The Order of Information Transfer into Short- Term Memory from Visual Pathways with Different Spatial-Frequency Tunings



Visual perceptual representations are formed from the results of processing the input image in parallel pathways with different spatial-frequency tunings. It is known that these representations are created gradually, starting from low spatial frequencies. However, the order of information transfer from the perceptual representation to short-term memory has not yet been determined. The purpose of our study is to determine the principle of entering information of different spatial frequencies in the short-term memory. We used the task of unfamiliar faces matching. Digitized photographs of faces were filtered by six filters with a frequency tuning step of 1 octave. These filters reproduced the spatial-frequency characteristics of the human visual pathways. In the experiment, the target face was shown first. Its duration was variable and limited by a mask. Then four test faces were presented. Their presentation was not limited in time. The observer had to determine the face that corresponds to the target one. The dependence of the accuracy of the solution of the task on the target face duration for different ranges of spatial frequencies was determined. When the target stimuli were unfiltered (broadband) faces, the filtered faces were the test ones, and vice versa. It was found that the short-term memory gets information about an unfamiliar face in a certain order, starting from the medium spatial frequencies, and this sequence does not depend on the processing method (holistic or featural).

General Information

Keywords: face, spatial frequency, sequence, short-term memory

Journal rubric: Cognitive Psychology

Article type: scientific article


Funding. This work was supported by the Ministry of Science and Higher Education of the Russian Federation (project № 25.3336.2017/Project Part).

For citation: Alekseeva D.S., Babenko V.V., Yavna D.V. The Order of Information Transfer into Short- Term Memory from Visual Pathways with Different Spatial-Frequency Tunings. Eksperimental'naâ psihologiâ = Experimental Psychology (Russia), 2020. Vol. 13, no. 2, pp. 72–89. DOI: 10.17759/exppsy.2020130206. (In Russ., аbstr. in Engl.)


  1. Barabanschikov V.A., Zhegallo A.V., Ivanova L.A. Raspoznavanie ekspressii perevernutogo izobrazheniya litsa [Recognition of expression of inverted face image] // Eksperimental’naia psikhologiia [Experimental Psychology (in Russia)]. 2010, Vol. 3, № 3. P. 66—83. (In Russ.).
  2. Shelepin Y. E. Vvedenie v neiroikoniku [Introduction to Neuroiconics]. St. Petersburg: Troitskii most Publ., 2017. 352 p. (In Russ.).
  3. Arsenault E., Yoonessi A., Baker C. Higher order texture statistics impair contrast boundary segmentation // J Vis. 2011. Vol. 11. № 10. P. 14. doi:10,1167/11.10,14
  4. Ashby F.G. Multidimensional models of perception and cognition. Psychology Press, 2014. 544 p.
  5. Behrmann M., Richler J.J., Avidan G., et al. Holistic face perception // Oxford handbook of perceptual organization. 2015. P. 758—774.
  6. Boulkenafet Z., Komulainen J., Hadid A. Face Spoofing Detection Using Colour Texture Analysis // IEEE Transactions on Information Forensics and Security. 2016. Vol. 11. № 8. P. 1818—1830, doi:10,1109/ TIFS.2016.2555286
  7. Brown C., Portch E., Skelton F.C., et al. The impact of external facial features on the construction of facial composites // Ergonomics. 2019. P. 1—18. doi:10,1080/00140139.2018.1556816
  8. Carbon C.-C., Leder H. When feature information comes first! Early processing of inverted faces. // Perception. 2005. Vol. 34. № 9. P. 1117—1134. doi:10,1068/p5192
  9. Carrasco M., Penpeci-Talgar C., Eckstein M. Spatial covert attention increases contrast sensitivity across the CSF: support for signal enhancement // Vision Res. 2000. Vol. 40. № 10—12. P. 1203—1215.
  10. Chaudhuri R., Fiete I. Computational principles of memory // Nat. Neurosci. 2016. Vol. 19, № 3. P. 394— 403. doi:10,1038/nn.4237
  11. Cohen E.H., Schnitzer B.S., Gersch T.M., et al. The relationship between spatial pooling and attention in saccadic and perceptual tasks // Vision Res. 2007. Vol. 47. № 14. P. 1907—1923. doi:10,1016/j. visres.2007.03.018
  12. Collin C.A., Rainville S., Watier N., et al. Configural and featural discriminations use the same spatial frequencies: a model observer versus human observer analysis // Perception. 2014. Vol. 43. № 6. P. 509— 526. doi:10,1068/p7531
  13. Collin C.A., Therrien M., Martin C., et al. Spatial frequency thresholds for face recognition when comparison faces are filtered and unfiltered // Percept Psychophys. 2006. Vol. 68. № 6. P. 879—889.
  14. Dimitriou D., Leonard H.C., Karmiloff-Smith A., et al. Atypical development of configural face recognition in children with autism, Down syndrome and Williams syndrome // J Intellect Disabil Res. 2015. Vol. 59, № 5. P. 422—438. doi:10,1111/jir.12141
  15. Finzi R.D., Susilo T., Barton J.J.S., et al. The role of holistic face processing in acquired prosopagnosia: evidence from the composite face effect // Visual Cognition. 2016. Vol. 24. № 4. P. 304—320, doi:10,1080/ 13506285.2016.1261976
  16. Gao Z., Bentin S. Coarse-to-fine encoding of spatial frequency information into visual short-term memory for faces but impartial decay // J Exp Psychol Hum Percept Perform. 2011. Vol. 37. № 4. P. 1051—1064. doi:10,1037/a0023091
  17. Gaspar C., Sekuler A.B., Bennett P.J. Spatial frequency tuning of upright and inverted face identification. // Vision Res. 2008. Vol. 48, № 28. P. 2817—2826. doi:10,1016/j.visres.2008.09.015
  18. Gold J., Bennett P.J., Sekuler A.B. Identification of band-pass filtered letters and faces by human and ideal observers. // Vision Res. 1999. Vol. 39. № 21. P. 3537—3560, doi:10,1016/S0042-6989(99)00080-2
  19. Hayward W.G., Crookes K., Chu M.H., et al. Holistic processing of face configurations and components // J Exp Psychol Hum Percept Perform. 2016. Vol. 42. № 10, P. 1482—1489. doi:10,1037/xhp0000246
  20. Jennings B.J., Yu Y., Kingdom F.A.A. The role of spatial frequency in emotional face classification // Atten Percept Psychophys. 2017. Vol. 79. № 6. P. 1573—1577. doi:10,3758/s13414-017-1377-7
  21. Johnson A.P., Prins N., Kingdom F.A.A., et al. Ecologically valid combinations of first- and second-order surface markings facilitate texture discrimination // Vision Res. 2007. Vol. 47. № 17. P. 2281—2290, doi:10,1016/j.visres.2007.05.003
  22. Kamps F.S., Morris E.J., Dilks D.D. A face is more than just the eyes, nose, and mouth: fMRI evidence that face-selective cortex represents external features // Neuroimage. 2019. Vol. 184. P. 90—100, doi:10,1016/j. neuroimage.2018.09.027
  23. Kauffmann L., Chauvin A., Guyader N., et al. Rapid scene categorization: role of spatial frequency order, accumulation mode and luminance contrast. // Vision Res. 2015. Vol. 107. P. 49—57. doi:10,1016/j.visres.2014.11.013
  24. Musel B., Kauffmann L., Ramanoël S., et al. Coarse-to-fine categorization of visual scenes in scene-selective cortex // J Cogn Neurosci. 2014. Vol. 26. № 10, P. 2287—2297. doi:10,1162/jocn_a_00643
  25. Näsänen R. Spatial frequency bandwidth used in the recognition of facial images // Vision Research. 1999. Vol. 39. № 23. P. 3824—3833. doi:10,1016/S0042-6989(99)00096-6
  26. Olzak L.A., Thomas J.P. Neural recoding in human pattern vision: model and mechanisms // Vision Res. 1999. Vol. 39. № 2. P. 231—256.
  27. Ouyang S., Hospedales T.M., Song Y.-Z., et al. ForgetMe№t: Memory-Aware Forensic Facial Sketch Matching // The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. P. 5571— 5579.
  28. Parker D.M., Costen N.P. One extreme or the other or perhaps the golden mean? Issues of spatial resolution in face processing // Current Psychology. 1999. Vol. 18. № 1. P. 118—127. doi:10,1007/s12144-999-1021-3
  29. Peli E., Lee E., Trempe C.L., et al. Image enhancement for the visually impaired: the effects of enhancement on face recognition. // J Opt Soc Am A Opt Image Sci Vis. 1994. Vol. 11. № 7. P. 1929—1939.
  30. Peters J.C., Vlamings P., Kemner C. Neural processing of high and low spatial frequency information in faces changes across development: qualitative changes in face processing during adolescence // Eur. J. Neurosci. 2013. Vol. 37. № 9. P. 1448—1457. doi:10,1111/ejn.12172
  31. Petras K., Ten Oever S., Jacobs C., et al. Coarse-to-fine information integration in human vision // Neuroimage. 2019. Vol. 186. P. 103—112. doi:10,1016/j.neuroimage.2018.10,086
  32. Ramon M., Vizioli L., Liu-Shuang J., et al. Neural microgenesis of personally familiar face recognition // Proc. Natl. Acad. Sci. U.S.A. 2015. Vol. 112. № 35. P. E4835-4844. doi:10,1073/pnas.1414929112
  33. Rohr M., Tröger J., Michely N., et al. Recognition memory for low- and high-frequency-filtered emotional faces: Low spatial frequencies drive emotional memory enhancement, whereas high spatial frequencies drive the emotion-induced recognition bias // Mem Cognit. 2017. Vol. 45. № 5. P. 699—715. doi:10,3758/s13421- 017-0695-2
  34. Royer J., Willenbockel V., Blais C., et al. The influence of natural contour and face size on the spatial frequency tuning for identifying upright and inverted faces // Psychol Res. 2017. Vol. 81. № 1. P. 13—23. doi:10,1007/s00426-015-0740-3
  35. Ruiz-Soler M., Beltran F.S. Face perception: an integrative review of the role of spatial frequencies // Psychol Res. 2006. Vol. 70, № 4. P. 273—292. doi:10,1007/s00426-005-0215-z
  36. Sakai K., Inui T. A feature-segmentation model of short-term visual memory. // Perception. 2002. Vol. 31. № 5. P. 579—589. doi:10,1068/p3320
  37. Tanaka J.W., Sung A. The «Eye Avoidance» Hypothesis of Autism Face Processing // J Autism Dev Disord. 2016. Vol. 46. № 5. P. 1538—1552. doi:10,1007/s10803-013-1976-7
  38. Thomas S.R., Barsalou N. Applying Human Spatial Vision Models to Real-World Target Detection and Identification: A Test of the Wilson Model // Vision Models For Target Detection And Recognition: In Memory of Arthur Menendez. World Scientific, 1995. P. 219—244.
  39. Tobin A., Favelle S., Palermo R. Dynamic facial expressions are processed holistically, but not more holistically than static facial expressions // Cogn Emot. 2016. Vol. 30. № 6. P. 1208—1221. doi:10,1080/02 699931.2015.1049936
  40. Van Rheenen T.E., Joshua N., Castle D.J., et al. Configural and Featural Face Processing Influences on Emotion Recognition in Schizophrenia and Bipolar Disorder // J Int Neuropsychol Soc. 2017. Vol. 23. № 3. P. 287—291. doi:10,1017/S1355617716001211
  41. Williams N.R., Willenbockel V., Gauthier I. Sensitivity to spatial frequency and orientation content is not specific to face perception. // Vision Res. 2009. Vol. 49. № 19. P. 2353—2362. doi:10,1016/j. visres.2009.06.019
  42. Wilson H.R., Gelb D.J. Modified line-element theory for spatial-frequency and width discrimination. // J Opt Soc Am A. 1984. Vol. 1. № 1. P. 124—131.

Information About the Authors

Daria S. Alekseeva, MA in Psychology, Junior Researcher, Regional Research Center of the Russian Academy of Education in the Southern Federal District, Southern Federal University, Rostov-na-Donu, Russia, ORCID:, e-mail:

Vitaliy V. Babenko, Doctor of Biology, Professor, Professor at the Department of Psychophysiology and Clinical Psychology, Southern Federal University, Rostov-na-Donu, Russia, ORCID:, e-mail:

Denis V. Yavna, PhD in Psychology, Associcate Professor at the Department of Psychophysiology and Clinical Psychology, Southern Federal University, Rostov-na-Donu, Russia, ORCID:, e-mail:



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