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Deferred imitations assess declarative memory in infants. Many cross-sectional and a few longitudinal studies revealed that, with development, infants learn faster,and retain more target actions over longer retention intervals. Longitudinal stabilities are modest and increase through the second year. To date, there are only few multivariate deferred imitation studies pointing to interactions between declarative memory, language and self-development. However, as these studies applied variable-centered data analysis approaches, the individual stance was not taken into account.Therefore, the present dissertation focuses on the explanation of inter-individual differences of deferred imitation through the second year. In the multivariate, longitudinal Frankfurt Memory Study (FRAMES), declarative memory (deferred imitation), non-declarative memory (train task), as well as cognitive, language, motor, social, emotional and body self-awareness development (Developmental Test for 6-month- to 6-year-olds, ET6-6) were assessed on three measurement occasions (12-, 18- and 24-month-olds). From a psychometric perspective, sound tests for the assessment of deferred imitation in the respective age groups were developed (Paper 1 & 2). Reliability analyses (Paper 3) indicated relatively high short-term-stability for the deferred imitation test (12-month-olds). The co-development of declarative and nondeclarative memory in 12- and 18-month-olds provided evidence for discriminative validity (Paper 4). Longitudinally, deferred imitation performance tremendously increased throughout the second year, and performance was moderately stable between 12 and 18 months and stability increased between 18 and 24 months. Using a person-centered analysis approach (relative difference scores; cluster analysis), developmental subgroups were extracted out of the total sample. These groups differed in terms of mean growth and stability. However, between the first and second measurement occasion, the groups did not differ with respect to motor, cognitive and language development (Paper 5). Using the data of three measurement occasions, subgroups were extracted showing significant differences with respect to language, motor and body self-awareness development (Paper 6). The results are discussed against the background of infancy development theories.
At present, there is a huge lag between the artificial and the biological information processing systems in terms of their capability to learn. This lag could be certainly reduced by gaining more insight into the higher functions of the brain like learning and memory. For instance, primate visual cortex is thought to provide the long-term memory for the visual objects acquired by experience. The visual cortex handles effortlessly arbitrary complex objects by decomposing them rapidly into constituent components of much lower complexity along hierarchically organized visual pathways. How this processing architecture self-organizes into a memory domain that employs such compositional object representation by learning from experience remains to a large extent a riddle. The study presented here approaches this question by proposing a functional model of a self-organizing hierarchical memory network. The model is based on hypothetical neuronal mechanisms involved in cortical processing and adaptation. The network architecture comprises two consecutive layers of distributed, recurrently interconnected modules. Each module is identified with a localized cortical cluster of fine-scale excitatory subnetworks. A single module performs competitive unsupervised learning on the incoming afferent signals to form a suitable representation of the locally accessible input space. The network employs an operating scheme where ongoing processing is made of discrete successive fragments termed decision cycles, presumably identifiable with the fast gamma rhythms observed in the cortex. The cycles are synchronized across the distributed modules that produce highly sparse activity within each cycle by instantiating a local winner-take-all-like operation. Equipped with adaptive mechanisms of bidirectional synaptic plasticity and homeostatic activity regulation, the network is exposed to natural face images of different persons. The images are presented incrementally one per cycle to the lower network layer as a set of Gabor filter responses extracted from local facial landmarks. The images are presented without any person identity labels. In the course of unsupervised learning, the network creates simultaneously vocabularies of reusable local face appearance elements, captures relations between the elements by linking associatively those parts that encode the same face identity, develops the higher-order identity symbols for the memorized compositions and projects this information back onto the vocabularies in generative manner. This learning corresponds to the simultaneous formation of bottom-up, lateral and top-down synaptic connectivity within and between the network layers. In the mature connectivity state, the network holds thus full compositional description of the experienced faces in form of sparse memory traces that reside in the feed-forward and recurrent connectivity. Due to the generative nature of the established representation, the network is able to recreate the full compositional description of a memorized face in terms of all its constituent parts given only its higher-order identity symbol or a subset of its parts. In the test phase, the network successfully proves its ability to recognize identity and gender of the persons from alternative face views not shown before. An intriguing feature of the emerging memory network is its ability to self-generate activity spontaneously in absence of the external stimuli. In this sleep-like off-line mode, the network shows a self-sustaining replay of the memory content formed during the previous learning. Remarkably, the recognition performance is tremendously boosted after this off-line memory reprocessing. The performance boost is articulated stronger on those face views that deviate more from the original view shown during the learning. This indicates that the off-line memory reprocessing during the sleep-like state specifically improves the generalization capability of the memory network. The positive effect turns out to be surprisingly independent of synapse-specific plasticity, relying completely on the synapse-unspecific, homeostatic activity regulation across the memory network. The developed network demonstrates thus functionality not shown by any previous neuronal modeling approach. It forms and maintains a memory domain for compositional, generative object representation in unsupervised manner through experience with natural visual images, using both on- ("wake") and off-line ("sleep") learning regimes. This functionality offers a promising departure point for further studies, aiming for deeper insight into the learning mechanisms employed by the brain and their consequent implementation in the artificial adaptive systems for solving complex tasks not tractable so far.