How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.     

Customer-Initiated Support and Employees’ Proactive Customer Service Performance: A Multilevel Examination of Proactive Motivation as the Mediator

Drawing on the proactive motivation model and theories of motivation, the current study examined customer-initiated support as an antecedent of employees’ proactive customer service performance (PCSP) and investigated the mediating role of proactive motivations and the moderating role of serving culture. We conducted two multilevel studies to test the proposed hypotheses. In study 1, we collected diary data from 158 nurses across 10 working days, and results showed that the relationship between nurses’ daily experience of customer-initiated support and their PCSP was mediated by self-efficacy, work meaning, and positive affect. In study 2, we collected multilevel multi-source data from 373 nurses nested within 80 units and found that nurses’ experience of customer-initiated support was positively related to their supervisor-rated PCSP via role-breadth self-efficacy and work meaning, but not through positive affect. Further, team-level serving culture strengthened the positive relationship between customer-initiated support and role-breadth self-efficacy. These findings shed light on understanding the effect of customer-initiated support as an external factor on prompting service employees’ proactive behaviors, potential motivational mediators to explain the underlying mechanisms, and potential boundary conditions inside of service organizations to strengthen the positive effects of customer-initiated support.