The role of microorganisms in the formation of a stalactite in Botovskaya Cave, Siberia – paleoenvironmental implications

Calcitic speleothems in caves can form through abiogenic or biogenic processes, or through a combination of both. Many issues conspire to make the assessment of biogenicity difficult, especially when focusing on old speleothem deposits. This study reports on a multiproxy analysis of a Siberian stalactite, combining high-resolution microscopy, isotope geochemistry and microbially enhanced mineral precipitation laboratory experiments. The contact between growth layers in a stalactite exhibits a biogenic isotopic signature; coupled with morphological evidence, this supports a microbial origin of calcite crystals. SIMS δ¹³C data suggest that microbially mediated speleothem formation occurred repeatedly at short intervals before abiotic precipitation took over. The studied stalactite also contains iron and manganese oxides that have been mediated by microbial activity through extracellular polymeric substance (EPS)-influenced organomineralization processes. The latter reflect paleoenvironmental changes that occurred more than 500 000 yr ago, possibly related to the presence of a peat bog above the cave at that time. Microbial activity can initiate calcite deposition in the aphotic zone of caves before inorganic precipitation of speleothem carbonates. This study highlights the importance of microbially induced fractionation that can result in large negative δ¹³C excursions. The microscale biogeochemical processes imply that microbial activity has only negligible effects on the bulk δ¹³C signature in speleothems, which is more strongly affected by CO₂ degassing and the host rock signature

Primitive computations in speech processing

Previous research suggests that artificial-language learners exposed to quasi-continuous speech can learn that the first and the last syllables of words have to belong to distinct classes (e.g., Endress & Bonatti, 2007; Peña, Bonatti, Nespor, & Mehler, 2002). The mechanisms of these generalizations, however, are debated. Here we show that participants learn such generalizations only when the crucial syllables are in edge positions (i.e., the first and the last), but not when they are in medial positions (i.e., the second and the fourth in pentasyllabic items). In contrast to the generalizations, participants readily perform statistical analyses also in word middles. In analogy to sequential memory, we suggest that participants extract the generalizations using a simple but specific mechanism that encodes the positions of syllables that occur in edges. Simultaneously, they use another mechanism to track the syllable distribution in the speech streams. In contrast to previous accounts, this model explains why the generalizations are faster than the statistical computations, require additional cues, and break down under different conditions, and why they can be performed at all. We also show that that similar edge-based mechanisms may explain many results in artificial-grammar learning and also various linguistic observations

Learning to spell in a deep orthography.

In R. A. Berman (Ed.)

Implicit statistical learning of graphotactic patterns: When and how do spellers use this knowledge?

International audienc