Estimación del tiempo de respuesta en los experimentos de banda de frecuencia espacial que implican percepción visual
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may 22, 2017
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Resumen
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.
Keywords
Spatial frequency bands, Gaussian filters, response time, object recognitionSpatial frequency bands, Gaussian filters, response time, object recognition
References
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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 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1351748/
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. http://dx.doi.org/10.1016/j.neulet.2010.10.053
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. http://dx.doi.org/10.1371/journal.pone.0037082
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 http://www.redalyc.org/pdf/784/78430206.pdf
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. http://dx.doi.org/10.1016/S0042-6989(02)00092-5
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. http://dx.doi.org/10.3758/BF03213093
De Valois, R.L., & De Valois, K. K. (1980). Spatial vision. Annual Review of Psychology. 31, 309-341. http://dx.doi.org/10.1146/annurev.ps.31.020180.001521
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 http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0120-62302015000100006&lng=es&nrm=iso&tlng=en
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. http://dx.doi.org/10.1068/p120195
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 https://pdfs.semanticscholar.org/46f9/5b9829922ba0d212ed5a91d2a06fd91391fe.pdf
Jakowski, P., & Sobieralska, K. (2004). Effect of stimulus intensity on manual and saccadic reaction time. Perception & Psychophysics, 66(4), 535-544. http://dx.doi.org/10.3758/BF03194899
Luna, R. (2011). Personality factor effect inside traffic signs perception. Securitas Vialis, 3(3), 95-101. http://dx.doi.org/10.1007/s112615-012-9049-9
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. http://dx.doi.org/10.3758/BF03196180
Norman, J., & Ehrlich, S. (1987). Spatial frequency filtering and target identification. Vision Research, 27(1), 87–96. http://dx.doi.org/10.1016/0042-6989(87)90145-3
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 http://cvcl.mit.edu/Papers/IJCV01-Oliva-Torralba.pdf
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. http://dx.doi.org/10.1016/0042-6989(91)90060-I
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 http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0121-49932011000300002
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. http://dx.doi.org/10.1111/j.1467-9280.1994.tb00500.x
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 http://www.redalyc.org/pdf/453/45326926003.pdf
Sowden, P.T., & Schyns, P.G. (2006). Channel surfing in the visual brain. Trends in Cognitive Sciences, 10(12), 538–545. http://dx.doi.org/10.1016/j.tics.2006.10.007
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. http://dx.doi.org/10.1016/j.actpsy.2010.02.006
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 http://web.mit.edu/torralba/www/ne3302.pdf
Ullman, S. (1981). Analysis of visual motion by biological and computer systems. IEEE Computer, 14(8), 57-69. http://dx.doi.org/10.1109/C-M.1981.220564
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 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1351748/
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. http://dx.doi.org/10.1016/j.neulet.2010.10.053
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. http://dx.doi.org/10.1371/journal.pone.0037082
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 http://www.redalyc.org/pdf/784/78430206.pdf
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. http://dx.doi.org/10.1016/S0042-6989(02)00092-5
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. http://dx.doi.org/10.3758/BF03213093
De Valois, R.L., & De Valois, K. K. (1980). Spatial vision. Annual Review of Psychology. 31, 309-341. http://dx.doi.org/10.1146/annurev.ps.31.020180.001521
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 http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0120-62302015000100006&lng=es&nrm=iso&tlng=en
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. http://dx.doi.org/10.1068/p120195
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 https://pdfs.semanticscholar.org/46f9/5b9829922ba0d212ed5a91d2a06fd91391fe.pdf
Jakowski, P., & Sobieralska, K. (2004). Effect of stimulus intensity on manual and saccadic reaction time. Perception & Psychophysics, 66(4), 535-544. http://dx.doi.org/10.3758/BF03194899
Luna, R. (2011). Personality factor effect inside traffic signs perception. Securitas Vialis, 3(3), 95-101. http://dx.doi.org/10.1007/s112615-012-9049-9
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. http://dx.doi.org/10.3758/BF03196180
Norman, J., & Ehrlich, S. (1987). Spatial frequency filtering and target identification. Vision Research, 27(1), 87–96. http://dx.doi.org/10.1016/0042-6989(87)90145-3
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 http://cvcl.mit.edu/Papers/IJCV01-Oliva-Torralba.pdf
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. http://dx.doi.org/10.1016/0042-6989(91)90060-I
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 http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0121-49932011000300002
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. http://dx.doi.org/10.1111/j.1467-9280.1994.tb00500.x
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 http://www.redalyc.org/pdf/453/45326926003.pdf
Sowden, P.T., & Schyns, P.G. (2006). Channel surfing in the visual brain. Trends in Cognitive Sciences, 10(12), 538–545. http://dx.doi.org/10.1016/j.tics.2006.10.007
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. http://dx.doi.org/10.1016/j.actpsy.2010.02.006
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 http://web.mit.edu/torralba/www/ne3302.pdf
Ullman, S. (1981). Analysis of visual motion by biological and computer systems. IEEE Computer, 14(8), 57-69. http://dx.doi.org/10.1109/C-M.1981.220564
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). https://doi.org/10.11144/Javeriana.upsy16-1.erts
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