Perceptual representations mediate effects of stimulus properties on liking for music.

Perceptual pleasure and its concomitant hedonic value play an essential role in everyday life, motivating behavior and thus influencing how individuals choose to spend their time and resources. However, how pleasure arises from perception of sensory information remains relatively poorly understood. In particular, research has neglected the question of how perceptual representations mediate the relationships between stimulus properties and liking (e.g., stimulus symmetry can only affect liking if it is perceived). The present research addresses this gap for the first time, analyzing perceptual and liking ratings of 96 nonmusicians (power of 0.99) and finding that perceptual representations mediate effects of feature-based and information-based stimulus properties on liking for a novel set of melodies varying in balance, contour, symmetry, or complexity. Moreover, variability due to individual differences and stimuli accounts for most of the variance in liking. These results have broad implications for psychological research on sensory valuation, advocating a more explicit account of random variability and the mediating role of perceptual representations of stimulus properties.

Probabilistic modelling of microtiming perception.

Music performances are rich in systematic temporal irregularities called "microtiming", too fine-grained to be notated in a musical score but important for musical expression and communication. Several studies have examined listeners' preference for rhythms varying in microtiming, but few have addressed precisely how microtiming is perceived, especially in terms of cognitive mechanisms, making the empirical evidence difficult to interpret. Here we provide evidence that microtiming perception can be simulated as a process of probabilistic prediction. Participants performed an XAB discrimination test, in which an archetypal popular drum rhythm was presented with different microtiming. The results indicate that listeners could implicitly discriminate the mean and variance of stimulus microtiming. Furthermore, their responses were effectively simulated by a Bayesian model of entrainment, using a distance function derived from its dynamic posterior estimate over phase. Wide individual differences in participant sensitivity to microtiming were predicted by a model parameter likened to noisy timekeeping processes in the brain. Overall, this suggests that the cognitive mechanisms underlying perception of microtiming reflect a continuous inferential process, potentially driving qualitative judgements of rhythmic feel.

Modeling enculturated bias in entrainment to rhythmic patterns.

Long-term and culture-specific experience of music shapes rhythm perception, leading to enculturated expectations that make certain rhythms easier to track and more conducive to synchronized movement. However, the influence of enculturated bias on the moment-to-moment dynamics of rhythm tracking is not well understood. Recent modeling work has formulated entrainment to rhythms as a formal inference problem, where phase is continuously estimated based on precise event times and their correspondence to timing expectations: PIPPET (Phase Inference from Point Process Event Timing). Here we propose that the problem of optimally tracking a rhythm also requires an ongoing process of inferring which pattern of event timing expectations is most suitable to predict a stimulus rhythm. We formalize this insight as an extension of PIPPET called pPIPPET (PIPPET with pattern inference). The variational solution to this problem introduces terms representing the likelihood that a stimulus is based on a particular member of a set of event timing patterns, which we initialize according to culturally-learned prior expectations of a listener. We evaluate pPIPPET in three experiments. First, we demonstrate that pPIPPET can qualitatively reproduce enculturated bias observed in human tapping data for simple two-interval rhythms. Second, we simulate categorization of a continuous three-interval rhythm space by Western-trained musicians through derivation of a comprehensive set of priors for pPIPPET from metrical patterns in a sample of Western rhythms. Third, we simulate iterated reproduction of three-interval rhythms, and show that models configured with notated rhythms from different cultures exhibit both universal and enculturated biases as observed experimentally in listeners from those cultures. These results suggest the influence of enculturated timing expectations on human perceptual and motor entrainment can be understood as approximating optimal inference about the rhythmic stimulus, with respect to prototypical patterns in an empirical sample of rhythms that represent the music-cultural environment of the listener.

Leveraging FPGAs for Accelerating Short Read Alignment.

One of the key challenges facing genomics today is how to efficiently analyze the massive amounts of data produced by next-generation sequencing platforms. With general-purpose computing systems struggling to address this challenge, specialized processors such as the Field-Programmable Gate Array (FPGA) are receiving growing interest. The means by which to leverage this technology for accelerating genomic data analysis is however largely unexplored. In this paper, we present a runtime reconfigurable architecture for accelerating short read alignment using FPGAs. This architecture exploits the reconfigurability of FPGAs to allow the development of fast yet flexible alignment designs. We apply this architecture to develop an alignment design which supports exact and approximate alignment with up to two mismatches. Our design is based on the FM-index, with optimizations to improve the alignment performance. In particular, the n-step FM-index, index oversampling, a seed-and-compare stage, and bi-directional backtracking are included. Our design is implemented and evaluated on a 1U Maxeler MPC-X2000 dataflow node with eight Altera Stratix-V FPGAs. Measurements show that our design is 28 times faster than Bowtie2 running with 16 threads on dual Intel Xeon E5-2640 CPUs, and nine times faster than Soap3-dp running on an NVIDIA Tesla C2070 GPU.

SCHIP expansion and parental coverage: an evaluation of Wisconsin's BadgerCare.

The Wisconsin BadgerCare program, which became operational in July 1999, expanded public health insurance eligibility to both parents and children in families with incomes below 185% of the U.S. poverty line (200% for those already enrolled). This eligibility expansion was part of a federal initiative known as the State Children's Health Insurance Program (SCHIP). Wisconsin was one of only four states that initially expanded coverage to parents of eligible children. In this paper, we attempt to answer the following question: To what extent does a public program with the characteristics of Wisconsin's BadgerCare program reduce the proportion of the low-income adult population without health care coverage? Using a coordinated set of administrative databases, we track three cohorts of mother-only families: those who were receiving cash assistance under the Wisconsin AFDC and TANF programs in September 1995, 1997, and 1999, and who subsequently left welfare. We follow these 19,201 "welfare leaver" families on a quarterly basis for up to 25 quarters, from 2 years before they left welfare through the end of 2001, making it possible to use the labor market information and welfare history of the women in analyzing outcomes. We apply multiple methods to address the policy evaluation question, including probit, random effects, and two difference-in-difference strategies, and compare the results across methods. All of our estimates indicate that BadgerCare substantially increased public health care coverage for mother-only families leaving welfare. Our best estimate is that BadgerCare increased the public health care coverage of all adult leavers by about 17-25% points.