Research

16 pages, 1929 KiB

Open AccessArticle

Figure–Ground Segmentation and Biological Motion Perception in Peripheral Visual Field

by Ilze Ceple, Jurgis Skilters, Vsevolod Lyakhovetskii, Inga Jurcinska and Gunta Krumina

Brain Sci. 2023, 13(3), 380; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13030380 - 22 Feb 2023

Cited by 1 | Viewed by 1330

Biological motion perception is a specific type of perceptual organization, during which a clear image of a moving human body is perceptually generated in virtue of certain core light dots representing the major joint movements. While the processes of biological motion perception have [...] Read more.

Biological motion perception is a specific type of perceptual organization, during which a clear image of a moving human body is perceptually generated in virtue of certain core light dots representing the major joint movements. While the processes of biological motion perception have been studied extensively for almost a century, there is still a debate on whether biological motion task performance can be equally precise across all visual field or is central visual field specified for biological motion perception. The current study explores the processes of biological motion perception and figure–ground segmentation in the central and peripheral visual field, expanding the understanding of perceptual organization across different eccentricities. The method involved three different tasks of visual grouping: (1) a static visual grouping task, (2) a dynamic visual grouping task, and (3) a biological motion detection task. The stimuli in (1) and (2) were generated from 12–13 dots grouped by proximity and common fate, and, in (3), light dots representing human motion. All stimuli were embedded in static or dynamics visual noise and the threshold value for the number of noise dots in which the elements could still be grouped by proximity and/or common fate was determined. The results demonstrate that biological motion can be differentiated from the scrambled set of moving dots in a more intensive visual noise than static and dynamic visual grouping tasks. Furthermore, in all three visual tasks (static and dynamic grouping, and biological motion detection) the performance was significantly worse in the periphery than in the central visual field, and object magnification could not compensate for the reduced performance in any of the three grouping tasks. The preliminary results of nine participants indicate that (a) human motion perception involves specific perceptual processes, providing the high-accuracy perception of the human body and (b) the processes of figure–ground segmentation are governed by the bottom-up processes and the best performance can be achieved only when the object is demonstrated in the central visual field. Full article

(This article belongs to the Special Issue Perceptual Organization from Phenomenology to Neuroscience and Artificial Intelligence: Principles, Phenomena, Illusions, and Issues)

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29 pages, 5602 KiB

Open AccessArticle

Biologically-Based Computation: How Neural Details and Dynamics Are Suited for Implementing a Variety of Algorithms

by Nicole Sandra-Yaffa Dumont, Andreas Stöckel, P. Michael Furlong, Madeleine Bartlett, Chris Eliasmith and Terrence C. Stewart

Brain Sci. 2023, 13(2), 245; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13020245 - 31 Jan 2023

Viewed by 1921

Abstract

The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been expanded to incorporate more biological details and applied to new tasks. This paper brings together [...] Read more.

The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been expanded to incorporate more biological details and applied to new tasks. This paper brings together these ongoing research strands, presenting them in a common framework. We expand on the NEF’s core principles of (a) specifying the desired tuning curves of neurons in different parts of the model, (b) defining the computational relationships between the values represented by the neurons in different parts of the model, and (c) finding the synaptic connection weights that will cause those computations and tuning curves. In particular, we show how to extend this to include complex spatiotemporal tuning curves, and then apply this approach to produce functional computational models of grid cells, time cells, path integration, sparse representations, probabilistic representations, and symbolic representations in the brain. Full article

(This article belongs to the Special Issue Perceptual Organization from Phenomenology to Neuroscience and Artificial Intelligence: Principles, Phenomena, Illusions, and Issues)

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17 pages, 332 KiB

Open AccessArticle

Is Reduced Visual Processing the Price of Language?

by Christer Johansson and Per Olav Folgerø

Brain Sci. 2022, 12(6), 771; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12060771 - 12 Jun 2022

Cited by 2 | Viewed by 5445

Abstract

We suggest a later timeline for full language capabilities in Homo sapiens, placing the emergence of language over 200,000 years after the emergence of our species. The late Paleolithic period saw several significant changes. Homo sapiens became more gracile and gradually lost significant [...] Read more.

We suggest a later timeline for full language capabilities in Homo sapiens, placing the emergence of language over 200,000 years after the emergence of our species. The late Paleolithic period saw several significant changes. Homo sapiens became more gracile and gradually lost significant brain volumes. Detailed realistic cave paintings disappeared completely, and iconic/symbolic ones appeared at other sites. This may indicate a shift in perceptual abilities, away from an accurate perception of the present. Language in modern humans interact with vision. One example is the McGurk effect. Studies show that artistic abilities may improve when language-related brain areas are damaged or temporarily knocked out. Language relies on many pre-existing non-linguistic functions. We suggest that an overwhelming flow of perceptual information, vision, in particular, was an obstacle to language, as is sometimes implied in autism with relative language impairment. We systematically review the recent research literature investigating the relationship between language and perception. We see homologues of language-relevant brain functions predating language. Recent findings show brain lateralization for communicative gestures in other primates without language, supporting the idea that a language-ready brain may be overwhelmed by raw perception, thus blocking overt language from evolving. We find support in converging evidence for a change in neural organization away from raw perception, thus pushing the emergence of language closer in time. A recent origin of language makes it possible to investigate the genetic origins of language. Full article

(This article belongs to the Special Issue Perceptual Organization from Phenomenology to Neuroscience and Artificial Intelligence: Principles, Phenomena, Illusions, and Issues)

13 pages, 1764 KiB

Open AccessArticle

Superordinate Categorization Based on the Perceptual Organization of Parts

by Henning Tiedemann, Filipp Schmidt and Roland W. Fleming

Brain Sci. 2022, 12(5), 667; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12050667 - 20 May 2022

Cited by 2 | Viewed by 2136

Abstract

Plants and animals are among the most behaviorally significant superordinate categories for humans. Visually assigning objects to such high-level classes is challenging because highly distinct items must be grouped together (e.g., chimpanzees and geckos) while more similar items must sometimes be separated (e.g., [...] Read more.

Plants and animals are among the most behaviorally significant superordinate categories for humans. Visually assigning objects to such high-level classes is challenging because highly distinct items must be grouped together (e.g., chimpanzees and geckos) while more similar items must sometimes be separated (e.g., stick insects and twigs). As both animals and plants typically possess complex multi-limbed shapes, the perceptual organization of shape into parts likely plays a crucial rule in identifying them. Here, we identify a number of distinctive growth characteristics that affect the spatial arrangement and properties of limbs, yielding useful cues for differentiating plants from animals. We developed a novel algorithm based on shape skeletons to create many novel object pairs that differ in their part structure but are otherwise very similar. We found that particular part organizations cause stimuli to look systematically more like plants or animals. We then generated other 110 sequences of shapes morphing from animal- to plant-like appearance by modifying three aspects of part structure: sprouting parts, curvedness of parts, and symmetry of part pairs. We found that all three parameters correlated strongly with human animal/plant judgments. Together our findings suggest that subtle changes in the properties and organization of parts can provide powerful cues in superordinate categorization. Full article

(This article belongs to the Special Issue Perceptual Organization from Phenomenology to Neuroscience and Artificial Intelligence: Principles, Phenomena, Illusions, and Issues)

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20 pages, 912 KiB

Open AccessArticle

The Perceptual Organisation of Visual Elements: Lines

by Liliana Albertazzi, Luisa Canal, Rocco Micciolo and Iacopo Hachen

Brain Sci. 2021, 11(12), 1585; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci11121585 - 30 Nov 2021

Cited by 2 | Viewed by 2051

Abstract

The aim of this study is to verify the conditions under which a series of visual stimuli (line segments) will be subjectively perceived as visual lines or surfaces employing four experiments. Two experiments were conducted with the method of subjective evaluation of the [...] Read more.

The aim of this study is to verify the conditions under which a series of visual stimuli (line segments) will be subjectively perceived as visual lines or surfaces employing four experiments. Two experiments were conducted with the method of subjective evaluation of the line segments, and the other two with the Osgood semantic differential. We analysed five variables (thickness, type, orientation, and colour) potentially responsible for the lines’ categorisation. The four experiments gave similar results: higher importance of the variables thickness and type; general lower significance of the variable colour; and general insignificance of the variable orientation. Interestingly, for the variable type, straight lines are evaluated as surfaces more frequently than curved lines and perceived as geometrical, flat, hard, static, rough, sharp, bound, sour, frigid, masculine, cold and passive. Curved lines are prevalently evaluated as lines, and categorised as organic, rounded, soft, dynamic, fluffy, blunt, free, sweet, sensual, feminine, warm and active. These results highlight the specificity of perceptual characteristics for the considered variables and confirm the relevance of the characteristics of variables such as thickness and type. Full article

(This article belongs to the Special Issue Perceptual Organization from Phenomenology to Neuroscience and Artificial Intelligence: Principles, Phenomena, Illusions, and Issues)

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Review

Jump to: Research, Other

33 pages, 813 KiB

Open AccessReview

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

by Martin Do Pham, Amedeo D’Angiulli, Maryam Mehri Dehnavi and Robin Chhabra

Brain Sci. 2023, 13(9), 1316; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13091316 - 13 Sep 2023

Cited by 2 | Viewed by 2421

Abstract

We examine the challenging “marriage” between computational efficiency and biological plausibility—A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that [...] Read more.

We examine the challenging “marriage” between computational efficiency and biological plausibility—A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man’s land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics. Full article

(This article belongs to the Special Issue Perceptual Organization from Phenomenology to Neuroscience and Artificial Intelligence: Principles, Phenomena, Illusions, and Issues)

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28 pages, 3872 KiB

Open AccessFeature PaperReview

Multidisciplinary Intersections on Artificial-Human Vividness: Phenomenology, Representation, and the Brain

by Violetta Molokopoy and Amedeo D’Angiulli

Brain Sci. 2022, 12(11), 1495; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12111495 - 03 Nov 2022

Cited by 2 | Viewed by 1846

Abstract

This article will explore the expressivity and tractability of vividness, as viewed from the interdisciplinary perspective of the cognitive sciences, including the sub-disciplines of artificial intelligence, cognitive psychology, neuroscience, and phenomenology. Following the precursor work by Benussi in experimental phenomenology, seminal papers by [...] Read more.

This article will explore the expressivity and tractability of vividness, as viewed from the interdisciplinary perspective of the cognitive sciences, including the sub-disciplines of artificial intelligence, cognitive psychology, neuroscience, and phenomenology. Following the precursor work by Benussi in experimental phenomenology, seminal papers by David Marks in psychology and, later, Hector Levesque in computer science, a substantial part of the discussion has been around a symbolic approach to the concept of vividness. At the same time, a similar concept linked to semantic memory, imagery, and mental models has had a long history in cognitive psychology, with new emerging links to cognitive neuroscience. More recently, there is a push towards neural-symbolic representations which allows room for the integration of brain models of vividness to a symbolic concept of vividness. Such works lead to question the phenomenology of vividness in the context of consciousness, and the related ethical concerns. The purpose of this paper is to review the state of the art, advances, and further potential developments of artificial-human vividness while laying the ground for a shared conceptual platform for dialogue, communication, and debate across all the relevant sub-disciplines. Within such context, an important goal of the paper is to define the crucial role of vividness in grounding simulation and modeling within the psychology (and neuroscience) of human reasoning. Full article

(This article belongs to the Special Issue Perceptual Organization from Phenomenology to Neuroscience and Artificial Intelligence: Principles, Phenomena, Illusions, and Issues)

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Other

Jump to: Research, Review

8 pages, 1265 KiB

Open AccessBrief Report

Time Perception in Cocaine-Dependent Patients

by Giovanna Mioni, Naomi Sanguin, Graziella Madeo and Stefano Cardullo

Brain Sci. 2022, 12(6), 745; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12060745 - 06 Jun 2022

Cited by 5 | Viewed by 1662

Abstract

The involvement of the dopamine system in modulating time perception has been widely reported. Clinical conditions (e.g., Parkinson’s disease, addictions) that alter dopaminergic signaling have been shown to affect motor timing and perceived duration. The present study aimed at investigating whether the effects [...] Read more.

The involvement of the dopamine system in modulating time perception has been widely reported. Clinical conditions (e.g., Parkinson’s disease, addictions) that alter dopaminergic signaling have been shown to affect motor timing and perceived duration. The present study aimed at investigating whether the effects of chronic stimulant use on temporal processing are time-interval dependent. All participants performed two different time bisection tasks (480/1920 ms and 1200/2640 ms) in which we analysed the proportion of long responses for each stimulus duration as well as an index of perceived duration and one of sensitivity. Regarding the proportion of long responses, we found no differences between groups in either time bisection task but patients had more variable results than controls did in both tasks. This study provides new insight into temporal processing in stimulant-dependent patients. Regardless of the time interval tested, the results showed comparable temporal ability in patients and controls, but higher temporal variability in patients. This finding is consistent with impairment of frontally-mediated cognitive functions involved in time perception rather than impairment in time processing per se. Full article

(This article belongs to the Special Issue Perceptual Organization from Phenomenology to Neuroscience and Artificial Intelligence: Principles, Phenomena, Illusions, and Issues)

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