@proceedings {2022,
	title = {Comparing Musicians and Non-musicians{\textquoteright} Expectations in Music and Vision},
	year = {2022},
	month = {09/2022},
	pages = {1-8},
	publisher = {ACM},
	address = {Austria and online},
	abstract = {The role of expectation in music has been of research interest for decades. Expectation mechanisms have also received considerable attention in vision, due in part to the widespread interest in predictive coding. Past research has uncovered different types of expectations that may be formed when exposed to a sequential stimulus, such as schematic expectation (based on general knowledge) and dynamic expectation (based on properties within the current stimulus). Yet to our knowledge, a direct comparison of the relative contribution of these types of expectation has not been performed within the same subjects through careful manipulation of stimuli, nor has this comparison been made across the musical and visual domains. This listener study aims to uncover the relative influence of dynamic and schematic expectations in musical and visual stimuli, and investigate the role of expertise in forming expectations by testin g both musicians and non-musicians. Our findings suggest that musicians are indeed more sensitive than non-musicians to the dynamic and schematic properties of musical stimuli, and they generally produce a wider range of expectedness ratings than non-musicians. Interestingly, musicians also interpret schematic information in the visual condition differently than non-musicians, suggesting that musical training may have influenced their expectation mechanisms more generally.},
	keywords = {cross-modal comparison, Expectation, music, musician vs non-musician, vision},
	author = {Kat R Agres and Ting Yuan Tay and Marcus Pearce}
}
@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 {2017,
	title = {Information-Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory},
	journal = {Cognitive Science},
	volume = {42},
	year = {2018},
	month = {01/2018},
	pages = {43-76},
	abstract = {A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners{\textquoteright} memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners{\textquoteright} expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session.},
	keywords = {Auditory perception, computational modeling, Expectation, Information theory, music cognition, Predictive coding, Recognition memory},
	issn = {1551-6709},
	doi = {10.1111/cogs.12477},
	url = {http://dx.doi.org/10.1111/cogs.12477},
	author = {Agres, Kat and Abdallah, Samer and Pearce, Marcus}
}
@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}
}
@proceedings {2016,
	title = {The Information Dynamics of Thinking: a cognitive architecture for human creative cognition},
	year = {2016},
	month = {2016},
	abstract = {We present IDyOT (Wiggins, 2012b), the Information Dynamics of Thinking, a novel cognitive architecture that instantiates
the (much more abstract) Global Workspace Theory of Baars (1988), in a way motivated by a principle of
efficiency in information processing. IDyOT simulates a hypothetical fundamental perceptual/cognitive loop, explicating
bottom-up learning of concepts, the hierarchical structure in them, and the correlations between them from
percepts. Even though the proposal is basically cognitive, embodiment is not ruled out, and the tight connection between
perception and cognition potentially affords a future extension of the account to an embodied view.},
	author = {Wiggins, Geraint A and Agres, Kat and Forth, Jamie and Purver, Matt}
}
@proceedings {2016,
	title = {Modeling metaphor perception with distributional semantics vector space models},
	year = {2016},
	month = {08/2016},
	abstract = {In this paper, we present a novel application of a computational model of word meaning to capture human judgments of the linguistic properties of metaphoricity, familiarity, and meaningfulness. We present data gathered from human subjects regarding their ratings of these properties over a set of word pairs specifically designed to exhibit varying degrees of metaphoricity. We then investigate whether these properties can be measured in terms of geometric features of a model of distributional lexical semantics. We compare the performance of two models, our own Concept Discovery Model which dynamically constructs context-sensitive subspaces, and a state-of-the-art static distributional semantic model, and find that our dynamic model performs significantly better in its measurement of metaphoricity.},
	keywords = {computational creativity, distributional semantics, metaphor, vector space models},
	author = {Kat R. Agres and Stephen McGregor and Karolina Rataj and Matthew Purver and Geraint A. Wiggins}
}
@conference {27,
	title = {Conceptualizing Creativity: From Distributional Semantics to Conceptual Spaces},
	booktitle = {Proceedings of the Sixth International Conference on Computational Creativity},
	year = {2015},
	author = {Agres, Kat and McGregor, Stephen and Purver, Matthew and Wiggins, Geraint}
}
@article {39,
	title = {From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation},
	journal = {Journal of Artificial General Intelligence},
	volume = {6},
	year = {2015},
	pages = {55-86},
	abstract = {We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discovering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynamically select characteristic dimensions associated with seed terms, and thus a subspace of terms defining the related concept. This approach performs as well as, and in some cases better than, leading distributional semantic models on a WordNet-based concept discovery task, while also providing a model of concepts as convex regions within a space with interpretable dimensions. In particular, it performs well on more specific, contextualized concepts; to investigate this we therefore move beyond WordNet to a set of human empirical studies, in which we compare output against human responses on a membership task for novel concepts. Finally, a separate panel of judges rate both model output and human responses, showing similar ratings in many cases, and some commonalities and divergences which reveal interesting issues for computational concept discovery.},
	keywords = {behavioural validation, computational creativity, concept discovery, conceptual spaces, distributional semantics},
	issn = {1946-0163},
	doi = {10.1515/jagi-2015-0004},
	url = {http://www.degruyter.com/view/j/jagi.2015.6.issue-1/jagi-2015-0004/jagi-2015-0004.xml?format=INT$\#$fn1},
	author = {Stephen McGregor and Kat Agres and Matthew Purver and Geraint A. Wiggins}
}
@conference {29,
	title = {An information-theoretic account of musical expectation and memory},
	booktitle = {Proceedings of the 35th Annual Conference of the Cognitive Science Society},
	year = {2013},
	author = {Agres, Kat and Abdallah, Samer and Pearce, Marcus}
}
