Jim Davies - Academia.edu (original) (raw)
Papers by Jim Davies
Lecture Notes in Computer Science, 2005
Knowledge-Based Systems, 2008
Cognitive Systems Research, 2009
Imagination, Cognition and Personality, 2011
In this article we review work on mental imagery in adults and children. We argue that the fundam... more In this article we review work on mental imagery in adults and children. We argue that the fundamental issue of how we respond to a stimulus to create a descriptive, propositional scene description, and then render this description into an image is poorly understood. In addition to providing a new framework by which to address this issue, we highlight several other topics within the study of mental imagery that require further investigation. These include inhibiting irrelevant information and making inferences, knowing the difference between reality and imagination (or even beliefs and imagination), and point of view. We conclude by suggesting future directions of study to address these additional topics.
Case-based problem solving refers to reasoning about new problems by reusing past cases. Visual c... more Case-based problem solving refers to reasoning about new problems by reusing past cases. Visual case-based problem solving pertains to reuse of past cases that contain only visual knowledge. In this paper, we explore the problem of automated adaptation of diagrammatic cases, i.e., automatic transfer of diagrammatic knowledge from a source case to a target problem. We describe Galatea, a computer program that adapts diagrammatic cases. Galatea explicitly represents knowledge states in the source case in the form of propositions, and transfers visual transformations from the source case to the target problem. A companion paper in the same conference [15] addresses the task of retrieving diagrammatic cases.
Complex problem solving typically involves the generation of a procedure consisting of an ordered... more Complex problem solving typically involves the generation of a procedure consisting of an ordered sequence of steps. Analogical reasoning is one strategy for solving complex problems, and visual reasoning is another. Visual analogies pertain to analogies based only on visual knowledge. In this paper, we describe the use of Galatea, a computational model of visual analogies in problem solving, to model the problem solving of a human subject (L14). L14 was a given the task of solving a complex problem using analogy in a domain that contained both visual and non-visual knowledge, and was encouraged to use visual analogy. We describe how Galatea models L14's use of visual analogy in problem solving.
Lecture Notes in Computer Science, 2007
Proceedings of the 8th ACM conference on Creativity and cognition, 2011
A cognitive model of the visual imagination will produce "incoherent" results when it adds elemen... more A cognitive model of the visual imagination will produce "incoherent" results when it adds elements to an imagined scene that come from different contexts (e.g., "computer" and "cheese" with "mouse"). We approach this problem with a model that infers coherence relations from co-occurrence probabilities of labels in images. We show that this algorithm's serial traversal of networks of co-occurrence relations for a particular query produces greater coherence than one leading model in the field of computational coherence: Thagard's connectionist model.
Proceedings of the International Conference on Evolutionary Computation Theory and Applications, 2014
Lecture Notes in Computer Science, 2014
The Knowledge Engineering Review, 2013
Visuo is an implemented Python program that models visual reasoning. It takes as input a descript... more Visuo is an implemented Python program that models visual reasoning. It takes as input a description of a scene in words (e.g. ‘small dog on a sunny street’) and produces estimates of the quantitative magnitudes of the qualitative input (e.g. the size of the dog and the brightness of the street). We claim that reasoners transfer quantitative knowledge to new concepts from distributions of familiar concepts in memory. We also claim that visuospatial magnitudes should be stored as distributions over fuzzy sets. We show that Visuo successfully predicts quantitative knowledge to new concepts.
Computational Intelligence, 2006
We show that visio-spatial representations and reasoning can be used as a similarity metric for c... more We show that visio-spatial representations and reasoning can be used as a similarity metric for case-based protein structure prediction. Our system retrieves pairs of α-helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair. We show that similar protein contact maps predict similar 3D protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in case-based reasoning.
Information Systems Frontiers, 2006
Determining the three-dimensional structure of a protein is an important step in understanding bi... more Determining the three-dimensional structure of a protein is an important step in understanding biological function. Despite advances in experimental methods (crystallography and NMR) and protein structure prediction techniques, the gap between the number of known protein sequences and determined structures continues to grow. Approaches to protein structure prediction vary from those that apply physical principles to those that consider known amino acid sequences and previously determined protein structures. In this paper we consider a two-step approach to structure prediction: (1) predict contacts between amino acids using sequence data; (2) predict protein structure using the predicted contact maps. Our focus is on the second step of this approach. In particular, we apply a case-based reasoning framework to determine the alignment of secondary structures based on previous experiences stored in a case base, along with detailed knowledge of the chemical and physical properties of proteins. Case-based reasoning is founded on the premise that similar problems have similar solutions. Our hypothesis is that we can use previously determined structures and their contact maps to predict the structure for novel proteins from their contact maps.
Cognitive science, Jan 10, 2017
An incoherent visualization is when aspects of different senses of a word (e.g., the biological &... more An incoherent visualization is when aspects of different senses of a word (e.g., the biological "mouse" vs. the computer "mouse") are present in the same visualization (e.g., a visualization of a biological mouse in the same image with a computer tower). We describe and implement a new model of creating contextual coherence in the visual imagination called Coherencer, based on the SOILIE model of imagination. We show that Coherencer is able to generate scene descriptions that are more coherent than SOILIE's original approach as well as a parallel connectionist algorithm that is considered competitive in the literature on general coherence. We also show that co-occurrence probabilities are a better association representation than holographic vectors and that better models of coherence improve the resulting output independent of the association type that is used. Theoretically, we show that Coherencer is consistent with other models of cognitive generation. In ...
Visual and spatial representations seem to play a significant role in analogy. In this paper, we ... more Visual and spatial representations seem to play a significant role in analogy. In this paper, we describe a specific role of visual representations: two situations that appear dissimilar non-visuospatially may appear similar when rerepresented visuospatially. We present a computational theory of analogy in which visuospatial re-representation enables analogical transfer in cases where there are ontological mismatches in the non-visuospatial representation. Realizing this theory in a computational model with specific data structures and algorithms first requires a computational model of visuospatial analogy, i.e., a model of analogy that only uses visuospatial knowledge. We have developed a computer program, called Galatea, which implements a core part of this model: it transfers problem-solving procedures between analogs that contain only visual and spatial knowledge. In this paper, we describe both how Galatea accomplishes analogical transfer using only visuospatial knowledge, and how it might be extended to support visuospatial re-representation of situations represented non-visually.
Lecture Notes in Computer Science, 2005
Knowledge-Based Systems, 2008
Cognitive Systems Research, 2009
Imagination, Cognition and Personality, 2011
In this article we review work on mental imagery in adults and children. We argue that the fundam... more In this article we review work on mental imagery in adults and children. We argue that the fundamental issue of how we respond to a stimulus to create a descriptive, propositional scene description, and then render this description into an image is poorly understood. In addition to providing a new framework by which to address this issue, we highlight several other topics within the study of mental imagery that require further investigation. These include inhibiting irrelevant information and making inferences, knowing the difference between reality and imagination (or even beliefs and imagination), and point of view. We conclude by suggesting future directions of study to address these additional topics.
Case-based problem solving refers to reasoning about new problems by reusing past cases. Visual c... more Case-based problem solving refers to reasoning about new problems by reusing past cases. Visual case-based problem solving pertains to reuse of past cases that contain only visual knowledge. In this paper, we explore the problem of automated adaptation of diagrammatic cases, i.e., automatic transfer of diagrammatic knowledge from a source case to a target problem. We describe Galatea, a computer program that adapts diagrammatic cases. Galatea explicitly represents knowledge states in the source case in the form of propositions, and transfers visual transformations from the source case to the target problem. A companion paper in the same conference [15] addresses the task of retrieving diagrammatic cases.
Complex problem solving typically involves the generation of a procedure consisting of an ordered... more Complex problem solving typically involves the generation of a procedure consisting of an ordered sequence of steps. Analogical reasoning is one strategy for solving complex problems, and visual reasoning is another. Visual analogies pertain to analogies based only on visual knowledge. In this paper, we describe the use of Galatea, a computational model of visual analogies in problem solving, to model the problem solving of a human subject (L14). L14 was a given the task of solving a complex problem using analogy in a domain that contained both visual and non-visual knowledge, and was encouraged to use visual analogy. We describe how Galatea models L14's use of visual analogy in problem solving.
Lecture Notes in Computer Science, 2007
Proceedings of the 8th ACM conference on Creativity and cognition, 2011
A cognitive model of the visual imagination will produce "incoherent" results when it adds elemen... more A cognitive model of the visual imagination will produce "incoherent" results when it adds elements to an imagined scene that come from different contexts (e.g., "computer" and "cheese" with "mouse"). We approach this problem with a model that infers coherence relations from co-occurrence probabilities of labels in images. We show that this algorithm's serial traversal of networks of co-occurrence relations for a particular query produces greater coherence than one leading model in the field of computational coherence: Thagard's connectionist model.
Proceedings of the International Conference on Evolutionary Computation Theory and Applications, 2014
Lecture Notes in Computer Science, 2014
The Knowledge Engineering Review, 2013
Visuo is an implemented Python program that models visual reasoning. It takes as input a descript... more Visuo is an implemented Python program that models visual reasoning. It takes as input a description of a scene in words (e.g. ‘small dog on a sunny street’) and produces estimates of the quantitative magnitudes of the qualitative input (e.g. the size of the dog and the brightness of the street). We claim that reasoners transfer quantitative knowledge to new concepts from distributions of familiar concepts in memory. We also claim that visuospatial magnitudes should be stored as distributions over fuzzy sets. We show that Visuo successfully predicts quantitative knowledge to new concepts.
Computational Intelligence, 2006
We show that visio-spatial representations and reasoning can be used as a similarity metric for c... more We show that visio-spatial representations and reasoning can be used as a similarity metric for case-based protein structure prediction. Our system retrieves pairs of α-helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair. We show that similar protein contact maps predict similar 3D protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in case-based reasoning.
Information Systems Frontiers, 2006
Determining the three-dimensional structure of a protein is an important step in understanding bi... more Determining the three-dimensional structure of a protein is an important step in understanding biological function. Despite advances in experimental methods (crystallography and NMR) and protein structure prediction techniques, the gap between the number of known protein sequences and determined structures continues to grow. Approaches to protein structure prediction vary from those that apply physical principles to those that consider known amino acid sequences and previously determined protein structures. In this paper we consider a two-step approach to structure prediction: (1) predict contacts between amino acids using sequence data; (2) predict protein structure using the predicted contact maps. Our focus is on the second step of this approach. In particular, we apply a case-based reasoning framework to determine the alignment of secondary structures based on previous experiences stored in a case base, along with detailed knowledge of the chemical and physical properties of proteins. Case-based reasoning is founded on the premise that similar problems have similar solutions. Our hypothesis is that we can use previously determined structures and their contact maps to predict the structure for novel proteins from their contact maps.
Cognitive science, Jan 10, 2017
An incoherent visualization is when aspects of different senses of a word (e.g., the biological &... more An incoherent visualization is when aspects of different senses of a word (e.g., the biological "mouse" vs. the computer "mouse") are present in the same visualization (e.g., a visualization of a biological mouse in the same image with a computer tower). We describe and implement a new model of creating contextual coherence in the visual imagination called Coherencer, based on the SOILIE model of imagination. We show that Coherencer is able to generate scene descriptions that are more coherent than SOILIE's original approach as well as a parallel connectionist algorithm that is considered competitive in the literature on general coherence. We also show that co-occurrence probabilities are a better association representation than holographic vectors and that better models of coherence improve the resulting output independent of the association type that is used. Theoretically, we show that Coherencer is consistent with other models of cognitive generation. In ...
Visual and spatial representations seem to play a significant role in analogy. In this paper, we ... more Visual and spatial representations seem to play a significant role in analogy. In this paper, we describe a specific role of visual representations: two situations that appear dissimilar non-visuospatially may appear similar when rerepresented visuospatially. We present a computational theory of analogy in which visuospatial re-representation enables analogical transfer in cases where there are ontological mismatches in the non-visuospatial representation. Realizing this theory in a computational model with specific data structures and algorithms first requires a computational model of visuospatial analogy, i.e., a model of analogy that only uses visuospatial knowledge. We have developed a computer program, called Galatea, which implements a core part of this model: it transfers problem-solving procedures between analogs that contain only visual and spatial knowledge. In this paper, we describe both how Galatea accomplishes analogical transfer using only visuospatial knowledge, and how it might be extended to support visuospatial re-representation of situations represented non-visually.