Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Novel domain formation reveals proto-architecture in inferotemporal cortex


Primate inferotemporal cortex is subdivided into domains for biologically important categories, such as faces, bodies and scenes, as well as domains for culturally entrained categories, such as text or buildings. These domains are in stereotyped locations in most humans and monkeys. To ask what determines the locations of such domains, we intensively trained seven juvenile monkeys to recognize three distinct sets of shapes. After training, the monkeys developed regions that were selectively responsive to each trained set. The location of each specialization was similar across monkeys, despite differences in training order. This indicates that the location of training effects does not depend on function or expertise, but rather on some kind of proto-organization. We explore the possibility that this proto-organization is retinotopic or shape-based.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Symbol training.
Figure 2: Effects of training on functional organization of IT.
Figure 3: Overall organization of selective responsiveness to trained symbol sets and face patches in seven monkeys.
Figure 4: Maps of eccentricity bias, curvature and category selectivity in three monkeys.
Figure 5: Relationship between eccentricity, spatial frequency, curvature and category.
Figure 6: Average z-scores for eccentricity (peripheral-minus-central) and curvature (straight-minus-curvy) contrasts for monkey face, cartoon face, Helvetica and Tetris ROIs combined across monkeys Y1, Y2, B1 and R2; values are mean across monkeys ±s.e.m.


  1. 1

    Halgren, E. et al. Location of human face-selective cortex with respect to retinotopic areas. Hum. Brain Mapp. 7, 29–37 (1999).

    CAS  PubMed  Google Scholar 

  2. 2

    Tsao, D.Y., Freiwald, W.A., Knutsen, T.A., Mandeville, J.B. & Tootell, R.B. Faces and objects in macaque cerebral cortex. Nat. Neurosci. 6, 989–995 (2003).

    CAS  PubMed  Google Scholar 

  3. 3

    Shapiro, P.N. & Penod, S.D. Meta-analysis of facial identification studies. Psychol. Bull. 100, 139–156 (1986).

    Google Scholar 

  4. 4

    Gogtay, N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl. Acad. Sci. USA 101, 8174–8179 (2004).

    CAS  PubMed  Google Scholar 

  5. 5

    Golarai, G., Liberman, A., Yoon, J.M. & Grill-Spector, K. Differential development of the ventral visual cortex extends through adolescence. Front. Hum. Neurosci. 3, 80 (2010).

    PubMed  PubMed Central  Google Scholar 

  6. 6

    Cohen, L. & Dehaene, S. Specialization within the ventral stream: the case for the visual word form area. Neuroimage 22, 466–476 (2004).

    PubMed  Google Scholar 

  7. 7

    Gauthier, I., Skudlarski, P., Gore, J.C. & Anderson, A.W. Expertise for cars and birds recruits brain areas involved in face recognition. Nat. Neurosci. 3, 191–197 (2000).

    CAS  PubMed  Google Scholar 

  8. 8

    Srihasam, K., Mandeville, J.B., Morocz, I.A., Sullivan, K.J. & Livingstone, M.S. Behavioral and anatomical consequences of early versus late symbol training in macaques. Neuron 73, 608–619 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Dehaene, S. & Cohen, L. Cultural recycling of cortical maps. Neuron 56, 384–398 (2007).

    CAS  PubMed  Google Scholar 

  10. 10

    Changizi, M.A., Zhang, Q., Ye, H. & Shimojo, S. The structures of letters and symbols throughout human history are selected to match those found in objects in natural scenes. Am. Nat. 167, E117–E139 (2006).

    PubMed  Google Scholar 

  11. 11

    Mahon, B.Z. & Caramazza, A. What drives the organization of object knowledge in the brain? Trends Cogn. Sci. 15, 97–103 (2011).

    PubMed  PubMed Central  Google Scholar 

  12. 12

    Quartz, S.R. & Sejnowski, T.J. The neural basis of cognitive development: a constructivist manifesto. Behav. Brain Sci. 20, 537–556; discussion 556–596 (1997).

    CAS  PubMed  Google Scholar 

  13. 13

    Gauthier, I. & Tarr, M.J. Becoming a “Greeble” expert: exploring mechanisms for face recognition. Vision Res. 37, 1673–1682 (1997).

    CAS  PubMed  Google Scholar 

  14. 14

    Freiwald, W.A., Tsao, D.Y. & Livingstone, M.S. A face feature space in the macaque temporal lobe. Nat. Neurosci. 12, 1187–1196 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Kornblith, S., Cheng, X., Ohayon, S. & Tsao, D.Y. A network for scene processing in the macaque temporal lobe. Neuron 79, 766–781 (2013).

    CAS  PubMed  Google Scholar 

  16. 16

    Bell, A.H., Hadj-Bouziane, F., Frihauf, J.B., Tootell, R.B. & Ungerleider, L.G. Object representations in the temporal cortex of monkeys and humans as revealed by functional magnetic resonance imaging. J. Neurophysiol. 101, 688–700 (2009).

    PubMed  Google Scholar 

  17. 17

    Hasson, U., Harel, M., Levy, I. & Malach, R. Large-scale mirror-symmetry organization of human occipito-temporal object areas. Neuron 37, 1027–1041 (2003).

    CAS  PubMed  Google Scholar 

  18. 18

    Hasson, U., Levy, I., Behrmann, M., Hendler, T. & Malach, R. Eccentricity bias as an organizing principle for human high-order object areas. Neuron 34, 479–490 (2002).

    CAS  PubMed  Google Scholar 

  19. 19

    Kanwisher, N., McDermott, J. & Chun, M.M. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17, 4302–4311 (1997).

    CAS  Google Scholar 

  20. 20

    Kravitz, D.J., Saleem, K.S., Baker, C.I., Ungerleider, L.G. & Mishkin, M. The ventral visual pathway: an expanded neural framework for the processing of object quality. Trends Cogn. Sci. 17, 26–49 (2013).

    PubMed  PubMed Central  Google Scholar 

  21. 21

    Malach, R., Levy, I. & Hasson, U. The topography of high-order human object areas. Trends Cogn. Sci. 6, 176–184 (2002).

    PubMed  Google Scholar 

  22. 22

    Pinsk, M.A., Desimone, K., Moore, T., Gross, C.G. & Kastner, S. Representations of faces and body parts in macaque temporal cortex: A functional MRI study. Proc. Natl. Acad. Sci. USA 102, 6996–7001 (2005).

    CAS  PubMed  Google Scholar 

  23. 23

    Tsao, D.Y., Freiwald, W.A., Tootell, R.B. & Livingstone, M.S. A cortical region consisting entirely of face-selective cells. Science 311, 670–674 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Nasr, S. et al. Scene-selective cortical regions in human and nonhuman primates. J. Neurosci. 31, 13771–13785 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Janssens, T., Zhu, Q., Popivanov, I.D. & Vanduffel, W. Probabilistic and single-subject retinotopic maps reveal the topographic organization of face patches in the macaque cortex. J. Neurosci. 34, 10156–10167 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Lafer-Sousa, R. & Conway, B.R. Parallel, multi-stage processing of colors, faces and shapes in macaque inferior temporal cortex. Nat. Neurosci. 16, 1870–1878 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Op de Beeck, H.P. & Baker, C.I. The neural basis of visual object learning. Trends Cogn. Sci. 14, 22–30 (2010).

    PubMed  Google Scholar 

  28. 28

    Nasr, S. & Tootell, R.B. A cardinal orientation bias in scene-selective visual cortex. J. Neurosci. 32, 14921–14926 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Op de Beeck, H.P., Deutsch, J.A., Vanduffel, W., Kanwisher, N.G. & DiCarlo, J.J. A stable topography of selectivity for unfamiliar shape classes in monkey inferior temporal cortex. Cereb. Cortex 18, 1676–1694 (2008).

    PubMed  Google Scholar 

  30. 30

    Tootell, R.B., Silverman, M.S., Hamilton, S.L., Switkes, E. & De Valois, R.L. Functional anatomy of macaque striate cortex. V. Spatial frequency. J. Neurosci. 8, 1610–1624 (1988).

    CAS  PubMed  Google Scholar 

  31. 31

    Heeger, D.J., Simoncelli, E.P. & Movshon, J.A. Computational models of cortical visual processing. Proc. Natl. Acad. Sci. USA 93, 623–627 (1996).

    CAS  PubMed  Google Scholar 

  32. 32

    Levy, I., Hasson, U., Avidan, G., Hendler, T. & Malach, R. Center-periphery organization of human object areas. Nat. Neurosci. 4, 533–539 (2001).

    CAS  PubMed  Google Scholar 

  33. 33

    Kourtzi, Z. & DiCarlo, J.J. Learning and neural plasticity in visual object recognition. Curr. Opin. Neurobiol. 16, 152–158 (2006).

    CAS  PubMed  Google Scholar 

  34. 34

    Baker, C.I. et al. Visual word processing and experiential origins of functional selectivity in human extrastriate cortex. Proc. Natl. Acad. Sci. USA 104, 9087–9092 (2007).

    CAS  PubMed  Google Scholar 

  35. 35

    Szwed, M. et al. Specialization for written words over objects in the visual cortex. Neuroimage 56, 330–344 (2011).

    PubMed  Google Scholar 

  36. 36

    Moore, M.W., Durisko, C., Perfetti, C.A. & Fiez, J.A. Learning to read an alphabet of human faces produces left-lateralized training effects in the fusiform gyrus. J. Cogn. Neurosci. 26, 896–913 (2014).

    PubMed  Google Scholar 

  37. 37

    Dehaene, S. et al. How learning to read changes the cortical networks for vision and language. Science 330, 1359–1364 (2010).

    CAS  PubMed  Google Scholar 

  38. 38

    Wertheim, T. Über die indirekte Sehschärfe. Z. Psychol. Physiol. Sinnesorgane 7, 172–187 (1894).

    Google Scholar 

  39. 39

    Carlson, E.T., Rasquinha, R.J., Zhang, K. & Connor, C.E. A sparse object coding scheme in area V4. Curr. Biol. 21, 288–293 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Hubel, D.H. & Wiesel, T.N. Uniformity of monkey striate cortex: a parallel relationship between field size, scatter, and magnification factor. J. Comp. Neurol. 158, 295–305 (1974).

    CAS  PubMed  Google Scholar 

  41. 41

    Hubel, D.H. & Wiesel, T.N. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. (Lond.) 195, 215–243 (1968).

    CAS  Google Scholar 

  42. 42

    Livingstone, M.S. & Hubel, D.H. Anatomy and physiology of a color system in the primate visual cortex. J. Neurosci. 4, 309–356 (1984).

    CAS  PubMed  Google Scholar 

  43. 43

    Hubel, D.H. & Livingstone, M.S. Segregation of form, color, and stereopsis in primate area 18. J. Neurosci. 7, 3378–3415 (1987).

    CAS  PubMed  Google Scholar 

  44. 44

    Hubel, D.H. & Wiesel, T.N. Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J. Neurophysiol. 28, 229–289 (1965).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Konkle, T. & Oliva, A. A real-world size organization of object responses in occipitotemporal cortex. Neuron 74, 1114–1124 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Van Essen, D.C. Windows on the brain: the emerging role of atlases and databases in neuroscience. Curr. Opin. Neurobiol. 12, 574–579 (2002).

    CAS  PubMed  Google Scholar 

  47. 47

    Van Essen, D.C. et al. An integrated software suite for surface-based analyses of cerebral cortex. J. Am. Med. Inform. Assoc. 8, 443–459 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Felleman, D.J. & Van Essen, D.C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Van Essen, D.C. & Dierker, D.L. Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 56, 209–225 (2007).

    CAS  PubMed  Google Scholar 

  50. 50

    Srihasam, K., Sullivan, K., Savage, T. & Livingstone, M.S. Noninvasive functional MRI in alert monkeys. Neuroimage 51, 267–273 (2010).

    PubMed  PubMed Central  Google Scholar 

  51. 51

    Leite, F.P. et al. Repeated fMRI using iron oxide contrast agent in awake, behaving macaques at 3 Tesla. Neuroimage 16, 283–294 (2002).

    PubMed  Google Scholar 

  52. 52

    Cox, R.W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


T. Savage trained the monkeys and helped with scanning. This work was supported by US National Institutes of Health (NIH) grants EY 16187 and EY 24187, and the Nancy Lurie Marks Foundation. This research was carried out in part at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, using resources provided by the Center for Functional Neuroimaging Technologies, P41EB015896, a P41 Biotechnology Resource Grant supported by the US National Institute of Biomedical Imaging and Bioengineering, NIH, and NIH Shared Instrumentation Grant S10RR021110.

Author information




M.S.L. did the behavioral experiments. K.S., J.L.V. and M.S.L. did the scanning. K.S. analyzed the data. M.S.L. wrote the manuscript.

Corresponding author

Correspondence to Margaret S Livingstone.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Training, testing and scanning timelines.

Training, testing, & scanning for each symbol set is indicated by the color of the bar. Functional MRI scanning sessions for post-training localization are indicated by hatching; between scanning days the monkeys continued their in-cage training. All multiple-symbol-set trained monkeys were scanned on at least two separate days (1 week apart) at the following time points: 1) before any training; 2) after the first symbol set was learned (and before the second symbol set training began); 3) after the second symbol set was learned (and before the third symbol set training began); 4) after the third symbol set was learned (except for Y2&G1 who did not get trained on a third symbol set before final scanning); 5) after training on all symbol sets. After training on all symbol sets was completed we did behavioral testing on each symbol set (data in Fig. 1); this testing is indicated by black outlined regions of the appropriate color. Note that Y2 was trained on Tetris after all scanning was finished because he grew too large to scan in a helmet. The post-training patches shown in Figs. 2 & 3 were calculated using data obtained immediately after training on each symbol set; these patches are the ROIs that were used to calculate the pre- and post-training bar graphs in Fig. 2d. Post-training data for the bar-graphs in Fig. 2d were obtained in the orange hatched scanning epochs. The pre-training data for the bar graphs in Fig. 2d were collected immediately before training on each symbol set. The centers of mass of each of the training-induced regions shown in Fig. 3c were calculated from data obtained both immediately after training on each symbol set (squares in Fig. 3c) and from data obtained immediately after training on a subsequent symbol set (circles in Fig. 3c). Scanning for the eccentricity, curvature, spatial frequency, and category maps in Figs 4 & S5 was performed in the yellow hatched epochs.

Supplementary Figure 2 Image sets used in this study in addition to those shown in Figure 1.

Images were 20° × 20° presented for 0.5 seconds each in 16 second blocks separated by 20 seconds of blank gray screen. A fixation spot was always superimposed at the center of each image.

Supplementary Figure 3 Significant activations for each trained symbol set and for faces overlaid on each monkey’s raw functional slices (thus minimizing registration errors).

Significance criterion: p<0.002; cluster size>=31 voxels. Red indicates significant activations for monkey faces > Helvetica AND monkey faces > Tetris; scans obtained before Cartoon face training. Cyan indicates Cartoon faces > monkey faces; scans obtained immediately after Cartoon face training. Blue indicates Helvetica>control; scans obtained immediately after Helvetica training. Green indicates Tetris>control; scans obtained immediately after Tetris training. In images with iron-oxide contrast agent major sulci are directly visible in the functional slices so registration of data obtained at different times is clear; left hemisphere is on the left. Each row shows the activations for one monkey; each column shows alternate 1mm slices from A+4 to A-4. Color indicates image category; two-color hatching indicates voxels with significant activations to two categories. T-score maps for each contrast for each monkey are shown in Fig. S4,S5,S6,S7,S8,S9,S10,S11.

Supplementary Figure 4 t-score maps for significant activations on functional slices for monkey B1 on alternate 1-mm coronal slices from AP +4 to AP –4 for each contrast; left hemisphere is on the left.

Supplementary Figure 5 t-score maps for significant activations on functional slices for monkey B2.

Supplementary Figure 6 t-score maps for significant activations on functional slices for monkey R2.

Supplementary Figure 7 t-score maps for significant activations on functional slices for monkey G2.

Supplementary Figure 8 t-score maps for significant activations on functional slices for monkey Y1.

Supplementary Figure 9 t-score maps for significant activations on functional slices for monkey Y2.

Supplementary Figure 10 t-score maps for significant activations on functional slices for monkey G1.

Supplementary Figure 11 Spatial frequency maps for the same monkeys as in Figure 4 for the contrast of 0.4 cpd patterns minus 2.5 cpd patterns, as well as graphs of the correlation coefficients between spatial frequency maps and all other maps.

Dotted line indicates zero correlation; correlation can vary between -1 and 1. Asterisks at the top indicate correlations that were significantly greater than zero at p<0.05; asterisks at the bottom indicate correlations significantly less than zero at p<0.05.

Supplementary Figure 12 Distribution of average t-scores for eccentricity and curvature contrasts for monkey face, cartoon face, Helvetica and Tetris ROIs.

Three monkeys were scanned with a periphery-minus-central contrast and a straight-minus-curvy contrast, as in Fig. 4. T-scores were averaged over each ROI in each monkey for both contrasts. (The same ROIs as were used as in Fig. 2d.) Black lines at the center of each plot show the 95% confidence limits for distribution of t-scores for the same ROIs calculated from stimulus-shuffled maps.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12 and Supplementary Tables 1 and 2 (PDF 2408 kb)

Supplementary Methods Checklist

(PDF 415 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Srihasam, K., Vincent, J. & Livingstone, M. Novel domain formation reveals proto-architecture in inferotemporal cortex. Nat Neurosci 17, 1776–1783 (2014).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing