Publicado May 22, 2017


Google Scholar
Search GoogleScholar

María del Prado Rivero Expósito

Enrique Vila Abad

Francisco Pablo Holgado Tello

Javier Aparicio

Georgio de Marchis



The present study aimed to determine the optimum response time (RT) needed to identify images of everyday objects when filtered using different spatial frequency bands. Subjects were randomly presented with different images of familiar objects that were both serialized and progressive in their spatial frequencies. The time needed to recognize them was then measured. The results showed that the optimum RT for identifying an image filtered in different spatial frequency bands was approximately 2000 ms of exposure. Specifically, stimuli presented using spatial frequency bands with Gaussian filters of variance V26-V32, which were familiar and of medium size to the viewer, were recognized in a mean time of 2126 ms.


Spatial frequency bands, Gaussian filters, response time, object recognitionSpatial frequency bands, Gaussian filters, response time, object recognition

Bell, A.H., Meredith, M.A., Van Opstal, A.J., & Muñóz, D.P. (2006). Stimulus intensity modifies saccadic reaction time and visual response latency in the superior colliculus. Experimental Brain Research, 174(1), 53-59.

Campbell, F.W., & Robson, J.G. (1968). Application of Fourier Analysis to the visibility of gratings. The Journal of Physiology, 197(3), 551-566. Retrieved from

Carrasco, M. (2006). Covert attention increases contrast sensitivity: psychophysical, neurophysiological, and neuroimaging studies. In S. Martínez-Conde, S.L. Macknik, L.M. Martínez, J.M. Alonso, & P.U. Tse (Eds.). Visual Perception. Part. I. Fundamentals of Vision: Low and Mid-level Processes in Perception. Progress in Brain Research, vol. 154 (pp. 33-70). Amsterdam: Elsevier.

Carreiro, L.R., Haddad, H., & Baldo, M.V. (2011). Effects of intensity and positional predictability of a visual stimulus on simple reaction time. Neuroscience Letters, 487(3), 345-349.

Carretié, L., Ríos, M., Periañez, J.A., Kessel, D., & Álvarez-Linera, J. (2012). The role of low and high spatial frequencies in exogenous attention to biologically salient stimuli. PloS ONE, 7(5), e37082.

Chotse Wai, O. (2004). Sistema de reconocimiento de patrones visuals basado en técnicas de procesamiento de imágenes y redes neuronales. Télématique, 3(2), 75-99. Retrieved from

Chung, S.T., Legge, G.E., & Tjan, B.S. (2002). Spatial-frequency characteristics of letter identification in central and peripheral vision. Vision Research, 42(18), 2137-2152.

Costen, N.P., Parker, D.M., & Craw, I. (1996). Effects of high-pass and low-pass spatial filtering on face identification. Perception and Psychophysics, 58(4), 602–612.

De Valois, R.L., & De Valois, K. K. (1980). Spatial vision. Annual Review of Psychology. 31, 309-341.

Díaz Pardo, I., Suárez Fajardo, C.A., Puerto-Leguizamón, G.P., & Zona Ortiz, T. (2015). Band-pass filters using OSRRcells. Revista Facultad Ingeniería Universidad de Antioquía, 74, 60-69. Retrieved from

Fernández-Trespalacios, J.L. (2004). Espectros de imágenes mediante los análisis de Fourier y de Gabor en relación con la psicofísica del Sistema Visual Humano [Spectra of images using Fourier analysis and Gabor psychophysics concerning Human Visual System]. V Semana de Investigación de la UNED. UNED: Madrid.

Fiorentini, A., Maffei, L., & Sandini, G. (1983). The role of high spatial frequencies in face perception. Perception, 12, 195–201.

Gold, J., Bennett, P.J., & Sekuler, A.B. (1999). Identification of bandpass filtered letters and faces by human and ideal observers. Vision Research, 39, 3537–3560. Retrieved from

Jakowski, P., & Sobieralska, K. (2004). Effect of stimulus intensity on manual and saccadic reaction time. Perception & Psychophysics, 66(4), 535-544.

Luna, R. (2011). Personality factor effect inside traffic signs perception. Securitas Vialis, 3(3), 95-101.

Morrison, D.J., & Schyns, P.G. (2001). Usage of spatial scales for the categorization of face, objects, and scenes. Psychonomic Bulletin & Review, 8(3), 454-469.

Norman, J., & Ehrlich, S. (1987). Spatial frequency filtering and target identification. Vision Research, 27(1), 87–96.

Oliva, A., & Schyns, P. G. (1997). Coarse blobs or fine edges? Evidence that information diagnosticity changes the perception of complex visual stimuli. Cognitive psychology, 34, 72-107.

Oliva, A., & Torralba, A. (2002). Scene-centred description from spatial envelope properties. In H. Bulthoff, S.W. Lee, T. Poggio, & C. Wallraven (Eds.), Lecture Note in Computer Science Series Proc. 2nd International Workshop on Biologically Motivated Computer Vision (pp. 263-272). Germany: Springer-Verlag.

Oliva, A., & Torralba, A. (2001). Modelling the shape of the scene: a holistic representation of the spatial envelope. International Journal in Computer Vision, 42, 145-175. Retrieved from

Palmer, S.E. (1999). Vision Science. Cambrigde, M.A: The MIT Press.

Parish, D.H., & Sperling, G. (1991). Object spatial frequencies, retinal spatial frequencies, noise, and the efficiency of letter discrimination. Vision Research, 31(7-8), 1399–1415.

Ramírez-Moreno, D.F., & Ramírez-Villegas, J.F. (2011). A computational implementation of a bottom-up visual attention model applied to natural scenes. Revista de Ingeniería, 35, 6-11. Retrieved from

Schyns, P.G., & Oliva, A. (1994). From blobs to boundary edges: Evidence for time- and spatial-scale-dependent scene recognition. Psychological Science, 5(4), 195-200.

Serrano, E.P., Fabio, M., & Figliola, A. (2012). Time-frequency methods based on the wavelet transform. Revista de Matemáticas: Teoría y Aplicaciones, 19(2), 157-168. Retrieved from

Sowden, P.T., & Schyns, P.G. (2006). Channel surfing in the visual brain. Trends in Cognitive Sciences, 10(12), 538–545.

Shipley, T.F., & Kellman, P.J. (2001). From fragments to objects: Segmentation and grouping in vision. Amsterdam: Elsevier Science Press.

Theeuwes, J. (2010). Top-down and bottom-up control of visual selection. Acta Psychological, 135(2), 77-99.

Thurman, S.M., & Grossman, E.D. (2011). Diagnostic spatial frequencies and human efficiency for discriminating actions. Attention Perception and Psychophysiology, 73(2), 572-580.

Torralba, A., & Oliva, A. (2003). Statistics of natural image categories Network: Computation in Neural System, 14, 391-412. Retrieved from

Ullman, S. (1981). Analysis of visual motion by biological and computer systems. IEEE Computer, 14(8), 57-69.
Cómo citar
Rivero Expósito, M. del P., Vila Abad, E., Holgado Tello, F. P., Aparicio, J., & de Marchis, G. (2017). Estimación del tiempo de respuesta en los experimentos de banda de frecuencia espacial que implican percepción visual. Universitas Psychologica, 16(1).

Artículos más leídos del mismo autor/a