The temporal dynamics of effect anticipation in course of action planning

“Strong” versions of the ideomotor theory of action control claim that anticipations of the environmental effects that actions bring about are mandatory for response selection. This is considered to be the one and only way of how actions can be voluntarily selected. We studied this notion in a series of four experiments where we adapted the flanker paradigm to investigate the involvement of effect codes in the preparation of motor responses. Participants first learned that their responses to stimulus letters were contingently followed by the presentation of a new letter on the screen. In the second phase of the experiments, the action-demanding letters were presented together with the effects of the correct response, effects of other responses, or neutral letters. Varying the stimulus onset asynchrony (SOA) between target stimuli and the flanking effect stimuli provides the opportunity to investigate the temporal dynamics of the activation of effect codes. Hence, flanker stimuli were presented before, simultaneously with, or after the onset of the target. The results indicate that effect-related information from the flanker stimuli is involved in the preparation process, but mainly in later phases of response preparation. The observed pattern of results suggests that, at least under conditions where responses are determined by stimuli, effect codes are activated in course of response planning to enable the evaluation of the executed response and the monitoring of response execution, but they do not automatically activate the responses themselves

A robust adaptive wavelet-based method for classification of meningioma histology images

Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy

TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images

BIIGLE 2.0 - Browsing and Annotating Large Marine Image Collections

Combining state-of-the art digital imaging technology with different kinds of marine exploration techniques such as modern AUV (autonomous underwater vehicle), ROV (remote operating vehicle) or other monitoring platforms enables marine imaging on new spatial and/or temporal scales. A comprehensive interpretation of such image collections requires the detection, classification and quantification of objects of interest in the images usually performed by domain experts. However, the data volume and the rich content of the images makes the support by software tools inevitable. We define some requirements for marine image annotation and present our new online tool Biigle 2.0. It is developed with a special focus on annotating benthic fauna in marine image collections with tools customized to increase efficiency and effectiveness in the manual annotation process. The software architecture of the system is described and the special features of Biigle 2.0 are illustrated with different use-cases and future developments are discussed

Introduction of image-based water transparency descriptors to quantify marine snow and turbidity features. A study with data from a stationary observatory

Möller T, Nilssen I, Nattkemper TW. Introduction of image-based water transparency descriptors to quantify marine snow and turbidity features. A study with data from a stationary observatory. Presented at the MIW 2014 - Marine Imaging Workshop, Southampton

Binding in voluntary action control

The last decade has seen a proliferation of empirical studies that seek to understand how the cognitive system links voluntary motor actions with their perceptual effects. A view that has found considerable support in this research is the ideomotor approach to action control which holds that actors select, initiate, and execute a movement by activating anticipatory codes of the movement's sensory effects. We, first review the empirical evidence from different paradigms showing that effects of voluntary actions become anticipated during response production. In a second step we survey empirical data investigating the nature of the mechanisms that link voluntary motor actions with their intended and expected perceptual effects. We argue that the integration, or binding, of perceptual and motor codes occurs in action planning where features of intended effects are selectively bound to features of the actions that are selected to achieve these effects in the environment. As a final step we will summarize empirical findings that may elucidate the particular roles of effect-code activation in response production and control

A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages

Loyek C, Kölling J, Langenkämper D, Niehaus K, Nattkemper TW. A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages. In: Gama J, Bradley E, Hollmén J, eds. Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science. Vol 7014. Berlin, Heidelberg: Springer; 2011: 258-269

Strategic influences on implementing instructions for future actions

Temporal and strategic factors that might influence the transformation of verbal task rules into functional stimulus–response associations were investigated in three experiments. In a dual task paradigm of the ABBA type participants were presented new S–R instructions for the A-task at the beginning of each trial. On varying proportions of trials No-go signals rendered the instructed A-task mappings irrelevant before instruction implementation was assessed during performance of an unrelated B-task. Our results indicate that participants refrain from implementing the mappings during instruction presentation when No-go signals appear frequently and late (Exp. 2), and that they can interrupt implementing instructed S–R mappings when frequent No-go signals appear early enough during implementation (Exp. 3). When No-go signals are rare and late, however (Exp. 1), the instructed stimulus features always activate their associated responses during performance of the embedded B-task in an automatic manner. Together, these findings suggest that participants strategically control whether or not they implement verbal instructions. Once implemented, however, instructed S–R associations influence behaviour even when the instructed mappings are no longer task relevant

A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification

Wei N, Flaschel E, Friehs K, Nattkemper TW. A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification. BMC Bioinformatics. 2008;9(1):449.Background: Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable and versatile, however, some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed. Results: This system is based on supervised learning technique. It learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via dark field microscopy. Wavelet decomposition is performed on the cell images. Energy and entropy are computed for each wavelet subimage as features. A feature selection algorithm is implemented to achieve better performance. Correlation between the results from the machine vision system and commonly accepted gold standards becomes stronger if wavelet features are utilized. The best performance is achieved with a selected subset of wavelet features. Conclusion: The machine vision system based on dark field microscopy in conjugation with supervised machine learning and wavelet feature selection automates the cell viability assessment, and yields comparable results to commonly accepted methods. Wavelet features are found to be suitable to describe the discriminative properties of the live and dead cells in viability classification. According to the analysis, live cells exhibit morphologically more details and are intracellularly more organized than dead ones, which display more homogeneous and diffuse gray values throughout the cells. Feature selection increases the system's performance. The reason lies in the fact that feature selection plays a role of excluding redundant or misleading information that may be contained in the raw data, and leads to better results