# A Comparative Study of Real and Virtual Environment via Psychological and Physiological Responses
## 2 Materials and Methods
### Data Analysis  
The research protocol and the compilation-synchronization of the recordings were carried out using iMotions software (v.6.1). Psychological and physiological data were recorded for all participants, and they completed a basic demographic questionnaire. The focus of the psychological data was on quantifying cognitive performance (attention and memory) and sense of presence. Psychological attention task. The task was to react as soon as possible to a specific auditory stimulus (target) with a mouse click and avoid clicking the mouse when four other auditory stimuli were presented (distractors); the task is similar to the auditory continuous performance test [39]. The task was configured with 20% object stimuli (8 objects and 32 distractors); the minimum time between stimuli was 800 ms and the maximum was 1600 ms. The participants had 750 ms to react to the stimuli; the number of errors made (that is, reactions slower than the time limit or made to distractors) and reaction times were recorded (Task-Time and Task-Errors, respectively). Figure 3: Participants during the IVE experiment. Figure 2: Comparison of the physical classroom (**a**) and its IVE replica (**b**). Psychological memory task. The psychological memory task involved a structured exercise that bore resemblance to the experimental setup of the Deese, Roediger, and McDermott (DRM) paradigm experiments [40]. Within this task, participants were instructed to commit to memory sets of words that were connected to a broader concept but were not directly presented as explicit words themselves. This memory exercise comprised a list of 15 words, and the retention performance was observed to be comparable to findings of previous research [41]. The presentation of the word lists was delivered orally with the utilization of Loquendo TTS 7 (www.loquendo.com). Each participant was tasked with listening to the provided word lists and subsequently repeating them accurately within a designated time frame of no more than 30 s before progressing to the subsequent word set. Each subject performed 3 memory tasks for each scenario (real and virtual), so 6 counterbalanced lists were chosen. After the test, the number of words that subjects remembered was quantified and corrected for the recall rate reported [41] for each word (Memory-Correct answers metric). Presence. Sense of presence is the illusion of \"being there\" [42] in an environmental simulation. To quantify sense of presence, the participants completed the SUS (after the surname of its authors [PERSON], [PERSON], and [PERSON]; for further information see [43]) questionnaire. The questionnaire consists of six items, which assesses the user's impression of three facets of the simulated environment: the participant's immersion; the realism of the spatial experience; and the degree to which it is remembered as a space. The items are valued using a Likert-type scale from 1 to 7. The sum of the six questionnaire items (SUS-Total) was quantified. Physiological data. These focused on quantifying complementary aspects related to the attention process. The metrics were based on EEG, HRV, and EDA recordings. Electroencephalogram (EEG). EEGs measure variations in the electrical activity of the surface of the scalp [44]. In the present study, two metrics were calculated: the relative power (to reduce data variability between subjects; [45]) of the beta band (13-30 Hz) of the C3 electrode, which is associated with increased attention [46; 47]; and the high-beta band (21-30 Hz), which is associated with alertness [48], specifically of electrode F3, since the left prefrontal lobe has concentration and working memory among its functions [49]. The means (EEG-C3-Beta and EEG-F3-Highbeta, respectively) were quantified during each experiment. The recordings were made using the b-Alert X10 device (www.advancedbrainmonitoring.com). The raw signal, sampled at 256 Hz, was pre-processed and analysed using the EEGLAB toolbox [50] via Matlab (v.2016a). Heart rate variability (HRV).