scholarly journals PPM-Decay: A computational model of auditory prediction with memory decay

2020 ◽  
Vol 16 (11) ◽  
pp. e1008304
Author(s):  
Peter M. C. Harrison ◽  
Roberta Bianco ◽  
Maria Chait ◽  
Marcus T. Pearce
Keyword(s):  
Computational Model ◽  
Predictive Performance ◽  
Pattern Detection ◽  
Human Cognition ◽  
Variable Order ◽  
Probabilistic Prediction ◽  
Auditory Pattern ◽  
New Variant ◽  
Memory Decay ◽  
With Memory

Statistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from training sequences. However, PPM has limitations as a cognitive model: in particular, it has a perfect memory that weights all historic observations equally, which is inconsistent with memory capacity constraints and recency effects observed in human cognition. We address these limitations with PPM-Decay, a new variant of PPM that introduces a customizable memory decay kernel. In three studies—one with artificially generated sequences, one with chord sequences from Western music, and one with new behavioral data from an auditory pattern detection experiment—we show how this decay kernel improves the model’s predictive performance for sequences whose underlying statistics change over time, and enables the model to capture effects of memory constraints on auditory pattern detection. The resulting model is available in our new open-source R package, ppm (https://github.com/pmcharrison/ppm).

scholarly journals PPM-Decay: A Computational Model of Auditory Prediction with Memory Decay

2020 ◽  
Author(s):  
Peter M. C. Harrison ◽  
Roberta Bianco ◽  
Maria Chait ◽  
Marcus T. Pearce
Keyword(s):  
Computational Model ◽  
Predictive Performance ◽  
Pattern Detection ◽  
Human Cognition ◽  
Variable Order ◽  
Probabilistic Prediction ◽  
Auditory Pattern ◽  
New Variant ◽  
Memory Decay ◽  
With Memory

AbstractStatistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from training sequences. However, PPM has limitations as a cognitive model: in particular, it has a perfect memory that weights all historic observations equally, which is inconsistent with memory capacity constraints and recency effects observed in human cognition. We address these limitations with PPM-Decay, a new variant of PPM that introduces a customizable memory decay kernel. In three studies – one with artificially generated sequences, one with chord sequences from Western music, and one with new behavioral data from an auditory pattern detection experiment – we show how this decay kernel improves the model’s predictive performance for sequences whose underlying statistics change over time, and enables the model to capture effects of memory constraints on auditory pattern detection. The resulting model is available in our new open-source R package, ppm (https://github.com/pmcharrison/ppm).

scholarly journals Correction: PPM-Decay: A computational model of auditory prediction with memory decay

2021 ◽  
Vol 17 (5) ◽  
pp. e1008995
Author(s):  
Peter M. C. Harrison ◽  
Roberta Bianco ◽  
Maria Chait ◽  
Marcus T. Pearce
Keyword(s):  
Computational Model ◽  
Memory Decay ◽  
With Memory

scholarly journals Information-theoretic Modeling of Perceived Musical Complexity

2019 ◽  
Vol 37 (2) ◽  
pp. 165-178
Author(s):  
Sarah A. Sauvé ◽  
Marcus T. Pearce
Keyword(s):  
Individual Differences ◽  
Computational Model ◽  
Information Content ◽  
Statistical Learning ◽  
Probabilistic Prediction ◽  
Information Theoretic ◽  
Musical Background ◽  
Musical Excerpts ◽  
Definition Of ◽  
Perceived Complexity

What makes a piece of music appear complex to a listener? This research extends previous work by Eerola (2016), examining information content generated by a computational model of auditory expectation (IDyOM) based on statistical learning and probabilistic prediction as an empirical definition of perceived musical complexity. We systematically manipulated the melody, rhythm, and harmony of short polyphonic musical excerpts using the model to ensure that these manipulations systematically varied information content in the intended direction. Complexity ratings collected from 28 participants were found to positively correlate most strongly with melodic and harmonic information content, which corresponded to descriptive musical features such as the proportion of out-of-key notes and tonal ambiguity. When individual differences were considered, these explained more variance than the manipulated predictors. Musical background was not a significant predictor of complexity ratings. The results support information content, as implemented by IDyOM, as an information-theoretic measure of complexity as well as extending IDyOM's range of applications to perceived complexity.

scholarly journals Memory in a New Variant of King’s Family for Solving Nonlinear Systems

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1251
Author(s):  
Munish Kansal ◽  
Alicia Cordero ◽  
Sonia Bhalla ◽  
Juan R. Torregrosa
Keyword(s):  
Nonlinear Systems ◽  
Convergence Analysis ◽  
Numerical Experiments ◽  
Order Of Convergence ◽  
Recent Literature ◽  
Divided Difference ◽  
Difference Operators ◽  
New Variant ◽  
Order Four ◽  
With Memory

In the recent literature, very few high-order Jacobian-free methods with memory for solving nonlinear systems appear. In this paper, we introduce a new variant of King’s family with order four to solve nonlinear systems along with its convergence analysis. The proposed family requires two divided difference operators and to compute only one inverse of a matrix per iteration. Furthermore, we have extended the proposed scheme up to the sixth-order of convergence with two additional functional evaluations. In addition, these schemes are further extended to methods with memory. We illustrate their applicability by performing numerical experiments on a wide variety of practical problems, even big-sized. It is observed that these methods produce approximations of greater accuracy and are more efficient in practice, compared with the existing methods.

scholarly journals Modeling the potential energy field caused by mass density distribution with Eton approach

Open Physics ◽  
2016 ◽  
Vol 14 (1) ◽  
pp. 106-113 ◽  
Author(s):  
Badr Saad T. Alkahtani ◽  
Abdon Atangana
Keyword(s):  
Potential Energy ◽  
Fractional Derivative ◽  
Mass Density ◽  
Variable Order ◽  
Modified Model ◽  
New Approach ◽  
Energy Field ◽  
Variable Order Derivative ◽  
With Memory ◽  
Real World Problems

AbstractA new approach for modeling real world problems called the “Eton Approach” was presented in this paper. The "Eton approach" combines both the concept of the variable order derivative together with Atangana derivative with memory derivative. The Atangana derivative with memory is used to account for the memory and fractional derivative for its filter effect. The approach was used to describe the potential energy field that is caused by a given charge or mass density distribution.We solve the modified model numerically and present supporting numerical simulations.

scholarly journals Wayeb: a Tool for Complex Event Forecasting

10.29007/2s9t ◽  
2018 ◽  
Author(s):  
Elias Alevizos ◽  
Alexander Artikis ◽  
Georgios Paliouras
Keyword(s):  
Markov Chains ◽  
Computational Model ◽  
Real Time ◽  
Complex Event Processing ◽  
Pattern Detection ◽  
Event Processing ◽  
Timely Manner ◽  
Probabilistic Description ◽  
Event Forecasting ◽  
Symbolic Automata

Complex Event Processing (CEP) systems have appeared in abundance during the last two decades. Their purpose is to detect in real–time interesting patterns upon a stream of events and to inform an analyst for the occurrence of such patterns in a timely manner. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CEP engine. We present Wayeb, a tool that attempts to address the issue of Complex Event Forecasting. Wayeb employs symbolic automata as a computational model for pattern detection and Markov chains for deriving a probabilistic description of a symbolic automaton.

scholarly journals Chronic adolescent exposure to cannabis in mice leads to long-term sex-biased changes in gene expression networks across brain regions

2021 ◽  
Author(s):  
Yanning Zuo ◽  
Attilio Iemolo ◽  
Patricia Montilla-Perez ◽  
Hai-Ri Li ◽  
Xia Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Brain Regions ◽  
Human Cognition ◽  
Genome Wide Association Studies ◽  
Cognitive Behaviors ◽  
Network Analyses ◽  
With Memory ◽  
Adolescent Exposure

Background: The molecular mechanisms underlying the long-lasting behavioral changes associated with adolescent cannabis use are poorly understood. To this end, we performed gene network analyses of multiple brain regions in adult mice exposed during the entire adolescence to Delta-9-tetrahydrocannabinol (THC), the major psychoactive component of cannabis. Methods: Two weeks after the last exposure to THC (10 mg/kg) or vehicle, we measured cognitive behaviors and profiled the transcriptomes of 5 brain regions from 12 female and 12 male mice. We performed differential gene expression analysis and constructed gene coexpression networks (modules) to identify THC-induced transcriptional alterations at the level of individual genes, gene networks, and biological pathways. We integrated the THC-correlated modules with human traits from genome-wide association studies to identify potential regulators of disease-associated networks. Results: THC impaired cognitive behaviors of mice, with memory being more impacted in females than males, which coincided with larger transcriptional changes in the female brain. Modules involved in endocannabinoid signaling and inflammation were correlated with memory deficits in the female dorsal medial striatum and ventral tegmental area, respectively. Converging pathways related to dopamine signaling and addiction were altered in the female amygdala and male nucleus accumbens. Moreover, the connectivity map of THC-correlated modules uncovered intra- and inter-region molecular circuitries influenced by THC. Lastly, modules altered by THC were enriched in genes relevant for human cognition and neuropsychiatric disorders. Conclusions: These findings provide novel insights concerning the genes, pathways and brain regions underlying persistent behavioral deficits induced by adolescent exposure to THC in a sex-specific manner.

scholarly journals TonalEmergence: An Agent-Based Model of Tonal Coordination

2021 ◽  
Author(s):  
Matthew Setzler ◽  
Robert Goldstone
Keyword(s):  
Computational Model ◽  
Empirical Studies ◽  
Human Cognition ◽  
Coordination Mechanisms ◽  
Agent Based Model ◽  
Agent Based ◽  
Music Improvisation ◽  
Interacting Agents ◽  
Mutual Adaptation ◽  
Complex Coordination

Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner's previous notes. The model replicates an interesting empirical result from a previous study of professional jazz pianists: that feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by 1) a tendency to strive toward consonance, 2) stochasticity, and 3) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.

scholarly journals A computational model of language functions in flexible goal-directed behaviour

2020 ◽  
Author(s):  
Giovanni Granato ◽  
Anna M. Borghi ◽  
Gianluca Baldassarre
Keyword(s):  
Computational Model ◽  
Cognitive Flexibility ◽  
Neuropsychological Test ◽  
Wisconsin Card Sorting Test ◽  
Inner Speech ◽  
Human Cognition ◽  
Card Sorting ◽  
Feedback Processing ◽  
Language Functions ◽  
Stimulus Features

The function of language in high-order goal-directed human cognition is an important topic at the centre of current debates. Experimental evidence shows that inner speech, representing a self-directed form of language, empowers cognitive processes such as working memory, perception, categorization, and executive functions. Here we study the relations between inner speech and processes like feedback processing and cognitive flexibility. To this aim we propose a computational model that controls an artificial agent who uses inner speech to internally manipulate its representations. The agent is able to reproduce human behavioural data collected during the solution of the Wisconsin Card Sorting test, a neuropsychological test measuring cognitive flexibility, also when a verbal shadowing protocol is used. The components of the model were systematically lesioned to clarify the specific impact of inner speech on the agent’s behaviour. The results indicate that inner speech improves the efficiency of internal manipulation. Specifically, it makes the representations linked to specific visual features more disentangled, thus improving the agent’s capacity to engage/disengage attention on stimulus features after positive/negative action outcomes. Overall, the model shows how inner speech could improve goal-directed internal manipulation of representations and enhance behavioural flexibility.

scholarly journals Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model

2013 ◽  
Vol 7 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Vlad Chiriacescu ◽  
Leen-Kiat Soh ◽  
Duane F. Shell
Keyword(s):  
Computational Model ◽  
Cognitive Science ◽  
Computational Simulation ◽  
Single Agent ◽  
Learning Model ◽  
Human Learning ◽  
Human Cognition ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Multi Agent

Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented.

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