Visual analogy is believed to be important in human problem solving. Yet, there are few computational models of visual analogy. In this paper, we present a preliminary computational model of visual analogy in problem solving. The model is instantiated in a computer program, called Galatea, which uses a language for representing and transferring visual information called Privlan. We describe how the computational model can account for a small slice of a cognitive-historical analysis of Maxwell’s reasoning (...) about electromagnetism. (shrink)

One of the main limitations of natural language‐based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on (...) text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic‐level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed. (shrink)

A fundamental question in visual neuroscience is: Why are the response properties of visual neurons as they are? A modern approach to this problem emphasizes the importance of adaptation to ecologically valid input, and it proceeds by modeling statistical regularities in ecologically valid visual input (natural images). A seminal model was linear sparse coding, which is equivalent to independent component analysis (ICA), and provided a very good description of the receptive fields of simple cells. Further models (...) based on modeling residual dependencies of the ‘‘independent” components have later been introduced. These models lead to emergence of further properties of visual neurons: the complex cell receptive fields, the spatial organization of the cells, and some surround suppression and Gestalt effects. So far, these models have concentrated on the response properties of neurons, but they hold great potential to model various forms of inference and learning. (shrink)

At the heart of the model minority myth—often associated with Asian Americans—is the concept of civility. In this groundbreaking book, Picturing Model Citizens, Thy Phu exposes the complex links between civility and citizenship, and argues that civility plays a crucial role in constructing Asian American citizenship. Featuring works by Arnold Genthe, Carl Iwasaki, Toyo Miyatake, Nick Ut, and others, Picturing Model Citizens traces the trope of civility from the nineteenth to the twenty-first centuries. Through an examination of photographs of Chinese (...) immigrants, Japanese internment camps, the Hiroshima Maidens project, napalm victims, and the SARS epidemic, Phu explores civility's unexpected appearance in images that draw on discourses of intimacy, cultivation, apology, and hygiene. She reveals how Asian American visual culture illustrates not only cultural ideas of civility, but also contests the contradictions of state-defined citizenship. (shrink)

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in (...) real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm. (shrink)

The Area Activation Model (Pomplun, Reingold, Shen, & Williams, 2000) is a computational model predicting the statistical distribution of saccadic endpoints in visual search tasks. Its basic assumption is that saccades in visual search tend to foveate display areas that provide a maximum amount of task‐relevant information for processing during the subsequent fixation. In the present study, a counterintuitive prediction by the model is empirically tested, namely that saccadic selectivity towards stimulus features depends on the spatial arrangement of (...) search items. We find good correspondence between simulated and empirically observed selectivity patterns, providing strong support for the Area Activation Model. (shrink)

According to the two visual systems model, the visual processing of objects divides into semantic and pragmatic processing. We provide various criteria for this distinction. Further, we argue that both the semantic and pragmatic processing of visual information about objects should be divided into low-level processing and high-level processing. Finally, we re-evaluate the contribution of the human parietal lobe to the concious visual perception of spatial relations among objects.

Theoretical models propose that attentional biases might account for the maintenance of social anxiety symptoms. However, previous eye-tracking studies have yielded mixed results. One explanation i...

This paper investigates the stabilization and trajectory tracking problem of wheeled mobile robot with a ceiling-mounted camera in complex environment. First, an adaptive visual servoing controller is proposed based on the uncalibrated kinematic model due to the complex operation environment. Then, an adaptive controller is derived to provide a solution of uncertain dynamic control for a wheeled mobile robot subject to parametric uncertainties. Furthermore, the proposed controllers can be applied to a more general situation where the parallelism requirement between (...) the image plane and operation plane is no more needed. The overparameterization of regressor matrices is avoided by exploring the structure of the camera-robot system, and thus, the computational complexity of the controller can be simplified. The Lyapunov method is employed to testify the stability of a closed-loop system. Finally, simulation results are presented to demonstrate the performance of the suggested control. (shrink)

The Necker cube and the productive class of related stimuli involving multiple depth interpretations driven by corner-like line junctions are often taken to be ambiguous. This idea is normally taken to be as little in need of defense as the claim that the Necker cube gives rise to multiple distinct percepts. In the philosophy of language, it is taken to be a substantive question whether a stimulus that affords multiple interpretations is a case of ambiguity. If we take into account (...) what have been identified as hallmark features of ambiguity and look at the empirical record, it appears that the Necker cube and related stimuli are not ambiguous. I argue that this raises problems for extant models of multistable perception in cognitive neuroscience insofar as they are purported to apply to these stimuli. Helpfully, similar considerations also yield reasons to suggest that the relevant models are well motivated for other instances of multistable perception. However, a different breed of model seems to be required for the Necker cube and related stimuli. I end with a sketch how one may go about designing such a model relying on oscillatory patters in neural firing. I suggest that distinctions normally confined to the philosophy of language are important for the study of perception, a perspective with a growing number of adherents. (shrink)

Frost's article advocates for universal models of reading and critiques recent models that concentrate in what has been described as “cracking the orthographic code.” Although the challenge to develop models that can account for word recognition beyond Indo-European languages is welcomed, we argue that reading models should also be constrained by general principles of visual processing and object recognition.

Visual narratives like comics involve a range of complex cognitive operations in order to be understood. The Parallel Interfacing Narrative‐Semantics (PINS) Model integrates an emerging literature showing that comprehension of wordless image sequences balances two representational levels of semantic and narrative structure. The neurocognitive mechanisms that guide these processes are argued to overlap with other domains, such as language and music.

Attempts to introduce Gestalt theory into the realm of visual neuroscience are discussed on both theoretical and experimental grounds. To define the framework in which these proposals can be defended, this paper outlines the characteristics of a standard model, which qualifies as a received view in the visual neurosciences, and of the research into natural images statistics. The objections to the standard model and the main questions of the natural images research are presented. On these grounds, this paper (...) defends the view that Gestalt psychology and experimental phenomenology provide a contribution to the research into perception by the construction of phenomenological models for an ecologically meaningful interpretation of the empirical evidence and the hypothetical constructs of the natural image research within the visual neuroscience. A formal framework for the phenomenological models is proposed, wherein Gestalt theoretical principles and empirical evidence are represented in terms of topological properties and relationships, which account for the order and structures that make the environment accessible to observers at a relevant behavioural level. It is finally argued that these models allow us to evaluate the principles and the empirical evidence of various natures which are integrated from different fields into the research into perception, and in particular into visual neurosciences. (shrink)

A comprehensive and stimulating study which presents the views of 71 leading theorists on the underlying mechanisms and functions of the primary visual cortex.

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to (...) raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

A neurally plausible model of how the process of visually perceiving a letter in the context of a word is learned, and how such processing occurs in adults is proposed. The model consists of a collection of abstract letter feature detector neurons and their interconnections. The model also includes a learning rule that specifies how these interconnections evolve with experience. The interconnections between neurons can be interpreted as representing the spatially redundant, sequentially redundant, and transgraphemic information in letter string displays. (...) Anderson, Silverstein, Ritz, and Jones's (1977) “Brain‐State‐in‐a‐Box” (BSB) neural mechanism is then used to implement the proposed model. The resulting system makes explicit qualitative predictions using both letter recognition accuracy and reaction time as dependent measures. In particular, the model offers an integrated explanation of some experiments involving manipulations of orthographic regularity, masking, case alternations, and experience with words. The similarities and differences between the model and models proposed by Adams (1979) and McClelland and Rumelhart (1981) are also discussed. (shrink)

Tense/aspect morphology on verbs is often thought to depend on event features like telicity, but it is not known how speakers identify these features in visual scenes. To examine this question, we asked Japanese speakers to describe computer‐generated animations of simple actions with variation in visual features related to telicity. Experiments with adults and children found that they could use goal information in the animations to select appropriate past and progressive verb forms. They also produced a large number (...) of different verb forms. To explain these findings, a deep‐learning model of verb production from visual input was created that could produce a human‐like distribution of verb forms. It was able to use visual cues to select appropriate tense/aspect morphology. The model predicted that video duration would be related to verb complexity, and past tense production would increase when it received the endpoint as input. These predictions were confirmed in a third study with Japanese adults. This work suggests that verb production could be tightly linked to visual heuristics that support the understanding of events. (shrink)

Current deep neural networks (DNNs) are far from being able to model the rich landscape of human visual experience. Beyond visual recognition, we explore the neural substrates of visual mental imagery and other visual experiences. Rather than shared visual representations, temporal dynamics and functional connectivity of the process are essential. Generative adversarial networks may drive future developments in simulating human visual experience.

When we read, we do not fixate on each word! How does that work? By deep theory those sorts of decisions must be the result of complex decisions involving the specific “word,” the linguistic context in which it appears, and visual information. But is reading really all that difficult? After all, simple heuristics models of reading seem to do sort of okay by only considering the additive effects of word and context. Entropy measures do well at predicting word (...) “skipping” and suggest that “word frequency” may play the dominant role in the “decision” to skip or not skip! (shrink)

Although seeing is commonly experienced as a unitary activity, the scientific description of vision resists such an intuitive account. Both psychologists and neuroscientists are in agreement with the idea that the elaboration of visual information is distributed across several different routes provided with different functions. Importantly, these routes can be mapped onto well-identified anatomical subdivision of the visual system. Crucially, although originally based on the assumption that different visual information are elaborated via different neural channels, such a (...) model is nowadays used as a tool for indicating a common neural basis between action and perception. The present review is aimed at providing a description of how the modular model of visual system has developed from a model where action and perception are considered as segregate to a model where action and perception are considered ass two labels of the same concept. (shrink)

Differences in the ability of students to judge images can be assessed by analyzing the individual preference order of images. To gain insights into potential heterogeneity in judgement of visual abstraction among students, we combine Bradley–Terry preference modeling and model-based recursive partitioning. In an experiment a sample of 1,020 high-school students ranked five sets of images, three of which with respect to their level of visual abstraction. Additionally, 24 art experts and 25 novices were given the same task, (...) while their eye movements were recorded. Results show that time spent on the task, the students’ age, and self-reported interest in visual puzzles had significant influence on rankings. Fixation time of experts and novices revealed that both groups paid more attention to ambiguous images. The presented approach makes the underlying latent scale of visual judgments quantifiable. (shrink)

As many as two million people in the United Kingdom repeatedly see people, animals, and objects that have no objective reality. Hallucinations on the border of sleep, dementing illnesses, delirium, eye disease, and schizophrenia account for 90% of these. The remainder have rarer disorders. We review existing models of recurrent complex visual hallucinations (RCVH) in the awake person, including cortical irritation, cortical hyperexcitability and cortical release, top-down activation, misperception, dream intrusion, and interactive models. We provide evidence that (...) these can neither fully account for the phenomenology of RCVH, nor for variations in the frequency of RCVH in different disorders. We propose a novel Perception and Attention Deficit (PAD) model for RCVH. A combination of impaired attentional binding and poor sensory activation of a correct proto-object, in conjunction with a relatively intact scene representation, bias perception to allow the intrusion of a hallucinatory proto-object into a scene perception. Incorporation of this image into a context-specific hallucinatory scene representation accounts for repetitive hallucinations. We suggest that these impairments are underpinned by disturbances in a lateral frontal cortex–ventral visual stream system. We show how the frequency of RCVH in different diseases is related to the coexistence of attentional and visual perceptual impairments; how attentional and perceptual processes can account for their phenomenology; and that diseases and other states with high rates of RCVH have cholinergic dysfunction in both frontal cortex and the ventral visual stream. Several tests of the model are indicated, together with a number of treatment options that it generates. Key Words: Blindness; Charles Bonnet; cholinergic; cortical release; delirium; dementia; dream intrusion; hallucination; Perception and Attention Deficit (PAD) model; schizophrenia. (shrink)

Nijhawan argues convincingly that predictive mechanisms are pervasive in the central nervous system (CNS). However, scientific understanding of visual prediction requires one to formulate empirically testable neurophysiological models. The author's suggestions in this direction are to be evaluated on the basis of more realistic experimental methodologies and more plausible assumptions on the hierarchical character of the human visual cortex.

Previous research with speech and non-speech stimuli suggested that in audiovisual perception, visual information starting prior to the onset of corresponding sound can provide visual cues, and form a prediction about the upcoming auditory sound. This prediction leads to audiovisual interaction. Auditory and visual perception interact and induce suppression and speeding up of the early auditory event-related potentials such as N1 and P2. To investigate AV interaction, previous research examined N1 and P2 amplitudes and latencies in response (...) to audio only, video only, audiovisual, and control stimuli, and compared AV with auditory perception based on four AV interaction models. The current study addresses how different models of AV interaction express N1 and P2 suppression in music perception. Furthermore, the current study took one step further and examined whether previous musical experience, which can potentially lead to higher N1 and P2 amplitudes in auditory perception, influenced AV interaction in different models. Musicians and non-musicians were presented the recordings of a keyboard /C4/ key being played, as well as CO stimuli. Results showed that AV interaction models differ in their expression of N1 and P2 amplitude and latency suppression. The calculation of model and has consequences for the resulting N1 and P2 difference waves. Furthermore, while musicians, compared to non-musicians, showed higher N1 amplitude in auditory perception, suppression of amplitudes and latencies for N1 and P2 was similar for the two groups across the AV models. Collectively, these results suggest that when visual cues from finger and hand movements predict the upcoming sound in AV music perception, suppression of early ERPs is similar for musicians and non-musicians. Notably, the calculation differences across models do not lead to the same pattern of results for N1 and P2, demonstrating that the four models are not interchangeable and are not directly comparable. (shrink)