Neuromatch
Published April 28, 2025 | Version 1.0
Publication Open

Investigating Perceptual Thresholds for Faces and Houses Using A General Linear Model Approach

Authors/Creators

  • 1. Physics Department, Boğaziçi University, Istanbul, Turkey
  • 2. Biomedical Engineering, Islamic Azad University Science and Research Branch, Tehran, Iran
  • 3. School of Psychology, University of East London, London, UK
  • 4. Centre for Human Neuroimaging, University College London, London, United Kingdom

Description

This study investigates how perceptual thresholds for recognising faces and houses differ under varying levels of visual noise, using a General Linear Model (GLM) framework. We examine how individual electrode locations and their corresponding anatomical regions contribute to perceptual thresholds by analysing electrocorticography (ECoG) data. Channel-specific analyses reveal localised brain activity highly correlated with task performance, offering new perspectives on neural selectivity and perceptual robustness. Additionally, we compare GLM approach implementations using Python and MATLAB to evaluate differences in efficiency, accuracy, and analytical insights across computational platforms. This comparison highlights how analytical tools can influence the interpretation of neural data.

Files

ISP2025-LittleZebras- Manuscript.pdf

Files (1.8 MB)

Name Size Download all
md5:8e5ed2215fbacc8c2f72b95da2010d4f
392.7 kB Preview Download
md5:f7002983aa0edccbf9b555c319161961
815.1 kB Preview Download
md5:0eb6bd71e5a736735762b8e0e7de7bae
7.1 kB Download
md5:f77ddc26db4519d5326c836f120289c7
536.9 kB Preview Download

Additional details

Related works

Is referenced by
Presentation: https://youtu.be/3VfaU_FzmWw?feature=shared (URL)

Funding

Neuromatch
Impact Scholars Program

References

  • Pitcher, D., Walsh, V., & Duchaine, B. (2011). The role of the occipital face area in the cortical face perception network. Experimental Brain Research, 209(4), 481–493. https://doi.org/10.1007/s00221-011-2579-1
  • Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17(11), 4302–4311. https://doi.org/10.1523/JNEUROSCI.17-11-04302.1997
  • Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2002). Human neural systems for face recognition and social communication. Biological Psychiatry, 51(1), 59–67. https://doi.org/10.1016/S0006-3223(01)01330-0
  • Zheng, X., & Liu, J. (2021). Neural mechanism of noise affecting face recognition. NeuroImage, 224, Article 117414. https://doi.org/10.1016/j.neuroimage.2020.117414
  • Hassaballah, M., & Aly, S. (2015). Face recognition: Challenges, achievements, and future directions. IET Computer Vision, 9(4), 614–626. https://doi.org/10.1049/iet-cvi.2014.0084
  • Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2002). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425–2430. https://doi.org/10.1126/science.1063736
  • Hermes, D., Miller, K. J., Wandell, B. A., & Winawer, J. (2019). Human visual cortical gamma reflects natural image structure. NeuroImage, 200, 635–643. https://doi.org/10.1016/j.neuroimage.2019.06.017
  • Lee, Y., Anaki, D., Grady, C. L., & Moscovitch, M. (2012). Neural correlates of temporal integration in face recognition: An fMRI study. NeuroImage, 61(4), 1287–1299. https://doi.org/10.1016/j.neuroimage.2012.02.073
  • Miller, K. J., Hermes, D., Pestilli, F., Wig, G. S., & Ojemann, J. G. (2017). Face percept formation in human ventral temporal cortex. Journal of Neurophysiology, 118(5), 2614–2627. https://doi.org/10.1152/jn.00113.2017
  • Quinn, A. J., Atkinson, L. Z., Gohil, C., Kohl, O., Pitt, J., Zich, C., Nobre, A. C., & Woolrich, M. W. (2017). The GLM-spectrum: A multilevel framework for spectrum analysis with covariate and confound modeling. NeuroImage, 153, 250–267. https://doi.org/10.1016/j.neuroimage.2017.03.021
  • Miller, K. J., Hermes, D., Witthoft, N., Rao, R. P., & Ojemann, J. G. (2015). The physiology of perception in human temporal lobe is specialized for contextual novelty. Journal of Neurophysiology, 114(1), 256–263. https://doi.org/10.1152/jn.00118.2015
  • Miller, K. J., Schalk, G., Hermes, D., Ojemann, J. G., & Rao, R. P. N. (2016). Spontaneous decoding of the timing and content of human object perception from cortical surface recordings reveals complementary information in the event-related potential and broadband spectral change. PLoS Computational Biology, 12(1), Article e1004660. https://doi.org/10.1371/journal.pcbi.1004660
  • Miller, K. J., Hermes, D., Pestilli, F., Wig, G. S., & Ojemann, J. G. (2017). Face percept formation in human ventral temporal cortex. Journal of Neurophysiology, 118(5), 2614–2627. https://doi.org/10.1152/jn.00113.2017
  • Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., & Frackowiak, R.S.J. (1995). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2(4), 189–210. https://doi.org/10.1002/hbm.460020402
  • Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4. https://doi.org/10.3389/neuro.06.004.2008