290 research outputs found
The Effect of Aphantasia on Visual Memory Retention
Aphantasia is the inability to create mental imagery which affects approximately 2% of people in the world (Zeman et al., 2015). The vividness of visual imagery questionnaire (VVIQ) was used to sort participants into four groups (aphantasia, low, medium, and high vividness) based on their vividness score with aphantasia participants scoring a 16 (the minimum). I tested the effect of aphantasia on scores of a visual memory recall test. Participants saw two of six possible images and answered five questions per image. Analysis of the results did not show any significant difference between aphantasia and non-aphantasia scores on the recall test however there was a significant difference between the image type (clipart or real-world scenes) and recall score. Future directions should examine the subconsciousâ role in mental imagery as a possible explanation as to why there was no significant difference between VVIQ groups on recall score
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
An Analogical Paradox for Nonhuman Primates: Bridging the Perceptual-Conceptual Gap
Over the past few decades, the dominant view by comparative psychologists of analogical reasoning in nonhuman primates was one of dichotomy between apes, including humans, and monkeys: the distinction between the analogical ape and paleological monkey (Thompson & Oden, 2000). Whereas evidence for analogy proper by representation reinterpretation in monkeys is sparse and debated, the gap between that which is analogic and paleologic has been narrowed by the studies presented here. Representation of relational concepts important for analogy proves difficult for rhesus and capuchin monkeys without the ability to rely on a greater amount of perceptual variability, implicating a perceptually-bound predisposition in problem-solving (Chapters 2-3). A shift in attention from perceptual features to abstract concepts for employment in relational matching is again difficult, but not impossible given cognitive incentive in the form of differential outcomes to refocus attention on conceptual properties (Chapter 4). Finally, chimpanzees unlike monkeys appear more apt to reason by analogy, perhaps due to a more default conceptual focus (Chapter 5). Taken together, these studies provide an account for the emergence of analogical reasoning skills throughout the primate lineage in contrast to views regarding analogy a hallmark of human intelligence
Object knowledge modulates colour appearance
We investigated the memory colour effect for colour diagnostic artificial objects. Since knowledge about these objects and their colours has been learned in everyday life, these stimuli allow the investigation of the influence of acquired object knowledge on colour appearance. These investigations are relevant for questions about how object and colour information in high-level vision interact as well as for research about the influence of learning and experience on perception in general. In order to identify suitable artificial objects, we developed a reaction time paradigm that measures (subjective) colour diagnosticity. In the main experiment, participants adjusted sixteen such objects to their typical colour as well as to grey. If the achromatic object appears in its typical colour, then participants should adjust it to the opponent colour in order to subjectively perceive it as grey. We found that knowledge about the typical colour influences the colour appearance of artificial objects. This effect was particularly strong along the daylight axis
Study of Different Approaches of Creature Detection in DIP
Creature identification assumes a critical part in everyday life. In the region like an air terminal where the nearness of any sort of creature nearness is entirely confined, creature location assumes an exceptionally fundamental part in such territories. In the horticultural regions put close to the timberland numerous creatures demolishes the harvests or even assault on individuals hence there is a need of framework which identifies the animal nearness and gives cautioning about that in the perspective of security reason. What's more, it is additionally helpful in the timberlands. Where wild animal can distinguish for the wellbeing reason.
Distributed Encoding of Spatial and Object Categories in Primate Hippocampal Microcircuits
The primate hippocampus plays critical roles in the encoding, representation, categorization and retrieval of cognitive information. Such cognitive abilities may use the transformational input-output properties of hippocampal laminar microcircuitry to generate spatial representations and to categorize features of objects, images, and their numeric characteristics. Four nonhuman primates were trained in a delayed-match-to-sample (DMS) task while multi-neuron activity was simultaneously recorded from the CA1 and CA3 hippocampal cell fields. The results show differential encoding of spatial location and categorization of images presented as relevant stimuli in the task. Individual hippocampal cells encoded visual stimuli only on specific types of trials in which retention of either, the Sample image, or the spatial position of the Sample image indicated at the beginning of the trial, was required. Consistent with such encoding, it was shown that patterned microstimulation applied during Sample image presentation facilitated selection of either Sample image spatial locations or types of images, during the Match phase of the task. These findings support the existence of specific codes for spatial and numeric object representations in primate hippocampus which can be applied on differentially signaled trials. Moreover, the transformational properties of hippocampal microcircuitry, together with the patterned microstimulation are supporting the practical importance of this approach for cognitive enhancement and rehabilitation, needed for memory neuroprosthetics
Applying blended conceptual spaces to variable choice and aesthetics in data visualisation
Computational creativity is an active area of research within the artificial intelligence domain that investigates what aspects of computing can be considered as an analogue to the human creative process. Computers can be programmed to emulate the type of things that the human mind can. Artificial creativity is worthy of study for two reasons. Firstly, it can help in understanding human creativity and secondly it can help with the design of computer programs that appear to be creative. Although the implementation of creativity in computer algorithms is an active field, much of the research fails to specify which of the known theories of creativity it is aligning with.
The combination of computational creativity with computer generated visualisations has the potential to produce visualisations that are context sensitive with respect to the data and could solve some of the current automation problems that computers experience. In addition theories of creativity could theoretically compute unusual data combinations, or introducing graphical elements that draw attention to the patterns in the data. More could be learned about the creativity involved as humans go about the task of generating a visualisation.
The purpose of this dissertation was to develop a computer program that can automate the generation of a visualisation, for a suitably chosen visualisation type over a small domain of knowledge, using a subset of the computational creativity criteria, in order to try and explore the effects of the introduction of conceptual blending techniques. The problem is that existing computer programs that generate visualisations are lacking the creativity, intuition, background information, and visual perception that enable a human to decide
what aspects of the visualisation will expose patterns that are useful to the consumer of the visualisation. The main research question that guided this dissertation was, âHow can criteria derived from theories of creativity be used in the generation of visualisations?â. In order to answer this question an analysis was done to determine which creativity theories and artificial intelligence techniques could potentially be used to implement the theories in the context of those relevant to computer generated visualisations. Measurable attributes and criteria that were sufficient for an algorithm that claims to model creativity were explored. The parts of the visualisation pipeline were identified and the aspects of visualisation generation that humans are better at than computers was explored. Themes that emerged in both the computational creativity and the visualisation literature were highlighted.
Finally a prototype was built that started to investigate the use of computational creativity methods in the âvariable choiceâ, and âaestheticsâ stages of the data visualisation pipeline.School of ComputingM. Sc. (Computing
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