A Novel Electrical Equipment Status Diagnosis Method Based on Super-Resolution Reconstruction and Logical Reasoning.

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system's situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.

Inferential reasoning abilities in wild-caught bumblebees.

The ability to make a decision by excluding alternatives (i.e. inferential reasoning) is a type of logical reasoning that allows organisms to solve problems with incomplete information. Several species of vertebrates have been shown to find hidden food using inferential reasoning abilities. Yet little is known about invertebrates' logical reasoning capabilities. In three experiments, I examined wild-caught bumblebees' abilities to locate a 'rewarded' stimulus using direct information or incomplete information-the latter requiring bees to use inferential reasoning. To do so, I adapted three paradigms previously used with primates-the two-cup, three-cup and double two-cup tasks. Bumblebees saw either two paper strips (experiment 1), three paper strips (experiment 2) or two pairs of paper strips (experiment 3) and experienced one of them being rewarded or unrewarded. At the test, they could choose between two (experiment 1), three (experiment 2) or four paper strips (experiment 3). Bumblebees succeeded in the three tasks and their performance was consistent with inferential reasoning. These findings highlight the importance of comparative studies with invertebrates to comprehensively track the evolution of reasoning abilities, in particular, and cognition, in general.

Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model.

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.

Inferior parietal cortex represents relational structures for explicit transitive inference.

The human brain is distinguished by its ability to perform explicit logical reasoning like transitive inference. This study investigated the functional role of the inferior parietal cortex in transitive inference with functional MRI. Participants viewed premises describing abstract relations among items. They accurately recalled the relationship between old pairs of items, effectively inferred the relationship between new pairs of items, and discriminated between true and false relationships for new pairs. First, the inferior parietal cortex, but not the hippocampus or lateral prefrontal cortex, was associated with transitive inference. The inferior parietal activity and functional connectivity were modulated by inference (new versus old pairs) and discrimination (true versus false pairs). Moreover, the new/old and true/false pairs were decodable from the inferior parietal representation. Second, the inferior parietal cortex represented an integrated relational structure (ordered and directed series). The inferior parietal activity was modulated by serial position (larger end versus center pairs). The inferior parietal representation was modulated by symbolic distance (adjacent versus distant pairs) and direction (preceding versus following pairs). It suggests that the inferior parietal cortex may flexibly integrate observed relations into a relational structure and use the relational structure to infer unobserved relations and discriminate between true and false relations.

Cross-frequency and inter-regional phase synchronization in explicit transitive inference.

Explicit logical reasoning, like transitive inference, is a hallmark of human intelligence. This study investigated cortical oscillations and their interactions in transitive inference with EEG. Participants viewed premises describing abstract relations among items. They accurately recalled the relationship between old pairs of items, effectively inferred the relationship between new pairs of items, and discriminated between true and false relationships for new pairs. First, theta (4-7 Hz) and alpha oscillations (8-15 Hz) had distinct functional roles. Frontal theta oscillations distinguished between new and old pairs, reflecting the inference of new information. Parietal alpha oscillations changed with serial position and symbolic distance of the pairs, representing the underlying relational structure. Frontal alpha oscillations distinguished between true and false pairs, linking the new information with the underlying relational structure. Second, theta and alpha oscillations interacted through cross-frequency and inter-regional phase synchronization. Frontal theta-alpha 1:2 phase locking appeared to coordinate spectrally diverse neural activity, enhanced for new versus old pairs and true versus false pairs. Alpha-band frontal-parietal phase coherence appeared to coordinate anatomically distributed neural activity, enhanced for new versus old pairs and false versus true pairs. It suggests that cross-frequency and inter-regional phase synchronization among theta and alpha oscillations supports human transitive inference.

It's not just what we don't know: The mapping problem in the acquisition of negation.

How do learners learn what no and not mean when they are only presented with what is? Given its complexity, abstractness, and roles in logic, truth-functional negation might be a conceptual accomplishment. As a result, young children's gradual acquisition of negation words might be due to their undergoing a gradual conceptual change that is necessary to represent those words' logical meaning. However, it's also possible that linguistic expressions of negation take time to learn because of children's gradually increasing grasp of their language. To understand what no and not mean, children might first need to understand the rest of the sentences in which those words are used. We provide experimental evidence that conceptually equipped learners (adults) face the same acquisition challenges that children do when their access to linguistic information is restricted, which simulates how much language children understand at different points in acquisition. When watching a silenced video of naturalistic uses of negators by parents speaking to their children, adults could tell when the parent was prohibiting the child and struggled with inferring that negators were used to express logical negation. However, when provided with additional information about what else the parent said, guessing that the parent had expressed logical negation became easy for adults. Though our findings do not rule out that young learners also undergo conceptual change, they show that increasing understanding of language alone, with no accompanying conceptual change, can account for the gradual acquisition of negation words.

Electrical analysis of logical complexity: an exploratory eeg study of logically valid/invalid deducive inference.

INTRODUCTION: Logically valid deductive arguments are clear examples of abstract recursive computational procedures on propositions or on probabilities. However, it is not known if the cortical time-consuming inferential processes in which logical arguments are eventually realized in the brain are in fact physically different from other kinds of inferential processes. METHODS: In order to determine whether an electrical EEG discernible pattern of logical deduction exists or not, a new experimental paradigm is proposed contrasting logically valid and invalid inferences with exactly the same content (same premises and same relational variables) and distinct logical complexity (propositional truth-functional operators). Electroencephalographic signals from 19 subjects (24.2 ± 3.3 years) were acquired in a two-condition paradigm (100 trials for each condition). After the initial general analysis, a trial-by-trial approach in beta-2 band allowed to uncover not only evoked but also phase asynchronous activity between trials. RESULTS: showed that (i) deductive inferences with the same content evoked the same response pattern in logically valid and invalid conditions, (ii) mean response time in logically valid inferences is 61.54% higher, (iii) logically valid inferences are subjected to an early (400 ms) and a late reprocessing (600 ms) verified by two distinct beta-2 activations (p-value < 0,01, Wilcoxon signed rank test). CONCLUSION: We found evidence of a subtle but measurable electrical trait of logical validity. Results put forward the hypothesis that some logically valid deductions are recursive or computational cortical events.

The importance of domain-specific number abilities and domain-general cognitive abilities for early arithmetic achievement and development.

BACKGROUND: Children's numerical and arithmetic skills differ greatly already at an early age. Although research focusing on accounting for these large individual differences clearly demonstrates that mathematical performance draws upon several cognitive abilities, our knowledge concerning key abilities underlying mathematical skill development is still limited. AIMS: First, to identify key cognitive abilities contributing to children's development of early arithmetic skills. Second, to examine the extent to which early arithmetic performance and early arithmetic development rely on different or similar constellations of domain-specific number abilities and domain-general cognitive abilities. SAMPLE: In all, 134 Swedish children (Mage = 6 years and 4 months, SD = 3 months, 74 boys) participated in this study. METHOD: Verbal and non-verbal logical reasoning, non-symbolic number comparison, counting knowledge, spatial processing, verbal working memory and arithmetic were assessed. Twelve months later, arithmetic skills were reassessed. A latent change score model was computed to determine whether any of the abilities accounted for variations in arithmetic development. RESULTS: Arithmetic performance was supported by counting knowledge, verbal and non-verbal logical reasoning and spatial processing. Arithmetic skill development was only supported by spatial processing. CONCLUSIONS: Results show that young children's early arithmetic performance and arithmetic development are supported by different cognitive processes. The findings regarding performance supported Fuchs et al.'s model (Dev Psychol, 46, 2010b, 1731) but the developmental findings did not. The developmental findings align partially to Geary et al.'s (J Educ Psychol, 109, 2017, 680) hypothesis stating that young children's early arithmetic development is more dependent on general cognitive abilities than number abilities.

Language as a mechanism for reasoning about possibilities.

The ability to entertain and reflect on possibilities is a crucial component of human reasoning. However, the origin of this reasoning-whether it is language-based or not-is highly debated. We contribute to this debate by investigating the relation between language and thought in the domain of possibility from a developmental perspective. Our investigation focuses on disjunctive syllogism, a specific type of possibility reasoning that has been explored extensively in the developmental literature and has clear linguistic correlates. Seeking links between conceptual and linguistic representations, we review evidence on how children reason by the disjunctive syllogism and how they acquire logical and modal language. We sketch a proposal for how language and thought interact during development. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments.

Machine learning models are biased toward data seen during the training steps. The models will tend to give good results in classes where there are many examples and poor results in those with few examples. This problem generally occurs when the classes to predict are imbalanced and this is frequent in educational data where for example, there are skills that are very difficult or very easy to master. There will be less data on students that correctly answered questions related to difficult skills and who incorrectly answered those related to skills easy to master. In this paper, we tackled this problem by proposing a hybrid architecture combining Deep Neural Network architectures- especially Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)-with expert knowledge for user modeling. The proposed solution uses attention mechanism to infuse expert knowledge into the Deep Neural Network. It has been tested in two contexts: knowledge tracing in an intelligent tutoring system (ITS) called Logic-Muse and prediction of socio-moral reasoning in a serious game called MorALERT. The proposed solution is compared to state-of-the-art machine learning solutions and experiments show that the resulting model can accurately predict the current student's knowledge state (in Logic-Muse) and thus enable an accurate personalization of the learning process. Other experiments show that the model can also be used to predict the level of socio-moral reasoning skills (in MorALERT). Our findings suggest the need for hybrid neural networks that integrate prior expert knowledge (especially when it is necessary to compensate for the strong dependency-of deep learning methods-on data size or the possible unbalanced datasets). Many domains can benefit from such an approach to building models that allow generalization even when there are small training data.

Reasoning outside the box: Divergent thinking is related to logical reasoning.

When people reason, they do so in a way that suggests they are thinking beyond the premises and actively using background knowledge. This study explored the hypothesis that divergent thinking, a key component of creativity, is a unique predictive factor of logical reasoning. A total of 96 adults completed a divergent thinking task and logical reasoning problems with varying forms and contents. Cognitive capacity was measured as a confounding factor. Individual differences in ideational fluency and originality were derived from the divergent thinking task. As hypothesized, originality was predictive of logical reasoning beyond fluency and cognitive capacity.

The Sydney declaration - Revisiting the essence of forensic science through its fundamental principles.

Unlike other more established disciplines, a shared understanding and broad acceptance of the essence of forensic science, its purpose, and fundamental principles are still missing or mis-represented. This foundation has been overlooked, although recognised by many forensic science forefathers and seen as critical to this discipline's advancement. The Sydney Declaration attempts to revisit the essence of forensic science through its foundational basis, beyond organisations, technicalities or protocols. It comprises a definition of forensic science and seven fundamental principles that emphasise the pivotal role of the trace as a vestige, or remnant, of an investigated activity. The Sydney Declaration also discusses critical features framing the forensic scientist's work, such as context, time asymmetry, the continuum of uncertainties, broad scientific knowledge, ethics, critical thinking, and logical reasoning. It is argued that the proposed principles should underpin the practice of forensic science and guide education and research directions. Ultimately, they will benefit forensic science as a whole to be more relevant, effective and reliable.

The influence of classroom seating arrangement on children's cognitive processes in primary school: the role of individual variables.

To date, despite the great debate regarding the best seating arrangement for learning in classrooms, no empirical studies have examined the direct effects of different seating arrangements on children's cognitive processes. This is particularly important nowadays that the COVID-19 measures include maintaining distance in the classroom. Aim of this study was experimentally investigating the effect of changing the seating arrangement (clusters vs. single desks), on logical reasoning, creativity and theory of mind, in children attending primary school. Furthermore, some individual characteristics (e.g., gender, loneliness, popularity) were analysed as potential moderators. Results on 77 participants showed that, when children were seated in single desks, their score in logical reasoning was globally higher. Furthermore, when seated in single desks, girls showed a better performance in the theory of mind, and lonelier children performed better in theory of mind and creativity. This on field experimental study suggests the importance of considering both the nature of the task and children's individual characteristics when deciding on a seating arrangement in the classroom.

High-definition transcranial direct current stimulation modulates performance and alpha/beta parieto-frontal connectivity serving fluid intelligence.

Fluid intelligence (Gƒ) includes logical reasoning abilities and is an essential component of normative cognition. Despite the broad consensus that parieto-prefrontal connectivity is critical for Gƒ (e.g. the parieto-frontal integration theory of intelligence, P-FIT), the dynamics of such functional connectivity during logical reasoning remains poorly understood. Further, given the known importance of these brain regions for Gƒ, numerous studies have targeted one or both of these areas with non-invasive stimulation with the goal of improving Gƒ, but to date there remains little consensus on the overall stimulation-related effects. To examine this, we applied high-definition direct current anodal stimulation to the left and right dorsolateral prefrontal cortex (DLPFC) of 24 healthy adults for 20 min in three separate sessions (sham, left, and right active). Following stimulation, participants completed a logical reasoning task during magnetoencephalography (MEG). Significant neural responses at the sensor-level were imaged using a beamformer, and peak task-induced activity was subjected to dynamic functional connectivity analyses to evaluate the impact of distinct stimulation montages on network activity. We found that participants responded faster following right DLPFC stimulation vs. sham. Moreover, our neural findings followed a similar trajectory of effects such that left parieto-frontal connectivity decreased following right and left DLPFC stimulation compared to sham, with connectivity following right stimulation being significantly correlated with the faster reaction times. Importantly, our findings are consistent with P-FIT, as well as the neural efficiency hypothesis (NEH) of intelligence. In sum, this study provides evidence for beneficial effects of right DLPFC stimulation on logical reasoning. KEY POINTS: Logical reasoning is an indispensable component of fluid intelligence and involves multispectral oscillatory activity in parietal and frontal regions. Parieto-frontal integration is well characterized in logical reasoning; however, its direct neural quantification and neuromodulation by brain stimulation remain poorly understood. High-definition transcranial direct current stimulation of dorsolateral prefrontal cortex (DLPFC) had modulatory effects on task performance and neural interactions serving logical reasoning, with right stimulation showing beneficial effects. Right DLPFC stimulation led to a decrease in the response time (i.e. better task performance) and left parieto-frontal connectivity with a marginal positive association between behavioural and neural metrics. Other modes of targeted stimulation of DLPFC (e.g. frequency-specific) can be employed in future studies.

Is clinical, musculoskeletal pain associated with poorer logical reasoning?

INTRODUCTION: It has been hypothesized that pain disrupts system 2 processes (eg, working memory) presumed to underlie logical reasoning. A recent study examining the impact of experimentally induced pain on logical reasoning found no evidence of an effect. OBJECTIVES: The aim of this study was to examine whether clinical pain, which is qualitatively different from experimental pain, would lower the ability to reason logically. METHODS: Ninety-six participants completed a questionnaire containing 3 different logical reasoning tasks (the cognitive reflection test, the belief bias syllogisms task, and the conditional inference task), questions about pain variables (present pain intensity, pain intensity during the last 24 hours, the influence of pain on daily activities, pain duration, and pain persistence), questions about other pain-related states (anxiety, depression, and fatigue), and pain-relieving medication. Correlations between the logical reasoning tasks and the pain variables were calculated. RESULTS: For 2 of the 3 logical reasoning tasks (the cognitive reflection test and the belief bias syllogisms task), clinical pain was unrelated to logical reasoning. Performance on context-free logical reasoning showed a significant negative correlation with present pain intensity, but not with the other pain variables. CONCLUSION: This finding that logical reasoning ability is largely unrelated to clinical pain is highly consistent with previous research on experimentally induced pain. Pain should probably not constitute a significant barrier to logical reasoning in everyday life.

Age-related differences in structural and functional prefrontal networks during a logical reasoning task.

In logical reasoning, difficulties in inhibition of currently-held beliefs may lead to unwarranted conclusions, known as belief bias. Aging is associated with difficulties in inhibitory control, which may lead to deficits in inhibition of currently-held beliefs. No study to date, however, has investigated the underlying neural substrates of age-related differences in logical reasoning and the impact of belief load. The aim of the present study was to delineate age differences in brain activity during a syllogistic logical reasoning task while the believability load of logical inferences was manipulated. Twenty-nine, healthy, younger and thirty, healthy, older adults (males and females) completed a functional magnetic resonance imaging experiment in which they were asked to determine the logical validity of conclusions. Unlike younger adults, older adults engaged a large-scale network including anterior cingulate cortex and inferior frontal gyrus during conclusion stage. Our functional connectivity results suggest that while older adults engaged the anterior cingulate network to overcome their intuitive responses for believable inferences, the inferior frontal gyrus network contributed to higher control over responses during both believable and unbelievable conditions. Our functional results were further supported by structure-function-behavior analyses indicating the importance of cingulum bundle and uncinate fasciculus integrity in rejection of believable statements. These novel findings lend evidence for age-related differences in belief bias, with potentially important implications for decision making where currently-held beliefs and given assumptions are in conflict.

Human vs. machine: the psychological and behavioral consequences of being compared to an outperforming artificial agent.

While artificial agents (AA) such as Artificial Intelligence are being extensively developed, a popular belief that AA will someday surpass human intelligence is growing. The present research examined whether this common belief translates into negative psychological and behavioral consequences when individuals assess that an AA performs better than them on cognitive and intellectual tasks. In two studies, participants were led to believe that an AA performed better or less well than them on a cognitive inhibition task (Study 1) and on an intelligence task (Study 2). Results indicated that being outperformed by an AA increased subsequent participants' performance as long as they did not experience psychological discomfort towards the AA and self-threat. Psychological implications in terms of motivation and potential threat as well as the prerequisite for the future interactions of humans with AAs are further discussed.

Class inclusion versus quantifiers comprehension tasks: an experimental study with school-aged children.

Literature in developmental psychology pays special attention to the difficulties met by preschool children when confronted with (universal vs. existential) quantified sentences. According to the pivotal Piagetian view, the difficulties exhibited in quantifier comprehension during the preoperational period (age 2-6) derive from the same limitations in logical reasoning that cause bad performance outcomes in class inclusion problems. Nevertheless, as far as we know, a direct comparison between the two tasks has never been produced. In this research we tested the logical hypotheses concerning the failure in quantifier comprehension of preschool children by administering a sentence-picture matching task to two groups of children (5-6 vs. 7-8 years old). Pictures, obtained by partially pairing two entity sets, one of which outnumbers the other, were presented to participants. After each picture, children were asked to answer questions involving quantified versus class inclusion contents. Main findings showed that younger children performed the quantifier task worse than older children and their performance in that task was also worse with respect to the class inclusion task. This difference was not observed with older children who obtained better results than younger children in both tasks. These findings suggest that the specific abilities involved in solving the two problems evolve independently from each other during cognitive development. The results have been discussed in the light of the recent developmental theories.

Unifying neural learning and symbolic reasoning for spinal medical report generation.

Automated medical report generation in spine radiology, i.e., given spinal medical images and directly create radiologist-level diagnosis reports to support clinical decision making, is a novel yet fundamental study in the domain of artificial intelligence in healthcare. However, it is incredibly challenging because it is an extremely complicated task that involves visual perception and high-level reasoning processes. In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the spinal medical report generation. Generally speaking, the NSL framework firstly employs deep neural learning to imitate human visual perception for detecting abnormalities of target spinal structures. Concretely, we design an adversarial graph network that interpolates a symbolic graph reasoning module into a generative adversarial network through embedding prior domain knowledge, achieving semantic segmentation of spinal structures with high complexity and variability. NSL secondly conducts human-like symbolic logical reasoning that realizes unsupervised causal effect analysis of detected entities of abnormalities through meta-interpretive learning. NSL finally fills these discoveries of target diseases into a unified template, successfully achieving a comprehensive medical report generation. When employed in a real-world clinical dataset, a series of empirical studies demonstrate its capacity on spinal medical report generation and show that our algorithm remarkably exceeds existing methods in the detection of spinal structures. These indicate its potential as a clinical tool that contributes to computer-aided diagnosis.

Role of the Hippocampus During Logical Reasoning and Belief Bias in Aging.

Reasoning requires initial encoding of the semantic association between premises or assumptions, retrieval of these semantic associations from memory, and recombination of information to draw a logical conclusion. Currently-held beliefs can interfere with the content of the assumptions if not congruent and inhibited. This study aimed to investigate the role of the hippocampus and hippocampal networks during logical reasoning tasks in which the congruence between currently-held beliefs and assumptions varies. Participants of younger and older age completed a series of syllogistic reasoning tasks in which two premises and one conclusion were presented and they were required to decide if the conclusion logically followed the premises. The belief load of premises was manipulated to be either congruent or incongruent with currently-held beliefs. Our whole-brain results showed that older adults recruited the hippocampus during the premise integration stage more than their younger counterparts. Functional connectivity using a hippocampal seed revealed that older, but not younger, adults recruited a hippocampal network that included anterior cingulate and inferior frontal regions when premises were believable. Importantly, this network contributed to better performance in believable inferences, only in older adults group. Further analyses suggested that, in older adults group, the integrity of the left cingulum bundle was associated with the higher rejection of believable premises more than unbelievable ones. Using multimodal imaging, this study highlights the importance of the hippocampus during premise integration and supports compensatory role of the hippocampal network during a logical reasoning task among older adults.