We present a method for rejecting competing models from noisy time-course data that does not rely on parameter inference. First we characterize ordinary differential equation models in only measurable variables using differential-algebra elimination. This procedure gives input-output equations, which serve as invariants for time series data. We develop a model comparison test using linear algebra and statistics to reject incorrect models from their invariants. This algorithm exploits the dynamic properties that are encoded in the structure of the (...) class='Hi'>model equations without recourse to parameter values, and, in this sense, the approach is parameter-free. We demonstrate this method by discriminating between different models from mathematical biology. (shrink)

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them (...) consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational. (shrink)

We agree that the relationship between phenomenology and accessibility can be fruitfully investigated via meshing, but we want to emphasise the importance of proper comparison between meshes, as well as considerations that make comparison especially difficult in this domain. We also argue that Block's interpretation of the neural data in his exemplar mesh is incorrect, and propose an alternative.

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven (...) established similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity. (shrink)

In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi‐coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the (...) new model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model. (shrink)

Electricity is an indicator that shows the progress of a civilization; it is a product that has greatly changed the way we think about the world. Electricity price forecasting became a fundamental task in all countries due to the deregulation of the electricity market in the 1990s. This work examines the effectiveness of using multiple variables for price prediction given the large number of factors that could influence the price of the electricity market. The tests were carried out over four (...) periods using data from Spain and deep learning models. Two different attribute selection methods based on Pearson’s correlation coefficient have been used to improve the efficiency of the training process. The variables used as input to the different prediction models were chosen, considering those most commonly used previously in the literature. This study attempts to test whether using time series lags improves the non-use of lags. The results obtained have shown that lags improve the results compared to a previous work in which no lags were used. (shrink)

Cognitive diagnostic assessment has been developed rapidly to provide fine-grained diagnostic feedback on students’ subskills and to provide insights on remedial instructions in specific domains. To date, most cognitive diagnostic studies on reading tests have focused on retrofitting a single booklet from a large-scale assessment. Critical issues in CDA involve the scarcity of research to develop diagnostic tests and the lack of reliability and validity evidence. This study explored the development and validation of the Diagnostic Chinese Reading Comprehension Assessment for (...) primary students under the CDA framework. Reading attributes were synthesized based on a literature review, the national curriculum criteria, the results of expert panel judgments, and student think-aloud protocols. Then, the tentative attributes were used to construct three booklets of reading comprehension items for 2–6 graders at three key stages. The assessment was administered to a large population of students in grades 2–6 from 20 schools in a district of Changchun City, China. Q-matrices were compared and refined using the model-data fit and an empirical validation procedure, and five representative cognitive diagnostic models were compared for optimal performance. The fit indices suggested that a six-attribute structure and the G-DINA model were best fitted for the reading comprehension assessment. In addition, diagnostic reliability, construct, internal and external validity results were provided, supporting CDM classifications as reliable, accurate, and useful. Such diagnostic information could be utilized by students, teachers, and administrators of reading programs and instructions. (shrink)

It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to (...) evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short‐term predictions, and the third method again used a generalization criterion for long‐term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short‐versus long‐term predictions on simple gambling tasks. (shrink)

The framework of causal Bayes nets, currently influential in several scientific disciplines, provides a rich formalism to study the connection between causality and probability from an epistemological perspective. This article compares three assumptions in the literature that seem to constrain the connection between causality and probability in the style of Occam's razor. The trio includes two minimality assumptions—one formulated by Spirtes, Glymour, and Scheines (SGS) and the other due to Pearl—and the more well-known faithfulness or stability assumption. In terms of (...) logical strength, it is fairly obvious that the three form a sequence of increasingly stronger assumptions. The focus of this article, however, is to investigate the nature of their relative strength. The comparative analysis reveals an important sense in which Pearl's minimality assumption is as strong as the faithfulness assumption and identifies a useful condition under which it is as safe as SGS's relatively secure minimality assumption. Both findings have notable implications for the theory and practice of causal inference. 1 Introduction2 Background: Inference of Causal Structure in Markovian Causal Models3 Three Assumptions of Simplicity4 A Comparison of P-minimality and Faithfulness5 A Comparison of P-minimality and SGS-minimality6 Methodological Formulations and Prior Knowledge of Causal Order7 Conclusion. (shrink)

As moral decision making in financial markets incorporates moral considerations into investment decisions, some rational decision theorists argue that moral considerations would introduce inefficiency to investment decisions. However, market demand for socially responsible investment is increasing, suggesting that investment decisions are influenced by both financial and moral considerations. Several models can be applied to explain moral behavior. We test the suitability of (a) multiple attribute utility theory (MAUT), (b) theory of planned behavior, and (c) issue-contingent model of ethical decision (...) making in organizations. In an experimental setting, 141 participants traded company shares in a computerized asset market. Over 12 periods, companies varied in morality (i.e., treatment of employees) and in profitability (i.e., expected dividends per share). Participants’ bids and asks for shares were recorded. Results indicate that moral considerations influence investment decisions, controlling for profit. Differences between the three models are discussed. (shrink)

In this paper, we present an intensive investigation of the finite volume method compared to the finite difference methods. In order to show the main difference in the way of approaching the solution, we take the Burgers equation and the Buckley–Leverett equation as examples to simulate the previously mentioned methods. On the one hand, we simulate the results of the finite difference methods using the schemes of Lax–Friedrichs and Lax–Wendroff. On the other hand, we apply Godunov’s scheme to simulate the (...) results of the finite volume method. Moreover, we show how starting with a variational formulation of the problem, the finite element technique provides piecewise formulations of functions defined by a collection of grid data points, while the finite difference technique begins with a differential formulation of the problem and continues to discretize the derivatives. Finally, some graphical and numerical comparisons are provided to illustrate and corroborate the differences between these two main methods. (shrink)

The sense of agency over bodily actions is the feeling that one is the agent of one's actions. In this paper I examine the prospects of Frith and colleagues' influential comparator account of how the sense of agency over one's bodily actions is elicited, in comparison to the multifactorial weighting model advocated by Synofzik and colleagues in response to some problems with this account. I examine two problems for the comparator model. I consider the common objection that (...) the actual sensory consequences of action aren't needed to elicit the sense of agency with a look at the data which most strongly suggests this, namely the preserved sense of agency over phantom limb movements. I consider a problem for the comparator model in the behaviour of normal subjects placed in unusual circumstances, in particular I consider the 'wheel of fortune' studies which some take to be problematic for the comparator model. I argue that neither of these objections are devastating for the comparator model and that the comparator model plus some plausible assumptions can explain these data. However, these assumptions are not part of the original comparator model. In the end we get a version of the comparator model modified to deal with problematic cases in a manner that could be seen as somewhat ad hoc. To deal with this the multifactorial weighting model of Synofzik and colleagues is introduced. Although this model is incomplete a single version can be offered which is naturally constrained by the cases which are problematic for the comparator model. However, it is not clear what, if anything, could count as evidence against the multifactorial weighting model. Despite being generated with the data in mind it may be untestable. I conclude that currently the comparator model has stronger support than the multifactorial weighting model. (shrink)

Upholders of fictionalism about scientific models have not yet successfully explained how scientists can learn about the real world by making comparisons between models and the real phenomena they stand for. In this paper I develop an account of model-world comparisons in terms of what I take to be the best antirealist analyses of comparative claims that emerge from the current debate on fiction.

The different interpretations of quark mixing involved in weak interaction processes in the Standard Model and the Generation Model are discussed with a view to obtaining a physical understanding of the Cabibbo angle and related quantities. It is proposed that hadrons are composed of mixed-quark states, with the quark mixing parameters being determined by the Cabibbo-Kobayashi-Maskawa matrix elements. In this model, protons and neutrons contain a contribution of about 5% and 10%, respectively, of strange valency quarks.

It has been suggested in the literature that spatial coherence of the wave function can be dynamically suppressed by fluctuations in the spacetime geometry. These fluctuations represent the minimal uncertainty that is present when one probes spacetime geometry with a quantum probe. Two similar models have been proposed, one by Diósi and one by Karolyhazy and collaborators, based on apparently unrelated minimal spacetime bounds. The two models arrive at somewhat different expressions for the dependence of the localization coherence length on (...) the mass and size of the quantum object. In the present article we compare and contrast the two models from three aspects: comparison of the spacetime bounds, method of calculating decoherence time, comparison of noise correlation. We show that under certain conditions the minimal spacetime bounds in the two models can be derived one from the other. We argue that the methods of calculating the decoherence time are equivalent. We re-derive the two-point correlation for the fluctuation potential in the K-model, and confirm the earlier result of Diósi and Lukács that it is non-white noise, unlike in the D-model, where the corresponding correlation is white noise in time. This seems to be the origin of the different results in the two models. We derive the non-Markovian master equation for the K-model. We argue that the minimal spacetime bound cannot predict the noise correlation uniquely, and additional criteria are necessary to accurately determine the effects of gravitationally induced decoherence. (shrink)

The E-Z Reader model (Reichle et al. 1998; 1999) provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. In this article, we first review what is known about eye movements during reading. Then we provide an updated version of the model (E-Z Reader 7) and describe how it accounts for basic findings about eye movement control in reading. We then review several alternative (...) models of eye movement control in reading, discussing both their core assumptions and their theoretical scope. On the basis of this discussion, we conclude that E-Z Reader provides the most comprehensive account of eye movement control during reading. Finally, we provide a brief overview of what is known about the neural systems that support the various components of reading, and suggest how the cognitive constructs of our model might map onto this neural architecture. Key Words: attention; eye-movement control; E-Z Reader; fixations; lexical access; models; reading; regressions; saccades. (shrink)

Empirical evidence has demonstrated the validity and reliability of moral development instruments such as the Defining Issues Test (DIT) and Moral Judgment Test (MJT). Rasch item reliability for each was .95. A newer instrument generated using the Model of Hierarchical Complexity had item reliability of .97. Rasch scores of responses to each instruments' items correlated well with the items' measure of hierarchical complexity, r = .286, .372, .557, .767. Test items used to measure moral reasoning were significantly correlated. In (...) general, stage of moral development did not predict political affiliation or voting, supporting Kohlberg's claim that structure, not content, underlies stage. (shrink)

Does science progress toward some goal or merely away from primitive beginnings? Two agent-based models are built to explain how possibly both kinds of progressive scientific change can result from the interactions of individuals exploring an epistemic landscape. These models are shown to result in qualitatively different predictions about what the resulting system of science should be like.

Current artificial neural network or connectionist models of music cognition embody feature-extraction and feature-weighting principles. This paper reports two experiments which seek evidence for similar processes mediating recognition of short musical compositions by musically trained and untrained listeners. The experiments are cast within a pattern recognition framework based on the vision-audition analogue wherein music is considered an auditory pattern consisting of local and global features. Local features such as inter-note interval, and global features such as melodic contour, are derived from (...) a two-dimensional matrix in which music is represented as a series of frequencies plotted over time.Manipulation of inter-note interval affected accuracy and reaction time measures in a discrimination task, whereas the same variables were affected by manipulation of melodic contour in a classification task. Musical training is thought of as a form of practice in musical pattern recognition and, as predicted, accuracy and reaction time measures of musically trained subjects were significantly better than those of untrained subjects. Given the evidence for feature-extraction and weighting processes in music recognition tasks, two connectionist models are discussed. The first is a single-layer perceptron which has been trained to discriminate between compositions according to inter-note interval. A second network, using the back-propagation algorithm and sequential input of patterns, is also discussed. (shrink)

With small to modest sample sizes and complex models, maximum likelihood estimation of confirmatory factor analysis models can show serious estimation problems such as non-convergence or parameter estimates outside the admissible parameter space. In this article, we distinguish different Bayesian estimators that can be used to stabilize the parameter estimates of a CFA: the mode of the joint posterior distribution that is obtained from penalized maximum likelihood estimation, and the mean, median, or mode of the marginal posterior distribution that are (...) calculated by using Markov Chain Monte Carlo methods. In two simulation studies, we evaluated the performance of the Bayesian estimators from a frequentist point of view. The results show that the EAP produced more accurate estimates of the latent correlation in many conditions and outperformed the other Bayesian estimators in terms of root mean squared error. We also argue that it is often advantageous to choose a parameterization in which the main parameters of interest are bounded, and we suggest the four-parameter beta distribution as a prior distribution for loadings and correlations. Using simulated data, we show that selecting weakly informative four-parameter beta priors can further stabilize parameter estimates, even in cases when the priors were mildly misspecified. Finally, we derive recommendations and propose directions for further research. (shrink)

In this paper, a seven-parameter BRDF model with double-peak characteristic, which could fit double-peak data, was adopted to fit the BRDF of Mars simulation soil. At the same time, the three-dimensional figure of the original data of the sample and the three-dimensional curve of the fitted curve were given. The results proved that the model worked well for this type of data. In addition, the experimental data of four kinds of earth sand samples with different roughness were also (...) fitted and analyzed. It was found that the model still had a good fitting effect on such data. At last, the Mars simulation soil and the four kinds of earth sand samples with different roughness were compared horizontally and vertically. Moreover, the double-peak characteristics and other properties of the Mars simulation soil and the earth sand were analyzed under the same and different roughness. (shrink)

Synchronization is a special case of self-organization in which one can observe close mimicry in behavior of the system components. Synchrony in body movements, autonomic arousal, and EEG activity among human individuals has attracted considerable attention for their possible roles in social interaction. This article is specifically concerned with autonomic synchrony and finding the best model for the dyadic relationships, with regard to both theoretical and empirical accuracy, that could be extrapolated to synchrony levels for groups and teams of (...) three or more people. The four models that are compared in this study have different theoretical origins: the two-variable linear regression function, a three-parameter nonlinear regression function, the logistic map function stated in polynomial form, and the logistic map function stated as an exponential regression structure. The data for this study were electrodermal responses collected from a team of four people engaged in an emergency response simulation that produced 12 dyadic time series. Results shows strong levels of fit between the data and all four models, although there were significant differences among them. Further research directions point toward finding conditions that favor one model over another and exploring other possible nonlinear structures. (shrink)

Electroencephalography is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network. However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the (...) extraction and classification of preprocessed data, namely, common spatial patterns- CNN and wavelet transform- CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN. (shrink)

Increases in international travel and migratory flows have enabled infectious diseases to emerge and spread more rapidly than ever before. Hence, it is increasingly easy for local infectious diseases to become global infectious diseases (GIDs). National governments must be able to react quickly and effectively to GIDs, whether naturally occurring or intentionally instigated by bioterrorism. According to the World Health Organisation, global partnerships are necessary to gather the most up-to-date information and to mobilize resources to tackle GIDs when necessary. Communicable (...) disease control also depends upon national public health laws and policies. The containment of an infectious disease typically involves detection, notification, quarantine and isolation of actual or suspected cases; the protection and monitoring of those not infected; and possibly even treatment. Some measures are clearly contentious and raise conflicts between individual and societal interests. In Europe national policies against infectious diseases are very heterogeneous. Some countries have a more communitarian approach to public health ethics, in which the interests of individual and society are more closely intertwined and interdependent, while others take a more liberal approach and give priority to individual freedoms in communicable disease control. This paper provides an overview of the different policies around communicable disease control that exist across a select number of countries across Europe. It then proposes ethical arguments to be considered in the making of public health laws, mostly concerning their effectiveness for public health protection. (shrink)