1. The document proposes modifications to the ACT-R cognitive architecture to model mental rotation and imagery using explicit spatial representations and computational geometry processes.
2. It describes two mental rotation strategies - holistic and piecemeal - and presents productions that instantiate each strategy using the modified ACT-R representations and processes.
3. The models are able to qualitatively replicate the response time patterns found in human mental rotation experiments using different stimuli to encourage holistic or piecemeal strategies.
The document proposes a framework for systematically generating and evaluating creative ideas using a matrix representation of ideas and defined creativity operators. An idea is represented as a feature-attribute matrix where novelty and usefulness matrices are combined into a creativity matrix. Creativity operators are then used to generate new idea matrices which can be evaluated and improved using the matrix algebraic methods. The framework is intended to operationalize creativity in a repeatable way. An example application compares and improves competing product ideas for an iPad cover.
This document provides an overview of the Scale Invariant Feature Transform (SIFT) algorithm for feature detection and matching across images. It begins by introducing SIFT and its applications in computer vision. The document then outlines the key steps of the SIFT algorithm, including constructing scale space, approximating the Laplacian of Gaussian, finding keypoints, removing low-contrast keypoints, assigning orientations to keypoints, and generating SIFT features. Details are provided for each step, with examples to illustrate the process. The goal of SIFT is to detect features that are invariant to scale, rotation, illumination and viewpoint changes.
Year+8+extension+measurement+homework+task+blooms by kate johnsAngela Phillips
1. Students are assigned a group project to create an image with different shapes that relates mathematically to each other's images. They must decide on a theme.
2. For the individual task, each student must include examples of different shapes like a right triangle, hexagon, and other polygon. They must also include a circle with a specified area and another with a given circumference.
3. Students must show their work and calculations for finding the measurements of the required shapes within their images.
This document provides an overview of image matching techniques. It defines image matching as geometrically aligning two images so corresponding pixels represent the same scene region. Key aspects covered include detecting invariant local features, describing features in a scale and rotation invariant way using SIFT, and matching features between images. SIFT is highlighted as an extraordinarily robust technique that can handle various geometric and illumination changes. Feature matching is used in many computer vision applications such as image alignment, 3D reconstruction, and object recognition.
This document discusses using drawings and simulations to support model-based scientific reasoning. It argues that modeling is a key part of the scientific process and learning science. An experimental study is described that had children draw and simulate the solar system using a drawing-based modeling software called SimSketch. The study found that drawing static elements was easier than dynamic processes, and older children scored better on their models. Modeling scores also correlated with gains in solar system knowledge from pre- to post-test. However, younger children and girls showed the greatest knowledge increases. Overall, the results suggest drawing-based modeling is accessible for children and relates to improved understanding, but more research is still needed.
This document discusses optimization approaches for transferring animation setups between 3D characters. The author aims to transfer a character's skeleton, joints, bones, orientations, and skin weights from a source 3D model to one or more target models. The methodology includes retargeting the skeleton from the source to the targets using either energy minimization or Procrustes analysis. It also covers improving the skeleton transfer by aligning spines and mirroring joints. The document discusses techniques for transferring joint orientations and rotations accurately. Additionally, it proposes normalizing poses to reduce differences in limb orientations between characters. The animation setup transfer aims to preserve artist work and ease reusability while handling characters with different morphologies or skeleton structures.
The document proposes a framework for systematically generating and evaluating creative ideas using a matrix representation of ideas and defined creativity operators. An idea is represented as a feature-attribute matrix where novelty and usefulness matrices are combined into a creativity matrix. Creativity operators are then used to generate new idea matrices which can be evaluated and improved using the matrix algebraic methods. The framework is intended to operationalize creativity in a repeatable way. An example application compares and improves competing product ideas for an iPad cover.
This document provides an overview of the Scale Invariant Feature Transform (SIFT) algorithm for feature detection and matching across images. It begins by introducing SIFT and its applications in computer vision. The document then outlines the key steps of the SIFT algorithm, including constructing scale space, approximating the Laplacian of Gaussian, finding keypoints, removing low-contrast keypoints, assigning orientations to keypoints, and generating SIFT features. Details are provided for each step, with examples to illustrate the process. The goal of SIFT is to detect features that are invariant to scale, rotation, illumination and viewpoint changes.
Year+8+extension+measurement+homework+task+blooms by kate johnsAngela Phillips
1. Students are assigned a group project to create an image with different shapes that relates mathematically to each other's images. They must decide on a theme.
2. For the individual task, each student must include examples of different shapes like a right triangle, hexagon, and other polygon. They must also include a circle with a specified area and another with a given circumference.
3. Students must show their work and calculations for finding the measurements of the required shapes within their images.
This document provides an overview of image matching techniques. It defines image matching as geometrically aligning two images so corresponding pixels represent the same scene region. Key aspects covered include detecting invariant local features, describing features in a scale and rotation invariant way using SIFT, and matching features between images. SIFT is highlighted as an extraordinarily robust technique that can handle various geometric and illumination changes. Feature matching is used in many computer vision applications such as image alignment, 3D reconstruction, and object recognition.
This document discusses using drawings and simulations to support model-based scientific reasoning. It argues that modeling is a key part of the scientific process and learning science. An experimental study is described that had children draw and simulate the solar system using a drawing-based modeling software called SimSketch. The study found that drawing static elements was easier than dynamic processes, and older children scored better on their models. Modeling scores also correlated with gains in solar system knowledge from pre- to post-test. However, younger children and girls showed the greatest knowledge increases. Overall, the results suggest drawing-based modeling is accessible for children and relates to improved understanding, but more research is still needed.
This document discusses optimization approaches for transferring animation setups between 3D characters. The author aims to transfer a character's skeleton, joints, bones, orientations, and skin weights from a source 3D model to one or more target models. The methodology includes retargeting the skeleton from the source to the targets using either energy minimization or Procrustes analysis. It also covers improving the skeleton transfer by aligning spines and mirroring joints. The document discusses techniques for transferring joint orientations and rotations accurately. Additionally, it proposes normalizing poses to reduce differences in limb orientations between characters. The animation setup transfer aims to preserve artist work and ease reusability while handling characters with different morphologies or skeleton structures.
Evolving art using measures for symmetry, compositional balance and livelinessEelco den Heijer
Presented at the ECTA conference, Barcelona, 2012. Presents research of unsupervised or autonomous evolutionary art using measures for symmetry, compositional balance and liveliness. Won award for Best Student Paper.
This document discusses rotations and dilations of geometric shapes. It defines a rotation as turning a shape around a center point, with all points maintaining their distance from the center. A dilation stretches or shrinks a shape by a scale factor, where a factor greater than 1 results in enlargement and between 0 and 1 results in shrinking. Examples are provided of rotating and dilating triangles, with step-by-step workings to find the new point coordinates. Additional online practice resources are recommended for mastering these transformation techniques.
This legal document provides several notices and disclaimers regarding the information presented. Specifically:
- The presentation is for informational purposes only and Intel makes no warranties regarding the information or summaries of the information.
- Any performance claims depend on system configuration and hardware/software/service activation. Performance varies depending on system configuration.
- The sample source code is released under the Intel Sample Source Code License Agreement.
- Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries. Other names may belong to other owners.
- Copyright of the content is held by Intel Corporation and all rights are reserved.
This document discusses feature descriptors and matching in computer vision. It covers three main components: 1) detecting interest points in images, 2) extracting feature descriptors around each interest point, and 3) determining correspondences between descriptors to match features across images. The document focuses on SIFT (Scale Invariant Feature Transform) descriptors, which are histograms of gradient orientations computed over localized patches that provide robust matching across changes in scale, rotation and illumination. SIFT descriptors have been widely and successfully used for applications like image stitching, 3D reconstruction, object recognition and augmented reality.
This document discusses using a neural network model to simulate coordinate transformations performed by neurons in the brain. Specifically, it describes experiments using a three-layer backpropagation network to model neurons in area 7a of the macaque parietal cortex. The network takes in visual stimulus and eye position data and outputs head-centered coordinates. Initial results show the model can perform this transformation with average errors of less than 4 degrees. Gain modulation is discussed as a potential mechanism for these coordinate transformations computed by parietal neurons.
This document discusses real-time gesture recognition of the human hand. It begins by defining what gestures are and how they can be used for human-computer interfaces and sign language. It then discusses challenges like finding the hand in an image, recognizing its shape and motion, and determining its position in 3D space. Potential methods explored include datagloves, colored gloves, and vision-based systems using features like color segmentation and tracking hand shape trajectories over time. The document considers techniques such as hidden Markov models, hierarchical searching, and predicting and tracking hand positions.
(1) This document summarizes two case studies on human activity and face recognition using Bayesian decision theory and computer vision techniques. (2) The first case study recognizes 10 human actions such as sitting, standing, bending etc. by tracking head motion trajectories using a Bayesian classifier with Gaussian likelihood functions. (3) The second case study detects and tracks faces in real-time by modeling skin color distribution with a 2D Gaussian and adapting the model to account for lighting and viewpoint changes.
This document summarizes object tracking methods, including representations of objects, features for tracking, detection approaches, tracking algorithms, and future directions. It discusses representing objects as points, patches, or contours, using features like color, edges, texture, and optical flow for detection and tracking. Detection can be done through point detection, background subtraction, segmentation, and supervised learning. Tracking algorithms include point tracking, kernel tracking, and silhouette tracking. The document outlines challenges like occlusion, camera motion, and non-rigid objects that remain for future work in object tracking.
This document provides an introduction to data visualization. It discusses what data visualization is, why it is used, and the stages involved in creating visualizations from data. Key points include:
- Data visualization involves using visual representations of data to help people analyze and communicate information more effectively.
- Visualizations are used for tasks like recording information, analyzing data to support reasoning, and communicating information.
- The process of creating visualizations involves understanding the properties of the data, properties of images and perception, and rules for mapping data to visual encodings.
- Important considerations include which visual variables to use to encode different data properties, principles of visual perception, and enabling interaction with the data. Validation of the effectiveness of
1. The document discusses multiple representations in cognitive architectures, including symbolic and visual/imagery-based representations.
2. It reviews past and current attempts to model visual mental imagery in cognitive architectures using array-based and retinotopic representations.
3. The concept of multi-representation cognition is introduced, where problems can be solved using different mental representations, like mathematical/symbolic vs. visual imagery representations, each with their own advantages.
This document provides an introduction to the field of computer vision. It discusses (1) the goals of understanding and modeling visual processing in humans and building artificial vision systems, (2) how the human retina represents images and the brain processes them to recognize faces and objects, and (3) psychophysical experiments used to test hypotheses about visual processing, including inattentional blindness and change blindness. Figure-ground segregation is also introduced, where only one side of a contour is perceived as the figure based on cues like symmetry, convexity, and region.
The document provides information on sketching and technical drawing techniques. It discusses isometric and orthographic drawings, coded plans, and viewpoints. It describes the design process as having 8 steps: identifying the problem and criteria, brainstorming solutions, developing ideas, exploring possibilities, selecting an approach, building a model, refining the design. Dimensioning and proportions are important for sketches to convey accurate relative sizes despite not being to scale.
1) The document describes how to construct basic and advanced tessellations through step-by-step processes. For the basic tessellation, a master tile is created using rotations and is then tessellated using repeated rotations of 60 and 180 degrees. For the advanced tessellation, a master tile is created using a translation and glide reflection and is tessellated through repeated reflections and translations.
2) Tessellations demonstrate mathematical concepts like isometries and can engage students in exploring geometry. Regular tessellations by hexagons, for example, are found in nature in honeycombs.
3) Tessellations have uses beyond mathematics, such as in stained glass windows where abstract designs were used
Multiple representations and visual mental imagery in artificial cognitive sy...University of Huddersfield
1) The document discusses multiple representations in artificial cognitive systems, including both external representations like diagrams and internal mental representations like visual mental imagery.
2) It presents examples of how problems can be solved using either a mathematical/propositional representation or a visual/imagery-based representation.
3) Leading cognitive architectures are discussed in terms of how they have begun to incorporate multiple representations, with some exploring non-symbolic, array-based representations to model processes involved in visual mental imagery.
Dr. Subrat Panda gave an introduction to reinforcement learning. He defined reinforcement learning as dealing with agents that must sense and act upon their environment to receive delayed scalar feedback in the form of rewards. He described key concepts like the Markov decision process framework, value functions, Q-functions, exploration vs exploitation, and extensions like deep reinforcement learning. He listed several real-world applications of reinforcement learning and resources for learning more.
Dr. Subrat Panda gave an overview of reinforcement learning. He defined reinforcement learning as dealing with agents that must sense and act upon their environment to receive delayed scalar feedback in the form of rewards. The goal is to learn an optimal policy that maps states to actions to maximize total future discounted reward. Q-learning is introduced as a way to estimate state-action values directly without needing a model of the environment. Q-learning updates estimates based on new observations and prior estimates in a process called bootstrapping. Exploration must be balanced with exploitation of current knowledge. Real-world applications of deep reinforcement learning were discussed.
1. The document discusses multiple representations in human cognition and cognitive architectures. It focuses on visual mental imagery and how cognitive models can incorporate different representational formats like diagrams, images, and symbols.
2. Current cognitive architectures mainly use symbolic representations which are insufficient for modeling visual imagery. A few models employ array-based representations to better capture spatial reasoning and imagery abilities.
3. For cognitive models to exhibit human-level intelligence, they need mechanisms for flexibly selecting and coordinating multiple internal and external representations.
This document discusses spatial thinking in STEM education. It provides an introduction to spatial thinking, examples of how spatial thinking is important in various science disciplines like biology, geology and chemistry. It discusses spatial thinking from a cognitive perspective involving processes like encoding, storing and manipulating spatial information. The document also summarizes research on individual differences in spatial ability, including differences between high and low performers on spatial tasks. It outlines evidence that spatial thinking contributes to science learning and is malleable with training.
There are a few potential issues with modeling the data this way:
1. Students are nested within classrooms. A student's outcomes may be more similar to others in their classroom compared to students in other classrooms, due to shared classroom factors. This violates the independence assumption of ordinary least squares regression.
2. Classroom-level factors like teacher quality are not included in the model but likely influence student outcomes. Failing to account for these could lead to omitted variable bias.
3. The error terms for students within the same classroom may not be independent as assumed, since classroom factors induce correlation.
To properly account for the nested data structure, we need to model the classroom as a second level in a multilevel
Two methods are described for optimizing cognitive model parameters: differential evolution (DE) and high-throughput computing with HTCondor. DE is a genetic algorithm that uses a population of models to explore the parameter space in parallel. It is well-suited for models with few parameters or short run times. HTCondor allows running a population of models over a computer network, making it suitable for larger, more complex models or simulating many participants. Examples of using each method with an ACT-R paired associate model are provided.
Weitere ähnliche Inhalte
Ähnlich wie Modelling alternative strategies for mental rotation
Evolving art using measures for symmetry, compositional balance and livelinessEelco den Heijer
Presented at the ECTA conference, Barcelona, 2012. Presents research of unsupervised or autonomous evolutionary art using measures for symmetry, compositional balance and liveliness. Won award for Best Student Paper.
This document discusses rotations and dilations of geometric shapes. It defines a rotation as turning a shape around a center point, with all points maintaining their distance from the center. A dilation stretches or shrinks a shape by a scale factor, where a factor greater than 1 results in enlargement and between 0 and 1 results in shrinking. Examples are provided of rotating and dilating triangles, with step-by-step workings to find the new point coordinates. Additional online practice resources are recommended for mastering these transformation techniques.
This legal document provides several notices and disclaimers regarding the information presented. Specifically:
- The presentation is for informational purposes only and Intel makes no warranties regarding the information or summaries of the information.
- Any performance claims depend on system configuration and hardware/software/service activation. Performance varies depending on system configuration.
- The sample source code is released under the Intel Sample Source Code License Agreement.
- Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries. Other names may belong to other owners.
- Copyright of the content is held by Intel Corporation and all rights are reserved.
This document discusses feature descriptors and matching in computer vision. It covers three main components: 1) detecting interest points in images, 2) extracting feature descriptors around each interest point, and 3) determining correspondences between descriptors to match features across images. The document focuses on SIFT (Scale Invariant Feature Transform) descriptors, which are histograms of gradient orientations computed over localized patches that provide robust matching across changes in scale, rotation and illumination. SIFT descriptors have been widely and successfully used for applications like image stitching, 3D reconstruction, object recognition and augmented reality.
This document discusses using a neural network model to simulate coordinate transformations performed by neurons in the brain. Specifically, it describes experiments using a three-layer backpropagation network to model neurons in area 7a of the macaque parietal cortex. The network takes in visual stimulus and eye position data and outputs head-centered coordinates. Initial results show the model can perform this transformation with average errors of less than 4 degrees. Gain modulation is discussed as a potential mechanism for these coordinate transformations computed by parietal neurons.
This document discusses real-time gesture recognition of the human hand. It begins by defining what gestures are and how they can be used for human-computer interfaces and sign language. It then discusses challenges like finding the hand in an image, recognizing its shape and motion, and determining its position in 3D space. Potential methods explored include datagloves, colored gloves, and vision-based systems using features like color segmentation and tracking hand shape trajectories over time. The document considers techniques such as hidden Markov models, hierarchical searching, and predicting and tracking hand positions.
(1) This document summarizes two case studies on human activity and face recognition using Bayesian decision theory and computer vision techniques. (2) The first case study recognizes 10 human actions such as sitting, standing, bending etc. by tracking head motion trajectories using a Bayesian classifier with Gaussian likelihood functions. (3) The second case study detects and tracks faces in real-time by modeling skin color distribution with a 2D Gaussian and adapting the model to account for lighting and viewpoint changes.
This document summarizes object tracking methods, including representations of objects, features for tracking, detection approaches, tracking algorithms, and future directions. It discusses representing objects as points, patches, or contours, using features like color, edges, texture, and optical flow for detection and tracking. Detection can be done through point detection, background subtraction, segmentation, and supervised learning. Tracking algorithms include point tracking, kernel tracking, and silhouette tracking. The document outlines challenges like occlusion, camera motion, and non-rigid objects that remain for future work in object tracking.
This document provides an introduction to data visualization. It discusses what data visualization is, why it is used, and the stages involved in creating visualizations from data. Key points include:
- Data visualization involves using visual representations of data to help people analyze and communicate information more effectively.
- Visualizations are used for tasks like recording information, analyzing data to support reasoning, and communicating information.
- The process of creating visualizations involves understanding the properties of the data, properties of images and perception, and rules for mapping data to visual encodings.
- Important considerations include which visual variables to use to encode different data properties, principles of visual perception, and enabling interaction with the data. Validation of the effectiveness of
1. The document discusses multiple representations in cognitive architectures, including symbolic and visual/imagery-based representations.
2. It reviews past and current attempts to model visual mental imagery in cognitive architectures using array-based and retinotopic representations.
3. The concept of multi-representation cognition is introduced, where problems can be solved using different mental representations, like mathematical/symbolic vs. visual imagery representations, each with their own advantages.
This document provides an introduction to the field of computer vision. It discusses (1) the goals of understanding and modeling visual processing in humans and building artificial vision systems, (2) how the human retina represents images and the brain processes them to recognize faces and objects, and (3) psychophysical experiments used to test hypotheses about visual processing, including inattentional blindness and change blindness. Figure-ground segregation is also introduced, where only one side of a contour is perceived as the figure based on cues like symmetry, convexity, and region.
The document provides information on sketching and technical drawing techniques. It discusses isometric and orthographic drawings, coded plans, and viewpoints. It describes the design process as having 8 steps: identifying the problem and criteria, brainstorming solutions, developing ideas, exploring possibilities, selecting an approach, building a model, refining the design. Dimensioning and proportions are important for sketches to convey accurate relative sizes despite not being to scale.
1) The document describes how to construct basic and advanced tessellations through step-by-step processes. For the basic tessellation, a master tile is created using rotations and is then tessellated using repeated rotations of 60 and 180 degrees. For the advanced tessellation, a master tile is created using a translation and glide reflection and is tessellated through repeated reflections and translations.
2) Tessellations demonstrate mathematical concepts like isometries and can engage students in exploring geometry. Regular tessellations by hexagons, for example, are found in nature in honeycombs.
3) Tessellations have uses beyond mathematics, such as in stained glass windows where abstract designs were used
Multiple representations and visual mental imagery in artificial cognitive sy...University of Huddersfield
1) The document discusses multiple representations in artificial cognitive systems, including both external representations like diagrams and internal mental representations like visual mental imagery.
2) It presents examples of how problems can be solved using either a mathematical/propositional representation or a visual/imagery-based representation.
3) Leading cognitive architectures are discussed in terms of how they have begun to incorporate multiple representations, with some exploring non-symbolic, array-based representations to model processes involved in visual mental imagery.
Dr. Subrat Panda gave an introduction to reinforcement learning. He defined reinforcement learning as dealing with agents that must sense and act upon their environment to receive delayed scalar feedback in the form of rewards. He described key concepts like the Markov decision process framework, value functions, Q-functions, exploration vs exploitation, and extensions like deep reinforcement learning. He listed several real-world applications of reinforcement learning and resources for learning more.
Dr. Subrat Panda gave an overview of reinforcement learning. He defined reinforcement learning as dealing with agents that must sense and act upon their environment to receive delayed scalar feedback in the form of rewards. The goal is to learn an optimal policy that maps states to actions to maximize total future discounted reward. Q-learning is introduced as a way to estimate state-action values directly without needing a model of the environment. Q-learning updates estimates based on new observations and prior estimates in a process called bootstrapping. Exploration must be balanced with exploitation of current knowledge. Real-world applications of deep reinforcement learning were discussed.
1. The document discusses multiple representations in human cognition and cognitive architectures. It focuses on visual mental imagery and how cognitive models can incorporate different representational formats like diagrams, images, and symbols.
2. Current cognitive architectures mainly use symbolic representations which are insufficient for modeling visual imagery. A few models employ array-based representations to better capture spatial reasoning and imagery abilities.
3. For cognitive models to exhibit human-level intelligence, they need mechanisms for flexibly selecting and coordinating multiple internal and external representations.
This document discusses spatial thinking in STEM education. It provides an introduction to spatial thinking, examples of how spatial thinking is important in various science disciplines like biology, geology and chemistry. It discusses spatial thinking from a cognitive perspective involving processes like encoding, storing and manipulating spatial information. The document also summarizes research on individual differences in spatial ability, including differences between high and low performers on spatial tasks. It outlines evidence that spatial thinking contributes to science learning and is malleable with training.
There are a few potential issues with modeling the data this way:
1. Students are nested within classrooms. A student's outcomes may be more similar to others in their classroom compared to students in other classrooms, due to shared classroom factors. This violates the independence assumption of ordinary least squares regression.
2. Classroom-level factors like teacher quality are not included in the model but likely influence student outcomes. Failing to account for these could lead to omitted variable bias.
3. The error terms for students within the same classroom may not be independent as assumed, since classroom factors induce correlation.
To properly account for the nested data structure, we need to model the classroom as a second level in a multilevel
Ähnlich wie Modelling alternative strategies for mental rotation (20)
Two methods are described for optimizing cognitive model parameters: differential evolution (DE) and high-throughput computing with HTCondor. DE is a genetic algorithm that uses a population of models to explore the parameter space in parallel. It is well-suited for models with few parameters or short run times. HTCondor allows running a population of models over a computer network, making it suitable for larger, more complex models or simulating many participants. Examples of using each method with an ACT-R paired associate model are provided.
Machine Learning, Artificial General Intelligence, and Robots with Human MindsUniversity of Huddersfield
The document discusses different types of artificial intelligence and outlines a new project to install the ACT-R cognitive architecture onto a NAO robot to create a robot with human-level general intelligence and flexible goal-directed behavior through embodied cognition, perception, motor skills, communication, learning and adaptation. The goal is to gain insights into building advanced autonomous agents by modeling key aspects of human cognition and intelligence.
This document summarizes two studies on how people orient themselves using maps in urban environments. The first study found that people often make errors in orientation when relying on highly visually salient objects that are not clear on the map, ignoring important ground-level cues, or misjudging object distances. The second study found that strong 2D ground cues on maps can improve accuracy, but the presence of a salient 3D landmark can confuse people and reduce accuracy. A process model of map-based orientation is proposed based on these findings. The studies have implications for how to design maps to best support orientation.
Graph comprehension model talk, Birkbeck and Toulouse Le Mirail, February 2012University of Huddersfield
The document describes a computational model of graph comprehension built within the ACT-R cognitive architecture. The model simulates how experts and novices interpret interaction graphs by encoding spatial relationships between plotted points and applying prior knowledge about graphical representations. It identifies variables, encodes distances between points symbolically, and recognizes patterns to describe effects. While focused on expert-level understanding, the model represents an initial step toward accounting for individual differences and a broader range of graph types.
Diagrammatic Cognition: Discovery and Design workshop, Humboldt University, B...University of Huddersfield
This workshop is designed to integrate a wide variety of cognitive science perspectives on the roles diagrams play in cognition, addressing various ways in which people design and use diagrams to spatialize thought and make it public, to work through ideas and clarify thinking, to reduce working memory load, to communicate ideas to others, to promote collaborative work by providing an external representation that can be pointed to and animated by gestures and collectively revised. The morning session (talks by Tversky, Healey, and Kirsh) will focus on creating and diagrams and using them to coordinate various activities, the afternoon (talks by Bechtel, Cheng, and Hegarty) will examine uses of diagrams in science. Both session will also include blitz talks presenting one major idea; scholars who would like to present blitz talks should contact the organizer.
http://mechanism.ucsd.edu/diagrammaticcognition.html
A cognitive architecture-based modelling approach to understanding biases in ...University of Huddersfield
Title: "A cognitive architecture-based modelling approach to
understanding biases in visualisation behaviour". A talk given at the "Dealing with Cognitive Biases in Visualisations (DECISIVe 2014) workshop at IEEE VIS, Paris, November 2014.
Title: "Sources of bias when working with visualisations". Introduction to the "Dealing with Cognitive Biases in Visualisations (DECISIVe 2014) workshop at IEEE VIS, Paris, November 2014.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
2. Motivation for this work
• Several formal computational accounts of mental
imagery (e.g., Glasgow & Papadias, 1992; Just &
Carpenter, 1985; Kunda, McGreggor, & Goel, 2013;
Tabachneck-Schijf, Leonardo, & Simon, 1997)
• Long standing issue of whether imagery requires
• Some form of array based representation
• Abstract, amodal representations and processes
• All of the above employ an array representation
• Recent attempts using cognitive architectures
• Sigma (Rosenbloom, 2012)
• Soar (Lathrop, Wintermute, & Laird, 2011; Wintermute,
2012)
3. Motivation for this work
Questions
• How can mental imagery be modelled in ACT-R?
• What representations and processes can support it?
• Does it need an array based representation?
• What are the minimal changes necessary to do this?
4. Representations in ACT-R
• Many spatial imagery phenomena involve mental
representations of the shape, location, orientation and
spatial extent of imagined objects
• ACT-R has discrete symbolic representations in visual
module (e.g., shape = ‘square’)
• Only one x-y coordinate location for each object
5. Modifications made
(1,2)
(4,3)
(6,1)
(7,4)
(5,5)
(1,6)
(6,8)
(1)
VISUAL: POLYGON0-0 [POLYGON0]
POLYGON0-0
SCREEN-POS POLYGON-FEATURE8-0
VALUE “poly4”
COLOR WHITE
HEIGHT 8
WIDTH 8
POINTS ((1 2) (4 3) (6 1) (7 4) (5 5) (6 8) (1 6))
CENTRE-X 4
CENTRE-Y 4
REGULAR FALSE
SIDES 7
POLYGON T
(2)
x
y
1
=
x
y
1
·
cos θ − sin θ 0
sin θ cos θ 0
0 0 1
(3)
1. Explicit representation of vertex coordinate locations in
stimulus objects
2. Encoding of vertex coordinates in the visual buffer
3. Affine and Boolean operations on spatial objects using
computational geometry and matrix multiplication
4. Use of imaginal action function of the imaginal buffer
7. Mental rotation
Standard task
• Pairs of similar images, one rotated around its centre.
Decide whether the images are identical or not.
• RT increases monotonically with the degree of angular
rotation between the images.
Strategies
• Holistic. Rotated figure manipulated as a single, whole
unit (Cooper, 1975; Shepard & Metzler, 1971).
• Piecemeal. Rotated figure subdivided, component pieces
manipulated separately (Just & Carpenter, 1976, 1985).
8. Holistic rotation strategy
Requires greater capacity to build and maintain complete
images in working memory (Bethell-Fox & Shepard, 1988;
Mumaw, Pellegrino, Kail, & Carter, 1984).
1. Search. Look for corresponding regions in the figures.
2. Confirm. Determine that the figures have related
features.
3. Transform and compare. Re-rotate whole figure
towards target.
9. Piecemeal rotation strategy
Favoured by lower spatial ability individuals
1. Search. Look for corresponding regions in the figures.
2. Transform and compare. Re-rotate selected piece
towards its corresponding target piece.
3. Confirm. Repeat to see if same rotation applies to other
corresponding pieces (Just & Carpenter, 1976, 1985)
10. Khooshabeh et al. (2013)
• Forced people to use holistic or
piecemeal strategy by using
fragmented versions of Shepard
and Metzler (1971) stimuli.
• Categorised people into high
and low spatial ability based on
performance.
• Assume that low ability
people use piecemeal strategy
most of the time.
• Compared high ability
people’s performance on
whole (holistic) and
fragmented (piecemeal)
stimuli.
12. Creating the models
Structure
• Two models instantiating the two strategies
• Holistic (7 productions)
• Piecemeal (8 productions)
• Rotation process (Just & Carpenter, 1976, 1985)
• Not a single ballistic rotation
• Series of discrete “rotate and compare” steps until images
are sufficiently congruent to stop.
Key parameters
• Rotation distance at each step
• Threshold distance to stop
• Imaginal delay time determines completion time for
imaginal buffer modification. Set to .1s (default =.2s).
13. Creating the models
Target image Rotated 60◦ clockwise
Stimuli
• Four component pieces of five random points – complex
irregular polygons (Cooper, 1975; Cooper & Podgorny,
1976)
• Model can attend to compound image or individual
components
14. Piecemeal strategy
1. Search. Look for corresponding
regions of the two figures.
Start
Look for
rotated figure
Attend to
rotated figure
Store rotated
figure; look to
normal figure
Attend to
normal figure
Pieces
match?
Different piece;
look again
no
Figures
aligned?
yes
Figures not
aligned; rotate
no
Enough
pieces
matched?
Figures aligned;
respond
yes
yes
Stop
no
15. Piecemeal strategy
1. Search. Look for corresponding
regions of the two figures.
2. Transform and compare.
Re-rotate selected piece towards
its corresponding target piece.
Start
Look for
rotated figure
Attend to
rotated figure
Store rotated
figure; look to
normal figure
Attend to
normal figure
Pieces
match?
Different piece;
look again
no
Figures
aligned?
yes
Figures not
aligned; rotate
no
Enough
pieces
matched?
Figures aligned;
respond
yes
yes
Stop
no
16. Piecemeal strategy
1. Search. Look for corresponding
regions of the two figures.
2. Transform and compare.
Re-rotate selected piece towards
its corresponding target piece.
3. Confirm. Repeat to see if the
same rotation will work for
other corresponding pieces.
Start
Look for
rotated figure
Attend to
rotated figure
Store rotated
figure; look to
normal figure
Attend to
normal figure
Pieces
match?
Different piece;
look again
no
Figures
aligned?
yes
Figures not
aligned; rotate
no
Enough
pieces
matched?
Figures aligned;
respond
yes
yes
Stop
no
17. Holistic strategy
1. Search. Look for corresponding
regions of the two figures.
Start
Look for
rotated figure
Attend to
rotated figure
Store rotated
figure; look to
normal figure
Attend to
normal figure
Pieces
match?
Different piece;
look again
no
Enough
pieces
matched?
yes
Do another
match
no
Figures
aligned?
yes
Figures not
aligned; rotate
no
Figures aligned;
respond
yes
Stop
18. Holistic strategy
1. Search. Look for corresponding
regions of the two figures.
2. Confirm. Determine that the
figures have related features.
Start
Look for
rotated figure
Attend to
rotated figure
Store rotated
figure; look to
normal figure
Attend to
normal figure
Pieces
match?
Different piece;
look again
no
Enough
pieces
matched?
yes
Do another
match
no
Figures
aligned?
yes
Figures not
aligned; rotate
no
Figures aligned;
respond
yes
Stop
19. Holistic strategy
1. Search. Look for corresponding
regions of the two figures.
2. Confirm. Determine that the
figures have related features.
3. Transform and compare.
Re-rotate whole figure towards
target.
Start
Look for
rotated figure
Attend to
rotated figure
Store rotated
figure; look to
normal figure
Attend to
normal figure
Pieces
match?
Different piece;
look again
no
Enough
pieces
matched?
yes
Do another
match
no
Figures
aligned?
yes
Figures not
aligned; rotate
no
Figures aligned;
respond
yes
Stop
20. Model performance
Angular Difference in Orientation (degrees)
ResponseTime(s)
0 20 40 60 80 100 120 140 160 180
12345678
y = 0.026x + 2.276
q
q
q
q
q
q
q q
q
q
y = 0.019x + 1.703
q
Fragmented
Complete
Human data
Angular Difference in Orientation (degrees)
ResponseTime(s)
0 20 40 60 80 100 120 140 160 180
12345678
y = 0.027x + 2.294
q
q
q
q
q
q
q
q
q
q
y = 0.019x + 1.995
q
Piecemeal
Holistic
Models
• Piecemeal slower because it requires more piece rotations
• To fit the data, rotation distance for additional piecemeal
rotations was larger than the initial rotation.
• Confirmatory action faster because distance known.
21. Conclusions
Representations and processes
• Not pixel arrays nor discrete symbols – intermediate
numerical level that abstracts from pixel level.
• Quantitative, subsymbolic processes assumed to be at a
level closer to the visual system but controlled and
monitored by higher level actions.
• Approach works well within the constraints of the
architecture with minimal adaptations
• May allow ACT-R to interact with other standard
vector-based images (e.g., SVG)
22. Conclusions
Future work
• Mental scanning and rotation are relatively simple —
repeated actions producing linear RT functions.
• Just use translation, rotation and Euclidean distance
measuring processes.
• More stringent test by modelling more challenging tasks.
• Raven’s Progressive Matrices (c.f. Kunda et al., 2013)
• “Pedestal blocks world” or “Nonholonomic car motion
planning” task (Wintermute, 2012)
• https://github.com/djpeebles/act-r-mental-rotation-models
23. References I
Bethell-Fox, C. E., & Shepard, R. N. (1988). Mental rotation:
Effects of stimulus complexity and familiarity.. Journal
of Experimental Psychology: Human Perception and
Performance, 14(1), 12–23.
Cooper, L. A. (1975). Mental rotation of random
two-dimensional shapes. Cognitive Psychology, 7(1),
20–43.
Cooper, L. A., & Podgorny, P. (1976). Mental transformations
and visual comparison processes: Effects of complexity
and similarity. Journal of Experimental Psychology:
Human Perception and Performance, 2(4), 503–514.
Glasgow, J., & Papadias, D. (1992). Computational imagery.
Cognitive Science, 16(3), 355–394.
Just, M. A., & Carpenter, P. A. (1976). Eye fixations and
cognitive processes. Cognitive psychology, 8(4), 441–480.
24. References II
Just, M. A., & Carpenter, P. A. (1985). Cognitive coordinate
systems: Accounts of mental rotation and individual
differences in spatial ability. Psychological Review, 92(2),
137–172.
Khooshabeh, P., Hegarty, M., & Shipley, T. F. (2013). Individual
differences in mental rotation. Experimental Psychology,
60(3), 164–171.
Kosslyn, S. M., Ball, T. M., & Reiser, B. J. (1978). Visual images
preserve metric spatial information: Evidence from
studies of image scanning.. Journal of Experimental
Psychology: Human Perception and Performance, 4(1),
47–60.
25. References III
Kunda, M., McGreggor, K., & Goel, A. K. (2013). A
computational model for solving problems from the
Raven’s Progressive Matrices intelligence test using
iconic visual representations. Cognitive Systems
Research, 22, 47–66.
Lathrop, S. D., Wintermute, S., & Laird, J. E. (2011). Exploring
the functional advantages of spatial and visual
cognition from an architectural perspective. Topics in
Cognitive Science, 3(4), 796–818.
Mumaw, R. J., Pellegrino, J. W., Kail, R. V., & Carter, P. (1984).
Different slopes for different folks: Process analysis of
spatial aptitude. Memory & Cognition, 12(5), 515–521.
26. References IV
Peebles, D. (2019). Modelling mental imagery in the ACT-R
cognitive architecture. In A. Goel, C. Seifert, & C. Freksa
(Eds.), Proceedings of the 41st annual conference of the
cognitive science society, Montreal, Canada: Cognitive
Science Society.
Rosenbloom, P. S. (2012). Extending mental imagery in Sigma.
In J. Bach, B. Goertzel, & M. Iklé (Eds.), International
conference on artificial general intelligence (pp. 272–281).
Berlin, Heidelberg: Springer.
Shepard, R. N., & Metzler, J. (1971). Mental rotation of
three-dimensional objects. Science, 171(3972), 701–703.
Tabachneck-Schijf, H. J. M., Leonardo, A. M., & Simon, H. A.
(1997). CaMeRa: A computational model of multiple
representations. 21, 305–350.
27. References V
Wintermute, S. (2012). Imagery in cognitive architecture:
Representation and control at multiple levels of
abstraction. Cognitive Systems Research, 19, 1–29.