ARTICLE INFO

Article Type

Original Research

Authors

Nejati   V. (* )
Shahidi   Sh. (1 )
Barzegar   B. (1 )






(* ) Psychology Department, Educational Sciences & Psychology Faculty, Shahid Beheshti University, Tehran, Iran
(1 ) Psychology Department, Educational Sciences & Psychology Faculty, Shahid Beheshti University, Tehran, Iran

Correspondence


Article History

Received:  July  10, 2013
Accepted:  September 23, 2013
ePublished:  July 7, 2014

BRIEF TEXT


… [1-4] In the virtual environments and alongside the oral and written languages, visual non-verbal information including static and animated pictures play an important role in learning [5]. Attention to the information should be considered in learning processes, since persons’ learning and information processing capacity are limited. Attentional functions including selective attention, attention shifting, and inhibition control play important roles in this field. Information is not processed and learned by persons, until they be attracted [6]. There are different types of attention including selective attention (the determining factor of inputs information to the nervous system for processing), sustained attention (the determining factor of sustainability of information), shifting attention (the ability to change the focus on one information to another), and divided attention (the factor of simultaneously processing of some matters) [7]. Selective attention is the most important type of information in virtual environment. Selective attention is the factor determining allocation or non-allocation of cognitive resources to information, i. e. processing [8]. Visual information plays an important role in learning. In natural environment, all the images consist of certain frequency range and our visual system filters inputs with some frequency channels [9]. Image details and its generalities are transferred by high and low frequencies, respectively. High and low processes of frequency are related to partial and general processing strategies of the images, respectively.

Frequency processing of the images evolves from infancy to adulthood, with promotion in using the filters [10]. In addition, low frequency filtering forms general image processing, alongside more inclination to the filtering in information processing in the adults [11].

The aim of this study was to investigate the difference between attentions to the filtered and non-filtered images of information in the elementary and junior high school students.

This is a cross-sectional comparative study.

All elementary and junior high school students of one and six education districts of Tehran, Iran, were studied in 2012.

Using available sampling method, 132 participants from three elementary and junior high schools were selected.

To collect data, the modified visual dot-probe test was used. The test is the modified copy of the original test [12]. Face images were used in the test. The images were filtered with MatLab software and the faces were entered in the program as quasi-random in high-filtered, low-filtered, and non-filtered categories (Fig. 1). Each face image (4*6cm) was simultaneously shown at the two ends of a monitor. The subject was in 50cm distance from the monitor. At first, in order to make the person’s field of vision the monitor, fixation point (+) was presented for 500ms. Then, for 500ms, 2 faces were presented at the left and right sides of the fixation point of the monitor. Seeing the point (asterisk), the subject has to show direction of the point (asterisk) through pressing the arrow keys on the keyboards; and based on this, the computer recorded the subject’s reaction time up to 1ms. Congruent and incongruent stimuli were presented. In the congruent presentation, the target image (face image) and asterisk were on the same side. In the incongruent presentation, asterisk and the target image were not on the same side. Congruent and incongruent presentations of the stimuli were done to investigate the effects of attention. Laptops were used for the test. After introducing themselves and taking the subjects’ confirmations, the researchers recorded demographic information including age, sex, and education level. Data were evaluated by dot-probe test software. To introduce the program and ensure the subject’s understanding of the tester’s guidelines, the duty was experimentally done for some minutes. Then, the original program was conducted and the subject’s performance was recorded. Data were entered to the SPSS 18 software. Regarding one group and three different frequency filters, repeated measurement statistical evaluation was used to investigate attention differences to the three filters. Using repeated measurement evaluation needs sphericity assumption. Mauchley test was used to investigate sphericity assumption and in case of absence of the assumption, Greenhouse test was used. Chi-Square test was used for the mentioned tests.

The mean correct responses of the subjects in congruent display at the low frequency (18.96±2.10) and in non-filtered incongruent presentation (17.04±1.81) were higher. The mean reaction time of the subjects in congruent display at high frequency (0.65±0.36) and in non-filtered incongruent presentation (0.59±0.33) were higher. There was significant difference between the subjects’ correct responses and reaction time to the different kinds of stimuli regarding congruent and incongruent presentations (Table 1). Only when the stimulus was congruent with Mauchely test, sphericity assumption for the replies was applied. In other cases, Greenhouse test was used (Table 2).  

There were some differences between the subjects’ correct responses and reaction time to the presented images, with the most right replies to the low-filtered images being congruent with the target stimulus (asterisk), alongside lower reaction times of responses to high-filtered and non-filtered images. Nevertheless, there was no such situation in incongruent presentation. So, when low filtered images were presented to the subjects, the rightness of responses and the speed of responding were higher indicating that the correct responses and more attention to children in the low-frequency filter which is consistent with several studies. Normal children, in case of low frequency information presentation, form more replies that are correct than the autistic children [13]. In contrary, with high frequencies in image presentation, the autistic children form more responses that are correct [13]. Using low frequency filtering in image comprehension is very more important than high frequency filtering [14]. The general processing strategies and low frequency filtering are mostly used by persons in image processing [15, 16]. … [17-25]

In general processing learning tasks, when visual information are intended, information presentation with low frequency ought to be done; and while there is need for partial characteristics processing, high frequency information ought to be used. In addition, based on a hypothesis, while information, which are related to the left semi-sphere (such as symbols, numbers, and formulae), are learned, high spatial frequency ought to be used; and since healthy persons tend to low spatial frequency in information processing in other cases, this frequency ought to be used in learning environment.

The small sample size and available sampling method were of the limitations of the study.

In normal children and adolescents, low frequency filtered images are processed more exactly and more rapidly than high frequency filtered images and non-filtered images; and like the adults, there are more tendencies to low frequency filtering than the high one.

The researchers feel grateful to the Behaviour and Cognitive Neuroscience Research Center for providing research tools.

Non-declared

Non-declared

The researchers themselves funded the study.

TABLES and CHARTS

Show attach file


CITIATION LINKS

[1]Johari A, Chen CJ, Toh SC. A feasible constructivist instructional development model for virtual reality (VR)-based learning environments: Its efficacy in the novice car driver instruction of Malaysia. Educ Technol Res Dev. 2005;53(1):111-23.
[2]Saleeb N, Dafoulas G. Effects of virtual world environments in student satisfaction: An examination of the role of architecture in 3D education. Int J Knowledge Soc Res. 2011;2(1):29-48.
[3]Jou M, Wang J. Investigation of effects of virtual reality environments on learning performance of technical skills. Comput Human Behav. 2013;29(2):433-8.
[4]Wood E, Zivcakova L, Gentile P, Archer K, De Pasquale D, Nosko A. Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Comput Educ. 2012;58(1):365-374.
[5]Lee DY, Shin DH. An empirical evaluation of multi-media based learning of a procedural task. Comput Human Behav. 2012;28(3):1072-81.
[6]Yli-Krekola A, Särelä J, Valpola H. Selective attention improves learning. In: Alippi C, Polycarpou M, Panayiotou C, Ellinas G. Artificial Neural Networks-ICANN 2009. Lecture Notes in Computer Science. New York: Springer Berlin Heidelberg; 2009. Pp. 285-94.
[7]Seidman LJ. Neuropsychological functioning in people with ADHD across the life span. Clin Psychol Rev. 2006;26(4):466-85.
[8]Leung K, Lee T, Yip P, Li LS, Wong MM. Selective attention biases of people with depression: Positive and negative priming of depression-related information. Psychiatry Res. 2009;165(3):241-51.
[9]Flevaris AV, Robertson LC, Bentin S. Using spatial frequency scales for processing face features and face configuration: An ERP analysis. Brain Res. 2008;1194:100-9.
[10]Leat SJ, Yadav NK, Irving EL. Development of visual acuity and contrast sensitivity in children. J Optometry. 2009;2(1):19-26.
[11]Maurer D, Grand RL, Mondloch CJ. The many faces of configural processing. Trends Cogn Sci. 2002;6(6):255-60.
[12]Ehrman RN, Robbins SJ, Bromwell MA, Lankford ME, Monterosso JR, O'Brien CP.. Comparing attentional bias to smoking cues in current smokers, former smokers, and non-smokers using a dot-probe task. Drug Alcohol Depend. 2002;67(2):185-91.
[13]Deruelle C, Rondan C, Gepner B, Tardif C. Spatial frequency and face processing in children with autism and Asperger syndrome. J Autism Dev Disord. 2004;34(2):199-210.
[14]Goffaux V, Rossion B. Face inversion disproportionately impairs the perception of vertical but not horizontal relations between features. J Exp Ment Psychol Human Percept Perform. 2006;33(4):995-1002.
[15]Rossion B. Picture-plane inversion leads to qualitative changes of face perception. Acta Psychologica. 2008;128(2):274-89.
[16]Rossion B. Distinguishing the cause and consequence of face inversion: The perceptual field hypothesis. Acta Psychologica. 2009;132(3):300-12.
[17]Leonard HC, Karmiloff-Smith A, Johnson MH. The development of spatial frequency biases in face recognition. J Exp Child Psychol. 2010;106(4):193-207.
[18]Awasthi B, Friedman J, Williams M. Faster, stronger, lateralized: Low spatial frequency information supports face Processing. Neuropsychologia. 2011;49(13):3583-90.
[19]de Heering A, Turati C, Rossion B, Bulf H, Goffaux V, Simion F. Newborns’ face recognition is based on spatial frequencies below 0.5 cycles per degree. Cognition. 2008;106(1):444-54.
[20]Adams RJ, Courage ML. Using a single test to measure human contrast sensitivity from early childhood to maturity. Vision Res. 2002;42(9):1205-10.
[21]Deruelle C, Rondan C, Salle-Collemiche X, Bastard-Rosset D, Da Fonséca D. Attention to low-and high-spatial frequencies in categorizing facial identities, emotions and gender in children with autism. Brain Cogn. 2008;66(2):115-23.
[22]Deruelle C, Fagot J. Categorizing facial identities, emotions, and genders: Attention to high- and low-spatial frequencies by children and adults. J Exp Child Psychol. 2005;90(2):172-84.
[23]Neta M, Whalen P. The primacy of negative interpretations when resolving the valence of ambiguous facial expressions. Psychol Sci. 2010;21(7):901-7.
[24]Goffaux V, Hault B, Michel C, Vuong QC, Rossion B. The respective role of low and high spatial frequencies in supporting configural and featural processing of faces. Perception. 2005;34(1):77-86.
[25]Gazzaniga MS, Lvry RB, Mangun GR. Cognitive neuroscience: The biology of the mind. 4th ed. New York: W. W. Norton & Company; 2013.