Attribution of intentional causation influences the perception of observed movements: behavioral evidence and neural correlates

Recent research on human agency suggests that intentional causation is associated with a subjective compression in the temporal interval between actions and their effects. That is, intentional movements and their causal effects are perceived as closer together in time than equivalent unintentional movements and their causal effects. This so-called intentional binding effect is consistently found for one's own self-generated actions. It has also been suggested that intentional binding occurs when observing intentional movements of others. However, this evidence is undermined by limitations of the paradigm used. In the current study we aimed to overcome these limitations using a more rigorous design in combination with functional Magnetic Resonance Imaging (fMRI) to explore the neural underpinnings of intentional binding of observed movements. In particular, we aimed to identify brain areas sensitive to the interaction between intentionality and causality attributed to the observed action. Our behavioral results confirmed the occurrence of intentional binding for observed movements using this more rigorous paradigm. Our fMRI results highlighted a collection of brain regions whose activity was sensitive to the interaction between intentionality and causation. Intriguingly, these brain regions have previously been implicated in the sense of agency over one's own movements. We discuss the implications of these results for intentional binding specifically, and the sense of agency more generally

Optimal Estimation of Several Linear Parameters in the Presence of Lorentzian Thermal Noise

In a previous article we developed an approach to the optimal (minimum variance, unbiased) statistical estimation technique for the equilibrium displacement of a damped, harmonic oscillator in the presence of thermal noise. Here, we expand that work to include the optimal estimation of several linear parameters from a continuous time series. We show that working in the basis of the thermal driving force both simplifies the calculations and provides additional insight to why various approximate (not optimal) estimation techniques perform as they do. To illustrate this point, we compare the variance in the optimal estimator that we derive for thermal noise with those of two approximate methods which, like the optimal estimator, suppress the contribution to the variance that would come from the irrelevant, resonant motion of the oscillator. We discuss how these methods fare when the dominant noise process is either white displacement noise or noise with power spectral density that is inversely proportional to the frequency ($1/f$ noise). We also construct, in the basis of the driving force, an estimator that performs well for a mixture of white noise and thermal noise. To find the optimal multi-parameter estimators for thermal noise, we derive and illustrate a generalization of traditional matrix methods for parameter estimation that can accommodate continuous data. We discuss how this approach may help refine the design of experiments as they allow an exact, quantitative comparison of the precision of estimated parameters under various data acquisition and data analysis strategies.Comment: 16 pages, 10 figures. Accepted for publication in Classical and Quantum Gravit

Sense of agency, associative learning, and schizotypy

Despite the fact that the role of learning is recognised in empirical and theoretical work on sense of agency (SoA), the nature of this learning has, rather surprisingly, received little attention. In the present study we consider the contribution of associative mechanisms to SoA. SoA can be measured quantitatively as a temporal linkage between voluntary actions and their external effects. Using an outcome blocking procedure, it was shown that training action-outcome associations under conditions of increased surprise augmented this temporal linkage. Moreover, these effects of surprise were correlated with schizotypy scores, suggesting that individual differences in higher level experiences are related to associative learning and to its impact on SoA. These results are discussed in terms of models of SoA, and our understanding of disrupted SoA in certain disorders

Structure of the chromosphere-corona transition region

Structure and energy distribution of chromosphere-corona transition regio

A Reading Lesson Observation Framework for Elementary Teachers, Principals, and Literacy Supervisors

Henk et al present a framework which will provide reference points for assessment and help refine instructional practices in reading

Interface free energies in p-spin glass models

The replica method has been used to calculate the interface free energy associated with the change from periodic to anti-periodic boundary conditions in finite-dimensional p-spin glass models in the phase which at mean-field level has one-step replica symmetry breaking (1RSB). In any finite dimension the interface free energy is exponentially small for a large system. This result implies that in finite dimensions, the 1RSB state does not exist, as it is destroyed by thermal excitation of arbitrarily large droplets. The implications of this for the theory of structural glasses are discussed.Comment: 4 page

Performance of a single-stage transonic compressor with a blade-tip solidity of 1.5 and comparison with 1.3 and 1.7 solidity stages

The overall and blade-element performance of a transonic compressor stage with a tip solidity of 1.5 is presented over the stable operating range at rotative speeds from 50 to 100 percent of design speed. State peak efficiency of 0.82 was obtained at a weight flow of 29.4 kg.sec (200.4 (kg/sec)/m2 of annulus area) and a pressure ratio of 1.71. Stall margin at design speed was 14 percent. A comparison of three stages in a solidity study showed that the performance of the 1.5 solidity stage and the 1.3 solidity stage were nearly identical but that the performance of the 1.7 solidity stage was significantly lower

SPAR demonstration problems

A series of examples are presented to indicate some of the principal functions of the SPAR system and to illustrate SPAR's control card-data card structure. Information in the following categories is given: (1) a description of the problem and, in most cases, comparisons with analytical solutions; (2) a list of the input cards; (3) a printout of the table of contents of the direct access library into which all SPAR output was directed; and (4) a few representative plots