Direction Of Inference Research Articles

Improved eigenfilter-based time-domain wideband beamforming

An improved eigenfilter-based wideband beamforming method with frequency invariance and nulls was proposed. Based on the optimization model formed by maximum energy array cost function and linear constraints of array response in main great direction,array spatial response variation was integrated into the cost function to achieve frequency invariance. Two methods,integrating the extended sidelobe energy into cost function and using linear constraints of array response in the direction of inference,were designed to achieve nulls,so that two new constraint optimization models were formed,which could be solved by eigenfilter. Simulation results show that frequency invariance and nulls of beam response are achieved at the specified angle region in the frequency passband,and the gain of the sidelobe is small,the effectiveness and satisfactory performance of the proposed method are proved.

How can you capture cultural dynamics?

Cross-cultural comparison is a critical method by which we can examine the interaction between culture and psychological processes. However, comparative methods tend to overlook cultural dynamics – the formation, maintenance, and transformation of cultures over time. The present article gives a brief overview of four different types of research designs that have been used to examine cultural dynamics in the literature: (1) cross-temporal methods that trace medium- to long-term changes in a culture; (2) cross-generational methods that explore medium-term implications of cultural transmission; (3) experimental simulation methods that investigate micro-level mechanisms of cultural dynamics; and (4) formal models and computer simulation methods often used to investigate long-term and macro-level implications of micro-level mechanisms. These methods differ in terms of level of analysis for which they are designed (micro vs. macro-level), scale of time for which they are typically used (short-, medium-, or long-term), and direction of inference (deductive vs. empirical method) that they imply. The paper describes examples of these methods, discuss their strengths and weaknesses, and point to their complementarity in inquiries about cultural change. Because cultural dynamics research is about meaning over time, issues deriving from interpretation of meaning and temporal distance between researchers and objects of inquiry can pose threats to the validity of the research and its findings. The methodological question about hermeneutic circle is recalled and further inquiries are encouraged.

Commentary on "The role of domain-general cognitive control in language comprehension" by Fedorenko.

Commentary on "The role of domain-general cognitive control in language comprehension" by Fedorenko.

Probing neural mechanisms of music perception, cognition, and performance using multivariate decoding.

Recent neuroscience research has shown increasing use of multivariate decoding methods and machine learning. These methods, by uncovering the source and nature of informative variance in large data sets, invert the classical direction of inference that attempts to explain brain activity from mental state variables or stimulus features. However, these techniques are not yet commonly used among music researchers. In this position article, we introduce some key features of machine learning methods and review their use in the field of cognitive and behavioral neuroscience of music. We argue for the great potential of these methods in decoding multiple data types, specifically audio waveforms, electroen- cephalography, functional MRI, and motion capture data. By finding the most informative aspects of stimulus and performance data, hypotheses can be generated pertaining to how the brain processes incoming musical information and generates behavioral output, respectively. Importantly, these methods are also applicable to different neural and physiological data types such as magnetoencephalography, near-infrared spectroscopy, positron emission tomography, and electromyography.Keywords: machine learning, classification, music neuroscienceSupplemental materials: http://dx.doi.org/10.1037/a0031014.suppMusic is acoustic information with complex temporal and spa- tial features. Research into perception and cognition of multifac- eted aspects of music aims to decode the information from neural signals elicited by listening to music. Music performance, on the other hand, entails the encoding of musical information to neural commands issued to the muscles. To understand the neural pro- cesses underlying music perception, cognition, and performance, therefore, researchers face issues of extracting meaningful infor- mation from extremely large data sets with regard to neural, physiological, and biomechanical signals. This is nontrivial in light of recent technological advances in data collection, which can lead to a potentially overwhelming amount of data. The supervised and unsupervised methods of machine learning are powerful tools for uncovering unseen patterns in these large data sets. In this way, not only can the means of specified conditions be compared, but data-driven methods are used to uncover sources of informative variance in the signals. Moreover, machine learning allows for quantitative evaluation of individual differences in music percep- tion and performance.In this article, we introduce key features of machine learning and highlight some examples of their use on a range of data types. After reviewing basic concepts and terminology, we discuss di- mensionality reduction and the impact of the choice of algorithm. We then turn to data types we judge to be most relevant to the neural processing of music. For an audio waveform, it is possible to elucidate the most perceptually informative part of the signal, by determining which aspects of the signal are most salient or useful to the brain in determining specific characteristics of the sound. In the same way, it is possible to uncover neural representations of musical attributes such as rhythm and harmony in a data-driven way by applying supervised and unsupervised learning to single- trial electroencephalography (EEG) or functional MRI (fMRI) data. Finally, machine learning methods are also useful in behav- ioral research, allowing characterization of fundamental patterns of movements that use a large number of joints and muscles during musical performance.MethodsAlthough we do not aim to give a complete overview of these methods here (for a detailed review of machine learning methods for brain imaging, see Lemm, Blankertz, Dickhaus, & Muller, 2011), we introduce a number of key aspects of machine learning as they pertain to music cognition research.Basic TerminologyMachine learning (or statistical learning) involves uncovering meaningful patterns in collections of observations, often with the goal of classifying, or categorizing, the observations in some way. …

Effects of Directionality in Deductive Reasoning: II. Premise Integration and Conclusion Evaluation

Previous research (Oberauer & Wilhelm, 2000) has shown an inherent directionality between the two terms linked in premises of typical deductive reasoning tasks. With three experiments we investigated the effect of inherent directionality on the time to integrate two premises and for the derivation of a conclusion. We varied figure (i.e., order of terms in the premises) and direction of inference (i.e., order of terms in the conclusion) in deduction tasks from various domains (propositional reasoning, syllogisms, spatial, temporal, and linear order reasoning). Effects of figure on premise reading times varied with the directionality of the relations. Effects of direction of inference reflected the same directionality for a subset of relations. We propose that two factors are jointly responsible for a large part of observed directionality effects in premise integration: the inherent directionality of relational statements and a general advantage for a given-new order of terms in the second premise. Difficulty of deriving a conclusion is affected by the directionality or relations if and only if the relation is semantically asymmetric, so that the directionality must be preserved in the integrated mental model.

A one-sided confidence set hidden under a two-sided sequential test of a normal mean

This article demonstrates how to construct a 1 − α level confidence set naturally associated a size α two-sided sequential test of a normal mean μ. If the null hypothesis H0: μ = 0 is rejected by the test, then the confidence set provides a one-sided confidence interval excluding zero for μ. Therefore, based on this confidence set, we can not only reject H0, infer the direction (μ > 0 or μ < 0) of μ, but also have the extra information on the magnitude of μ in the direction of inference. The confidence set is clearly more informative than the original test.

Strategy Shifts and Expertise in Solving Transformation Rule Problems

The acquisition of expertise in formal problem solving has been assumed to involve either a shift from backwards to forwards inference, or a shift from unguided to guided forwards inference. In a longitudinal study, the acquisition of formal problem-solving expertise was investigated. Participants were tested as novices before undertaking controlled practice in the problem domain which involved transformation rule problems, and were finally tested as experts. The direction of inference in problem solutions was found to be inadequate to describe the strategic differences between novices and experts. Therefore, a new solution coding system was applied, based on atomic components of problem solution. Analysis of novice and expert solutions revealed no systematic strategy in the novice stage—solutions were confused and contained unproductive steps and backtracking. Several strategies were found in the expert solutions, but they did not agree with previously reported results. It was therefore proposed that the acquisition of expertise does not involve a change from one specific solution strategy to another, but rather the development of an efficient strategy, which can differ between participants.

Self-anchoring and differentiation processes in the minimal group setting.

In-group favoritism in the minimal group setting was hypothesized to be a function of 2 processes: a tendency to base in-group judgments on the self (self-anchoring) and a tendency to assume 1 group to be opposite of the other (differentiation). In the first 3 experiments, in which the order of rating the self and target group was varied, was categorized and uncategorized participants were given trait information about 1 group and were asked to estimate the level of those traits in the other group. In-group judges tended to base group ratings on the self, whereas out-group and uncategorized judges inferred the 2 groups to be opposite of one another. Experiment 4 attempted to directly assess the direction of inference between self and in-group by giving feedback about self or in-group on unfamiliar dimensions and found that participants were more willing to generalize from self to in-group than from in-group to self.

Auditors' causal judgments: Effects of forward vs backward inference on information processing

This paper has two purposes: (1) to provide a conceptual basis for empirical research on auditors' causal judgments by reviewing the philosophical and psychological literatures on causality, and (2) to report two experiments on auditors' information processing when making causal judgments. The experiments examined the association between the direction of inference (forward inference from cause to effect vs backward inference from effect to cause) and type of information about a casual relation (conditionality vs multiplicity). The results supported the hypotheses that auditors place a significant weight on conditionality information when making forward inferences and a significant weight on multiplicity information when making backward inferences.

A class of statistics based on the information concept

The methodic use of Shannon's entropy as a basic concept, complementing probability, leads to a new class of statistics which provides, inter alia, a measure of mutual dissimilarity y between several frequency distributions. Application to contin-gency tables with any number of dimensions yields a dimension-less, standardised contingency coefficient which depends on the direction of inference and will combine multiplicatively with the number of observed events. This class of statistics further in-cludes a continuous modification W of the number of degrees of freedom in a table, and a measure Q of its overall information content. Numerical illustrations and comparisons with former re-sults are worked out. Direct applications include the optimal partition of a quasicontinuum into cells by maximizing Q, the ordering of unordered tables by minimising local values of y, and a tentative absolute weighting of inductive inference based on the minimal necessary shift, required by an hypothesis, between the actuall...

A cognitive determinant of impression formation.

Abstract : Concrete functioning individuals were shown to generalize in their impressions further than abstract functioning individuals at both higher and lower levels of informational input when the input was hedonically consistent with the direction of inference. Concrete functioning individuals generalized less than abstract functioning individuals, however, when the input information was hedonically inconsistent with the direction of inference. These findings may be interpreted as supporting the notion that concrete persons, more than abstract individuals, are disposed toward seeing the 'good' person as all good and the 'bad' person as all bad, minimizing the plausibility that the same person could simultaneously possess both good and bad characteristics. The findings thus point to concrete-abstract functioning as a variable of considerable weight in the extent of inferences people make of others. (Author)