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Computational-Model-Based Analysis of Context Effects on Harmonic Expectancy.

ABSTRACT: Expectancy for an upcoming musical chord, harmonic expectancy, is supposedly based on automatic activation of tonal knowledge. Since previous studies implicitly relied on interpretations based on Western music theory, the underlying computational processes involved in harmonic expectancy and how it relates to tonality need further clarification. In particular, short chord sequences which cannot lead to unique keys are difficult to interpret in music theory. In this study, we examined effects of preceding chords on harmonic expectancy from a computational perspective, using stochastic modeling. We conducted a behavioral experiment, in which participants listened to short chord sequences and evaluated the subjective relatedness of the last chord to the preceding ones. Based on these judgments, we built stochastic models of the computational process underlying harmonic expectancy. Following this, we compared the explanatory power of the models. Our results imply that, even when listening to short chord sequences, internally constructed and updated tonal assumptions determine the expectancy of the upcoming chord.

SUBMITTER: Morimoto S

PROVIDER: S-EPMC4803284 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Computational-Model-Based Analysis of Context Effects on Harmonic Expectancy.

Morimoto Satoshi S Remijn Gerard B GB Nakajima Yoshitaka Y

PloS one 20160322 3

Expectancy for an upcoming musical chord, harmonic expectancy, is supposedly based on automatic activation of tonal knowledge. Since previous studies implicitly relied on interpretations based on Western music theory, the underlying computational processes involved in harmonic expectancy and how it relates to tonality need further clarification. In particular, short chord sequences which cannot lead to unique keys are difficult to interpret in music theory. In this study, we examined effects of ...[more]

PMID: 27003807

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Project description:BACKGROUND:Patient expectancy of therapeutic improvement is a primary mediator of placebo effects in antidepressant clinical trials, but its mechanisms are poorly understood. This study employed a novel antidepressant trial design, with integrated functional magnetic resonance imaging (fMRI), to manipulate patient outcome expectancy and examine its neural mediators. METHOD:Twenty-three depressed outpatients, in a randomized controlled trial were assigned to either Open (high outcome expectancy) or Placebo-controlled (low outcome expectancy) treatment with citalopram for eight weeks. fMRI scans were acquired before and after the expectancy manipulation (before medication treatment), while participants performed a masked emotional face task. Focusing on an amygdala region-of-interest (ROI), we tested a model where reduction in amygdala activation mediated outcome expectancy effects on the slope of change in depressive symptoms. RESULTS:Following the manipulation, significant differences between conditions were found in neural activation changes in the amygdala, as well as in superior temporal gyrus, insula, and thalamus. Findings support the proposed mediation model according to which activation in the left amygdala ROI decreased significantly in the Open as opposed to the Placebo-controlled group following randomization (p?=?0.009) for sad vs. neutral face contrast. The reduced left amygdala activation, in turn, was a significant predictor of decreased depressive symptoms during the trial (p?=?0.007), and the mediation model was significant. CONCLUSIONS:Results from this study, the first designed to identify the neural mechanisms of expectancy augmentation in an antidepressant randomized control trial, suggest that therapeutic modulation of amygdala activity may be an important pathway by which patient outcome expectancy influences depressive symptoms. CLINICALTRIALS. GOV IDENTIFIER:NCT01919216; Trial name: Placebo Effects in the Treatment of Depression: Cognitive and Neural Mechanisms, URL: https://clinicaltrials.gov/ct2/show/NCT01919216.

Project description:BackgroundIn the literature-based discovery, considerable research has been done based on the ABC model developed by Swanson. ABC model hypothesizes that there is a meaningful relation between entity A extracted from document set 1 and entity C extracted from document set 2 through B entities that appear commonly in both document sets. The results of ABC model are relations among entity A, B, and C, which is referred as paths. A path allows for hypothesizing the relationship between entity A and entity C, or helps discover entity B as a new evidence for the relationship between entity A and entity C. The co-occurrence based approach of ABC model is a well-known approach to automatic hypothesis generation by creating various paths. However, the co-occurrence based ABC model has a limitation, in that biological context is not considered. It focuses only on matching of B entity which commonly appears in relation between two entities. Therefore, the paths extracted by the co-occurrence based ABC model tend to include a lot of irrelevant paths, meaning that expert verification is essential.MethodsIn order to overcome this limitation of the co-occurrence based ABC model, we propose a context-based approach to connecting one entity relation to another, modifying the ABC model using biological contexts. In this study, we defined four biological context elements: cell, drug, disease, and organism. Based on these biological context, we propose two extended ABC models: a context-based ABC model and a context-assignment-based ABC model. In order to measure the performance of the both proposed models, we examined the relevance of the B entities between the well-known relations "APOE-MAPT" as well as "FUS-TARDBP". Each relation means interaction between neurodegenerative disease associated with proteins. The interaction between APOE and MAPT is known to play a crucial role in Alzheimer's disease as APOE affects tau-mediated neurodegeneration. It has been shown that mutation in FUS and TARDBP are associated with amyotrophic lateral sclerosis(ALS), a motor neuron disease by leading to neuronal cell death. Using these two relations, we compared both of proposed models to co-occurrence based ABC model.ResultsThe precision of B entities by co-occurrence based ABC model was 27.1% for "APOE-MAPT" and 22.1% for "FUS-TARDBP", respectively. In context-based ABC model, precision of extracted B entities was 71.4% for "APOE-MAPT", and 77.9% for "FUS-TARDBP". Context-assignment based ABC model achieved 89% and 97.5% precision for the two relations, respectively. Both proposed models achieved a higher precision than co-occurrence-based ABC model.

Dataset Information

Computational-Model-Based Analysis of Context Effects on Harmonic Expectancy.

Publications

Computational-Model-Based Analysis of Context Effects on Harmonic Expectancy.

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