FamFac – Una base de datos de caras famosas para experimentos de psicología

Introducción. La existencia de una gran variación en las propiedades de bajo nivel de estímulos visuales y la ocurrencia de diversos grados de familiaridad con rostros famosos pueden haber causado un sesgo en los resultados de las investigaciones que intentaron desentrañar los procesos involucrados...

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Autores:
Monteiro, Fábio
Rodrigues , Paulo
Santos, Isabel M.
Bem-Haja, Pedro
Rosa, Pedro J.
Tipo de recurso:
Article of journal
Fecha de publicación:
2023
Institución:
Universidad de San Buenaventura
Repositorio:
Repositorio USB
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.usb.edu.co:10819/28975
Acceso en línea:
https://hdl.handle.net/10819/28975
https://doi.org/10.21500/20112084.6498
Palabra clave:
face processing
face recognition
control of low-level features
methodology
Procesamiento facial
reconocimiento facial
control de propiedades de bajo nivel
metodología
Rights
openAccess
License
http://purl.org/coar/access_right/c_abf2
id SANBUENAV2_0e7db4999afb40f3e6c7fc0bebfff617
oai_identifier_str oai:bibliotecadigital.usb.edu.co:10819/28975
network_acronym_str SANBUENAV2
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repository_id_str
dc.title.spa.fl_str_mv FamFac – Una base de datos de caras famosas para experimentos de psicología
dc.title.translated.spa.fl_str_mv FamFac – Una base de datos de caras famosas para experimentos de psicología
title FamFac – Una base de datos de caras famosas para experimentos de psicología
spellingShingle FamFac – Una base de datos de caras famosas para experimentos de psicología
face processing
face recognition
control of low-level features
methodology
Procesamiento facial
reconocimiento facial
control de propiedades de bajo nivel
metodología
title_short FamFac – Una base de datos de caras famosas para experimentos de psicología
title_full FamFac – Una base de datos de caras famosas para experimentos de psicología
title_fullStr FamFac – Una base de datos de caras famosas para experimentos de psicología
title_full_unstemmed FamFac – Una base de datos de caras famosas para experimentos de psicología
title_sort FamFac – Una base de datos de caras famosas para experimentos de psicología
dc.creator.fl_str_mv Monteiro, Fábio
Rodrigues , Paulo
Santos, Isabel M.
Bem-Haja, Pedro
Rosa, Pedro J.
dc.contributor.author.eng.fl_str_mv Monteiro, Fábio
Rodrigues , Paulo
Santos, Isabel M.
Bem-Haja, Pedro
Rosa, Pedro J.
dc.subject.eng.fl_str_mv face processing
face recognition
control of low-level features
methodology
topic face processing
face recognition
control of low-level features
methodology
Procesamiento facial
reconocimiento facial
control de propiedades de bajo nivel
metodología
dc.subject.spa.fl_str_mv Procesamiento facial
reconocimiento facial
control de propiedades de bajo nivel
metodología
description Introducción. La existencia de una gran variación en las propiedades de bajo nivel de estímulos visuales y la ocurrencia de diversos grados de familiaridad con rostros famosos pueden haber causado un sesgo en los resultados de las investigaciones que intentaron desentrañar los procesos involucrados en el procesamiento de rostros familiares y desconocidos (por ejemplo, las diferencias temporales en la detección de los primeros potenciales relacionados con eventos especializados en el procesamiento de rostros puede ser explicada por diferentes métodos para controlar la variación en las propiedades de bajo nivel de los estímulos visuales). Objetivo. Para mitigar estos problemas,desarrollamos una base de datos de 183 caras famosas, disponible gratuitamente, cuyas propiedades de bajo nivel (brillo, tamaño, resolución) fueron homogeneizados y elnivel de familiaridad medido. Método. El brillo de los estímulos fue estandarizado por un algoritmo personalizado. El tamaño y la resolución de las imágenes fueran homogeneizadas en Gimp. El nivel de familiaridad de los rostros famosos fue medido por un grupo de 48 estudiantes universitarios portugueses. Resultados. Nuestros resultados sugirieron que el brillo de cada imagen no difiere significativamente del valor de brillomedio del conjunto de estímulos. Cuarenta y un rostros famosos fueron clasificados como altamente familiares. Principales implicaciones. Este estudio proporciona dosrecursos importantes, ya que tanto el algoritmo como la base de datos están disponibles gratuitamente para fines de investigación. Los procedimientos de homogeneizacióndeben garantizar que los estímulos incluidos en la base de datos no provoquen efectos de confusión como los verificados en estudios anteriores.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-24T00:00:00Z
2025-08-22T16:59:23Z
dc.date.available.none.fl_str_mv 2023-07-24T00:00:00Z
2025-08-22T16:59:23Z
dc.date.issued.none.fl_str_mv 2023-07-24
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.doi.none.fl_str_mv 10.21500/20112084.6498
dc.identifier.eissn.none.fl_str_mv 2011-7922
dc.identifier.issn.none.fl_str_mv 2011-2084
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10819/28975
dc.identifier.url.none.fl_str_mv https://doi.org/10.21500/20112084.6498
identifier_str_mv 10.21500/20112084.6498
2011-7922
2011-2084
url https://hdl.handle.net/10819/28975
https://doi.org/10.21500/20112084.6498
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language eng
dc.relation.bitstream.none.fl_str_mv https://revistas.usb.edu.co/index.php/IJPR/article/download/6498/5194
dc.relation.citationedition.eng.fl_str_mv Núm. 2 , Año 2023 : Psychophysiology and Experimental Psychology
dc.relation.citationendpage.none.fl_str_mv 41
dc.relation.citationissue.eng.fl_str_mv 2
dc.relation.citationstartpage.none.fl_str_mv 31
dc.relation.citationvolume.eng.fl_str_mv 16
dc.relation.ispartofjournal.eng.fl_str_mv International Journal of Psychological Research
dc.relation.references.eng.fl_str_mv Andrews, T. J., Watson, D. M., Rice, G. E., & Hartley, T. (2015). Low-level properties of natural images predict topographic patterns of neural response in the ventral visual pathway. Journal of Vision, 15(7), 1–12. https://doi.org/10.1167/15.7.3.
Bainbridge, W. A., & Oliva, A. (2015). A toolbox and sample object perception data for equalization of natural images. Data in Brief, 5, 846–851. https://doi.org/10.1016/j.dib.2015.10.030.
Bentin, S., & Deouell, L. Y. (2000) Structural encoding and identification in face processing: ERP evidence for separate mechanisms. Cognitive Neuropsychology, 17(1–3), 35–55. https://doi.org/10.1080/026432900380472.
Bizzozero, I., Lucchelli, F., Saetti, M. C., & Spinnler, H. (2007). “Whose face is this?”: Italian norms of naming celebrities. Neurological Sciences, 28(6), 315-322. https://doi.org/10.1007/s10072-007-0845-6.
Brannan, J. R., Solan, H. A., Ficarra, A. P., & Ong, E. (1998). Effect of luminance on visual evoked potential amplitudes in normal and disabled readers. Optometry and Vision Science: Official Publication of the American Academy of Optometry, 75(4), 279-283. https://doi.org/10.1097/00006324-199804000-00025.
Bruce, V., Henderson, Z., Greenwood, K., Hancock, P. J., Burton, A. M., & Miller, P. (1999). Verification of face identities from images captured on video. Journal of Experimental Psychology: Applied, 5(4), 339. https://doi.org/10.1037/1076-898X.5.4.339.
Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305–327. https://doi.org/10.1111/j.2044-8295.1986.tb02199.x.
Burton, A. M., Bruce, V., & Hancock, P. J. (1999). From pixels to people: a model of familiar face recognition. Cognitive Science, 23(1), 1–31. https://doi.org/10.1207/s15516709cog2301_1.
Cocker, K. D., Moseley, M. J., Bissenden, J. G., Fielder, A. R. (1994). Visual acuity and pupillary responses to spatial structure in infants. Investigative Ophthalmology & Visual Science. 35, 2620-2625.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Dubois, S., Rossion, B., Schiltz, C., Bodart, J. M., Michel, C., Bruyer, R., & Crommelinck, M. (1999). Effect of familiarity on the processing of human faces. Neuroimage, 9, 278-289. https://doi.org/10.1006/nimg.1998.0409.
Dragoi, V., Sharma, J. & Sur, M. (2000). Adaptation-induced plasticity of orientation tuning in adult visual cortex. Neuron, 28(1), 287–298. https://doi.org/10.1016/S0896-6273(00)00103-3.
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191. https://doi.org/10.3758/B F03193146.
Fife-Schaw, C. (2006). Levels of measurement In G. M. Breakwell, S. Hammond, C. Fife-Schaw, & J. A. Smith (Eds.), Research Methods in Psychology. (pp. 147-157). Sage Publications.
Gosling, A., & Eimer, M. (2011). An event-related brain potential study of explicit face recognition. Neuropsychologia, 49(9), 2736–2745. https://doi.org/10.1016/j.neuropsychologia.2011.05.025.
Heap, L. A., Vanwalleghem, G., Thompson, A. W., Favre-Bulle, I. A., & Scott, E. K. (2018). Luminance changes drive directional startle through a thalamic pathway. Neuron, 99(2), 293-301. https://doi.org/10.1016/j.neuron.2018.06.013.
Johnston, R. J., & Edmonds, A. J. (2009). Familiar and unfamiliar face recognition: a review. Memory, 17(5), 577–596. https://doi.org/10.1080/09658210902976969.
Kamitani, Y. & Tong, F. (2006). Decoding seen and attended motion directions from activity in the human Visual Cortex. Current Biology, 16(11), 1096–1102. https://doi.org/10.1016/j.cub.2006.04.003.
Knebel, J. F., Toepel, U., Hudry, J., Le Coutre, J., & Murray, M. M. (2008). Generating controlled image sets in cognitive neuroscience research. Brain Topography, 20(4), 284–289. https://doi.org/10.1007/s10548-008-0046-5.
Lakens, D., Fockenberg, D. A., Lemmens, K. P., Hamm, J., & Miden, C. J. (2013). Brightness differences influence the evaluation of affective pictures. Cognition and Emotion, 27(7), 1225–1246. https://doi.org/10.1080/02699931.2013.781501.
Leibenluft, E., Gobbini, M. I., Harrison, T., & Haxby, J. V. (2004). Mothers’ neural activation in response to pictures of their children and other children. Biological Psychiatry, 56(4), 225–232. https://doi.org/10.1016/j.biopsych.2004.05.017.
Lima, D., Pinto, R., & Albuquerque, P. B. (2021). Recognition and naming test of the Portuguese population for national and international celebrities. Behavior Research Methods, 53(6), 2326-2337. https://doi.org/10.3758/s13428-021-01572-y.
Longmore, C. A., Santos, I. M., Silva, C. F., Hall, A., Faloyin, D., Little, E. (2017). Image dependency in the recognition of newly learnt faces. Quarterly Journal of Experimental Psychology, 70(5), 863-873. https://doi.org/10.1080/17470218.2016.1236825.
Marful, A., Díez-Álamo, A. M., Plaza-Navas, S., & Fernandez, A. (2018). A normative study for photographs of celebrities in Spain. Plos One, 13(5), e0197554. https://doi.org/10.1371/journal.pone.0197554.
McCourt, M. E., & Foxe, J. J. (2003). Brightening prospects for “early” corticol coding of perceived luminance. Journal of Vision, 3(9), 49–56. https://doi.org/10.1167/3.9.424.
Natu, V., & O’Toole, A. J. (2011). The neural processing of familiar and unfamiliar faces: a review and synopsis. British Journal of Psychology, 102(4), 726–747. https://doi.org/10.1111/j.2044-8295.2011.02053.x.
Nessler, D., Mecklinger, A., & Penney, T. B. (2005). Perceptual fluency, semantic familiarity and recognition-related familiarity: an electrophysiological exploration. Cognitive Brain Research, 22(2), 265–288. https://doi.org/10.1016/j.cogbrainres.2004.03.023.
Orquin, J. L., Loose, S. M. (2013). Attention and choice: A review on eye movements in decision making. Acta Psychologica, 144(1), 190-206. https://doi.org/10.1016/j.actpsy.2013.06.003.
Park, S., Konkle, T., & Oliva, A. (2015). Parametric coding of the size and clutter of natural scenes in the human brain. Cerebral Cortex, 25(7), 1792-1805. https://doi.org/10.1093/cercor/bht418.
Prajapati, B., Dunne, M., & Armstrong, R. (2010). Sample size estimation and statistical power analyses. Optometry Today, 16(7), 10-18. Ramon, M., Caharel, S., & Rossion, B. (2011). The speed of recognition of personally familiar faces. Perception, 40(4), 437–449. https://doi.org/10.1068/p6794.
Schettino, A., Keil, A., Porcu, E., & Müller, M. M. (2016). Shedding light on emotional perception: interaction of brightness and semantic content in extrastriate visual cortex. NeuroImage, 133, 341–353. https://doi.org/10.1016/j.neuroimage.2016.03.020.
Schindler, S., Schettino, A., Pourtois, G. (2018). Electrophysiological correlates of the interplay between low-level visual features and emotional content during word reading. Scientific Reports, 8, 1-13. https://doi.org/10.1038/s41598-018-30701-5.
Stacey, P. C., Walker, S., & Underwood, J. D. (2005). Face processing and familiarity: evidence from eye-movement data. British Journal of Psychology, 96(4), 407–422. https://doi.org/10.1348/000712605X47422.
UNESCO. (2018). The Creative Commons licenses. UNESCO. https://en.unesco.org/open-access/creative-commons-licenses. Valentine, T., Lewis, M. B., & Hills, P. J. (2016). Face-space: A unifying concept in face recognition research. Quarterly Journal of Experimental Psychology, 69(10), 1996-2019. https://doi.org/10.1080/17470218.2014.990392.
Willenbockel, V., Sadr, J., Fiset, D., Horne, G. O., Gosselin, F., & Tanaka, J. W. (2010). Controlling low-level image properties: The SHINE toolbox. Behavior Research Methods, 42(3), 671–684. https://doi.org/10.3758/BRM.42.3.671.
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spelling Monteiro, FábioRodrigues , PauloSantos, Isabel M.Bem-Haja, PedroRosa, Pedro J.2023-07-24T00:00:00Z2025-08-22T16:59:23Z2023-07-24T00:00:00Z2025-08-22T16:59:23Z2023-07-24Introducción. La existencia de una gran variación en las propiedades de bajo nivel de estímulos visuales y la ocurrencia de diversos grados de familiaridad con rostros famosos pueden haber causado un sesgo en los resultados de las investigaciones que intentaron desentrañar los procesos involucrados en el procesamiento de rostros familiares y desconocidos (por ejemplo, las diferencias temporales en la detección de los primeros potenciales relacionados con eventos especializados en el procesamiento de rostros puede ser explicada por diferentes métodos para controlar la variación en las propiedades de bajo nivel de los estímulos visuales). Objetivo. Para mitigar estos problemas,desarrollamos una base de datos de 183 caras famosas, disponible gratuitamente, cuyas propiedades de bajo nivel (brillo, tamaño, resolución) fueron homogeneizados y elnivel de familiaridad medido. Método. El brillo de los estímulos fue estandarizado por un algoritmo personalizado. El tamaño y la resolución de las imágenes fueran homogeneizadas en Gimp. El nivel de familiaridad de los rostros famosos fue medido por un grupo de 48 estudiantes universitarios portugueses. Resultados. Nuestros resultados sugirieron que el brillo de cada imagen no difiere significativamente del valor de brillomedio del conjunto de estímulos. Cuarenta y un rostros famosos fueron clasificados como altamente familiares. Principales implicaciones. Este estudio proporciona dosrecursos importantes, ya que tanto el algoritmo como la base de datos están disponibles gratuitamente para fines de investigación. Los procedimientos de homogeneizacióndeben garantizar que los estímulos incluidos en la base de datos no provoquen efectos de confusión como los verificados en estudios anteriores.Introduction. High variation in the low-level proprieties of visual stimuli and varying degrees of familiarity with famous faces may have caused a bias in the results of investigations that tried to disentangle the processes involved in familiar and unfamiliar face processing (e.g., temporal differences in the detection of the first event-related potentials specialized in face processing may have been caused by different methods of controlling variance in the low-level proprieties of visual stimuli). Objective. To address these problems, we developed a freely available database of 183 famous faces whose low-level proprieties (brightness, size, resolution) have been homogenized and the level of familiarity established. Method. The brightness of the stimuli was standardized by a custom-developed algorithm. The size and the resolution of the pictures were homogenized in Gimp. The familiarity level of the famous faces was established by a group of 48 Portuguese college students. Results. Our results suggest that the brightness of each image did not differ significantly from the mean brightness value of the stimuli set, confirming the standardizing ability of the algorithm. Forty-one famous faces were classified as highly familiar. Main findings and implications. This study provides two important resources, as both the algorithm and the database are freely available for research purposes. The homogenization of the low-level features and the control of the level of familiarity of the famous faces included in our database should ensure that they do not elicit confounding effects such as the ones verified in past studies.  application/pdf10.21500/20112084.64982011-79222011-2084https://hdl.handle.net/10819/28975https://doi.org/10.21500/20112084.6498engUniversidad San Buenaventura - USB (Colombia)https://revistas.usb.edu.co/index.php/IJPR/article/download/6498/5194Núm. 2 , Año 2023 : Psychophysiology and Experimental Psychology4123116International Journal of Psychological ResearchAndrews, T. J., Watson, D. M., Rice, G. E., & Hartley, T. (2015). Low-level properties of natural images predict topographic patterns of neural response in the ventral visual pathway. Journal of Vision, 15(7), 1–12. https://doi.org/10.1167/15.7.3.Bainbridge, W. A., & Oliva, A. (2015). A toolbox and sample object perception data for equalization of natural images. Data in Brief, 5, 846–851. https://doi.org/10.1016/j.dib.2015.10.030.Bentin, S., & Deouell, L. Y. (2000) Structural encoding and identification in face processing: ERP evidence for separate mechanisms. Cognitive Neuropsychology, 17(1–3), 35–55. https://doi.org/10.1080/026432900380472.Bizzozero, I., Lucchelli, F., Saetti, M. C., & Spinnler, H. (2007). “Whose face is this?”: Italian norms of naming celebrities. Neurological Sciences, 28(6), 315-322. https://doi.org/10.1007/s10072-007-0845-6.Brannan, J. R., Solan, H. A., Ficarra, A. P., & Ong, E. (1998). Effect of luminance on visual evoked potential amplitudes in normal and disabled readers. Optometry and Vision Science: Official Publication of the American Academy of Optometry, 75(4), 279-283. https://doi.org/10.1097/00006324-199804000-00025.Bruce, V., Henderson, Z., Greenwood, K., Hancock, P. J., Burton, A. M., & Miller, P. (1999). Verification of face identities from images captured on video. Journal of Experimental Psychology: Applied, 5(4), 339. https://doi.org/10.1037/1076-898X.5.4.339.Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305–327. https://doi.org/10.1111/j.2044-8295.1986.tb02199.x.Burton, A. M., Bruce, V., & Hancock, P. J. (1999). From pixels to people: a model of familiar face recognition. Cognitive Science, 23(1), 1–31. https://doi.org/10.1207/s15516709cog2301_1.Cocker, K. D., Moseley, M. J., Bissenden, J. G., Fielder, A. R. (1994). Visual acuity and pupillary responses to spatial structure in infants. Investigative Ophthalmology & Visual Science. 35, 2620-2625.Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.Dubois, S., Rossion, B., Schiltz, C., Bodart, J. M., Michel, C., Bruyer, R., & Crommelinck, M. (1999). Effect of familiarity on the processing of human faces. Neuroimage, 9, 278-289. https://doi.org/10.1006/nimg.1998.0409.Dragoi, V., Sharma, J. & Sur, M. (2000). Adaptation-induced plasticity of orientation tuning in adult visual cortex. Neuron, 28(1), 287–298. https://doi.org/10.1016/S0896-6273(00)00103-3.Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191. https://doi.org/10.3758/B F03193146.Fife-Schaw, C. (2006). Levels of measurement In G. M. Breakwell, S. Hammond, C. Fife-Schaw, & J. A. Smith (Eds.), Research Methods in Psychology. (pp. 147-157). Sage Publications.Gosling, A., & Eimer, M. (2011). An event-related brain potential study of explicit face recognition. Neuropsychologia, 49(9), 2736–2745. https://doi.org/10.1016/j.neuropsychologia.2011.05.025.Heap, L. A., Vanwalleghem, G., Thompson, A. W., Favre-Bulle, I. A., & Scott, E. K. (2018). Luminance changes drive directional startle through a thalamic pathway. Neuron, 99(2), 293-301. https://doi.org/10.1016/j.neuron.2018.06.013.Johnston, R. J., & Edmonds, A. J. (2009). Familiar and unfamiliar face recognition: a review. Memory, 17(5), 577–596. https://doi.org/10.1080/09658210902976969.Kamitani, Y. & Tong, F. (2006). Decoding seen and attended motion directions from activity in the human Visual Cortex. Current Biology, 16(11), 1096–1102. https://doi.org/10.1016/j.cub.2006.04.003.Knebel, J. F., Toepel, U., Hudry, J., Le Coutre, J., & Murray, M. M. (2008). Generating controlled image sets in cognitive neuroscience research. Brain Topography, 20(4), 284–289. https://doi.org/10.1007/s10548-008-0046-5.Lakens, D., Fockenberg, D. A., Lemmens, K. P., Hamm, J., & Miden, C. J. (2013). Brightness differences influence the evaluation of affective pictures. Cognition and Emotion, 27(7), 1225–1246. https://doi.org/10.1080/02699931.2013.781501.Leibenluft, E., Gobbini, M. I., Harrison, T., & Haxby, J. V. (2004). Mothers’ neural activation in response to pictures of their children and other children. Biological Psychiatry, 56(4), 225–232. https://doi.org/10.1016/j.biopsych.2004.05.017.Lima, D., Pinto, R., & Albuquerque, P. B. (2021). Recognition and naming test of the Portuguese population for national and international celebrities. Behavior Research Methods, 53(6), 2326-2337. https://doi.org/10.3758/s13428-021-01572-y.Longmore, C. A., Santos, I. M., Silva, C. F., Hall, A., Faloyin, D., Little, E. (2017). Image dependency in the recognition of newly learnt faces. Quarterly Journal of Experimental Psychology, 70(5), 863-873. https://doi.org/10.1080/17470218.2016.1236825.Marful, A., Díez-Álamo, A. M., Plaza-Navas, S., & Fernandez, A. (2018). A normative study for photographs of celebrities in Spain. Plos One, 13(5), e0197554. https://doi.org/10.1371/journal.pone.0197554.McCourt, M. E., & Foxe, J. J. (2003). 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Behavior Research Methods, 42(3), 671–684. https://doi.org/10.3758/BRM.42.3.671.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.http://creativecommons.org/licenses/by-nc-nd/4.0https://revistas.usb.edu.co/index.php/IJPR/article/view/6498face processingface recognitioncontrol of low-level featuresmethodologyProcesamiento facialreconocimiento facialcontrol de propiedades de bajo nivelmetodologíaFamFac – Una base de datos de caras famosas para experimentos de psicologíaFamFac – Una base de datos de caras famosas para experimentos de psicologíaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2689https://bibliotecadigital.usb.edu.co/bitstreams/b22871e7-3641-444d-b10c-7f3a42b29027/download9875f7395e73ef04b5d7b7a16f3c3ea2MD5110819/28975oai:bibliotecadigital.usb.edu.co:10819/289752025-08-22 11:59:24.067http://creativecommons.org/licenses/by-nc-nd/4.0https://bibliotecadigital.usb.edu.coRepositorio Institucional Universidad de San Buenaventura Colombiabdigital@metabiblioteca.com

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