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Das Wissen über Kognition oder metakognitives Wissen ist seit den 1970er Jahren Gegenstand der entwicklungspsychologischen Forschung. Besonders umfangreich wurde Entwicklung und Bedeutung des metakognitiven Wissens im Kontext der Gedächtnisentwicklung vom Vorschul- bis ins Grundschulalter untersucht. Das metakognitive Wissen im Inhaltsbereich der mathematischen Informationsverarbeitung ist – trotz elaborierter theoretischer Modelle über Struktur und Inhalt – empirisch weitgehend unerschlossen. Die vorliegende Studie wurde durchgeführt, um systematisch zu untersuchen, wie sich das mathematische metakognitive Wissen in der Sekundarstufe entwickelt, welche Faktoren für individuelle Unterschiede in der Entwicklung verantwortlich sind und in welchem Zusammenhang die metakognitive Wissensentwicklung mit der parallel verlaufenden Entwicklung mathematischer Kompetenzen steht. Zur Klärung der Fragestellungen wurden vier Messzeitpunkte einer breiter angelegten Längsschnittuntersuchung ausgewertet. Der dabei beobachtete Zeitraum umfasste die fünfte und sechste Jahrgangsstufe. Die Stichprobe bestand aus 928 Schülern der Schularten Gymnasium, Realschule und Hauptschule. Die Messinstrumente zur Erfassung der Entwicklungsveränderungen im mathematischen metakognitiven Wissen und der Mathematikleistung wurden auf Grundlage der item response theory konstruiert und mittels vertikalem linking fortlaufend an den Entwicklungsstand der Stichprobe angepasst. Zusätzlich wurden kognitive (Intelligenz und Arbeitsgedächtniskapazität), motivationale (mathematisches Interesse und Selbstkonzept) und sozioökonomische Merkmale (sozioökonomischer Status der Herkunftsfamilie) der Schüler erhoben. Die Lesekompetenz wurde als Methodenfaktor kontrolliert. Entwicklungsunterschiede und -veränderungen im metakognitiven Wissen wurde mit Hilfe von latenten Wachstumskurvenmodellen untersucht. Im beobachteten Zeitraum zeigte sich eine stetige Zunahme des metakognitiven Wissens. Allerdings verlief die Entwicklungsveränderung nicht linear, sondern verlangsamte sich im Verlauf der sechsten Jahrgangsstufe. Individuelle Unterschiede in Ausprägung und Veränderung des metakognitiven Wissens wurden durch kognitive und sozioökonomische Schülermerkmale vorhergesagt. Die motivationalen Merkmale wirkten sich demgegenüber nicht auf den Entwicklungsprozess aus. Geschlechtsunterschiede zeigten sich im Entwicklungsverlauf als Schereneffekt zugunsten der Mädchen. Unterschiede zwischen den Schülern der drei Schularten erreichten bereits zum Eintritt in die Sekundarstufe Signifikanz. Zudem gewannen Gymnasiasten und Hauptschüler im Entwicklungsverlauf stärker an metakognitivem Wissen hinzu als Realschüler. Explorative Mischverteilungsanalysen in der Stichprobe ergaben drei latente Entwicklungsklassen mit jeweils charakteristischem Veränderungsverlauf. Die Klassenzuweisung wurde von der besuchten Schulart sowie kognitiven und sozioökonomischen Schülermerkmalen vorhergesagt. Die Entwicklungsprozesse im mathematischen metakognitiven Wissen und der mathematischen Leistung standen in einem substanziellen, wechselseitigen Zusammenhang. Geschlechts- und Schulartunterschiede blieben ebenso wie die korrelativen Zusammenhänge zwischen den Entwicklungsprozessen auch nach Kontrolle der individuellen Unterschiede in kognitiven, motivationalen und sozioökonomischen Merkmalen erhalten. Die Befunde bestätigen die konstruktivistischen Entwicklungsannahmen der gedächtnispsychologisch geprägten Grundlagenforschung zum metakognitiven Wissen. Zudem wird mit der Untersuchung des mathematischen metakognitiven Wissens in der Sekundarstufe der traditionelle Forschungsfokus inhaltlich erweitert. Das im Rahmen der Studie konstruierte Instrument zur Erfassung des mathematischen metakognitiven Wissens ermöglicht die Untersuchung weiterer, bislang offener Fragen auf dem Gebiet der metakognitiven Entwicklung.
This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control.
This study examined the impact of three clinical psychological variables (non-pathological levels of depression and anxiety, as well as experimentally manipulated mood) on fat and taste perception in healthy subjects. After a baseline orosensory evaluation, ‘sad’, ‘happy’ and ‘neutral’ video clips were presented to induce corresponding moods in eighty participants. Following mood manipulation, subjects rated five different oral stimuli, appearing sweet, umami, sour, bitter, fatty, which were delivered at five different concentrations each. Depression levels were assessed with Beck’s Depression Inventory (BDI) and anxiety levels were assessed via the Spielberger’s STAI-trait and state questionnaire. Overall, subjects were able to track the concentrations of the stimuli correctly, yet depression level affected taste ratings. First, depression scores were positively correlated with sucrose ratings. Second, subjects with depression scores above the sample median rated sucrose and quinine as more intense after mood induction (positive, negative and neutral). Third and most important, the group with enhanced depression scores did not rate low and high fat stimuli differently after positive or negative mood induction, whereas, during baseline or during the non-emotional neutral condition they rated the fat intensity as increasing with concentration. Consistent with others’ prior observations we also found that sweet and bitter stimuli at baseline were rated as more intense by participants with higher anxiety scores and that after positive and negative mood induction, citric acid was rated as stronger tasting compared to baseline. The observation that subjects with mild subclinical depression rated low and high fat stimuli similarly when in positive or negative mood is novel and likely has potential implications for unhealthy eating patterns. This deficit may foster unconscious eating of fatty foods in sub-clinical mildly depressed populations.
Relief from pain is positively valenced and entails reward-like properties. Notably, stimuli that became associated with pain relief elicit reward-like implicit responses too, but are explicitly evaluated by humans as aversive. Since the unpredictability of pain makes pain more aversive, this study examined the hypotheses that the predictability of pain also modulates the valence of relief-associated stimuli. In two studies, we presented one conditioned stimulus \((_{FORWARD}CS+)\) before a painful unconditioned stimulus (US), another stimulus \((_{BACKWARD}CS+)\) after the painful US, and a third stimulus (CS−) was never associated with the US. In Study 1, \(_{FORWARD}CS+\) predicted half of the USs while the other half was delivered unwarned and followed by \(_{BACKWARD}CS+\). In Study 2, all USs were predicted by \(_{FORWARD}CS+\) and followed by \(_{BACKWARD}CS+\). In Study 1 both \(_{FORWARD}CS+\) and \(_{BACKWARD}CS+\) were rated as negatively valenced and high arousing after conditioning, while \(_{BACKWARD}CS+\) in Study 2 acquired positive valence and low arousal. Startle amplitude was significantly attenuated to \(_{BACKWARD}CS+\) compared to \(_{FORWARD}CS+\) in Study 2, but did not differ among CSs in Study 1. In summary, predictability of aversive events reverses the explicit valence of a relief-associated stimulus.
Objective
Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball.
Methods
Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude.
Results
Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy.
Conclusions
Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection.
Significance
Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.
Objective: Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball. Methods: Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude. Results: Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy. Conclusions: Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection. Significance: Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.
Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary.
Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance.
Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error).
Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance.
Significance: This confirms that structural brain traits contribute to individual performance in BCI use.