The Brain Computes Dynamic Facial Movements for Emotion Categorization Using a Third Pathway DOI Creative Commons
Yuening Yan, Jiayu Zhan,

Oliver Garrod

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 8, 2024

Abstract Recent theories suggest a new brain pathway dedicated to processing social movement is involved in understanding emotions from biological motion, beyond the well-known ventral and dorsal pathways. However, how this functions as network that computes dynamic motion signals for perceptual behavior unchartered. Here, we used generative model of important facial movements participants (N = 10) categorized “happy,” “surprise,” “fear,” “anger,” “disgust,” “sad” while recorded their MEG responses. Using representational interaction measures (between features, t source, behavioral responses), reveal per participant functional extending occipital cortex superior temporal gyrus. Its sources selectively represent, communicate compose disambiguate emotion categorization behavior, swiftly filters out task-irrelevant identity-defining face shape features. Our findings complex categorize individual participants.

Language: Английский

Tasks and their role in visual neuroscience DOI Creative Commons
Kendrick Kay, Kathryn Bonnen, Rachel N. Denison

et al.

Neuron, Journal Year: 2023, Volume and Issue: 111(11), P. 1697 - 1713

Published: April 10, 2023

Language: Английский

Citations

30

Using deep neural networks to disentangle visual and semantic information in human perception and memory DOI
Adva Shoham, Idan Grosbard, Or Patashnik

et al.

Nature Human Behaviour, Journal Year: 2024, Volume and Issue: 8(4), P. 702 - 717

Published: Feb. 8, 2024

Language: Английский

Citations

10

A large and rich EEG dataset for modeling human visual object recognition DOI
Alessandro T. Gifford, Kshitij Dwivedi, Gemma Roig

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 264, P. 119754 - 119754

Published: Nov. 15, 2022

Language: Английский

Citations

38

Improved region proposal network for enhanced few-shot object detection DOI
Zeyu Shangguan, Mohammad Rostami

Neural Networks, Journal Year: 2024, Volume and Issue: 180, P. 106699 - 106699

Published: Sept. 3, 2024

Language: Английский

Citations

6

Advancing Naturalistic Affective Science with Deep Learning DOI
Chujun Lin,

Landry S. Bulls,

Lindsey J. Tepfer

et al.

Affective Science, Journal Year: 2023, Volume and Issue: 4(3), P. 550 - 562

Published: Aug. 25, 2023

Language: Английский

Citations

14

Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning DOI Open Access
Xiaoyong Zhang, Weiwei Jia, Yuman Sun

et al.

Forests, Journal Year: 2023, Volume and Issue: 14(3), P. 483 - 483

Published: Feb. 27, 2023

Due to the complexity and difficulty of forest resource ground surveys, remote-sensing-based methods assess resources effectively plan management measures are particularly important, as they provide effective means explore changes in over long time periods. The objective this study was monitor spatiotemporal trends wood carbon stocks standing forests southeastern Xiaoxinganling Mountains by using Landsat remote sensing data collected between 1989 2021. Various indicators for predicting were constructed based on Google Earth Engine (GEE) platform. We initially used a multiple linear regression model, deep neural network model convolutional exploring stocks. Finally, we chose because it provided more robust predictions stock pixel-by-pixel basis hence mapping spatial distribution variable. Savitzky–Golay filter smoothing applied predicted annual average observe overall trend, autocorrelation analysis conducted. Sen’s slope Mann–Kendall statistical test It found that 59.5% area showed an increasing while 40.5% decreasing trend past 33 years, future development plotted combining results with Hurst exponent.

Language: Английский

Citations

12

Pre-frontal cortex guides dimension-reducing transformations in the occipito-ventral pathway for categorization behaviors DOI Creative Commons
Yaocong Duan, Jiayu Zhan, Joachim Groß

et al.

Current Biology, Journal Year: 2024, Volume and Issue: 34(15), P. 3392 - 3404.e5

Published: July 18, 2024

Highlights•Occipital cortex represents both task-relevant and irrelevant features before 120 ms•Only advance to the temporal region•During 121–150 ms, occipital representations reduce lower-dimensional manifolds•These manifolds then transform into from 161 350 msSummaryTo interpret our surroundings, brain uses a visual categorization process. Current theories models suggest that this process comprises hierarchy of different computations transforms complex, high-dimensional inputs (i.e., manifolds) in support multiple behaviors. Here, we tested hypothesis by analyzing these transformations reflected dynamic MEG source activity while individual participants actively categorized same stimuli according tasks: face expression, gender, pedestrian vehicle type. Results reveal three transformation stages guided pre-frontal cortex. At stage 1 (high-dimensional, 50–120 ms), sources represent task-irrelevant stimulus features; higher ventral/dorsal regions, whereas halt at occipital-temporal junction. 2 (121–150 feature manifolds, which underlying behavior over 3 (161–350 ms). Our findings shed light on how brain's network mechanisms specific behaviors.Graphical abstract

Language: Английский

Citations

4

High-level Visual Processing in the Lateral Geniculate Nucleus Revealed using Goal-driven Deep Learning DOI

Mai Gamal,

Seif Eldawlatly

Journal of Neuroscience Methods, Journal Year: 2025, Volume and Issue: unknown, P. 110429 - 110429

Published: March 1, 2025

Language: Английский

Citations

0

Computational reconstruction of mental representations using human behavior DOI Creative Commons
Laurent Caplette, Nicholas B. Turk‐Browne

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 17, 2024

Revealing how the mind represents information is a longstanding goal of cognitive science. However, there currently no framework for reconstructing broad range mental representations that humans possess. Here, we ask participants to indicate what they perceive in images made random visual features deep neural network. We then infer associations between semantic their responses and images. This allows us reconstruct multiple concepts, both those supplied by other concepts extrapolated from same space. validate these reconstructions separate further generalize our approach predict behavior new stimuli task. Finally, individual observers enables large-scale investigation conceptual representations.

Language: Английский

Citations

3

Effective ensemble based intrusion detection and energy efficient load balancing using sunflower optimization in distributed wireless sensor network DOI

V. S. Prasanth,

A. Mary Posonia,

A. Parveen Akhther

et al.

Multimedia Systems, Journal Year: 2024, Volume and Issue: 30(4)

Published: July 29, 2024

Language: Английский

Citations

3