We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning, developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies ridges of the overlapped multicomponent from a time-frequency representation. The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs first (...) extraction is decided using the framework of the intrinsic mode polynomial chirp transform, which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications. (shrink)

It is a contested question in contemporary theories of religion whether the concept of religion can be defined in a sound way or not. Many theorists maintain that a universal but delimiting definition is impossible. In this study, by contrast, it is argued that a conceptual analysis of religion that holds universally is perfectly possible because the following thesis can be seen as a necessary and sufficient conceptual condition of what religion is: X is a religion if and only if (...) X is a collection of artifacts which has the proper function of representing a supraphysical world. On this thesis, it is argued that artifacts such as pictorial and verbal representations, rituals, symbols, and various tools constitute religion as a cultural object, which, as a collection of artifacts, has the proper function of representing a conceived world that is not entirely physical, and which, allegedly, is a prerequisite for existential welfare in relation to observance. It is here important to understand what is constitutive for these kinds of conceived worlds. Supraphysical world is defined as follows. Given that the actual world is a physical world, a conception S is a construction of a supraphysical world if and only if both of the following conditions apply to S: Metaphysical component: S is a duplicate of the actual world with the addition of an anti-physical substance. Existential-normative component: S is an alleged prerequisite for existential welfare in relation to observance. The core argument of the study is that holds a priori for the concept of religion and as an a posteriori necessity for every instance of a religion. Apart from discussing the methodological problems of defining religion, the study introduces a new theory of religion in terms of. It addresses issues in the theory of artifacts; in the theory of representations; and in the theory of conceptual analysis. (shrink)

We provide a brief overview of the shift toward the intrinsic view of brain activity, describing in particular the structural and functional connectivity patterns of the “Default mode network”. We then consider the Default mode network in a specifically cognitive setting and ask what changes the focus on the Default mode network and other sorts of intrinsic activity require from models put forward by cognitive neuroscientists.

ObjectiveStatic regional homogeneity based on the resting-state functional magnetic resonance imaging has been used to study intrinsic brain activity in Alzheimer’s disease. However, few studies have examined dynamic ReHo in AD. In this study, we used rs-fMRI and dReHo to investigate the alterations in dynamic IBA in patients with AD to uncover dynamic imaging markers of AD.MethodIn total, 111 patients with AD, 29 patients with mild cognitive impairment, and 73 healthy controls were recruited for this study ultimately. After the (...) rs-fMRI scan, we calculated the dReHo values using the sliding window method. ANOVA and post hoc two-sample t-tests were used to detect the differences among the three groups. We used the mini-mental state examination and Montreal Cognitive Assessment to evaluate the cognitive function of the subjects. The associations between the MMSE score, MoCA score, and dReHo were assessed by the Pearson correlation analysis.ResultsSignificant dReHo variability in the right middle frontal gyrus and right posterior cingulate gyrus was detected in the three groups through ANOVA. In post hoc analysis, the AD group exhibited significantly greater dReHo variability in the right MFG than the MCI group. Compared with the HC group, the AD group exhibited significantly increased dReHo variability in the right PCG. Furthermore, dReHo variability in the right PCG was significantly negatively correlated with the MMSE and MoCA scores of patients with AD.ConclusionDisrupted dynamic IBA in the DMN might be an important characteristic of AD and could be a potential biomarker for the diagnosis or prognosis of AD. (shrink)

Climate dynamics and trends have significant environmental and socioeconomic impacts; however, in the Benin Republic, they are generally studied with diverse statistical methods ignoring the nonstationarity, nonlinearity, and self-similarity characteristics contained in precipitation time series. This can lead to erroneous conclusions and an unclear understanding of climatic dynamics. Based on daily precipitation data observed in the six synoptic stations of Benin Republic, in the period from 1951 to 2010, we have proposed determining the local trends of precipitations, investigating precipitation nonlinear (...) dynamics, and assessing climatic shift in the study period by Ensemble Empirical Mode Decomposition and Multifractal Detrended Fluctuation Analysis method. To overcome the detrending issue in the standard MFDFA method, the EEMD algorithm is embedded into the MFDFA. The study period is subdivided into three subperiods: 1951–1970, 1971–1990, and 1991–2010. Intrinsic Mode Functions are obtained according to the climatic region in which the stations are located. Results show that precipitation variation is significantly governed by the five first IMFs, in which oscillation periods vary from 1 to 25 days. The trend curves decrease at all the synoptic stations, and their slope values vary accordingly to the subperiods. Referring to the values of the multifractal spectrum parameters, α 0, and the width of the spectrum w, consistent changes are observed regardless of the subperiods and the concerned stations. The spatial and temporal variability of precipitation indicates that the multifractal properties are good indicators for assessing changes in precipitation dynamics, and the changes in their features could be explained by the global change than by the local climate variation. Despite the observed differences in multifractal spectra properties from the three subperiods, it is not possible to verify the subdivision of 1951–2010 in three subperiods as it is done by previous studies in West Africa. Our findings can be used in the validation of global and regional climate models since a valid model should explain empirically detected scaling properties in observed data. (shrink)

To effectively identify network rumors and block their spread, this paper uses fractal theory to analyze a network rumor spreading situation time series, reveal its inner regularity, extract features, and establish a network rumor recognition model. The model is based on an empirical mode decomposition correlation dimension and K-nearest neighbor approach. Firstly, a partition function is used to determine if the time series of the rumor spreading situation is a uniform fractal process. Secondly, the rumor spreading situation is subjected (...) to EMD to obtain a series of intrinsic mode functions, construct the IMF1–IMF6 components containing effective feature information as the principal components, and reconstruct the phase space of the principal components, respectively. Finally, the correlation dimensions of the principal components IMF1–IMF6 as obtained by the Grassberger-Procaccia algorithm are used as feature parameters and are imported into the KNN model for rumor recognition. The experimental results show that the correlation dimension of a spreading situation can better reflect the characteristic information; as combined with the KNN model for identifying rumors, the recognition rate reaches 87.5%. This result verifies the effectiveness of fractal theory in network rumors recognition, expands the thinking for the research of rumors recognition, and provides theoretical support for rumor governance. (shrink)

In this article, a new adaptive data-driven strategy for voice activity detection using empirical mode decomposition is proposed. Speech data are decomposed using an a posteriori, adaptive, data-driven EMD in the time domain to yield a set of physically meaningful intrinsic mode functions. Each IMF preserves the nonlinear and nonstationary property of the speech utterance. Among a set of IMFs, the IMF that contains source information dominantly called characteristic IMF can be identified and extracted by designing (...) a zero-frequency filter-assisted peaking resonator. The detected CIMF is used to compute energy using short-term processing. Choosing proper threshold, voiced regions in speech utterances are detected using frame energy. The proposed framework has been studied on both clean speech utterance and noisy speech utterance. The proposed method is used for voice activity detection in the presence of white noise and shows encouraging result in the presence of white noise up to 0 dB. (shrink)

Many philosophers, including direct reference theorists, appeal to naively to 'modes of presentation' in the analysis of belief reports. I show that a variety of such appeals can be analyzed in terms of a precise theory of modes of presentation. The objects that serve as modes are identified intrinsically, in a noncircular way, and it is shown that they can function in the required way. It is a consequence of the intrinsic characterization that some objects are well-suited to serve (...) as modes that present individuals and while others are well-suited to serve as modes that present properties (though such modes do not `determine' the objects they present---there is no necessary connection between a mode m and the individual x or property F that it presents). Moreover, it is also a consequence that the modes for properties and individuals can be organized into complexes that are structurally identical to Russellian propositions having the represented properties and individuals as constituents. Not only is the relationship between modes and Fregean senses and modes of presentation explored in the paper, but also the idea that the theory of modes of presentation developed in the paper can serve as a theory of concepts. (shrink)

Recently, we proposed a fundamental subdivision of the human cortex into two complementary networks—an “extrinsic” one which deals with the external environment, and an “intrinsic” one which largely overlaps with the “default mode” system, and deals with internally oriented and endogenous mental processes. Here we tested this hypothesis by contrasting decision making under external and internally-derived conditions. Subjects were presented with an external cue, and were required to either follow an external instruction or to ignore it and follow (...) a voluntary decision process . Our results show that a well defined component of the intrinsic system—the right inferior parietal cortex—was preferentially activated during the “free-will” condition. Importantly, this activity was significantly higher than the base-line resting state. The results support a self-related role for the intrinsic system and provide clear evidence for both hemispheric and regional specialization in the human intrinsic system. (shrink)

Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The (...) main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5–14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism. (shrink)

The objective of this paper is to discuss the relationship between the functional properties and information-processing modes of the human brain and the evolution of scientific thought. Science has emerged as a tool to carry out predictive operations that exceed the accuracy, temporal scale, and intrinsic operational limitations of the human brain. Yet the scientific method unavoidably reflects some fundamental characteristics of the information-acquisition and -analysis modes of the brain, which impose a priori boundary conditions upon how science can (...) develop and how the physical universe can be “understood.” A brief description of physical and biological interactions is given, with emphasis on the defining role played by the concept of information. Current views on the information-processing and information-generating mechanisms of the human brain are briefly reviewed. It is shown how some particular features of superstition, natural philosophy, physical thought, and intuition can be linked to certain characteristic information-processing modes of the brain. A discussion is given of how greatly expanded knowledge of brain functions might affect the future of science and technology. (shrink)

Neurofeedback is a complex learning scenario, as the task consists of trying out mental strategies while processing a feedback signal that signifies activation in the brain area to be self-regulated and acts as a potential reward signal. In an attempt to dissect these subcomponents, we obtained whole-brain networks associated with efficient self-regulation in two paradigms: parallel, where the task was performed concurrently, combining feedback with strategy execution; and serial, where the task was performed consecutively, separating feedback processing from strategy execution. (...) Twenty participants attempted to control their anterior midcingulate cortex using functional magnetic resonance imaging NF in 18 sessions over 2 weeks, using cognitive and emotional mental strategies. We analyzed whole-brain fMRI activations in the NF training runs with the largest aMCC activation for the serial and parallel paradigms. The equal length of the strategy execution and the feedback processing periods in the serial paradigm allows a description of the two task subcomponents with equal power. The resulting activation maps were spatially correlated with functionally annotated intrinsic connectivity brain maps. Brain activation in the parallel condition correlates with the basal ganglia network, the cingulo-opercular network, and the frontoparietal control network ; brain activation in the serial strategy execution condition with the default mode network, the FPCN, and the visual processing network; while brain activation in the serial feedback processing condition predominantly with the CON, the DMN, and the FPCN. Additional comparisons indicate that BG activation is characteristic to the parallel paradigm, while supramarginal gyrus and superior temporal gyrus activations are characteristic to the serial paradigm. The multifaceted view of the subcomponents allows describing the cognitive processes associated with strategy execution and feedback processing independently in the serial feedback task and as combined processes in the multitasking scenario of the conventional parallel feedback task. (shrink)

Protein disulfide isomerase (PDI) is one of the most abundant and critical protein folding catalysts in the endoplasmic reticulum of eukaryotic cells. PDI consists of four thioredoxin domains and interacts with a wide range of substrate and partner proteins due to its intrinsic conformational flexibility. PDI plays multifunctional roles in a variety of pathophysiological events, both as an oxidoreductase and a molecular chaperone. Recent studies have revealed that the conformation and activity of PDI can be regulated in multiple ways, (...) including posttranslational modification and substrate/ligand binding. Here, we summarize recent advances in understanding the function and regulation of PDI in different pathological and physiological events. We propose that the multifunctional roles of PDI are regulated by multiple mechanisms. Furthermore, we discuss future directions for the study of PDI, emphasizing how different regulatory modes are linked to the conformational changes and biological functions of PDI in the context of diverse pathophysiologies. (shrink)

Owing to the adverse impact of the COVID-19 pandemic on world economies, it is expected that information flows between commodities and uncertainties have been transformed. Accordingly, the resulting twisted risk among commodities and related uncertainties is presumed to rise during stressed market conditions. Therefore, investors feel pressured to find safe haven investments during the pandemic. For this reason, we model a mixture of asymmetric and non-linear bi-directional causality between global commodities and uncertainties at different frequencies through the information flow theory. (...) Consequently, we utilise the complete ensemble empirical mode decomposition with adaptive noise and the Rényi effective transfer entropy techniques to establish the dynamic flow of information. The intrinsic mode functions from the CEEMDAN are carefully extracted into multi-frequencies through cluster analysis to reconstruct the series into high, medium, and low frequencies in addition to the residue. We utilise daily data from December 31st, 2019, to March 31st, 2021, to provide insights into the COVID-19 pandemic. The correlation coefficients and variances demonstrate that the high frequency which measures the short-term dynamics is the dominant frequency, suggesting short-lived market fluctuations relative to real economic growth for institutional investors. Moreover, outcomes from the multi-frequency entropy indicate a negative bi-directional causality of information flow between global commodities and uncertainties, especially in the long term. Generally, the findings present pertinent inferences for portfolio diversification, policy decisions, and risk management schemes for global commodities and markets volatilities. We, therefore, advocate that market volatilities act as effective hedges for global commodities, and they clearly act as balancing assets rather than substitutes in the long-term dynamics of the COVID-19 pandemic. Investors who delayed in investing within financial markets of commodities and market volatilities are likely to minimise their portfolio risks. (shrink)

In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal (...) requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems. (shrink)

Electromechanical actuators are more and more widely used as actuation devices in flight control system of aircrafts and helicopters. The reliability of EMAs is vital because it will cause serious accidents if the malfunction of EMAs occurs, so it is significant to detect and diagnose the fault of EMAs timely. However, EMAs often run under variable conditions in realistic environment, and the vibration signals of EMAs are nonlinear and nonstationary, which make it difficult to effectively achieve fault diagnosis. This paper (...) proposed a fault diagnosis method of electromechanical actuators based on variational mode decomposition multifractal detrended fluctuation analysis and probabilistic neural network. First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions. Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. Then, the principal component analysis was introduced to realize dimension reduction of the extracted feature vectors. Finally, the probabilistic neural network was utilized to classify the fault modes. The experimental results show that this method can effectively achieve the fault diagnosis of EMAs even under diffident working conditions. Simultaneously, the diagnosis performance of the proposed method in this paper has an advantage over that of EMD-MFDFA method for feature extraction. (shrink)

An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- optimized kernel extreme learning machine model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (...) using the complete ensemble empirical mode decomposition based on adaptive noise. Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings. (shrink)

. Underwater communication is usually affected by ambient noise, which may be generated by different sources, such as the wind origin sea-surface sources, ships and under water life. The properties of background noise, which are non-stationary in nature, depend on location, sea depth, wind speed and sound propagation conditions in the area. Overall performance of underwater acoustic instruments can be improved by denoising the underwater signals. This paper proposes a novel denoising method using empirical mode decomposition technique. Frequency domain (...) based thresholding has been used to denoise the signal, which involves three steps: EMD is applied to the noisy signal to decompose the signal into intrinsic mode functions. Thresholding is applied to each IMF in the frequency domain to remove the noise. Thresholded IMFs are added to obtain the denoised signal. Real-time experiments were performed to validate the proposed technique on the basis of the records of the ambient noise data recorded at sea for various wind speed. It was observed that the experimental results are in good agreement with the proposed algorithm under different wind-noise levels. (shrink)

We propose an empirical mode decomposition (EMD-) based method to extract features from the multichannel recordings of local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. The feature extraction approach consists of three stages. First, we employ EMD to decompose nonstationary single-trial time series into narrowband components called intrinsic mode functions (IMFs) with time scales dependent on the data. Second, we adopt unsupervised (...) K-means clustering to group the IMFs and residues into several clusters across all trials and channels. Third, we use the supervised common spatial patterns (CSP) approach to design spatial filters for the clustered spatiotemporal signals. We exploit the support vector machine (SVM) classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that the CSP feature of the cluster in the gamma frequency band outperforms the features in other frequency bands and leads to the best decoding performance. We also show that the EMD-based feature extraction can be useful for evoked potential estimation. Our proposed feature extraction approach may have potential for many applications involving nonstationary multivariable time series such as brain-computer interfaces (BCI). (shrink)

I offer in this article an account of the distinction between intrinsic and extrinsic properties based on the ontology of modes. Modes are particular properties that directly depend for their identity on their "bearers". In Section 1, I shall introduce the ontology of modes. In Section 2, I shall examine the problem of distinguishing between intrinsic and extrinsic properties by considering another, related problem: that of distinguishing between internal and external relations. In Section 3, I shall present my (...) own account and I shall also single out different degrees of intrinsicality and extrinsicality. In Section 4, I shall deal with missing-relatum modes, namely, with modes that lack specific relata, such as Othello's loving someone. I shall introduce three different theories to deal with them. Finally, in Section 5, I shall solve some problems affecting the intrinsic/extrinsic distinction from the standpoint of my account. (shrink)

Over 300 years ago the father of microscopy, Antonie van Leeuwenhoek, observed dried rotifers “coming back to life” upon rehydration. Since then, scientists have been fascinated by the enduring mystery of how certain organisms survive losing essentially drying out completely. Historically sugars, such as the disaccharide trehalose, have been viewed as major functional mediators of desiccation tolerance. However, some desiccation tolerant organisms do not produce this sugar, hinting that additional mediators, and potentially novel mechanisms exist. It has become apparent that (...) a common theme among such organisms is the production and use of intrinsically disordered proteins to mediate survival in this dry state. However, the basic biology of these proteins – which unlike globular proteins lack persistent three-dimensional structure – is poorly understood, as are the functional mechanisms utilized by these enigmatic proteins that allow them to mediate desiccation tolerance. We purpose that probing the biochemical and biophysical nature of stress-related IDPs will provide mechanistic insights into these fascinating proteins. Remarkably, organisms from every kingdom of life can survive losing nearly all their intracellular water. A hallmark of desiccation tolerance is the use of intrinsically disordered proteins, but how IDPs contribute to tolerance is poorly understood. Here, we discuss possible mechanisms and avenues for studying these enigmatic proteins. (shrink)

Fred Dretske's theory of indicatory functions (Dretske 1988 & 1994)is undoubtedly one of the more ambitious attempts to articulate a sound naturalistic foundation for an adequate theory of intentional content. In what follows I argue that, contrary to Dretske's explicit intentions, his theory fails a crucial adequacy test - that of accounting for mental content as a system-intrinsic property. Once examined in light of the first-person perspective of an embodied psy- chological agent, I argue, it becomes clear that (...) neither 'indication', nor 'function', as used by Dretske, can be consistently applied. Dre- tske's theory of indicatory functions is, thus, doubly incoherent. It is then argued that the problems identified here stretch far beyond Dretske's specific theory - covering the better part of contemporary attempts to naturalize content. I conclude by suggesting that these general problems of representation, exemplified so vividly in Dretske's theory, also testify to the inadequacy of the quest to reduce tel- eological phenomena (function and purpose) to predominantly mechanistic variables. (shrink)

This paper describes one style of functional analysis commonly used in the neurosciences called task-bound functional analysis. The concept of function invoked by this style of analysis is distinctive in virtue of the dependence relations it bears to transient environmental properties. It is argued that task-bound functional analysis cannot explain the presence of structural properties in nervous systems. An alternative concept of neural function is introduced that draws on the theoretical neuroscience literature, and an argument is given to show that (...) this alternative concept of may help to overcome the explanatory limitations of task-bound functional analysis. (shrink)

Name der Zeitschrift: Semiotica Jahrgang: 2018 Heft: 225 Seiten: 457-487.

It is a characteristic feature of our mental make-up that the same perceptual input situation can simultaneously elicit conflicting mental perspectives. This ability pervades our perceptual and cognitive domains. Striking examples are the dual character of pictures in picture perception, pretend play, or the ability to employ metaphors and allegories. I argue that traditional approaches, beyond being inadequate on principle grounds, are theoretically ill equipped to deal with these achievements. I then outline a theoretical perspective that has emerged from a (...) theoretical convergence of perceptual psychology, ethology, linguistics, and developmental research. On the basis of this framework, I argue that corresponding achievements are brought forth by a specific type of functional architecture whose core features are as follows: (1) a perceptual system that is biologically furnished with a rich system of conceptual forms, (2) a triggering relation between the sensory input and conceptual forms by which the same sensory input can be exploited by different types or systems of conceptual forms, and (3) computational principles for handling semantically underspecified conceptual forms. Characteristic features of the proposed theoretical framework are pointed out using the Heider–Simmel phenomenon as an example. (shrink)

This paper deals with a philosophical question that arises within the theory of computational complexity: how to understand the notion of INTRINSIC complexity or difficulty, as opposed to notions of difficulty that depend on the particular computational model used. The paper uses ideas from Blum's abstract approach to complexity theory to develop an extensional approach to this question. Among other things, it shows how such an approach gives detailed confirmation of the view that subrecursive hierarchies tend to rank (...) class='Hi'>functions in terms of their intrinsic, and not just their model-dependent, difficulty, and it shows how the approach allows us to model the idea that intrinsic difficulty is a fuzzy concept. (shrink)

We propose a rudimentary formal framework for reasoning about recursion equations over inductively generated data. Our formalism admits all equational programs , and yet singles out none. While being simple, this framework has numerous extensions and applications. Here we lay out the basic concepts and definitions; show that the deductive power of our formalism is similar to that of Peano's Arithmetic; prove a strong normalization theorem; and exhibit a mapping from natural deduction derivations to an applied λ -calculus, à la (...) Curry–Howard, which provides a transparent proof of Gödel's result that the provably recursive functions of PA are definable by primitive recursion in finite types. Also of interest is the extent, uncommon among similar constructions, to which our mapping is oblivious to first order terms, including first order quantification and equality. Additional applications and extensions of the method will be presented in sequel papers. In particular, the methodology introduced here lends itself to systematic links between natural sub-formalisms and computational complexity classes. Since these sub-formalisms are largely transparent, allowing one to reason formally in the full system while leaving to automatic software tools the task of verifying that restrictions are complied with, the framework is particularly attractive for the development of Feasible Mathematics. (shrink)

Visual modules can be viewed as expressions of a marked analytic attitude in the study of vision. In vision psychology, this attitude is accompanied by hypotheses that characterize how visual modules are thought to operate in perceptual processes. Our thesis here is that there are what we call “intrinsic reasons” for the presence of such hypotheses in a vision theory, that is, reasons of a deductive kind, which are imposed by the partiality of the basic terms in the definition (...) of a module, and by peculiar characteristics of those terms. Specifically, we discuss three hypotheses of functional attributes: successive stages in the action of modules, residual indeterminacy of their effects, and the role of prior constraints. For each of the three, we indicate its occurrence in perceptual psychology, explain corresponding intrinsic reasons, and illustrate such reasons with examples. (shrink)

In this paper, I introduce an intrinsic account of the quantum state. This account contains three desirable features that the standard platonistic account lacks: (1) it does not refer to any abstract mathematical objects such as complex numbers, (2) it is independent of the usual arbitrary conventions in the wave function representation, and (3) it explains why the quantum state has its amplitude and phase degrees of freedom. -/- Consequently, this account extends Hartry Field’s program outlined in Science Without (...) Numbers (1980), responds to David Malament’s long-standing impossibility conjecture (1982), and establishes an important first step towards a genuinely intrinsic and nominalistic account of quantum mechanics. I will also compare the present account to Mark Balaguer’s (1996) nominalization of quantum mechanics and discuss how it might bear on the debate about “wave function realism.” In closing, I will suggest some possible ways to extend this account to accommodate spinorial degrees of freedom and a variable number of particles (e.g. for particle creation and annihilation). -/- Along the way, I axiomatize the quantum phase structure as what I shall call a “periodic difference structure” and prove a representation theorem as well as a uniqueness theorem. These formal results could prove fruitful for further investigation into the metaphysics of phase and theoretical structure. -/- (For a more recent version of this paper, please see "The Intrinsic Structure of Quantum Mechanics" available on PhilPapers.). (shrink)