Abductive conditionals as a test case for inferentialism

In previous published research (“Conditionals and Inferential Connections: A Hypothetical Inferential Theory,” Cognitive Psychology, 2018), we investigated experimentally what role the presence and strength of an inferential connection between a conditional’s antecedent and consequent plays in how people process that conditional. Our analysis showed the strength of that connection to be strongly predictive of whether participants evaluated the conditional as true, false, or neither true nor false. In this article, we re-analyse the data from our previous research, now focussing on the semantics of conditionals rather than on how they are processed. Specifically, we use those data to compare the main extant semantics with each other and with inferentialism, a semantics according to which the truth of a conditional requires the presence of an inferential connection between the conditional’s component parts.

Neurons in the brain form thousands of connections, or synapses, with one another, allowing signals to pass from one cell to the next. To activate a neuron, a high enough activating signal or ‘action potential’ must be reached. However, the accepted view of signal transmission assumes that the great majority of synapses are too weak to activate neurons. This means that often simultaneous inputs from many neurons are required to trigger a single neuron’s activation. However, such coordination is likely unreliable as neurons can react differently to the same stimulus depending on the circumstances. An alternative way of transmitting signals has been reported in turtle brains, where impulses from a single neuron can trigger activity across a network of connections. Furthermore, these responses are reliably repeatable, activating the same neurons in the same order. Riquelme et al. set out to understand the mechanism that underlies this type of neuron activation using a mathematical model based on data from the turtle brain. These data showed that the neural network in the turtle’s brain had many weak synapses but also a few, rare, strong synapses. Simulating this neural network showed that those rare, strong synapses promote the signal’s reliability by providing a consistent route for the signal to travel through the network. The numerous weak synapses, on the other hand, have a regulatory role in providing flexibility to how the activation spreads. This combination of strong and weak connections produces a system that can reliably promote or stop the signal flow depending on the context. Riquelme et al.’s work describes a potential mechanism for how signals might travel reliably through neural networks in the brain, based on data from turtles. Experimental work will need to address whether strong connections play a similar role in other animal species, including humans. In the future, these results may be used as the basis to design new systems for artificial intelligence, building on the success of neural networks.

We propose a neural mass model for anatomically-constrained effective connectivity among neuronal populations residing in four layers (L2/3, L4, L5 and L6) within a cortical column. Eight neuronal populations in a given column--an excitatory population and an inhibitory population per layer--are assumed to be coupled via effective connections of unknown strengths that need to be estimated. The effective connections are constrained to anatomical connections that have been shown to exist in previous anatomical studies. The neural input to a cortical column is directed into the two populations in L4. The anatomically-constrained effective connectivity is captured by a system of 16 stochastic differential equations. Solving these equations yields the average postsynaptic potentials and transmembrane currents generated in each population. The current source density (CSD) responses in each layer, which serve as the model observations, are equated in the model to the sum of all currents generated within that layer. The model is implemented in a continuous-discrete state-space framework, and the innovation method is used for estimating the model parameters from CSD data. To this end, local field potential (LFP) responses to forepaw stimulation were recorded in rat area S1 using multi-channel linear probes. LFPs were converted to CSD signals, which were averaged within each layer, yielding one CSD response per layer. To estimate the effective strengths of connections between all cortical layers, the model was fitted to these CSD signals. The results show that the pattern of effective interactions is strongly influenced by the pattern of strengths of the anatomical connections; however, these two patterns are not identical. The estimated anatomically-constrained effective connectivity matrix and the anatomical connectivity matrix shared five of their six strongest connections, although rankings according to connection strength differed. The strongest effective connections were from excitatory neurons in layer 4 to excitatory neurons in layer 2/3. Our study shows the feasibility of estimating anatomically-constrained effective connectivity within a cortical column, and indicates that there is a strong influence of anatomical connectivity on effective connectivity between cortical layers.

According to Inferentialism, a conditional is true precisely if there is a compelling argument for its consequent starting from its antecedent conjoined with contextually given background knowledge. As a direct consequence of this view, the truth of a conditional’s component parts does not guarantee the truth of the conditional, given that it is compatible with there not being an argument from the conditional’s antecedent to its consequent. Thus, Inferentialism invalidates the principle called Centering, according to which the joint truth of φ and ψ guarantees the truth of “If φ , ψ ”. This distinguishes Inferentialism from all standard semantics of conditionals, all of which validate Centering. Here, we report the results from a study that registered (i) participants’ truth evaluations of a set of conditionals; (ii) their truth evaluations of the component parts of those conditionals; (iii) their judgements of the strength of the inferential connection between antecedent and consequent for each conditional; and (iv) their metacognitive confidence in the inference strength judgements. The data from (i)–(iii) allowed us to test Centering and to investigate whether possible violations of the principle can be explained in inferentialist terms. The data from (iv) allowed us to make some progress on testing predictions derived from Hypothetical Inferential Theory, the psychological face of Inferentialism. Our findings show that almost 60% of the relevant responses violated Centering; that participants obeyed Centering only when there was a strong inferential link between true antecedent and true consequent; and that the best models were those that involved inference strength and metacognitive confidence, while the Centering model was weak.

The corticoreticulospinal tract (CReST) is a descending motor pathway that reorganizes after corticospinal tract (CST) injury in animals. In humans, the pattern of CReST innervation to upper limb muscles has not been carefully examined in healthy individuals or individuals with CST injury. In the present study, we assessed CReST projections to an arm and hand muscle on the same side of the body in healthy and chronic stoke subjects using transcranial magnetic stimulation. We show that CReST connection strength to the muscles differs between healthy and stroke subjects, with stronger connections to the hand than arm in healthy subjects, and stronger connections to the arm than hand in stroke subjects. These results help us better understand CReST innervation patterns in the upper limb, and may point to its role in normal motor function and motor recovery in humans. The corticoreticulospinal tract (CReST) is a major descending motor pathway in many animals, but little is known about its innervation patterns in proximal and distal upper extremity muscles in humans. The contralesional CReST furthermore reorganizes after corticospinal tract (CST) injury in animals, but it is less clear whether CReST innervation changes after stroke in humans. We thus examined CReST functional connectivity, connection strength, and modulation in an arm and hand muscle of healthy (n=15) and chronic stroke (n=16) subjects. We delivered transcranial magnetic stimulation to the contralesional hemisphere (assigned in healthy subjects) to elicit ipsilateral motor evoked potentials (iMEPs) from the paretic biceps (BIC) and first dorsal interosseous (FDI) muscle. We operationalized CReST functional connectivity as iMEP presence/absence, CReST projection strength as iMEP size and CReST modulation as change in iMEP size by head rotation. We found comparable CReST functional connectivity to the BICs and FDIs in both subject groups. However, the pattern of CReST connection strength to the muscles diverged between groups, with stronger connections to FDIs than BICs in healthy subjects and stronger connections to BICs than FDIs in stroke subjects. Head rotation modulated only FDI iMEPs of healthy subjects. Our findings indicate that the healthy CReST does not have a proximal innervation bias, and its strong FDI connections may have functional relevance to finger individuation. The reversed CReST innervation pattern in stroke subjects confirms its reorganization after CST injury, and its strong BIC connections may indicate upregulation for particular upper extremity muscles or their functional actions.

Associations between age, motor function, and resting state sensorimotor network connectivity in healthy older adults

Production trade-offs represent simultaneous and possible changes to the inputs and outputs in the technology under consideration. However, since trade-offs are illative and subjective, in many real applications, the data of production trade-offs cannot be precisely measured. Occasionally, a crisp trade-off cannot reflect desirable judgment of expert. This paper develops the trade-off approach in data envelopment analysis (DEA) using imprecise trade-offs represented by fuzzy sets. We develop some fuzzy versions of trade-off DEA models by using some ranking methods based on the comparison of α-cuts. We show in numerical examples how our models become useful for detecting sensitive trade-offs. In other words, our approach can be seen as extension of the trade-off approach that provides users with models, which represent real evaluation of decision making units (DMU) with good judgments as possible trade-offs.

The road in the remote sensing image has the characteristics of slender and tortuous shape, complex connectivity, large road span, strong connectivity, complex ground information of the remote sensing image, occlusion, and different scales, etc. Based on these characteristics, this paper will do some change in Unet, included three parts: encoding, center feature extraction, decoding. The encoding part is aimed at problems that Unet can't extract rich feature for few samples. We proposes to use a pre-trained VGG model as an encoding module to improve network performance, reduce the risk of overfitting, and extract rich feature; the center feature extraction part, for the problems of occlusion, scale change, connectivity, etc., extracts the dense Dilation convolution module to maintain the resolution while expanding the receptive field to extract more distinguishing features; the decoding part In view of the different importance of features at different scales, a channel attention module is proposed to realize the fusion of high and low features. Through experimental comparison and analysis, the model has a significant effect on solving the road extraction problem of remote sensing images. Then the skeleton is extracted from the segmentation results and processed by denoising and straight line connection. The road network map is extracted from the skeleton map and vectorized to realize the automatic grid. Grid data vectorization.

In this paper a study was conducted to determine the optimal sequence of teaching disciplines, the life safety and the bases of labor protection during the training of specialists in radio engineering according to the criterion of the strength of interdisciplinary connections of these disciplines. It was established that in the different academic years the sequence of teaching of disciplines the life safety and the bases of labor protection during the training of specialists in radio engineering at Vinnytsia National Technical University was different. The following variants of the sequence of teaching disciplines were considered: life safety and the bases of labor protection: the study of disciplines in parallel in one semester, the study of the initial discipline of life safety, and in the next semester – the bases of labor protection, the study of the discipline of the bases of labor protection, and in the next semester – the life safety. The research was conducted separately in two academic groups with different levels of success, where students received grades on a 100-point scale from different disciplines. The comparison of variances of different samples was performed according to Fisher's criterion. Determination of the strength of interdisciplinary connections is carried out by the correlation method. The calculation of the linear correlation coefficient took into account: the average and partial sample values of the comparable values, the total number of these values in the series of indicators, the variance, and the deviation of these values from the mean values. Homogeneity comparison of different samples and their belonging to one general population (future specialists of radio engineering profile) was conducted according to Student's criterion. Graphical interpretation of the obtained results is presented. On the basis of comparison and comparison of data, strong interdisciplinary connections are defined in all three studied variants of the sequence of studying the courses of life safety and the bases of labor protection. It was established that according to the criterion of the strength of interdisciplinary relations, the optimal sequence of teaching disciplines during the training of future specialists in the radio engineering profile is the teaching of the initial life safety, and then the bases of labor protection. The determined optimal sequence of studying the courses of life safety and the bases of labor protection is confirmed by the definition of life safety as a discipline.

A simple mathematical model describing the generation and propagation of epileptiform activity in a cerebral cortical network is presented. The model consists of a system of nonlinear delay differential equations. Physiological properties are taken into account as nonlinear transmission of signals at the synapse, temporal and spatial summation of incoming signals at the soma, active membrane characteristics, and dendritic and axonal propagation times. The influence of the connectivity and the temporal parameters on the oscillatory properties of the model is studied. The computer simulations are in agreement with experimental observations in cortical networks: whereas a weak excitatory or strong inhibitory synaptic connection strength produces a stationary status with short-lasting responses to external stimuli, increases in excitation or decreases in inhibition induce spontaneous and stimulus-evoked rhythmic discharges. Synaptic burst-like activity is observed only for an intermediate range of excitatory and inhibitory connection strengths and external inputs. The form and duration of the bursts can also be controlled by the temporal parameters. The results demonstrate that relatively simple mathematical equations are sufficient to model some of the network properties underlying the generation and propagation of epileptiform activity.

Currently, Pingyang County, Cangnan County, and Longgang City are promoting the integrated development of the three regions.Against this background, the transportation system, especially the highway passenger transport network, serves as an infrastructure carrier that connects different regions and facilitates the flow of factors.Its structural characteristics and operational efficiency directly affect the realization degree and development quality of regional integration.Taking Longgang City, Pingyang County, and Cangnan County in Zhejiang Province as the research area, this study utilizes highway passenger transport schedule data from 2020 and 2025, and applies social network analysis method, including network density, node centrality, and node connection strength, to investigate the spatiotemporal evolution of the structural characteristics of the highway passenger transport network among these three regions.The findings are as follows: 1) In terms of passenger transport network density, the density of the highway passenger transport network in 2025 was 22.23, which was higher than 16.67 in 2020.The passenger transport connection strength of the highway passenger transport network in 2025 was enhanced, indicating that the connections between nodes have become closer and the overall level of transportation integration of the highway passenger transport network has been improved.2) From the perspective of passenger transport connection strength between nodes, the number of node pairs with strong passenger transport connections in 2025 was greater than that in 2020, while the number of node pairs with weak passenger transport connections was How to cite this paper:

Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological attributes, such as connection strength, which may delete strong functional connections in brain functional networks. To overcome these limitations, we propose a novel method to construct dynamic functional networks (DFN) based on weighted regularization (WR) and tensor low-rank approximation (TLA), and apply it to identify eMCI subjects from normal subjects. First, we introduce the WR term into the DFN construction and obtain WR-based DFNs (WRDFN). Then, we combine the WRDFNs of all subjects into a third-order tensor for TLA processing, and obtain the DFN based on WR and TLA (WRTDFN) of each subject in the tensor. We calculate the weighted-graph local clustering coefficient of each region in each WRTDFN as the effective feature, and use the t-test for feature selection. Finally, we train a linear support vector machine (SVM) classifier to classify the WRTDFNs of all subjects. Experimental results demonstrate that the proposed method can obtain DFNs with the scale-free property, and that the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under curve (AUC) reach 87.0662% ± 0.3202%, 83.4363% ± 0.5076%, 90.6961% ± 0.3250% and 0.9431 ± 0.0023, respectively. We also achieve the best classification results compared with other comparable methods. This work can effectively improve the classification performance of DFNs constructed by existing methods for eMCI and has certain reference value for the early diagnosis of Alzheimer’s disease (AD).

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A complex network theory based approach to better understand the infiltration-excess runoff generation thresholds

In this paper, the notion of cycle connectivity of bipolar fuzzy graphs is introduced. The concept of strong edge, $P$ cut node, $P$-bridge and $\theta$ - evaluation of bipolar fuzzy graphs are studied. The cycle connectivity of bipolar fuzzy trees, bipolar fuzzy cycles and complete bipolar fuzzy graphs are obtained. A condition for complete bipolar fuzzy graphs to have cyclic cut-nodes is obtained.