scholarly journals Hierarchical structure in language and action: A formal comparison

2021 ◽  
Author(s):  
Cas Coopmans ◽  
Karthikeya Ramesh Kaushik ◽  
Andrea E. Martin
Keyword(s):  
Category Theory ◽  
Structural Similarity ◽  
Galois Connection ◽  
Syntactic Structures ◽  
Cross Domain ◽  
Action Sequences ◽  
Cognitive Revolution ◽  
System Properties ◽  
Formal Properties ◽  
The Brain

Since the cognitive revolution, language and action have been compared as cognitive systems, with cross-domain convergent views recently gaining renewed interest in biology, neuroscience, and cognitive science. Language and action are both combinatorial systems whose mode of combination has been argued to be hierarchical, combining elements into constituents of increasingly larger size. This structural similarity has led to the suggestion that they rely on shared cognitive and neural resources. In this paper, we compare the conceptual and formal properties of hierarchy in language and action using tools from category theory. We show that the strong compositionality of language requires a formalism that describes the mapping between sentences and their syntactic structures as an order-embedded Galois connection, while the weak compositionality of actions only requires a monotonic mapping between action sequences and their goals, which we model as a monotone Galois connection. We aim to capture the different system properties of language and action in terms of the distinction between hierarchical sets and hierarchical sequences, and discuss the implications for the way both systems are represented in the brain.

scholarly journals Veto and Vacillation: A Neural Precursor of the Decision to Withhold Action

2014 ◽  
Vol 26 (2) ◽  
pp. 296-304 ◽  
Author(s):  
Erman Misirlisoy ◽  
Patrick Haggard
Keyword(s):  
Human Behavior ◽  
Temporal Pattern ◽  
Neural Precursor ◽  
Rule Based ◽  
Action Sequences ◽  
Readiness Potentials ◽  
Preparatory Activity ◽  
The Brain ◽  
Motor Areas

The capacity to inhibit a planned action gives human behavior its characteristic flexibility. How this mechanism operates and what factors influence a decision to act or not act remain relatively unexplored. We used EEG readiness potentials (RPs) to examine preparatory activity before each action of an ongoing sequence, in which one action was occasionally omitted. We compared RPs between sequences in which omissions were instructed by a rule (e.g., “omit every fourth action”) and sequences in which the participant themselves freely decided which action to omit. RP amplitude was reduced for actions that immediately preceded a voluntary omission but not a rule-based omission. We also used the regular temporal pattern of the action sequences to explore brain processes linked to omitting an action by time-locking EEG averages to the inferred time when an action would have occurred had it not been omitted. When omissions were instructed by a rule, there was a negative-going trend in the EEG, recalling the rising ramp of an RP. No such component was found for voluntary omissions. The results are consistent with a model in which spontaneously fluctuating activity in motor areas of the brain could bias “free” decisions to act or not.

scholarly journals Compressibility of High-Density EEG Signals in Stroke Patients

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4107 ◽  
Author(s):  
Nadia Mammone ◽  
Simona De Salvo ◽  
Cosimo Ieracitano ◽  
Silvia Marino ◽  
Emanuele Cartella ◽  
...  
Keyword(s):  
Electrical Activity ◽  
Bayesian Learning ◽  
Similarity Index ◽  
Structural Similarity ◽  
High Density ◽  
Critical Event ◽  
Compression Rate ◽  
Eeg Signals ◽  
Neural Connections ◽  
The Brain

Stroke is a critical event that causes the disruption of neural connections. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. Intra- and extra-cellular currents are involved in the communication between neurons and the macroscopic effects of such currents can be detected at the scalp through electroencephalographic (EEG) sensors. EEG can be used to study the lesions in the brain indirectly, by studying their effects on the brain electrical activity. The primary goal of the present work was to investigate possible asymmetries in the activity of the two hemispheres, in the case one of them is affected by a lesion due to stroke. In particular, the compressibility of High-Density-EEG (HD-EEG) recorded at the two hemispheres was investigated since the presence of the lesion is expected to impact on the regularity of EEG signals. The secondary objective was to evaluate if standard low density EEG is able to provide such information. Eighteen patients with unilateral stroke were recruited and underwent HD-EEG recording. Each EEG signal was compressively sensed, using Block Sparse Bayesian Learning, at increasing compression rate. The two hemispheres showed significant differences in the compressibility of EEG. Signals acquired at the electrode locations of the affected hemisphere showed a better reconstruction quality, quantified by the Structural SIMilarity index (SSIM), than the EEG signals recorded at the healthy hemisphere (p < 0.05), for each compression rate value. The presence of the lesion seems to induce an increased regularity in the electrical activity of the brain, thus an increased compressibility.

M-Fuzzifying Approximation Spaces, M-Fuzzifying Pretopological Spaces and M-Fuzzifying Closure Spaces

2020 ◽  
Vol 16 (03) ◽  
pp. 609-626
Author(s):  
Anand P. Singh ◽  
I. Perfilieva
Keyword(s):  
Significant Role ◽  
Category Theory ◽  
Galois Connection ◽  
Fuzzy Relations ◽  
Approximation Spaces ◽  
Closure Spaces

In category theory, Galois connection plays a significant role in developing the connections among different structures. The objective of this work is to investigate the essential connections among several categories with a weaker structure than that of [Formula: see text]-fuzzifying topology, viz. category of [Formula: see text]-fuzzifying approximation spaces based on reflexive [Formula: see text]-fuzzy relations, category of [Formula: see text]-fuzzifying pretopological spaces and the category of [Formula: see text]-fuzzifying interior (closure) spaces. The interrelations among these structures are shown via the functorial diagram.

Visualization of Pterygomaxillary Fissure Structure and Shape in CT Image via Non-Linear Perspective Projection

2021 ◽  
Vol 11 (6) ◽  
pp. 1580-1589
Author(s):  
R. Partheepan ◽  
J. Raja Paul Perinbam ◽  
M. Krishnamurthy ◽  
N. R. Shanker
Keyword(s):  
Similarity Index ◽  
Occipital Lobe ◽  
Structural Similarity ◽  
Brain Regions ◽  
Medical Image Segmentation ◽  
Linear Perspective ◽  
Brain Images ◽  
Non Linear ◽  
Back Ground ◽  
The Brain

The neurologist analyses the brain images to diagnose disease via structure and shape of the part in scanned Medical images such as CT, MRI, and PET. The Medical image segmentation performs less in the regions where no or little contrast, artifacts over the different boundary regions. The manual process of segmentation shows poor boundary differentiation due to discernibility in shape and location, intra and inter observer reliability. In this paper, we propose dyadic CAT optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non-linear perspective Foreground and Back Ground projection. The DCO algorithm removes the artifacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm shows the region boundary for pterygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture in brain image with high visibility in the regions of inadequately visible boundary and distinguishes the deformable shape. The DCO algorithm applies on 50 images and eight images with complex bone and muscle mass structure for performance evaluation. The DCO algorithm shows the increased Structural similarity index (SSIM) with 90% accuracy.

scholarly journals Modified distance regularized level set evolution for brain ventricles segmentation

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Thirumagal Jayaraman ◽  
Sravan Reddy M. ◽  
Manjunatha Mahadevappa ◽  
Anup Sadhu ◽  
Pranab Kumar Dutta
Keyword(s):  
Level Set ◽  
Morphological Changes ◽  
Similarity Index ◽  
Neuronal Loss ◽  
Optimization Procedure ◽  
Ground Truth ◽  
Structural Similarity ◽  
Computed Tomography Images ◽  
Level Set Evolution ◽  
The Brain

AbstractNeurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%, specificity in the range of 98%–99%, and accuracy in the range of 95%–98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.

scholarly journals Promise Theory and the Alignment of Context, Processes, Types, and Transforms

2021 ◽  
Author(s):  
Jan Aldert Bergstra ◽  
Mark Burgess
Keyword(s):  
Category Theory ◽  
Structural Similarity ◽  
Additional Structure ◽  
Scaling Properties ◽  
Agent Based ◽  
Process Outcomes ◽  
Wide Range ◽  
Structural Details ◽  
Non Local ◽  
The Relationship

Promise Theory concerns the 'alignment', i.e. the degree of functional compatibility and the 'scaling' properties of process outcomes in agent-based models, with causality and intentional semantics. It serves as an umbrella for other theories of interaction, from physics to socio-economics, integrating dynamical and semantic concerns into a single framework. It derives its measures from sets, and can therefore incorporate a wide range of descriptive techniques, giving additional structure with predictive constraints. We review some structural details of Promise Theory, applied to Promises of the First Kind, to assist in the comparison of Promise Theory with other forms of physical and mathematical modelling, including Category Theory and Dynamical Systems.  We explain how Promise Theory is distinct from other kinds of model, but has a natural structural similarity to statistical mechanics and quantum theory, albeit with different goals; it respects and clarifies the bounds of locality, while incorporating non-local communication. We derive the relationship between promises and morphisms to the extent that this would be a useful comparison.

The architecture of mind and brain

2018 ◽  
Author(s):  
Paul F. M. J. Verschure
Keyword(s):  
Twentieth Century ◽  
Layered Structure ◽  
Historical Analysis ◽  
Cognitive Architectures ◽  
Detailed Understanding ◽  
Unified Theories ◽  
Important Challenge ◽  
Mind And Brain ◽  
Cognitive Revolution ◽  
The Brain

The components of a Living Machine must be integrated into a functioning whole, which requires a detailed understanding of the architecture of living machines. This chapter starts with a conceptual and historical analysis which from Plato brings us to nineteenth-century neuroscience and early concepts of the layered structure of nervous systems. These concepts were further captured in the cognitive behaviorism of Tolman and came to full fruition in the cognitive revolution of the second half of the twentieth century. Verschure subsequently describes the most relevant proposals of cognitive architectures followed by an overview of the few proposals stemming from modern neuroscience on the architecture of the brain. Subsequently, we will look at contemporary contenders that mediate between cognitive and brain architecture. An important challenge to any model of cognitive architectures is how to benchmark it. Verschure proposes the Unified Theories of Embodied Minds (UTEM) benchmark which advances from Newell’s classic Unified Theories of Cognition benchmark.

The Role of Creativity in the Cognitive Turn in Linguistics

2013 ◽  
Vol 5 (2) ◽  
pp. 271-292 ◽  
Author(s):  
István Kenesei
Keyword(s):  
Theory Of Mind ◽  
Natural Phenomenon ◽  
Long Term Memory ◽  
Word Meanings ◽  
Syntactic Structures ◽  
Variable Nature ◽  
Syntactic Relations ◽  
Formal Properties

The recent cognitive turn in linguistics is closely related to research into the creative nature of language. Formal creativity, or in other words, the recursive nature of language (with respect to both words, i.e., the basic units, and sentences, i.e., the end products) is what determines further domains of creativity, viz., at the level of meanings and in the theory of mind, providing for their unlimited and variable nature. Principles of the formal properties of language are presented at the levels of words and sentences, showing that recursion occurs both in words and sentences, indicating the local nature of syntactic relations, and demonstrating their neural correlates. Reference to neurolinguistic experiments is used to argue that metaphorical extensions of meanings are a natural phenomenon placing no burden on mental processing, even though literal meanings are not handled the same way as metaphors. It is claimed that sentential meanings have a primacy over word meanings, while words, and not sentences, are the basic units of the mental lexicon, i.e., long-term memory. In order to understand metaphors it is essential to have theory of mind (ToM), which develops in children parallel with the acquisition of complex syntactic structures involving mental verbs, as is shown by false-belief tasks. The nature and limits of the complexity of ToM is related to the limits of syntactic complexity in natural language.

scholarly journals Hypotheses about the relationship of cognition with psychopathology should be tested by embedding them into empirical priors

2018 ◽  
Author(s):  
Michael Moutoussis ◽  
Alexandra Kathryn Hopkins ◽  
Raymond J Dolan
Keyword(s):  
Computational Models ◽  
Point Of View ◽  
Model Parameters ◽  
Parameter Inference ◽  
Cross Domain ◽  
Empirical Priors ◽  
Relationship Of ◽  
The Relationship ◽  
The Brain ◽  
Decision Making Biases

Mechanistic hypotheses about psychiatric disorders are increasingly formalized as computational models of information-processing in the brain. Model parameters, characterizing for example decision-making biases, are hypothesized to correlate with clinical constructs. This is promising, but here we draw attention to some techniques used to minimize noise in parameter estimation which are in common use but may be unhelpful. Namely, the use of empirical priors that do not incorporate relationships between psychopathology and modelled processes will suppress the very relationships of interest. This is because the variability associated with psychopathology will be indistinguishable from that due to noise from the point of view of the hierarchical, or random-effects, fit that used the empirical priors in question. We advocate incorporating cross-domain, e.g. psychopathology-cognition relationships into the parameter inference itself.

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