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An important question in implicit sequence learning research is how the learned information is represented. In earlier models, the representations underlying implicit learning were viewed as being either purely motor or perceptual. These different conceptions were later integrated by multidimensional models such as the Dual System Model of Keele et al. (Psychol Rev 110(2):316-339, 2003). According to this model, different types of sequential information can be learned in parallel, as long as each sequence comprised only one single dimension (e.g., shapes, colors, or response locations). The term dimension, though, is underspecified as it remains an open question whether the involved learning modules are restricted to motor or to perceptual information. This study aims to show that the modules of the implicit learning system are not specific to motor or perceptual processing. Rather, each module processes an abstract feature code which represents both response- and perception-related information. In two experiments, we showed that perceiving a stimulus-location sequence transferred to a motor response-location sequence. This result shows that the mere perception of a sequential feature automatically leads to an activation of the respective motor feature, supporting the notion of abstract feature codes being the basic modules of the implicit learning system. This result could only be obtained, though, when the task instructions emphasized the encoding of the stimulus-locations as opposed to an encoding of the color features. This limitation will be discussed taking into account the importance of the instructed task set.

According to the Theory of Event Coding (TEC; Hommel, Musseler, Aschersleben, & Prinz, 2001), action and perception are represented in a shared format in the cognitive system by means of feature codes. In implicit sequence learning research, it is still common to make a conceptual difference between independent motor and perceptual sequences. This supposedly independent learning takes place in encapsulated modules (Keele, Ivry, Mayr, Hazeltine, & Heuer 2003) that process information along single dimensions. These dimensions have remained underspecified so far. It is especially not clear whether stimulus and response characteristics are processed in separate modules. Here, we suggest that feature dimensions as they are described in the TEC should be viewed as the basic content of modules of implicit learning. This means that the modules process all stimulus and response information related to certain feature dimensions of the perceptual environment. In 3 experiments, we investigated by means of a serial reaction time task the nature of the basic units of implicit learning. As a test case, we used stimulus location sequence learning. The results show that a stimulus location sequence and a response location sequence cannot be learned without interference (Experiment 2) unless one of the sequences can be coded via an alternative, nonspatial dimension (Experiment 3). These results support the notion that spatial location is one module of the implicit learning system and, consequently, that there are no separate processing units for stimulus versus response locations. (PsycINFO Database Record

Senescent cells contribute to tissue aging and dysfunction. Therefore, detecting senescent cells in skin is of interest for skin tumor diagnostics and therapy. Here, we studied the transition into senescence of human dermal fibroblasts (HDFs) in a three-dimensional (3D) human fibroblast-derived matrix (FDM). Senescent and proliferating cells were imaged by Raman spectroscopy (RS) and Fourier transform infrared (FTIR) spectroscopy. The obtained averaged spectra were analyzed using PLS-LDA. For these 3D cultured cells, RS and FTIR could clearly distinguish senescent from proliferating cells. For both techniques, we detected senescence-associated alterations in almost all cellular macromolecules. Furthermore, we identified different biochemical properties of 3D compared to two-dimensional (2D) cultured cells, indicating that cells in their natural, skin-like 3D environment act differently than in (2D) cell cultivations in vitro. Compared to 2D cultured cells, cells grown in 3D models displayed a sharper contrast between the proliferating and senescent state, also affecting the abundance of biomolecules including nucleic acids. The training accuracies of both vibrational spectroscopic techniques were >96%, demonstrating the suitability of these label-free measurements for detecting these cellular states in 3D skin models.

Dermal fibroblast cells can adopt different cell states such as proliferation, quiescence, apoptosis or senescence, in order to ensure tissue homeostasis. Proliferating (dividing) cells pass through the phases of the cell cycle, while quiescent and senescent cells exist in a non-proliferating cell cycle-arrested state. However, the reversible quiescence state is in contrast to the irreversible senescence state. Long-term quiescent cells transit into senescence indicating that cells age also when not passing through the cell cycle. Here, by label-free in vitro vibrational spectroscopy, we studied the biomolecular composition of quiescent dermal fibroblast cells and compared them with those of proliferating and senescent cells. Spectra were examined by multivariate statistical analysis using a PLS-LDA classification model, revealing differences in the biomolecular composition between the cell states mainly associated with protein alterations (variations in the side chain residues of amino acids and protein secondary structure), but also within nucleic acids and lipids. We observed spectral changes in quiescent compared to proliferating cells, which increased with quiescence cultivation time. Raman and infrared spectroscopy, which yield complementary biochemical information, clearly distinguished contact-inhibited from serum-starved quiescent cells. Furthermore, the spectra displayed spectral differences between quiescent cells and proliferating cells, which had recovered from quiescence. This became more distinct with increasing quiescence cultivation time. When comparing proliferating, (in particular long-term) quiescent and senescent cells, we found that Raman as well as infrared spectroscopy can separate these three cellular states from each other due to differences in their biomolecular composition. Our spectroscopic analysis shows that proliferating and quiescent fibroblast cells age by similar but biochemically not identical processes. Despite their aging induced changes, over long time periods quiescent cells can return into the cell cycle. Finally however, the cell cycle arrest becomes irreversible indicating senescence.

Over the last decade, Raman spectroscopy has gained more and more interest in research as well as in clinical laboratories. As a vibrational spectroscopy technique, it is complementary to the also well-established infrared spectroscopy. Through specific spectral patterns, substances can be identified and molecular changes can be observed with high specificity. Because of a high spatial resolution due to an excitation wavelength in the visible and near-infrared range, Raman spectroscopy combined with microscopy is very powerful for imaging biological samples. Individual cells can be imaged on the subcellular level. In vivo tissue examinations are becoming increasingly important for clinical applications. In this review, we present currently ongoing research in different fields of medical diagnostics involving linear Raman spectroscopy and imaging. We give a wide overview over applications for the detection of atherosclerosis, cancer, inflammatory diseases and pharmacology, with a focus on developments over the past 5 years. Conclusions drawn from Raman spectroscopy are often validated by standard methods, for example, histopathology or PCR. The future potential of Raman spectroscopy and its limitations are discussed in consideration of other non-linear Raman techniques.

Cellular senescence is a terminal cell cycle arrested state, assumed to be involved in tumor suppression. We studied four human fibroblast cell strains (BJ, MRC-5, IMR-90, and WI-38) from proliferation into senescence. Cells were investigated by label-free vibrational Raman and infrared spectroscopy, following their transition into replicative senescence. During the transition into senescence, we observed rather similar biomolecular abundances in all four cell strains and between proliferating and senescent cells; however, in the four aging cell strains, we found common molecular differences dominated by protein and lipid modifications. Hence, aging induces a change in the appearance of biomolecules (including degradation and storage of waste) rather than in their amount present in the cells. For all fibroblast strains combined, the partial least squares-linear discriminant analysis (PLS-LDA) model resulted in 75% and 81% accuracy for the Raman and infrared (IR) data, respectively. Within this validation, senescent cells were recognized with 93% sensitivity and 90% specificity for the Raman and 84% sensitivity and 97% specificity for the IR data. Thus, Raman and infrared spectroscopy can identify replicative senescence on the single cell level, suggesting that vibrational spectroscopy may be suitable for identifying and distinguishing different cellular states in vivo, e.g., in skin.

Although the existence of implicit motor learning is now widely accepted, the findings concerning perceptual implicit learning are ambiguous. Some researchers have observed perceptual learning whereas other authors have not. The review of the literature provides different reasons to explain this ambiguous picture, such as differences in the underlying learning processes, selective attention, or differences in the difficulty to express this knowledge. In three experiments, we investigated implicit visual learning within the original serial reaction time task. We used different response devices (keyboard vs. mouse) in order to manipulate selective attention towards response dimensions. Results showed that visual and motor sequence learning differed in terms of RT-benefits, but not in terms of the amount of knowledge assessed after training. Furthermore, visual sequence learning was modulated by selective attention. However, the findings of all three experiments suggest that selective attention did not alter implicit but rather explicit learning processes.

Implicit learning is one of the most fundamental learning mechanisms that enables humans to adapt to regularities inherent in the environment. Despite its high flexibility, it depends on constraints, such as selective attention. Here, we focused on the stimulus-to-response binding which defines the dimensions of the stimuli and the responses participants attend to. In a serial reaction time task with a visual sequence, we investigated whether this stimulus–response binding influences the amount of sequence learning. The results of Experiments 1 and 2 showed that visual sequence learning is reduced when participants do not attend to the relevant response dimension. Furthermore, the findings of Experiment 3 suggest that attention to the relevant response dimension increased the development of explicit knowledge without affecting implicit knowledge. This latter finding is difficult to reconcile with the assumption that explicit learning results from the gradual strengthening of sequence representations.