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Musical preference is highly individualized and is an area of active study to develop methods for its quantification. Recently, preference-based behavior, associated with activity in brain reward circuitry, has been shown to follow lawful, quantifiable patterns, despite broad variation across individuals. These patterns, observed using a keypress paradigm with visual stimuli, form the basis for relative preference theory (RPT). Here, we sought to determine if such patterns extend to non-visual domains (i.e., audition) and dynamic stimuli, potentially providing a method to supplement psychometric, physiological, and neuroimaging approaches to preference quantification. For this study, we adapted our keypress paradigm to two sets of stimuli consisting of seventeenth to twenty-first century western art music (Classical) and twentieth to twenty-first century jazz and popular music (Popular). We studied a pilot sample and then a separate primary experimental sample with this paradigm, and used iterative mathematical modeling to determine if RPT relationships were observed with high R2 fits. We further assessed the extent of heterogeneity in the rank ordering of keypress-based responses across subjects. As expected, individual rank orderings of preferences were quite heterogeneous, yet we observed mathematical patterns fitting these data similar to those observed previously with visual stimuli. These patterns in music preference were recurrent across two cohorts and two stimulus sets, and scaled between individual and group data, adhering to the requirements for lawfulness. Our findings suggest a general neuroscience framework that predicts human approach/avoidance behavior, while also allowing for individual differences and the broad diversity of human choices; the resulting framework may offer novel approaches to advancing music neuroscience, or its applications to medicine and recommendation systems.

In augmented reality (AR), audio markers can be alternatives to image markers for rendering virtual objects when an AR device camera fails to identify the image marker due to lighting conditions and/or the distance between the marker and device. However, conventional audio markers simply broadcast a rendering queue to anonymous devices, making it difficult to provide specific virtual objects of interest to the user. To overcome this limitation without relying on camera-based sensing, we propose a user-aware audio marker system using low frequency ultrasonic signal processing. The proposed system detects users who stay within the marker using ultrasonic-based object detection, and then it uses ultrasonic communication based on windowed differential phase shift keying modulation in order to send a rendering queue only to those users near the marker. Since the proposed system uses commercial microphones and speakers, conventional telecommunication systems can be employed to deliver the audio markers. The performance of the proposed audio marker system is evaluated in terms of object detection accuracy and communication robustness. First, the object detection accuracy of the proposed system is compared with that of a pyroelectric infrared (PIR) sensor-based system in indoor environments, and it is shown that the proposed system achieves a lower equal error rate than the PIR sensor-based system. Next, the successful transmission rate of the proposed system is measured for various distances and azimuths under noisy conditions, and it is also shown that the proposed audio marker system can successfully operate up to approximately 4 m without any transmission errors, even with 70 dBSPL ambient noise.

A noncoherent low-frequency ultrasonic (LFU) communication system is proposed for near-field communication using commercial off-the-shelf (COTS) speakers and microphones. Since the LFU communication channel is known to be a frequency-selective characteristic, the proposed system is basically designed by differential phase-shift keying (DPSK) modulation with forward error correction. In addition, automatic gain control of the carrier frequency band over the LFU communication channel is proposed. Then, in order to optimize the symbol length of the proposed LFU communication system under a realistic aerial acoustic channel, a propagation model of the LFU communication channel is proposed by incorporating aerial acoustic attenuation. The performance of the proposed LFU communication system is demonstrated on two different tasks: bit error rate (BER) measurement and successful transmission rate (STR) comparison with Google Tone for various distances between the transmitter and the receiver. Consequently, the proposed method can operate without a bit error at a distance of 8 m under various noise conditions with sound pressure level of 80 dB. Moreover, the proposed method achieves higher STR than Google Tone on a task of URL transmission using two laptops.

Individuals tend to give losses approximately 2-fold the weight that they give gains. Such approximations of loss aversion (LA) are almost always measured in the stimulus domain of money, rather than objects or pictures. Recent work on preference-based decision-making with a schedule-less keypress task (relative preference theory, RPT) has provided a mathematical formulation for LA similar to that in prospect theory (PT), but makes no parametric assumptions in the computation of LA, uses a variable tied to communication theory (i.e., the Shannon entropy or information), and works readily with non-monetary stimuli. We evaluated if these distinct frameworks described similar LA in healthy subjects, and found that LA during the anticipation phase of the PT-based task correlated significantly with LA related to the RPT-based task. Given the ease with which non-monetary stimuli can be used on the Internet, or in animal studies, these findings open an extensive range of applications for the study of loss aversion. Furthermore, the emergence of methodology that can be used to measure preference for both social stimuli and money brings a common framework to the evaluation of preference in both social psychology and behavioral economics.