Human “free will” has been made problematic by several recent arguments against mental causation, the unity of the I or “self,” and the possibility that conscious decision-making could be temporally prior to action. This paper suggests a pathway through this thicket for free will or self-determination. Doing so requires an account of mind as an emergent process in the context of animal psychology and mental causation. Consciousness, a palpable but theoretically more obscure property of some minds, is likely to (...) derive from complex animals’ real-time monitoring of internal state in relation to environment. Following Antonio Damasio, human mind appears to add to nonhuman “core consciousness” an additional narrative “self-consciousness.” The neurological argument against free will, most famously from Benjamin Libet, can be avoided as long as “free will” means, not an impossible event devoid of prior causation, but an occasional causal role played by narrative self-consciousness in behavioral determination. There is no necessary incompatibility between the scientific and evolutionary exploration of mind and consciousness and the uniquely self-determining capabilities of human mentality which are based on the former. (shrink)

Emotions are traditionally considered to be brief states that last for seconds or a few minutes at most. However, due to pioneering theoretical work of Frijda and recent empirical studies, it has become clear that the duration of emotions is actually highly variable with durations ranging from a few seconds to several hours, or even longer. We review research on determinants of emotion duration. Three classes of determinants are identified: features related to the emotion-eliciting event, emotion itself, and emotion-experiencing person. (...) Initial evidence on the psychological and neural mechanisms that underlie their effects is discussed. (shrink)

Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes analysis (...) of the behavior of reinforced concrete elements and linear structures exposed to actions of standard fire, were used for the development of a prognostic model with the application of fuzzy neural networks. As fire resistance directly affects the functionality and safety of structures, the significance which new methods and computational tools have on enabling quick, easy, and simple prognosis of the same is quite clear. This paper will consider the application of fuzzy neural networks by creating prognostic models for determining fire resistance of eccentrically loaded reinforced concrete columns. (shrink)

Short-term motor practice leads to plasticity in the primary motor cortex. The purpose of this study is to investigate the factors that determine the increase in corticospinal tract excitability after motor practice, with special focus on two factors; “the level of muscle activity” and “the presence/absence of a goal of keeping the activity level constant.” Fifteen healthy subjects performed four types of rapid thumb adduction in separate sessions. In the “comfortable task” and “forceful task”, the subjects adducted their thumb using (...) comfortable and strong forces. In the “comfortable with a goal task” and “forceful with a goal task”, subjects controlled the muscle activity at the same level as in the C and F, respectively, by adjusting the peak electromyographic amplitude within the target ranges. Paired associative stimulation, which combines peripheral nerve stimulation and transcranial magnetic stimulation, with an inter-stimulus interval of 25 ms was also done. Before and after the motor tasks and PAS25, TMS was applied to the M1. None of the four tasks showed any temporary changes in behavior, meaning no learning occurred. Motor-evoked potential amplitude increased only after the FG and it exhibited a positive correlation with the MEP increase after PAS25, suggesting that FG and PAS25 share at least similar plasticity mechanisms in the M1. Resting motor threshold decreased only after FG, suggesting that FG would also be associated with the membrane depolarization of M1 neurons. These results suggest task-dependent plasticity from the synergistic effect of forceful muscle activity and of setting a goal of keeping the activity level constant. (shrink)

Mental and neural states are related to one another by vertical interlevel relations and by horizontal intralevel relations. For particular choices of such relations, problems arise if causal efficacy is ascribed to mental states. In a series of influential papers and books, Kim has presented his much discussed “supervenience argument,” which ultimately amounts to the dilemma that mental states either are causally inefficacious or they hold the threat of overdetermining neural states. Forced by this disjunction, Kim votes in (...) favor of overdetermination and, ultimately, reduction. We propose a perspective on mental causation that dissolves the assumption of a tension between horizontal and vertical determination. For mental states to be causally efficacious, they must be dynamically stable. This important requirement can be implemented by combining a key idea of supervenience, multiple realization, with the recently introduced vertical interlevel relation of contextual emergence. Both together deflate Kim's dilemma and reflate the causal efficacy of mental states. 2012 APA, all rights reserved). (shrink)

China declared a long-term commitment at the United Nations General Assembly in 2020 to reduce CO2 emissions. This announcement has been described by Reuters as “the most important climate change commitment in years.” The allocation of China’s provincial CO2 emission quotas is crucial for building a unified national carbon market, which is an important policy tool necessary to achieve carbon emissions reduction. In the present research, we used historical quota data of China’s carbon emission trading policy pilot areas from 2014 (...) to 2017 to identify alternative features of corporate CO2 emissions and build a backpropagation neural network model to train the benchmark model. Later, we used the model to calculate the quotas for other regions, provided they implement the carbon emission trading policy. Finally, we added up the quotas to obtain the total national quota. Additionally, considering the perspective of carbon emission terminal, a new characteristic system of quota allocation was proposed in order to retrain BP including the following three aspects: enterprise production, household consumption, and regional environment. The results of the benchmark model and the new models were compared. This feature system not only builds a reasonable quota-related indicator framework but also perfectly matches China’s existing “bottom-up” total control quota approach. Compared with the previous literature, the present report proposes a quota allocation feature system closer to China’s policy and trains BP to obtain reasonable feature weights. The model is very important for the establishment of a unified national carbon emission trading market and the determination of regional quotas in China. (shrink)

While neuroscientists often characterize brain activity as representational, many philosophers have construed these accounts as just theorists’ glosses on the mechanism. Moreover, philosophical discussions commonly focus on finished accounts of explanation, not research in progress. I adopt a different perspective, considering how characterizations of neural activity as representational contributes to the development of mechanistic accounts, guiding the investigations neuroscientists pursue as they work from an initial proposal to a more detailed understanding of a mechanism. I develop one illustrative example (...) involving research on the information-processing mechanisms mammals employ in navigating their environments. This research was galvanized by the discovery in the 1970s of place cells in the hippocampus. This discovery prompted research in what the activity of these cells represents and how place representations figure in navigation. It also led to the discovery of a host of other types of neurons—grid cells, head-direction cells, boundary cells—that carry other types of spatial information and interact with place cells in the mechanism underlying spatial navigation. As I will try to make clear, the research is explicitly devoted to identifying representations and determining how they are constructed and used in an information processing mechanism. Construals of neural activity as representations are not mere glosses but are characterizations to which neuroscientists are committed in the development of their explanatory accounts. (shrink)

Spoken language consists of a linear sequence of units, from which the existence of particular underlying hierarchical processing mechanisms is inferred. Uddén et al. use graph theory to provide a framework for describing the possible structural relationships that may underlie a linear output sequence. Being more explicit in defining different structures can help identifying and testing for such structures in AGL experiments, as well as help showing how behavioral and neuroimaging data reveals signatures of hierarchical processing in humans.

Spoken language consists of a linear sequence of units, from which the existence of particular underlying hierarchical processing mechanisms is inferred. Uddén et al. use graph theory to provide a framework for describing the possible structural relationships that may underlie a linear output sequence. Being more explicit in defining different structures can help identifying and testing for such structures in AGL experiments, as well as help showing how behavioral and neuroimaging data reveals signatures of hierarchical processing in humans.

This paper presents a review of studies that were aimed at determining which brain regions are recruited during visual self-recognition, with a particular focus on self-face recognition. A complex bilateral network, involving frontal, parietal and occipital areas, appears to be associated with self-face recognition, with a particularly high implication of the right hemisphere. Results indicate that it remains difficult to determine which specific cognitive operation is reflected by each recruited brain area, in part due to the variability of used control (...) stimuli and experimental tasks. A synthesis of the interpretations provided by previous studies is presented. The relevance of using self-recognition as an indicator of self-awareness is discussed. We argue that a major aim of future research in the field should be to identify more clearly the cognitive operations induced by the perception of the self-face, and search for dissociations between neural correlates and cognitive components. (shrink)

The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the (...) continuous boundaries that they induced along their collinear edges. The shapes in some images had the same contrast (black or white) with respect to the background gray. Other images included opposite contrasts along each induced continuous boundary. Psychophysical results demonstrate conditions under which figure-ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. Studies are also reviewed in which both polarity-specific and polarity-invariant properties obtain perceptual figure-ground grouping results. The FACADE and 3D LAMINART models are used to explain these data. Keywords: figure-ground separation, border ownership, perceptual grouping, surface filling-in, V2, V4, FACADE Theory. (shrink)

The paper is an examination of the ways and extent to which neuroscience places constraints on cognitive science. In Part I, I clarify the issue, as well as the notion of levels in cognitive inquiry. I then present and address, in Part II, two arguments designed to show that facts from neuroscience are at a level too low to constrain cognitive theory in any important sense. I argue, to the contrary, that there are several respects in which facts from neurophysiology (...) will constrain cognitive theory. Part III then turns to an examination of Connectionism and Classical Cognitivism to determine which, if either, is in a better position to accomodate neural constraints in the ways suggested in Part II. (shrink)

Although the neural bases of the brain associated with movement disorders in children with developmental coordination disorder are becoming clearer, the information is not sufficient because of the lack of extensive brain function research. Therefore, it is controversial about effective intervention methods focusing on brain function. One of the rehabilitation techniques for movement disorders involves intervention using motor imagery. MI is often used for movement disorders, but most studies involve adults and healthy children, and the MI method for children (...) with DCD has not been studied in detail. Therefore, a review was conducted to clarify the neuroscientific basis of the methodology of intervention using MI for children with DCD. The neuroimaging review included 20 magnetic resonance imaging studies, and the neurorehabilitation review included four MI intervention studies. In addition to previously reported neural bases, our results indicate decreased activity of the bilateral thalamus, decreased connectivity of the sensory-motor cortex and the left posterior middle temporal gyrus, bilateral posterior cingulate cortex, precuneus, cerebellum, and basal ganglia, loss of connectivity superiority in the abovementioned areas. Furthermore, reduction of gray matter volume in the right superior frontal gyrus and middle frontal gyrus, lower fractional anisotropy, and axial diffusivity in regions of white matter pathways were found in DCD. As a result of the review, children with DCD had less activation of the left brain, especially those with mirror neurons system and sensory integration functions. On the contrary, the area important for the visual space processing of the right brain was activated. Regarding of characteristic of the MI methods was that children observed a video related to motor skills before the intervention. Also, they performed visual-motor tasks before MI training sessions. Adding action observation during MI activates the MNS, and performing visual-motor tasks activates the basal ganglia. These methods may improve the deactivated brain regions of children with DCD and may be useful as conditioning before starting training. Furthermore, we propose a process for sharing the contents of MI with the therapist in language and determining exercise strategies. (shrink)

In previous research Libet discovered that a critical time period for neural activation is necessary in order for a stimulus to become conscious. This necessary time period varies from subject to subject. In this current study, six subjects for whom the time for neural activation of consciousness had been previously determined were administered a battery of psychological tests on the basis of which ratings were made of degree of repressiveness. As hypothesized, repressive subjects had a longer critical time (...) period for neural activation of consciousness, suggesting the possibility that this neurophysiological time factor is a necessary condition for the development of repression. (shrink)

Empathy is often split into an affective facet for embodied simulation or sometimes sensorial processing, and a cognitive facet for mentalizing and perspective-taking. However, a recent neurophenomenological framework proposes a graded view on empathy (i.e., “Graded Empathy”) that extends this dichotomy and considers multiple levels while integrating complex neural patterns and representations of subjective experience. In the current magnetoencephalography study, we conducted a multidimensional investigation of neural oscillatory modulations and their cortical sources in 44 subjects while observing stimuli (...) that convey vicarious pain (vs no-pain) in a broad time window and frequency range to explore rich neural representations of pain empathy. Furthermore, we collected participants’ subjective-experience of sensitivity to vicarious pain, as well as their self-reported trait levels of affective and cognitive empathy to examine the possible associations between neural mechanisms and subjective experiences and reports. While extending previous electrophysiological studies that mainly focused on alpha suppression, we found here four significant power modulation patterns corresponding to multiple facets of empathy: an early central (peaking in the paracentral sulcus) alpha (6–11 Hz) suppression pattern plausibly reflecting sensory processing, two early beta (15–23 Hz) suppression patterns in the mid-cingulate cortex (plausibly reflecting the affective component) and in the precuneus (plausibly reflecting the cognitive component), and a late anterior (peaking in the orbitofrontal cortex) alpha-beta (11–19 Hz) enhancement pattern (plausibly reflecting cognitive-control inhibitory response). Interestingly, the latter measure was negatively correlated with the subjective sensitivity to vicarious pain, thereby possibly revealing a novel inhibitory neural mechanism determining the subjective sensitivity to vicarious pain. Altogether, these multilevel findings cannot be accommodated by the dichotomous model of empathy (i.e., affective-cognitive), and provide empirical support to theGraded Empathyneurophenomenological framework. Furthermore, this work emphasizes the importance of examining multiple neural rhythms, their cortical generators, and reports of subjective-experience in the aim of elucidating the complex nature of empathy. (shrink)

Humans are equipped with the remarkable ability to comprehend an infinite number of utterances. Relations between grammatical categories restrict the way words combine into phrases and sentences. How the brain recognizes different word combinations remains largely unknown, although this is a necessary condition for combinatorial unboundedness in language. Here, we used functional magnetic resonance imaging and multivariate pattern analysis to explore whether distinct neural populations of a known language network hub—Broca’s area—are specialized for recognizing distinct simple word combinations. The (...) phrases consisted of a noun (flag) occurring either with a content word, an adjective (green flag), or with a function word, a determiner (that flag). The key result is that the distribution of neural populations classifying word combination in Broca’s area seems sensitive to neuroanatomical subdivisions within this area, irrespective of task. The information patterns for adjective + noun were localized in its anterior part (BA45) whereas those for determiner + noun were localized in its posterior part (BA44). Our findings provide preliminary answers to the fundamental question of how lexical and grammatical category information interact during simple word combination, with the observation that Broca’s area is sensitive to the recognition of categorical relationships during combinatory processing, based on different demands placed on syntactic and semantic information. This supports the hypothesis that the combinatorial power of language consists of some neural computation capturing phrasal differences when processing linguistic input. (shrink)

Tactile distance perception is believed to require that immediate afferent signals be referenced to a stored representation of body size and shape (the body model). For this ability, recent studies have reported that the stored body representations involved are highly distorted, at least in the case of the hand, with the hand dorsum represented as wider and squatter than it actually is. Here, we aim to define the neural basis of this phenomenon. In a behavioural experiment participants estimated the (...) distance between touches on two points by adjusting the length of a visually-presented line on the screen. The technique of multidensional scaling (MDS) was used to reconstruct a perceptual map of tactile space. Analysis of spatial distortion using Procrustes alignment showed that maps were stretched in the mediolateral hand axis. In order to determine the neural correlates of these body distortions, we performed an fMRI study. For each participant, we used a searchlight pattern classifier with Euclidean distance on pre-defined regions of interests (ROIs). In order to relate the representations between the different points and to computational models, we compare response-pattern dissimilarity matrices in these ROIs. Similar to the behavioural experiment, we used MDS to reconstruct maps of the neural representation of tactile space using the values from the dissimilarities matrices. We were able to reconstruct the perceptual map of tactile space in the contralateral primary somatosensory and motor cortices. This suggests that these areas are critical to generate the tactile representations of the dorsum of the hand. (shrink)

In previous research Libet discovered that a critical time period for neural activation is necessary in order for a stimulus to become conscious. This necessary time period varies from subject to subject. In this current study, six subjects for whom the time for neural activation of consciousness had been previously determined were administered a battery of psychological tests on the basis of which ratings were made of degree of repressiveness. As hypothesized, repressive subjects had a longer critical time (...) period for neural activation of consciousness, suggesting the possibility that this neurophysiological time factor is a necessary condition for the development of repression. (shrink)

The confluence of creativity in music performance finds itself in performance practices and cultural motifs, the communication of the human body along with the instrument it interacts with, and individual performers’ perceptual, motor, and cognitive abilities that contribute to varied musical interpretations of the same piece or melodic line. The musical and artistic execution of a player, as well as the product of this phenomena can become determinant causes in a creative mental state. With advances in neurocognitive measures, the state (...) of one’s artistic intuition and execution has been a growing interest in understanding the creative thought process of human behavior, particularly in improvising artists. This article discusses the implementation on the concurrence of spontaneous (Type-1) and controlled (Type-2) processing modes that may be apparent in the perception of non-improvising artists on how melodic lines are perceived in music performance. Elucidating the cortical-subcortical activity in the dual-process model may extend to non-improvising musicians explored in the paradigm of neural correlates. These interactions may open new possibilities for expanding the repertoire of executive functions, creativity, and the coordinated activity of cortical-subcortical regions that regulate the free flow of artistic ideas and expressive spontaneity in future neuromusical research. (shrink)

This paper focuses on modeling and control of a class of serial variable stiffness actuators based on level mechanisms for robotic applications. A multi-input multi-output complex nonlinear dynamic model is derived to fully describe SVSAs and the relative degree of the model is determined accordingly. Due to nonlinearity, high coupling, and parametric uncertainty of SVSAs, a neural network-based adaptive control strategy based on feedback linearization is proposed to handle system uncertainties. The feasibility of the proposed approach for position and (...) stiffness tracking of SVSAs is verified by simulation results. (shrink)

With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function neural network modeling technique to predict preschool instructors’ technology usage in classes based (...) on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research. (shrink)

People are motivated by shared social values that, when held with moral conviction, can serve as compelling mandates capable of facilitating support for ideological violence. The current study examined this dark side of morality by identifying specific cognitive and neural mechanisms associated with beliefs about the appropriateness of sociopolitical violence, and determining the extent to which the engagement of these mechanisms was predicted by moral convictions. Participants reported their moral convictions about a variety of sociopolitical issues prior to undergoing (...) functional MRI scanning. During scanning, they were asked to evaluate the appropriateness of violent protests that were ostensibly congruent or incongruent with their views about sociopolitical issues. Complementary univariate and multivariate analytical strategies comparing neural responses to congruent and incongruent violence identified neural mechanisms implicated in processing salience and in the encoding of subjective value. As predicted, neuro-hemodynamic response was modulated parametrically by individuals’ beliefs about the appropriateness of congruent relative to incongruent sociopolitical violence in ventromedial prefrontal cortex, and by moral conviction in ventral striatum. Overall moral conviction was predicted by neural response to congruent relative to incongruent violence in amygdala. Together, these findings indicate that moral conviction about sociopolitical issues serves to increase their subjective value, overriding natural aversion to interpersonal harm. (shrink)

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We (...) find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses. (shrink)

Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or would (...) it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction. (shrink)

Current cognitive science models of perception and action assume that the objects that we move toward and perceive are represented as determinate in our experience of them. A proper phenomenology of perception and action, however, shows that we experience objects indeterminately when we are perceiving them or moving toward them. This indeterminacy, as it relates to simple movement and perception, is captured in the proposed phenomenologically based recurrent network models of brain function. These models provide a possible foundation from which (...) predicative structures may arise as an emergent phenomenon without the positing of a representing subject. These models go some way in addressing the dual constraints of phenomenological accuracy and neurophysiological plausibility that ought to guide all projects devoted to discovering the physical basis of human experience. (shrink)

Air temperature prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system, hybrid neural-fuzzy inference system, and adaptive neurofuzzy inference system for AT prediction. Modelling was done for three stations in North Dakota, USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when (...) handling highly variable data, such as AT, which shows a high positive correlation with average daily dew point, total solar radiation, and negative correlation with average wind speed. At the Robinson station, DENFIS performed the best with a coefficient of determination of 0.96 and a modified index of agreement of 0.92, followed by ANFIS with R2 of 0.94 and md of 0.89, and HyFIS with R2 of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R2: 0.953/0.960, md: 0.903/0.912, then ANFIS with R2: 0.943/0.942, md: 0.888/0.890, and HyFIS with R2: 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure. (shrink)

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments (...) made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration. (shrink)

Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and (...) clarify how functionalism and computationalism may or may not legitimately come together. (shrink)

I agree with Nachev and Hacker’s general approach. However, their criticism of claims of covert automaticity can be strengthened. I first say a few words on what voluntary action involves and on the consequent limited relevance of brain research for the determination of voluntariness. I then turn to Nachev and Hacker’s discussion of possible covert automaticity and show why the case for it is weaker than they allow.

How different neural crest derivatives differentiate in distinct embryonic locations in the vertebrate embryo is an intriguing issue. Many attempts have been made to understand the underlying mechanism of specific pathway choices made by migrating neural crest cells. In this speculative review we suggest a new mechanism for the regulation of neural crest cell migration patterns in avian and mammalian embryos, based on recent progress in understanding the expression and activity of receptor tyrosine kinases during embryogenesis. Distinct (...) subpopulations of crest‐derived cells express specific receptor tyrosine kinases while residing in a migration staging area. We postulate that the differential expression of receptor tyrosine kinases by specific subpopulations of neural crest cells allows them to respond to localized growth factor ligand activity in the embryo. Thus, the migration pathway taken by neural crest subpopulations is determined by their receptor tyrosine kinase response to the differential localization of their cognate ligand. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm (...) and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Philosophy, Psychiatry, & Psychology 10.2 (2003) 161-167 [Access article in PDF] Social, Cognitive, and Neural Constraints on Subjectivity and Agency:Implications for Dissociative Identity Disorder Peter Q. Deeley In this commentary, I consider Matthew's argument after making some general observations about dissociative identity disorder (DID). In contrast to Matthew's statement that "cases of DID, although not science fiction, are extraordinary" (p. 148), I believe that there are natural analogs (...) of the disorder that, when considered, make it seem less puzzling and exotic. After discussing these examples, I examine the relations between social, cognitive, and neural processes supporting subjectivity and agency, to identify sources of personal identity in cases of DID that can be used as a basis for assessing Matthew's points against Behnke and Sinnott-Armstrong. I am not proposing a comprehensive theory, but rather identifying what I believe are components of such a theory. I then outline an approach to determining criminal responsibility in DID, drawing on analogies with other psychiatric disorders. Dissociative Identity Disorder DID is a disorder described in DSM-IV, which "is characterised by the presence of two or more distinct identities or personality states that recurrently take control of the individual's behaviour accompanied by an inability to recall important personal information that is too extensive to be explained by ordinary forgetfulness" (American Psychiatric Association, p. 519). Recognizing Personal Identity How is personal identity recognized in normal cognition? If we take personhood to involve recognition of continuity of social, psychological, and physical identity for a given individual, then such judgments are routinely made rapidly and automatically as an aspect of what has come to be termed social cognition (Adolphs 2003). There is likely to be considerable redundancy in [End Page 161] the types of information used to make judgments about whether a person is the same as one who was previously encountered, or perhaps heard about—for example, facial features, movement patterns, speech prosody, dress, speech content, gender, ethnicity, social role, among other features, all provide information about individual identity, and hence personhood. When uncertainty exists, it is likely to reflect a loss or alteration of cues that would ordinarily be used in a rapid, automatic, unexamined way (Adolphs 2003). In such extreme cases (for example, severe mental illness or disfigurement), the problem of identification becomes the subject of explicit reasoning (and hence of potential disagreement, because we then engage in post hoc rationalizations to establish criteria for determining personhood, whereas normally we rely on essentially automatic, implicit abilities that work in most circumstances, some of which make little use of linguistic representations and formal reasoning—for example, individual facial identity recognition, which may occur as quickly as 170 ms after stimulus onset [Adolphs 2003]). Nevertheless, examining our judgments and intuitions in cases that force us to explicitly consider whether personal identity is preserved may provide analogies and clues for how we should proceed with DID. Let us consider, then, some natural analogs of DID. Natural Analogs of DID Method Acting I suggest that method acting is an analog of DID (Manderino 1985). Method acting refers to an approach to acting training and performance in which emphasis is placed on intensely imagining and identifying with the mental life of the character being portrayed. Training courses and manuals list many techniques and practices that can be used to facilitate absorption in a role, drawing on different kinds of memory—for example, experiential memories, memories of others, general knowledge (semantic memory), and memories of sensations and feelings, to creative a vivid sense of the subjective experience and actions of the character. Manderino, a method acting teacher, comments: "it is important to investigate the way in which your own mental makeup can improperly influence a character. Insecurities, self-consciousness, shyness or self-doubts can maintain a strong hold, and permitting any of these feelings to enter a performance is to remain the actor and not enter into the mental life of the character. For some, acting allows personal problems to be left behind, and they are thankful for the complete escape that... (shrink)

Philosophical proponents of predictive processing cast the novelty of predictive models of perception in terms of differences in the functional role and information content of neural signals. However, they fail to provide constraints on how the crucial semantic mapping from signals to their informational contents is determined. Beyond a novel interpretative gloss on neural signals, they have little new to say about the causal structure of the system, or even what statistical information is carried by the signals. That (...) means that the predictive framework for perception can be relabeled in traditional, non-predictive terms, with no empirical consequences relevant to existing or future data. To the extent that neuroscientific research based on predictive processing is both innovative and productive, it will be due to the framework’s suggestive heuristic effects, or perhaps auxiliary empirical claims about implementation, rather than a difference in the information-processing structure that it describes. (shrink)

Methods of machine learning (ML) are gradually complementing and sometimes even replacing methods of classical statistics in science. This raises the question whether ML faces the same methodological problems as classical statistics. This paper sheds light on this question by investigating a long-standing challenge to classical statistics: the reference class problem (RCP). It arises whenever statistical evidence is applied to an individual object, since the individual belongs to several reference classes and evidence might vary across them. Thus, the problem consists (...) in choosing a suitable reference class for the individual. I argue that deep neural networks (DNNs) are able to overcome specific instantiations of the RCP. Whereas the criteria of narrowness, reliability, and homogeneity, that have been proposed to determine a suitable reference class, pose an inextricable tradeoff to classical statistics, DNNs are able to satisfy them in some situations. On the one hand, they can exploit the high dimensionality in big-data settings. I argue that this corresponds to the criteria of narrowness and reliability. On the other hand, ML research indicates that DNNs are generally not susceptible to overfitting. I argue that this property is related to a particular form of homogeneity. Taking both aspects together reveals that there are specific settings in which DNNs can overcome the RCP. (shrink)

The article is devoted to the study of the nature of consciousness within the framework of the information concept. The paper proposes a definition of consciousness as an informational environment in which an extended model of reality is realized. The process of realization of this extended model is defined as thinking. The result of thinking is information objects that form a system in the form of information environment. Information objects are reflections of the real world properties, not directly, but by (...) means of translation through a special object – a carrier of consciousness. In the case of human consciousness, such a carrier is a human being (represented in the form of his nervous system). As a result, consciousness can be qualified as a simulacrum of reality, i.e., a model of a model: an information model of the carrier of consciousness, which in turn is a means of physical modeling of the real "big" world. Possible mechanisms mediating thinking are considered. For this purpose, two new concepts are introduced: neural circuit and neurophysical pattern. An approach to the study of self-consciousness based on the localization of the consciousness carrier in the multidimensional space of states of initial real objects, as well as their reflections in the form of information objects is proposed. This localization is ensured by the presence of feedbacks. Summarizing the results of the study, the article states the following connection between consciousness, self-consciousness and subjectness: under certain conditions (when consciousness is localized in the state space of the carrier of consciousness), consciousness acquires the property of self-consciousness, a special case of which (when the initiator of changes determining localization is the carrier of consciousness) is self-consciousness endowed with subjectness. (shrink)

. Fuzzy systems and artificial neural networks, as important components of soft-computation, can be applied together to model uncertainty. A composite block of the fuzzy system and the ANN shares a mutually beneficial association resulting in enhanced performance with smaller networks. It makes them suitable for application with time-varying multi-input multi-output channel modeling enabling such a system to track minute variations in propagation conditions. Here we propose a fuzzy neural system using a fuzzy time delay fully recurrent (...) class='Hi'>neural network that has the capability to tackle time-varying inputs in fuzzified form and is used to model MIMO channels. The inference engine is constituted by novel FTDFRNN blocks which determine the decision boundaries and tracks in-phase and quadrature components of input signals encompassing stochastic behavior of the MIMO channel. The system shows significant improvement in performance compared to statistical and ANN approaches in terms of faster processing time, lower bit error rate margins and better precision while carrying out symbol recovery of transmitted data through severely faded MIMO channels. (shrink)

I take issue with Saunders & van Brakel's claim that neural processes play no interesting role in determining color categorizations. I distinguish an aspect of color categorization, namely, color discrimination, from other aspects. The law of trichromacy describes conditions under which physical properties cannot be discriminated in terms of color. Trichromacy is explained by properties of neural processes.

The effective controlling and monitoring of an industrial process through the integration of statistical process control and engineering process control has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in (...) a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network based scheme to enhance the accurate identification rate for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine, and multivariate adaptive regression splines. Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system. (shrink)

Air transport is playing an increasing role in the world economy every year. This is facilitated by technological development and the latest developments in the aviation industry, globalization. This paper provides an overview of artificial neural network training methods for airfare predicting. The articles for 2017-2019 were analyzed in order to determine the model with the most accurate prediction. The researchers conducted research on open data collected by themselves and set themselves the goal of creating a model that would (...) advise a user the best time to buy a ticket when the price would be the lowest. The review of the papers by similar themes revealed that the Bagging Regression Tree model has the highest results with an accuracy of 88% and the random forest method has an accuracy of 87%. Civil aviation plays an important role in the economy of each country. Aviation is the best way to cover long distances in comfort in the shortest time. Airlines offer customers a variety of opportunities to travel both within the country and abroad. The main problem of interaction between airlines and customers is the airfare: the former want to sell more at the higher price, and the latter want to buy cheaper. Therefore, companies use their own private algorithms for dynamic pricing and constantly monitor the market situation, responsive to changes in demand and the actions of competitors. This behavior allows them to achieve a balance between the desires of airlines and customers. Scientists are trying to invent a way to predict airfare so that air travelers can buy them at the lowest price. The results of the work in this area provide general rules for the best purchase. For example, according to the article thebest day to buy a ticket by expedia.com for a domestic flight on the United States is Sunday, and the best period is 57 days before departure. This article provides an overview of the works, the authors of which compared the models of machine learning. Achievements in this area are limited to direct flights of a certain domestic market and 88% accuracy of the forecast. The Bagging Regression Tree model described in the article can be considered the best result. This trained model can make predictions based only on two parameters: the number of free cargo and the number of days left before departure and has an accuracy of 88%. (shrink)

Identifying the physiological processes in the central nervous system that underlie our conscious experiences has been at the forefront of cognitive neuroscience. While the principles of classical physics were long found to be unaccommodating for a causally effective consciousness, the inherent indeterminism of quantum physics, together with its characteristic dichotomy between quantum states and quantum observables, provides a fertile ground for the physical modeling of consciousness. Here, we utilize the Schrödinger equation, together with the Planck-Einstein relation between energy and frequency, (...) in order to determine the appropriate quantum dynamical timescale of conscious processes. Furthermore, with the help of a simple two-qubit toy model we illustrate the importance of non-zero interaction Hamiltonian for the generation of quantum entanglement and manifestation of observable correlations between different measurement outcomes. Employing a quantitative measure of entanglement based on Schmidt decomposition, we show that quantum evolution governed only by internal Hamiltonians for the individual quantum subsystems preserves quantum coherence of separable initial quantum states, but eliminates the possibility of any interaction and quantum entanglement. The presence of non-zero interaction Hamiltonian, however, allows for decoherence of the individual quantum subsystems along with their mutual interaction and quantum entanglement. The presented results show that quantum coherence of individual subsystems cannot be used for cognitive binding because it is a physical mechanism that leads to separability and non-interaction. In contrast, quantum interactions with their associated decoherence of individual subsystems are instrumental for dynamical changes in the quantum entanglement of the composite quantum state vector and manifested correlations of different observable outcomes. Thus, fast decoherence timescales could assist cognitive binding through quantum entanglement across extensive neural networks in the brain cortex. (shrink)

Associative memory is the ability to link together components of stimuli. Previous evidence suggests that prior familiarization with study items affects the nature of the association between stimuli. More specifically, novel stimuli are learned in a more context-dependent fashion than stimuli that have been encountered previously without the current context. In the current study, we first acquired behavioral data from 62 human participants to conceptually replicate this effect. Participants were instructed to memorize multiple object-scene pairs and were then tested on (...) their recognition memory for the objects. Importantly, 1 day prior, participants had been familiarized with half of the object stimuli. During the test phase, the objects were either matched to the same scene as during study or swapped with a different object’s scene. Our results conceptually replicated the context-dependency effect by showing that breaking up a studied object-context pairing is more detrimental to object recognition performance for non-familiarized objects than for familiarized objects. Second, we used functional magnetic resonance imaging to determine whether medial temporal lobe encoding-related activity patterns are reflective of this familiarity-related context effect. Data acquired from 25 human participants indicated a larger effect of familiarization on encoding-related hippocampal activity for objects presented within a scene context compared to objects presented alone. Our results showed that both retrieval-related accuracy patterns and hippocampal activation patterns were in line with a familiarization-mediated context-dependency effect. (shrink)

An understanding of the functional repertoire of neural circuits and their plasticity requires knowledge of neural connectivity diagrams and their dynamical evolution. However, one must additionally take into account the fast and reversible functional effects induced by neuromodulatory mechanisms which do not alter neural circuit diagrams. Neuromodulators contribute crucially to determine the performativity of a neural circuit, that is, its ability to change behavior, and especially behavioral changes occurring under temporal constraints that are incompatible with the (...) longer time scales of Hebbian learning and other forms of neural learning. This paper focuses on two properties of neuromodulatory action that have been relatively neglected so far. These properties are the functional soundness of neuromodulated circuits and the robustness of neuromodulatory action. Both properties are analyzed here as sources of functional specifications for the computational modeling of neural circuit performativity. In particular, taking dynamical systems that are based on CTRNNs as an exemplary class of computational models, it is argued that robustness is suitably modeled there by means of a hysteresis process, and functional soundness by means of a multiplicity of stable fixed points. (shrink)

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments (...) made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration. (shrink)

Shannon's theory lays the foundation for understanding the flow of information from world into brain: There must be a set of possible messages. Brain structure determines what they are. Many messages convey quantitative facts. It is impossible to consider how neural tissue processes these numbers without first considering how it encodes them.

The central task of cognitive neuroscience to map cognitive capacities to neural mechanisms faces three interlocking conceptual problems that together frame the problem of cognitive ontology. First, they must establish which tasks elicit which cognitive capacities, and specifically when different tasks elicit the same capacity. To address this operationalization problem, scientists often assess whether the tasks engage the same neural mechanisms. But to determine whether mechanisms are of the same or different kinds, we need to solve the abstraction (...) problem by determining which mechanistic differences are and are not relevant, and also the boundary problem by distinguishing the mechanism from its background conditions. Solving these problems, in turn, requires understanding how cognitive capacities are elicited in tasks. These three problems, which have been noted and discussed elsewhere in the literature, together form a ‘cycle of kinds’ that frames the central problem-space of cognitive ontology. We describe this cycle to clarify the intellectual challenges facing the cognitive ontologist and to reveal the kind of iterative process by which ontological revision in cognitive neuroscience is likely to unfold. (shrink)

It was recently shown that the Madelung equations, that is, a hydrodynamic form of the Schrödinger equation, can be derived from a canonical ensemble of neural networks where the quantum phase was identified with the free energy of hidden variables. We consider instead a grand canonical ensemble of neural networks, by allowing an exchange of neurons with an auxiliary subsystem, to show that the free energy must also be multivalued. By imposing the multivaluedness condition on the free energy (...) we derive the Schrödinger equation with “Planck’s constant” determined by the chemical potential of hidden variables. This shows that quantum mechanics provides a correct statistical description of the dynamics of the grand canonical ensemble of neural networks at the learning equilibrium. We also discuss implications of the results for machine learning, fundamental physics and, in a more speculative way, evolutionary biology. (shrink)