neural correlates of consciousness and task relevance using EEG and fMRI

Torge Dellert · Westfälischen Wilhelms-Universität (WWU) Münster

Wed, Oct 12, 2022 · 16:00 UTC

How does our brain generate consciousness, that is, the subjective experience of what it is like to see face or hear a sound? Do we become aware of a stimulus during early sensory processing or only later when information is shared in a wide-spread fronto-parietal network? Neural correlates of consciousness are typically identified by comparing brain activity when a constant stimulus (e.g., a face) is perceived versus not perceived. However, in most previous experiments, conscious perception was systematically confounded with post-perceptual processes such as decision-making and report. In thi

Seminar · Cognition

Networks for multi-sensory attention and working memory

Barbara Shinn-Cunningham · Carnegie Mellon University

Thu, May 13, 2021 · 16:00 UTC

Converging evidence from fMRI and EEG shows that audtiory spatial attention engages the same fronto-parietal network associated with visuo-spatial attention. This network is distinct from an auditory-biased processing network that includes other frontal regions; this second network is can be recruited when observers extract rhythmic information from visual inputs. We recently used a dual-task paradigm to examine whether this "division of labor" between a visuo-spatial network and an auditory-rhythmic network can be observed in a working memory paradigm. We varied the sensory modality (visual v

ePoster · Neuroscience

Human-like Behavior and Neural Representations Emerge in a Goal-driven Model of Overt Visual Search for Natural Objects

Motahareh Pourrahimi, Irina Rish, Pouya Bashivan · Bernstein Conference 2024

Visual search, the act of finding a target among multiple visually presented items, is a key paradigm in studying visual attention. While much is known about the brain networks underlying visual search, our understanding of the neural computations driving this behavior is limited, resulting in challenges in simulating such behavior in-silico. To address this gap, we developed an image-computable general-architecture visual search model (GVSM). We trained an artificial neural network to perform naturalistic visual search [Fig. A-B] using the same stimulus configuration as human psychophysics ex