To establish a reference system for representing spatial information was dependent on specific characteristics of the learning situation [1]. The direction was the crucial mechanism for higher-level memories of space [2]-[3]. Human possessed the cognitive ability to engage in spatial navigation and to generate mental maps of the environment. The spatial cognitive performance was decided by accessing the information of features, such as environment structure and objects. Human would promote their performance, when they performed several empirical navigations.
Siegel and White (1975) defined the landmarks of distinctive features that allowed them to be more easily remembered. In previous study, some of the subjects used only local landmarks while others relied exclusively on global landmarks. Other subjects used local landmarks at one location and global landmarks at the other location [4]. Gillner and Mallot manipulated the positions of landmarks following training showed that the navigation performances were the poorest when the new positions of the landmarks offered conflicting action choices [5]. Human might adopted several strategies according to their habits to navigate in the virtual environment. Their movements might be associated with landmarks.
The transfer of knowledge obtained in a virtual environment to the real world had been studied [6]. Subject sufficiently exposed to the virtual training environment.
The virtual environment training eventually would surpass real-world training [7].
There were structural landmarks and objective landmarks in the real world. We built the 3D virtual environment to simulate the real world. We adopted the simple structure, like as checkerboard lattice, to construct the virtual environment. This design could avoid the influence of the structural features and then we could not consider the influence of the structure during navigating in the maze.
In this study, we focused on the influence which was caused from single type landmarks, for example the global landmarks or the local landmarks. When subjects navigated in the virtual maze, only single typical landmarks were referred in the virtual maze. There were many walking paths in the simple structure and subjects might not always choose the specific walking path. There was no effect about router navigation experience because the starting position was randomly decided by program.
Subjects would use landmarks to navigate and then subjects could be familiar with the virtual environments by landmarks. Two types of landmarks were designed to ensure that each of them could be only dealt with as global or local landmarks. We would observe the affection that caused from different types of the landmarks. The global landmarks surrounded outside in the maze. In another maze, the local landmarks existed inside the maze on the wall. Subjects made decisions about the direction to move based on the relative location of the target. The landmarks provided the help and the guidance for subjects. The landmarks could be regarded as spatial cues that were associated with the relative location of the target. The landmarks played a role as mental reference points of spatial navigation. Subjects could build spatial memory of the relative relationships between the landmarks and the target. Subjects relied on a set of neighbor landmarks to determine the target’s location in the global landmarks but subjects relied on a sequence of intermediate local landmarks to navigate in the virtual environment. Subjects would recognize their location by the landmarks around themselves during navigating. Behavior experiment aimed to investigate the different types of the landmarks that affected the performance of subjects’ spatial navigation.
Brain activity related to the landmark information obtained from virtual environments [8]. Human could use the spatial memory to cognize the environment by recalling the landmarks and spatial relationships [9]. Participants were sensitive to the information content of landmarks and they would allocate memory resources form
the information of the landmarks [10].The cortical theta (4 – 8 Hz [11]) oscillations had been observed during the variety of learning tasks, including recognition [12] and recall [13]. The frequency of theta-wave episodes occurred more frequently in the complex mazes [14].The frequency of theta was more frequent in recall trials than in learning trials in the complex mazes [15]. Human cortical theta oscillations acted to coordinate sensory and motor brain activity in various brain regions to facilitate exploratory learning and navigational planning [16]. Many researchers had observed theta oscillations during cognitive tasks [17]-[22]. The scalp electroencephalogram (EEG) was recorded from 64 channels during human virtual maze navigation. The theta oscillation was usually localized at two regions which were the fontal region, possibly associated with spatial working memory, and the parietal-temporal region [23]. Neuroimaging studies suggested the role of the theta oscillations that was were associated with spatial navigation and wayfinding tasks [24]-[28]. The theta band in the scalp in the frontal midline was often present during working memory [29]. Brain activity was recorded when subjects navigated the virtual mazes by using landmarks.
We would observe the variation of brain activities and then we would see if different landmarks caused the different responses of brain dynamics. When the target was appeared, subjects would recall the relative location of the target by loading working memory and subjects would consult the landmarks to search the target. The theta power might increase in the frontal midline during navigating.
The different difficulty was closely related to the type of visual cues, such as landmarks, hall way structure, or location information. In previous study, landmark information and optic flow information significantly reduced the navigating time in the virtual maze [30]. In this study, we focused on the effect of different typical landmarks. Two types were divided into the global landmarks and the local landmarks.
We researched the effect of landmarks to affect spatial cognitive function by
physiological signals, such as brain waves. Through physiological signals we could objectively understand each cognitive state of the subjects. Both types of landmarks provided different style of help for guiding direction. We would adopt the brain dynamic of the EEG data recorded in the individual situation. We tested brain activities data recorded in between the global condition and location condition by using permutation tests to see if there was the significant difference of brain activities in the same independent component or not.
II. Materials and Methods
2.1. Implementation of the Virtual Environment
The virtual maze environment in the current study, FlexNavi (i.e., flexible navigation), was implemented in two independent modules: the 3D environment and the control module. The 3D environment is a 3D model in 3ds-Max (Autodesk®, San Rafael, CA, USA) compatible format (3ds format). The 3D model can be created with any 3D model software that can export 3ds format, and its layout is totally up to the research purpose of the users. The control module is implemented in Visual C++
using WorldToolKit (WTK) library (SENSE8®). The WTK library is an advanced cross-platform environment for the development of high-performance and three-dimensional graphics applications. The C program including the WTK library is used and its library function is called up to control the three-dimensional models.
The control-module loads the 3D model and allows the user to navigate inside the virtual environment in first-person view. This two-module framework of implementation gives users the flexibility to create any kind of maze layout that suits their research purpose, while avoiding the cumbersome process of acquiring programming skills. Figure 2-1 showed the 3D models for global landmarks and local landmarks.
For general application purpose, the environment was built for different purposes and the program allowed the user to use a text file to define various parameters, such as basic control, target environment, task mode, and input/output setups.
(a)
(b)
Figure 2-1: 3D model (a)Global (b)Local
2.1.1. Apparatus
The virtual maze environment was loaded on a PC-compatible desktop computer equipped with 2GB of RAM and a 22 TFT LCD monitor. The participants used four arrow keys on the keyboard to control their movement inside the maze. The view of the screen was in a first-person perspective. The participant pressed the “UP” key to move forward, “DOWN” key to move backward, and “RIGHT” and “LEFT” keys to turn leftwards and rightwards, respectively.
The layout of the maze used in the current experiment was a 5 × 5 grid of interleaving roads and blocks surrounded by walls. There were four targets to be searched. Targets were displayed on one of four sides of block or on one segment of the surrounding walls. Fig. 2-2 showed the targets in the virtual environment of different landmarks. The target was in the bottom of screen. Only one target was appeared in a navigating task.
Global
(a) (b)
(c)(d)
Local
(e) (f) (g) (h)
Figure 2-2: (a) 、 (b) 、 (c) 、 (d) Targets appeared in the virtual
environment of global landmarks. (e)、(f)、(g)、(g) Targets appeared
in the virtual environment of local landmarks.
In the global condition, ten large architectures were placed outside the surrounded walls. Participants could see from everywhere inside the maze and the target was searched in the maze (Figure 2-3 (a)). In the local condition, ten picture landmarks were placed on the sides of different blocks and targets were searched in the maze (Figure 2-3 (b)). We built two virtual mazes which were consisted of two typical landmarks (Figure 2-3 (c)). Participants could see a particular landmark only from a few restricted orientations at certain locations inside the maze. However, if participants control his/her view by rotating 360 degrees at any position, they will see at least one landmark. For a given type of maze, only one type of landmarks was displayed. There were no specific paths for subjects to walk in the maze composed of simple structure because there were several moving styles of the minimal paths.
(a)
(b)
(c)
Figure 2-3: (a) Global landmarks and targets. (b) Local landmarks and targets. (c) The structure of the maze and two typical landmarks in the virtual environment.
Targets were also pictures attached on the blocks, but their appearances and sizes were different from the local landmarks. At any given moment during the navigation task, the bottom of screen showed the target picture (0.1 width x 0.15 length of screen) indicating what the participant was supposed to look for. For each type of landmark condition, there were eight different targets. Locations and outlook of the targets differed between mazes of different types of landmarks.
2.1.2. Program Setup
2.1.2.1. Basic Control
The basic control settings include the program window size, forward and rotational speed of navigation, pitch angle of viewpoint, the brightness of environment light, the log file name, the control keys, joystick and mouse.
The target environment settings contain the total numbers of mazes and targets, the number of targets, the order of targets, whether showing the other target pictures
during searching one target or not, the farthest distance between view position and target to display the target picture, the user-defended positions and directions for random start points database of searching task, and the limited distances to make sure the available random start positions not too close to the end position of prior searching task and current target position.
2.1.2.2. Task Mode Settings
The task mode setup includes the order of task modes and durations, and the time to switch maze. The modes of task contain searching task, motion condition, navigation followed by defined sign, fixation marker in the center of screen, all black display, one full screen picture, and a center fixation marker with one picture background. Except the searching task and navigation followed by defined sign, the other mode screen was halted at the last position. During the motion condition, four white rectangles randomly display on the upper, bottom, left and right sides of screen then participants must respond the correct direction by control keys.
2.1.3.3. Input/Output Settings
The output signal is an 8 bits event and grid position signal transmitted from parallel port (2pin ~ 9pin) and the input signal is a one bit signal received from parallel port for synchronizing the tasks switch. The input/output setup includes the parallel port number, whether enabling the input or output port or not, and the pin number of input port.
2.2. Questionnaire
The questionnaire was a technique commonly used by researchers to realize the feelings of subjects who participated in the behavioral experiment. After completing the experiment, subjects were asked to fill the questionnaire associated with searching
style. Subjects might make use of different strategies to search the target in the different mazes. The information of the questionnaire could let us know that the characters related to different typical landmarks were gave rise to the different impacts for subjects.
2.3. Subjects
2.3.1. Behavior Experiment Subjects
Thirty right-handed healthy adults gave informed consent before the study approved by local Institutional Review Board. Fifteen males and females participated in the experiment. Mean age of the females was 20.5 ± 3.5years and mean age of the males was 22.3 ± 4.6 years. None had a history of neurological or psychiatric disorders or any sign of color blindness or visual field defects.All participants were rewarded with NT$ 200.
2.3.2. EEG Experiment Subjects
Thirteen right-handed healthy females gave informed consent before the study before the study approved by local Institutional Review Board. Mean age of the females was 21.5 ± 3.6years. None had a history of neurological or psychiatric disorders or any sign of color blindness or visual field defects. All participants were rewarded with NT$ 800.
2.4. Behavior Data Analysis
2.4.1. Corrected Travel Distance of Path Ratio Analysis
The travel distance was the length of covered path when the participant navigated from the start position to the target position in each trial, and the corrected
travel distance was the ratio of the real path length which the participants travelled during a trial to the shortest path length (Figure 2-4). Path ratio was used to determine the performance of the spatial navigation. If subjects were familiar with the virtual environment, the value of path ratio would decrease.
Figure 2-4: The method of calculating the path.
2.4.2 Analysis of Variance (ANOVA) Analysis
The analysis of variance (ANOVA) simultaneously tests if the means of the populations are the same in the same significant level [31]-[34]. There are several assumptions about ANOVA. (a) Variables must be normally distributed. (b) Samples are independent. (c) Variances of populations are equal. The ANOVA estimates the variance within the populations and the variance of inter-populations by sampling data. The freedom of each population is used to apply the F distribution test.
The one-way analysis of variance (one-way ANOVA) is a technique used to compare means of two or more samples. The one-way ANOVA is used to test the average of groups and one-way ANOVA is used to test whether these differences are significant. It appears to test the average, but it analyzes the variance in fact. The one-way ANOVA is a robust procedure relative to violations of the normality assumptions. (a) Variables must be normally distributed. (b)The sample is a simple random sample (SRS). (c)Samples are independent. (d)Variances of populations are
equal. The ratio of variance is a comparison of the variance amongst the different
populations.
The one-way ANOVA measures significant effects of one factor only and the two-way ANOVA measures the effects of two factors simultaneously. The methods of ANOVA tests are divided into non-repeat experiment and repeated experiment. For non-repeat experiment of two-way ANOVA, the data are classified with two factors and the factors are tested individually to see whether there are significant impacts on the average for two factors. Classification variables are used to test the relationship between the independent variables and the dependent variables. We can see if there is any interaction between them. For repeated experiment, one-way ANOVA is used to test the different values of the same group in the different situations. For repeated experiment of two-way ANOVA, the above test is processed in addition and then we can observe the impact of the interaction between two independent variables and the dependent variable.
2.5. EEG Data Analysis
2.5.1. EEG Data Analysis Procedures
Fig. 2-5 showed the schema of the data analysis procedure for EEG signals. The EEG data were recorded with 16-bit quantization level at a sampling rate of 2000 Hz .The EEG signals were down-sampled to sampling rate from 2000 to 500 Hz. The EEG signals were filtered with a low-pass with the cut-off frequencies at 50 Hz and then the EEG signals were filtered with high-pass filter with the cut-off frequencies at 0.5 Hz. The EEG data were extracted epochs from 1 s before the deviation onsets and 23 s after the deviation onsets. We would remove some epochs that were consisted too many noisy signals. These remainding epochs were combined with each other. The EEG data were processed by independent component analysis (ICA) and we applied
the method of Event-Related spectral perturbations (ERSP) to investigate the brain dynamics responses.
Figure 2-5: The schema for EEG analysis
The EEG responses related to different navigation stages of a block were extracted and combined together. There were several types of artifacts which were identified and eliminated from EEG data by using the EEGLAB toolbox. Criteria of artifact rejection included linear drift (Figure 2-6 (a)) and abnormally distributed data (Figure 2-6 (b)). The artifacts might be generated from equipments or the physiologic activity like as muscle artifact. Eye movements usually were observed in frontal electrodes. Sweat artifact was characterized by very low-frequency oscillations (Figure 2-6 (c)) [35]. The high impedance at posterior temporal electrode T6 resulted
in this electrode recording from the ground on the forehead (Figure 2-6 (d)) [35].
(a)
(b)
(c)
(d)
Figure 2-6: The artifacts were existed in the EEG signals. (a): Artifacts were generated from the bad channel T7. (b): Artifacts were muscle activities. (c) The distribution here (midtemporal electrode T3 and occipital electrode O1) were suggested sweat. Note that morphology and frequency were also consistent with slow rolling eye movements [35]. (d) The high impedance at posterior temporal electrode T6 results in this electrode recording from the ground on the forehead [35].
2.5.2. Independent Component Analysis (ICA)
The broad spread of EEG source potentials through the brain, skull, and scalp at each scalp electrode. The ICA algorithm was suitable to decompose the EEG data which was collected with single scalp channels. The ICA algorithm was related to the method called blind signals separation and it was a powerful approach to identify the complex spatiotemporal dynamic [36]-[37].The ICA was used to find a linear signal separation and feature extraction. The ICA performed a linear un-mixing of multi-channel EEG recordings into temporally independent statistical source signals.
After ICA decomposition, the spatial filters were chosen the maximum among temporally independent signals in the mixed channel data. These information sources might represent synchronous activity. The application of the ICA algorithm was solved identification, localization and separation [38]-[40]. (1) The component source locations to the scalp sensors were fixed throughout the data. (2) The source activities of mixing source activities were summed linearly and instantaneously. (3) The component source activity waveforms were temporally independent of one another. (4) There were no differential delays involved in projecting the source signals to the different sensors. (5) The probable distributions of the individual component source generated by nonlinear cortical dynamics activity values were not precise Gaussian.
(6)The signal source of physiological activity was not time locked to the sources of the EEG data. We would adopt independent component analysis (ICA) to find spatial filters for information sources of the EEG data. Figure 2-7 showed the process about ICA decomposition data which were transform into temporally independent processes.
ICA applied to a matrix of EEG scalp data to find a non-mixed matrix of weights and
ICA applied to a matrix of EEG scalp data to find a non-mixed matrix of weights and