Entraining IDyOT: Timing in the Information Dynamics of Thinking

Forth, Jamie; Agres, Kat; Purver, Matthew; Wiggins, Geraínt A.

doi:10.3389/fpsyg.2016.01575

Cited by 20 publications

(28 citation statements)

References 121 publications

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“…The present Deep Improviser, while yet shallow, when comprised of CNN and RNN probably imitates some of the sequential aspects of time series models. But we also plan in the longer run to integrate Deep Improviser with algorithmic approaches based on information theoretic and perceptual decision-making models [46,47].…”

Section: Discussion Conclusion and Future Workmentioning

confidence: 99%

Towards a Deep Improviser: a prototype deep learning post-tonal free music generator

Dean

Forth

2018

Neural Comput & Applic

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Two modest-sized symbolic corpora of post-tonal and post-metrical keyboard music have been constructed, one algorithmic, the other improvised. Deep learning models of each have been trained. The purpose was to obtain models with sufficient generalisation capacity that in response to separate fresh input seed material, they can generate outputs that are statistically distinctive, neither random nor recreative of the learned corpora or the seed material. This objective has been achieved, as judged by k-sample Anderson-Darling and Cramer tests. Music has been generated using the approach, and preliminary informal judgements place it roughly on a par with an example of composed music in a related form. Future work will aim to enhance the model such that it deserves to be fully evaluated in relation to expression, meaning and utility in real-time performance.

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Section: Discussion Conclusion and Future Workmentioning

confidence: 99%

Towards a Deep Improviser: a prototype deep learning post-tonal free music generator

Dean

Forth

2018

Neural Comput & Applic

Self Cite

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“…Examples of these are Nengo (Eliasmith, 2013), neural blackboard architectures (Van der Velde and De Kamps, 2006), and the previously mentioned Smolensky and Legendre (2006). Further, the use of conceptual spaces in cognitive architectures is an area of active research, as seen in, for example, Forth et al (2016); Lieto et al (2017). Whilst the current work does not model key aspects of human cognition such as dynamics or action selection, it provides a model of compositionality that can be utilized by these architectures in representing inputs and how they may combine to form novel representations.…”

Section: Related Workmentioning

confidence: 99%

Interacting Conceptual Spaces I: Grammatical Composition of Concepts

Bolt

Coecke

Genovese

et al. 2019

Conceptual Spaces: Elaborations and Applications

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The categorical compositional approach to meaning has been successfully applied in natural language processing, outperforming other models in mainstream empirical language processing tasks. We show how this approach can be generalized to conceptual space models of cognition. In order to do this, first we introduce the category of convex relations as a new setting for categorical compositional semantics, emphasizing the convex structure important to conceptual space applications. We then show how to construct conceptual spaces for various types such as nouns, adjectives and verbs. Finally we show by means of examples how concepts can be systematically combined to establish the meanings of composite phrases from the meanings of their constituent parts. This provides the mathematical underpinnings of a new compositional approach to cognition.

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“…Bearing in mind eye movement research in response to auditory experiment has shown results in pupil dilation response [45], the present ndings on motor modulation of attentional processing would we extended by a broader motor response. Moreover, the model would modify the Information Dynamics of Thinking (IDyOT) model for language and music of Forth and colleagues [44] may bear in mind previous motor response and unexpected external stimulus. Forth and colleagues proposed a mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic [44].…”

Section: Conclusion: Improve Of Modeling Novel Response Due To Pmentioning

confidence: 99%

“…Moreover, the model would modify the Information Dynamics of Thinking (IDyOT) model for language and music of Forth and colleagues [44] may bear in mind previous motor response and unexpected external stimulus. Forth and colleagues proposed a mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic [44].…”

Section: Conclusion: Improve Of Modeling Novel Response Due To Pmentioning

confidence: 99%

Prior context influences motor brain areas in an auditory oddball task and prefrontal cortex multitasking modelling

Mugruza-Vassallo

Potter

Tsiora³

et al. 2020

Preprint

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In this study, the relationship of orienting of attention, motor control and the Stimulus- (SDN) and Goal-Driven Network (GDN) was explored through an innovative method for fMRI analysis considering all voxels after every type condition. The task consisted of four conditions: standard target (G), novel (N), neutral (Z) and noisy target (NG). First running average reaction times of each condition was made. In the second level analysis, ‘distracted’ participants evoke brain activations and differences in both hemispheres neural network for selective attention as previously reported while the participants, as a whole, demonstrated mainly left cortical and subcortical activations. A context analysis was run in the behaviourally distracted participant group contrasting the trials immediately prior to the G trials, namely one of the Z, N or NG conditions. Results showed different prefrontal activations were evoked dependent on prior context in auditory modality with recruiting from 1 to 10 brain prefrontal areas. The more motor response and influence of the previous novel stimulus the more prefrontal areas, which extend hierarchical studies of prefrontal control of attention and explain better how the auditory processing interferes with movement. Also, it was addressed how subcortical loops and model of previous motor response affected the signal processing the novel stimulus, lateralized or simultaneously, with the Target. This model makes the first grasp to understand how auditory stimulus is changing motor responses in a kind of self-induced motor response. Moreover, current BCI works address some multimodal stimulus-driven systems.

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Entraining IDyOT: Timing in the Information Dynamics of Thinking

Cited by 20 publications

References 121 publications

Towards a Deep Improviser: a prototype deep learning post-tonal free music generator

Towards a Deep Improviser: a prototype deep learning post-tonal free music generator

Interacting Conceptual Spaces I: Grammatical Composition of Concepts

Prior context influences motor brain areas in an auditory oddball task and prefrontal cortex multitasking modelling

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