Bertram F. Malle, Zeynep Aydin
Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, USA
Title: Norm Representations are Graded: A Replication in Turkish
Abstract
Norms are social forces that influence people’s behavior, and they are cognitively represented so as to enable such social influence. Previous research suggests that norms not only come as classes of prescriptions (should) and prohibitions (must not) but have graded strength—that is, they are ordered from weak to strong. English speakers strongly agree in the graded ordering of norm expressions and appear to distinguish five grades of prescriptions and five grades of prohibitions. The present study replicated these results in Turkish, a language very different from English. Turkish speakers showed as high or higher agreement in the ordering of norm expressions and distinguished five levels of both prescriptions and prohibitions.
Koiti Hasida
RIKEN Center for Advanced Intelligence Project, The University of Tokyo, Tokyo, Japan
Title: Personal AI and Open Citizen Science
Abstract
Centralized AIs (CAIs) based on centralized management of personal data (PD) are threatening human rights and democracy and impairing value creation by PD. Worse still, it is impossible for the humanity to jointly restrict CAIs because they create winners. Personal AIs (PAIs) based on decentralized management of PD (DMPD) can displace CAIs, as they create much larger value than CAIs by fully utilizing your PD to more deeply and carefully intervening in your life. Second, DMPD also enables decentralized governance of PAIs and other services to maximize their added value.
Yukyung Kim, Jeongwook Kwon, Je-Hyeop Lee, Je-Choon Park, Sangbin Yun, Jeehye Seo, Byoung-Kyong Min
Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
Title: Out-of-phase transcranial alternating current stimulation between dorsolateral prefrontal and anterior cingulate cortices alters P300 amplitudes during inhibitory-control performance
Abstract
Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation technique that is considered to entrain the brain’s oscillations and affect cognitive functions. In this study, we investigated whether theta-frequency tACS with a phase lag between dorsolateral prefrontal and anterior cingulate cortices altered inhibitory-control performance. Twenty-one healthy participants were instructed to perform a Stroop task before and after tACS treatment. Participants were exposed to tACS for 9 minutes per session: one for the in-phase (0° phase lag) condition and the other for the out-of-phase (180° phase lag) condition between the left dorsolateral prefrontal cortex (lDLPFC) and the dorsal anterior cingulate cortex (dACC). We analyzed the reaction times and event-related potentials (ERPs) between the in-phase and out-of-phase conditions as compared with the no-tACS condition. In the incongruent condition, the out-of-phase condition yielded significant reduction in response times (in-phase = 664.45 ms, out-of-phase = 639.95 ms; t(20) = 2.14, p < 0.05) and P300 amplitudes (in-phase = 11.29 μV, out-of-phase = 9.07 μV; t(20) = 3.42, p < 0.01) compared to the in-phase condition. Particularly for the incongruent trials preceded by congruent trials, the out-of-phase stimulation resulted in significant reduction in both reaction times (in-phase = 674.18 ms, out-of-phase = 641.90 ms; t(20) = 2.23, p < 0.05) and P300 amplitudes (in-phase = 11.62 μV, out-of-phase = 9.01 μV; t(20) = 2.29, p < 0.05) compared to the in-phase condition. These findings imply that out-of-phase theta-frequency tACS plays a selectively disinhibitory control role between lDLPFC and dACC depending on the precedent congruency condition, resulting in the enhancement of inhibitory task performance.
Acknowledgement: This work was supported by the Convergent Technology R&D Program for Human Augmentation (grant number 2020M3C1B8081319 to B.-K.M.), which is funded by the Korean government through the National Research Foundation of Korea. The authors declare no competing interests.
Dong-Jin Sung, Ji-Hyeok Jeong, Keun-Tae Kim, Hyungmin Kim
Bionics Research Center, Korea Institute of Science & Technology, Seoul, Korea
Title: Data selection-based method to improve gait-related multi-session motor imagery brain-computer interface
Abstract
Prominent performance in lower limb motor imagery (MI)-based brain-computer interfaces (BCIs) is crucial for their successful application in real-life scenarios. Nonetheless, achieving stable performance across multiple sessions remains a challenge due to the non-stationarity of electroencephalogram (EEG) data. To address this issue, we propose a data selection-based method to mitigate the observed instability in gait-related multi-session MI.
In order to evaluate our method, we collected three gait-related classes of MIs (gait, sit-down, rest) from two healthy male participants and two participants with spinal cord injury (SCI). Each session included data acquisition of 30 trials for each class, and a total of 4 sessions were recorded for each participant. A convolutional neural network (CNN)-based model was used to extract features from the individual sessions, which were evaluated using the cosine similarity function with respect to the target session. Based on an empirically defined criterion of a cosine similarity above 0.1, we selected relevant feature data from each session. The selected target-relevant features corresponding to each class from previous sessions were then utilized in the training phase.
The proposed method showed an increase in accuracy compared to self-training, which employs only the training data from the target session (Data selection: 70.83% vs Self-training: 65.72%). These findings suggest that incorporating relevant data from past sessions enhances the stability of performance across multiple sessions in MI EEG data.
