The Unconscious Impact of Personalized Smartphone Notifications
Poster No:
620
Submission Type:
Abstract Submission
Authors:
Prakash Mishra1, Saurabh Gandhi2
Institutions:
1Indian Institute of Technology, Delhi, Delhi, Delhi, 2Indian Institute of Technology, Jodhpur, Jodhpur, Rajasthan
First Author:
Co-Author:
Introduction:
With increasing use and dependence on smartphones, smartphone notifications are expected to have strong behavioural associations, and have indeed been shown to have strong emotional associations for certain users (Stothart, 2015). Here we evaluate the unconscious impact of notifications on neural activity using the mismatch negativity ERP component, which is known to reflect pre-attentive emotion evaluation.
Methods:
Participants engage in a task-irrelevant auditory oddball paradigm with two blocks. In the 1st block, we use personally significant (PS) smartphone notifications as deviant along with non-PS (NPS) tones (beep) as the standard; and vice-versa in the 2nd block. Each block consists of 900 trials with 1/6th of the trials comprising of the deviant tone. The number of standard tones between two consecutive deviant tones varies between three and nine. Each trial lasts 1300 ms, and each auditory stimulus starts between 300 ms and 310 ms randomly within a trial. The length of each auditory stimulus fluctuates between 700 ms and 710 ms. The participant watches a silent movie while sitting in a comfortable chair and receives auditory stimuli from headphones (Fig. 1). To assess participants' attentiveness during the task, we pose five questions related to the video after each block. We collect electroencephalogram (EEG) data during the task using the 64-channel actiCHamp system at 1000 Hz sampling rate with reference electrode Cz. Following EEG data collection during the task, we gather participants' responses using the Smartphone Addiction Scale (SAS ) (Li et al., 2023) and Mobile Phone Problematic Use Scale (MPPUS) (Mach et al., 2020). EEG data pre-processing and analysis are conducted using MNE-Python. Raw EEG is re-referenced to mastoid channels TP9 and TP10. A bandpass filter of 0.1–30 Hz is applied, followed by epoch creation from -100 ms to 700 ms relative to auditory stimulus onset. Epochs containing EOG artifacts, identified via MNE's standard EOG pipeline with prefrontal electrodes, are excluded. Additionally, epochs with a peak-to-peak voltage exceeding 80 µV in frontocentral electrodes are removed to minimize muscle artifacts (overall, 37.9% deviant and 36.98% standard epochs are dropped per subject). Event-related potentials (ERPs) are obtained by averaging epochs for the same event across subjects for frontocentral electrodes. Mismatch negativity (MMN) is calculated by subtracting standard ERP responses from deviant ERP responses for NPS (Fig. 1 B) as well as PS (Fig. 1C) audio stimuli at Cz.
Results:
In this study, we analyze data from 30 subjects with an average age of 25 years. The MPPUS scores range from 83 to 230 (mean=146.5; standard deviation (SD) = 34.37). The SAS scores vary from 51 to 161(mean=109.83; SD = 26.09). A strong correlation (r = 0.79) between MPPUS and SAS scores (Fig. 2A) suggests that participants provide reliable, consistent responses across different assessments. 9 participants with SAS scores less than 97.4 (30th percentile of SAS score) are considered low smartphone users (LSU), and 9 participants with SAS scores greater than 119.6 (70th percentile of SAS score) are regarded as high smartphone users (HSU). Similar to a previous result with text notifications in the pre-smartphone era (Roye et al., 2007), PS tones evoke a strong and earlier MMN peak than NPS tones (Fig. 1D). Further, when PS tones act as deviants, a late positive peak, corresponding to the attention-related P3a component, is evoked only for the HSU group (Fig. 2B, C) suggesting a conscious attention shift occurring specifically in that group.
Conclusions:
We show that personally significant smartphone notification tones a stronger pre-attentive response compared to non-significant tones, reflecting the strong impact of notifications even without being conscious of them. Moreover, PS tones tend to cause an attention shift towards them for high smartphone users, but not so much for low users.
Emotion, Motivation and Social Neuroscience:
Emotional Learning
Emotional Perception 1
Social Cognition 2
Keywords:
Addictions
Electroencephaolography (EEG)
Emotions
1|2Indicates the priority used for review
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2. Kanjo, E., Kuss, D. J., & Ang, C. S. (2017). NotiMind: Utilizing Responses to Smart Phone Notifications as Affective Sensors. IEEE Access, 5, 22023–22035. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2755661
3. Schirmer, A., & Escoffier, N. (2010). Emotional MMN: Anxiety and heart rate correlate with the ERP signature for auditory change detection. Clinical Neurophysiology, 121(1), 53–59. https://doi.org/10.1016/j.clinph.2009.09.029
4. Li, J., Alghamdi, A., Li, H., Lepp, A., Barkley, J., Zhang, H., & Soyturk, I. (2023). Reassessing the smartphone addiction scale: Support for unidimensionality and a shortened scale from an American sample. Computers in Human Behavior, 139, 107552. https://doi.org/10.1016/j.chb.2022.107552
5. Mach, A., Demkow-Jania, M., Klimkiewicz, A., Jakubczyk, A., Abramowska, M., Kuciak, A., Serafin, P., Szczypiński, J., & Wojnar, M. (2020). Adaptation and Validation of the Polish Version of the 10-Item Mobile Phone Problematic Use Scale. Frontiers in Psychiatry, 11, 427. https://doi.org/10.3389/fpsyt.2020.00427
6. Roye, A., Jacobsen, T., & Schröger, E. (2007). Personal significance is encoded automatically by the human brain: An event-related potential study with ringtones. European Journal of Neuroscience, 26(3), 784–790. https://doi.org/10.1111/j.1460-9568.2007.05685.