@proceedings {2019,
	title = {A Comparative Study of Mental States in 2D and 3D Virtual Environments Using EEG},
	year = {2019},
	month = {10/2019},
	publisher = {IEEE},
	address = {Bari, Italy},
	abstract = {There is growing evidence that virtual reality (VR) can be used as an optional therapy method for stress relief and treatment of mental disorders such as a variety of anxiety disorders, depression and psychosis. However, more systematic studies to quantify the effects of VR for emotion elicitation and compare the effects of 3D and 2D environment for the same is necessary to design feasible and effective treatments. In this study, we design a cross-over experiment protocol comprising of two emotion eliciting environments relaxation and arousal - that is presented to the participant either in a 2D monitor or a 3D head-mounted (HMD) VR display. The EEG data is collected during the experiment and analyzed offline to classify emotion elicited by low and high arousal environments using SVM classification of band power features. A 10-fold cross validation is performed and classification accuracy for each subject is computed for 3D-VR and 2D-screen display. The average classification accuracies over subjects are obtained as 66.88\% and 59.27\% in 3D-VR and 2D-screen group respectively. The performance difference between 3D-VR and 2D screen is statistically significant (p < 0.05), indicating that 3D-VR generates more distinct EEG patterns associated with emotion elicitation. Further, significant differences in band powers from alpha, theta and beta bands are also observed between both groups. The results presented in this paper support the use of VR as an effective tool to study emotion elicitation and regulation, compared to a 2D screen.},
	author = {Pinar Bilgin and Kat Agres and Neethu Robinson and Aung Aung Phyo Wai and Cuntai Guan}
}
@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}
}
@proceedings {2017,
	title = {Intelligibility of Sung Lyrics: A Pilot Study},
	year = {2017},
	address = {Suzhou, China},
	author = {Karim Ibrahim and David Grunberg and Kat Agres and Chitra Gupta and Ye Wang}
}
@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}
}
@article {2017,
	title = {Evaluation of Musical Creativity and Musical Metacreation Systems},
	journal = {ACM Comput. Entertain.},
	volume = {14},
	year = {2016},
	month = {12/2016},
	pages = {3:1{\textendash}3:33},
	abstract = {The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher and not objectively at all. This article provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn among three types of creative systems: those that are purely generative, those that contain internal or external feedback, and those that are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers (1) evaluate their systems{\textquoteright} creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and (2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system and may be employed by the researcher to both better understand the efficacy of their system and its impact and to incorporate feedback into the system. Here we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition and can be used to demonstrate the impact and novelty of particular approaches.},
	keywords = {artificial intelligence, Music perception and cognition},
	issn = {1544-3574},
	doi = {10.1145/2967506},
	url = {http://doi.acm.org/10.1145/2967506},
	author = {Agres, Kat and Forth, Jamie and Wiggins, Geraint A.}
}
@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}
}
@proceedings {34,
	title = {Schematic processing as a framework for learning and creativity in CBR and CC},
	year = {2015},
	pages = {151-155},
	publisher = {ICCBR-15},
	author = {Agres, Kat and Wiggins, Geraint A.}
}
