@article {2019,
	title = {Re-Representing Metaphor: Modeling Metaphor Perception Using Dynamically Contextual Distributional Semantics},
	journal = {Frontiers in Psychology: Cognitive Science},
	volume = {10},
	year = {2019},
	month = {04/2019},
	pages = {1-18},
	type = {Original Research},
	abstract = {In this paper, we present a novel context-dependent approach to modeling word meaning, and apply it to the modeling of metaphor. In distributional semantic
approaches, words are represented as points in a high dimensional space generated from co-occurrence statistics; the distances between points may then be used to quantifying semantic relationships. Contrary to other approaches which use static, global representations, our approach discovers contextualized representations by dynamically projecting low-dimensional subspaces; in these ad hoc spaces, words can be re-represented in an open-ended assortment of geometrical and conceptual configurations as appropriate for particular contexts. We hypothesize that this context-specific re-representation enables a more effective model of the semantics of metaphor than standard static approaches. We test this hypothesis on a dataset of English word dyads rated for degrees of metaphoricity, meaningfulness, and familiarity by human participants. We demonstrate that our model captures these ratings more effectively than a state-of-the-art static model, and does so via the amount of contextualizing work inherent in the re-representational process.},
	keywords = {computational creativity, computational linguistics, conceptual models, distributional semantics, metaphor, vector space models},
	doi = {doi: 10.3389/fpsyg.2019.00765},
	author = {Stephen McGregor and Kat Agres and Karolina Rataj and Matthew Purver and Geraint Wiggins}
}
@article {9999,
	title = {Entraining IDyOT: timing in the information dynamics of thinking},
	journal = {Frontiers in Psychology: Auditory Cognitive Neuroscience},
	volume = {7:1575},
	year = {2016},
	abstract = {We present a novel hypothetical account of entrainment in music and language, in context of the Information Dynamics of Thinking model, IDyOT. The extended model affords an alternative view of entrainment, and its companion term, pulse, from earlier accounts. The model is based on hierarchical, statistical prediction, modeling expectations of both what an event will be and when it will happen. As such, it constitutes a kind of predictive coding, with a particular novel hypothetical implementation. Here, we focus on the model{\textquoteright}s mechanism for predicting when a perceptual event will happen, given an existing sequence of past events, which may be musical or linguistic. We propose a range of tests to validate or falsify the model, at various different levels of abstraction, and argue that computational modeling in general, and this model in particular, can offer a means of providing limited but useful evidence for evolutionary hypotheses.},
	keywords = {anticipatory cognition, cognitive architecture, evolutionary psychology, language, music perception, timing},
	author = {Jamie Forth and Kat Agres and Matt Purver and Geraint Wiggins}
}
