Presented at the 4th International Symposium on Innovative Approaches in Social, Human and Administrative Sciences (ISAS WINTER-2019 (SHS)), Samsun, Turkey, Nov 22, 2019

SETSCI Conference Proceedings, 2019, 11, Page (s): 10-21 , https://doi.org/10.36287/setsci.4.8.003

Published Date: 23 December 2019    | 2682     19

 P300, especially P3b, the third positive peak with near 350ms associated with occipito-patieto-temporal region of the brain, is mainly responsible for categorization of different objects. So, this study investigates the nature of amplitude and reaction time difference in ‘food’ and ‘no-food’ objects categorization task. Object categorization processes were investigated by measuring EEG with event-related potentials (ERP) method while participants were categorizing different ‘food’ and ‘no-food’ items. The EEG study of this experiment found no P3b amplitude differences for ‘food’ and ‘no-food’ category in the ultra-rapid categorization task. On the other hand, from the behavioral study we observed no significant difference in both reaction time (RT) and error rate (ER) in the above task. The result of this study is consistent with some previous experiments. For example, regarding the reaction, the findings can be compared with VanRullen & Thrope (2001) who also found no significant longer reaction time for ‘means of transport’ item in comparison with ‘animal’. The result of this study can also be interpreted from the perspective of ‘coarse-to-fine account’ hypothesis (Prass et al., 2013) which indicated that to recognize objects belong to basic level category one needs detail information with sufficient time. Since this study includes ultra-rapid visualization task participants did not get enough time to process objects of two different categories. Hence both reaction time and error rate were not significant in this regard.

Keywords - P3b, ultra-rapid categorization task, reaction time, amplitudes, error rate, coarse-to-fine account

Batty, M. & Taylor, M. J. (2002). Visual categorization during childhood: An ERP study. Psychophysiology 39, 482–490
Daliri, M.R., Taghizadeh, M. & Niksirat, K.S. (2013). EEG signature of Object Categorization from Event-related Potentials. Journal of Medical Signals & Sensors 3(1), 37-44
Donchin, E., Ritter, W. & McCallum, C. (1978). Cognitive psychology: the endogenous components of the ERP. In Callaway, E, Teuting P, Koslov S. (eds.). Brain event-related potentials in man. New York: Academic Press, 349-441
El-Lone, R., Hussan, M., Kabbara, A. & Hleiss, R. (2015). Visual objects categorization using dense EEG: A preliminary study. International Conference on Advances in Biomedical Engineering (ICABME), 115-118
Fabiani, M., Gratton, G., Karis, D. & Donchin, E. (1987). The definition, identification, and reliability of measurement of the P300 component of the event-related brain potential. In Ackles PK, Jennings JR, Coles MGH (eds.) Advances in psychophysiology, vol.2. Greenwich, CT: JAI Press, 1-78
Fabre-Thorpe M, Richard, G. & Thorpe, S.J. (1998). Rapid Categorization of natural images by rhesus monkeys. Neuroreport 9, 303-308
Ghosh, P., Mazumder, A., Bhattacharyya, S., & Tibarewala, D.N. (2015). An EEG Study on Working Memory and Cognition. 21-26, Retrieved on 10 June, 2018 from http://dx.doi.org/10.1145/2708463.2709065
Halgren, E., Squires, NK., Wilson, C., Rohrbaugh, JW, Babb TL, & Crandall PH. (1980). Endogenous potentials generated in the human hippocampal formation and amygdala by infrequent events. Science 210, 803-805
Hruby, T. & Marsalek, P. (2003). Event-Related Potentials – the P3 Wave. Acta Neurobiol. EXp. 63, 55-63
Israel, JB., Chesney, GL., Wickens CD. &Donchin E. (980). P300 and tracking difficulty: evidence for multiple resources in the dual-task performance. Psychophysiology 17, 259-273
Li, F.F., VanRullen, R., Koch, C. & Perona, P.(2002). Rapid natural scene categorization in the near absence of attention. PNAS, vol. 99(4), 9596-9601
Mace, MJ., Joubert, OR., Nespoulous, J.& Fabre-Thorpe, M. (2009). The time-course of visual categorization: you spot the animal faster than the bird. PloS ONE 4(6), e5927
Näätänen, R. (1990). The role of attention in the information processing as revealed by event-related potentials and other brain measures of cognitive function. Behavioral and Brain Sciences 13, 201-288
Patel, S.H. & Azzam, P.N. (2005). Characterization of N200 and P300: Studies of the Event-Related Potential. Int. J. Med. Sci.2(4), 147-154
Picton, T.W. (1992). The P300 Wave of the Human Event-Related Potential. Journal of Clinical Neurophysiology, 9(4), 456-479
Picton, TW,. Campbell, KB., Baribeau-Braun, J. & Proulx, GB. (1978). The neurophysiology of human attention: A tutorial review. In Requin, J. (ed.) Attention and Performance VII, Hillsdale, NJ: Erlbaum, 429-467
Prass, M., Grimsen, C., König, M. & Fahle, M. (2013). Ultra Rapid Object Categorization: Effects of Level Animacy and Context. Plos One. Vol. 8 (6), 1-10
Ragusa, F., Tomaselli, V. & Furnari, A. (2016). Food vs Non-Food Classification. MADiMa’16, 77-81
Rodin, E. (1991). Latency determination by global field power in normal subjects. J Clin Neurophysiol 8, 88-94
Ruchkin, DS., Johnson, R., Canoune, HL., Ritter, W. & Hammer, M. (1990). Multiple sources of P3b associated with different types of information, Psychophysiology 27, 157-176
Rutters, F., Kumar, S., Higgs, S. &Humphreys, G. W. (2015). Electrophysiological evidence for enhanced representation of food stimuli in working memory. Exp Brain Res. 233, 519–528
Sommer W & Matt, J. (1990). Awareness of P300-related cognitive process: a signal detection approach. Psychophysiology, 27, 575-585
Squires, NK., Squires, KC. & Hillard, SA. (1975). Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalogr Clin Neurophysiol 8, 387-401
Stapleton, JM. & Halgren, E. (1987). Endogenous potentials evoked in simple cognitive task: depth components and task correlates. Electroencephalogr Clin Neurophysiol, 67, 44-52
Sutton, S., Braren, M., Zubin, J & John ER. (1965). Evoked potential correlates of stimulus uncertainty, Science 150, 1187-1188
Thorpe, S.J., Fize, D. & Marlot, C. (1996). Speed of processing in the human visual system Nature 381, 520-522
Ungerer, F. & Schmid, H. (2006). An Introduction to Cognitive Linguistics. Harlow: Pearson Longman
VanRullen, R. & Thorpe, S.J. (2001) Is it a bird? Is it a plane? Ultra-rapid visual categorization of natural and artifactual objects. Perception, 30, 655-668
Vaughan, HG. & Ritter, W. (1970). The sources of auditory evoked responses recorded from the human scalp. Electroencephalogr Clin Neurophysiol 28, 360-367
Verleger, R., &Berg, P. (1991). The waltzing oddball. Psychophysiology 28, 468-477
Vogels, R. (1999). Categorization of complex visual images by rhesus monkeys. Part 1: Behavioral study. European Journal of Neuroscience 11, 1223-1238
Walter, WG., Cooper, R., Aldridge, VJ., McCallum, WC. & Winter, AL. (1964). Contingent negative variation: An electric sign of sensorimotor association and expectancy in the human brain. Nature 203, 380-384
Woods, DL. & Courchesne, E. (1986). The recovery functions of auditory event-related potentials during split-second discriminations. Electroencephalogr Clin Neurophysiol, 65, 304-315
Zhu,W., Drewes,J., Peatfield, N. A. &Melcher, D. (2016). Differential Visual Processing of Animal Images, with and without Conscious Awareness. Frontiers of Human Neuroscience, vol. 10, 1-19

This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.