Zahlreiche Untersuchungen belegen, dass räumlich selektive Aufmerksamkeit visuelle und auditive Reizverarbeitung beeinflusst. Bestehende Modellvorstellungen sind, aufgrund der geringen Kenntnisse vergleichbarer somatosensorischer Effekte, schwer zu einem allgemeinen Mechanismus generalisieren. Mittels zeitlich-räumlich hoch aufgelöster Messmethoden wurde in dieser Dissertation Effekte räumlich selektiver Aufmerksamkeit auf die bewusste Wahr-nehmung und kortikale Verarbeitung somatosensorischer Reize untersucht. Im Einzelnen wurde gezeigt, dass die räumlich selektive Aufmerksamkeit die Maskierung eines überschwelli-gen Reizes an einer Hand durch einen starken Reiz an der anderen Hand moduliert. Mittels Elektroenzephalografie (EEG) wurde nachgewiesen, dass nach der Stimulation die Verarbei-tung in einem fronto-parietalen Netzwerk den Zugang ins Bewusstsein signalisiert. Der Be-fund einer der bewussten Wahrnehmung zeitlich vorausgehenden neuronalen Desynchronisa-tion im frontalen Kortex und in S1 erlaubt eine Erweiterung bestehender Modellvorstellun-gen. In einer simultanen EEG-funktionelle Magnetresonanztomografie (fMRT) -Studie wurde gezeigt, dass räumlich selektive Aufmerksamkeit die Signalverarbeitung während einer frühen sensorischen Phase der Reizverarbeitung beeinflusst (50 ms). Dieser Effekt korrelierte mit den Blutflußveränderungen in S1. Zusammenfassend zeigen die Studien, dass räumlich selektive Aufmerksamkeit zwar frühe somatosensorische Aktivität in S1 sowie die Wahrnehmung so-matosensorischer Reize moduliert, dies jedoch keine hinreichende Bedingung für die bewusste Wahrnehmung ist. Hingegen ist die attentional kontrollierte Desynchronisation somatosenso-rischer Rhythmen vor der Stimulation, die eine verstärkte fronto-parietale Reizverarbeitung nach sich zieht, hierfür entscheidend. / Numerous studies have shown that selective orientation of attention to a stimulus location modulates visual and auditory stimulus processing. Due to the relatively little knowledge about comparable effects of attention in the somatosensory system, existing models can barely be assigned to general cortical mechanisms. The studies conducted in this dissertation should therefore contribute to this knowledge. Effects of spatial selective attention on conscious per-ception and cortical processing of somatosensory stimuli have been investigated by applying recording methods with high temporal and spatial resolutions. Specifically, it was shown that spatial selective attention modulates masking of supra-threshold stimulus on one hand by a strong stimulus applied to the other hand. Using electroencephalography (EEG), it was dem-onstrated that processing in a fronto-parietal network but not early S1-activation signals the entry into conscious perception. The finding of neuronal desynchronisation in the frontal cor-tex and S1 preceding conscious stimulus perception permits the extension of the existing models. With the aim of localizing the temporal effects of spatial selective attention, a simul-taneous EEG-functional magnetic resonance imaging (fMRI)-study was conducted. In con-trast to findings of visual attention, it was shown that orientation of attention enhances soma-tosensory processing at an early stage of stimulus processing (50 ms). This effect correlated with the changes of cortical blood flow in S1. Together, these studies show that spatial-selective attention modulates early activity in S1 as well as conscious perception of somatosen-sory stimuli. Nevertheless, this is not sufficient for an entrance into conscious perception. Instead, attentionally controlled pre-stimulus desynchronisation of somatosensory rhythmic activity, followed by an increased fronto-parietal stimulus processing are necessary prerequi-sites for conscious perception.

In the human visual system, one of the most prominent functions of the extensive feedback from the higher brain areas within and outside of the visual cortex is attentional modulation. The feedback helps the brain to concentrate its resources on visual features that are relevant for recognition, i. e. it iteratively selects certain aspects of the visual scene for refined processing by the lower areas until the inference process in the higher areas converges to a single hypothesis about this scene. In order to minimize a number of required selection-refinement iterations, one has to find a short sequence of maximally informative portions of the visual input. Since the feedback is not static, the selection process is adapted to a scene that should be recognized. To find a scene-specific subset of informative features, the adaptive selection process on every iteration utilizes results of previous processing in order to reduce the remaining uncertainty about the visual scene. This phenomenon inspired us to develop a computational algorithm solving a visual classification task that would incorporate such principle, adaptive feature selection. It is especially interesting because usually feature selection methods are not adaptive as they define a unique set of informative features for a task and use them for classifying all objects. However, an adaptive algorithm selects features that are the most informative for the particular input. Thus, the selection process should be driven by statistics of the environment concerning the current task and the object to be classified. Applied to a classification task, our adaptive feature selection algorithm favors features that maximally reduce the current class uncertainty, which is iteratively updated with values of the previously selected features that are observed on the testing sample. In information-theoretical terms, the selection criterion is the mutual information of a class variable and a feature-candidate conditioned on the already selected features, which take values observed on the current testing sample. Then, the main question investigated in this thesis is whether the proposed adaptive way of selecting features is advantageous over the conventional feature selection and in which situations. Further, we studied whether the proposed adaptive information-theoretical selection scheme, which is a computationally complex algorithm, is utilized by humans while they perform a visual classification task. For this, we constructed a psychophysical experiment where people had to select image parts that as they think are relevant for classification of these images. We present the analysis of behavioral data where we investigate whether human strategies of task-dependent selective attention can be explained by a simple ranker based on the mutual information, a more complex feature selection algorithm based on the conventional static mutual information and the proposed here adaptive feature selector that mimics a mechanism of the iterative hypothesis refinement. Hereby, the main contribution of this work is the adaptive feature selection criterion based on the conditional mutual information. Also it is shown that such adaptive selection strategy is indeed used by people while performing visual classification.

Arbeitsgedächtnisdefizite lernbehinderter Kinder sind bisher vor allem im phonologischen Bereich nachgewiesen worden. Die eigene Studie untersucht mögliche Defizite im Bereich der zentralen Exekutive mit zwei Schwerpunkten: (a) Lassen sich bei der Steuerung selektiver Aufmerksamkeit spezifische Defizite Lernbehinderter identifizieren? (b) Zeigen sich Besonderheiten in den Alterszugewinnen selektiver Aufmerksamkeit? In der Untersuchung wurden Grundschulkinder gleichen mentalen und gleichen chronologischen Alters mit einer Gruppe lernbehinderter Kinder einer vierten Klasse verglichen. Zusätzlich wurden ältere Schulkinder (7. Klasse, 4. Klasse, 7. Klasse einer Sonderschule L) einbezogen, so dass auch prüfbar wird, inwiefern sich Alterszugewinne der kognitiven Funktionen bei lernbehinderten im Vergleich zu unauffälligen Schülern unterscheiden. Einbezogen wurden nur L-Schüler mit einem IQ < 85. Zur Erfassung der zentral-exekutiven selektiven Aufmerksamkeit wurden drei verschiedene Aufgabenanforderungen (TAP, Stopp-Signal, TEZEK) gewählt. Die Befunde deuten auf ein frühes Funktionsdefizit der lernbehinderten Kinder hin, das bis 14 Jahre weitgehend kompensiert werden kann.

No description available.

In the human visual system, one of the most prominent functions of the extensive feedback from the higher brain areas within and outside of the visual cortex is attentional modulation. The feedback helps the brain to concentrate its resources on visual features that are relevant for recognition, i. e. it iteratively selects certain aspects of the visual scene for refined processing by the lower areas until the inference process in the higher areas converges to a single hypothesis about this scene. In order to minimize a number of required selection-refinement iterations, one has to find a short sequence of maximally informative portions of the visual input. Since the feedback is not static, the selection process is adapted to a scene that should be recognized. To find a scene-specific subset of informative features, the adaptive selection process on every iteration utilizes results of previous processing in order to reduce the remaining uncertainty about the visual scene. This phenomenon inspired us to develop a computational algorithm solving a visual classification task that would incorporate such principle, adaptive feature selection. It is especially interesting because usually feature selection methods are not adaptive as they define a unique set of informative features for a task and use them for classifying all objects. However, an adaptive algorithm selects features that are the most informative for the particular input. Thus, the selection process should be driven by statistics of the environment concerning the current task and the object to be classified. Applied to a classification task, our adaptive feature selection algorithm favors features that maximally reduce the current class uncertainty, which is iteratively updated with values of the previously selected features that are observed on the testing sample. In information-theoretical terms, the selection criterion is the mutual information of a class variable and a feature-candidate conditioned on the already selected features, which take values observed on the current testing sample. Then, the main question investigated in this thesis is whether the proposed adaptive way of selecting features is advantageous over the conventional feature selection and in which situations. Further, we studied whether the proposed adaptive information-theoretical selection scheme, which is a computationally complex algorithm, is utilized by humans while they perform a visual classification task. For this, we constructed a psychophysical experiment where people had to select image parts that as they think are relevant for classification of these images. We present the analysis of behavioral data where we investigate whether human strategies of task-dependent selective attention can be explained by a simple ranker based on the mutual information, a more complex feature selection algorithm based on the conventional static mutual information and the proposed here adaptive feature selector that mimics a mechanism of the iterative hypothesis refinement. Hereby, the main contribution of this work is the adaptive feature selection criterion based on the conditional mutual information. Also it is shown that such adaptive selection strategy is indeed used by people while performing visual classification.:1. Introduction 2. Conventional feature selection 3. Adaptive feature selection 4. Experimental investigations of ACMIFS 5. Information-theoretical strategies of selective attention 6. Discussion Appendix Bibliography