@article{StoneNicenboimVasishthetal.2023,
  author    = {Stone, Kate and Nicenboim, Bruno and Vasishth, Shravan and R{\"o}sler, Frank},
  title     = {Understanding the effects of constraint and predictability in ERP},
  series = {Neurobiology of language},
  volume    = {4},
  journal   = {Neurobiology of language},
  number    = {2},
  publisher = {MIT Press},
  address   = {Cambridge, MA, USA},
  issn      = {2641-4368},
  doi       = {10.1162/nol_a_00094},
  pages     = {221 -- 256},
  year      = {2023},
  abstract  = {Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.},
  language  = {en}
}
@article{BauerSommerGaedke2013,
  author    = {Bauer, Barbara and Sommer, Ulrich and Gaedke, Ursula},
  title     = {High predictability of spring phytoplankton biomass in mesocosms at the species, functional group and community level},
  series = {Freshwater biology},
  volume    = {58},
  journal   = {Freshwater biology},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0046-5070},
  doi       = {10.1111/j.1365-2427.2012.02780.x},
  pages     = {588 -- 596},
  year      = {2013},
  abstract  = {1. Models aim to predict phytoplankton dynamics based on observed initial conditions and a set of equations and parameters. However, our knowledge about initial conditions in nature is never perfect. Thus, if phytoplankton dynamics are sensitive to small variations in initial conditions, they are difficult to predict. 2. We used time-series data from indoor mesocosm experiments with natural phyto- and zooplankton communities to quantify the extent to which small initial differences in the species, functional group and community biomass in parallel treatments were amplified or buffered over time. We compared the differences in dynamics between replicates and among all mesocosms of 1year. 3. Temperature-sensitive grazing during the exponential growth phase of phytoplankton caused divergence. In contrast, negative density dependence caused convergence. 4. Mean differences in biomass between replicates were similar for all hierarchical levels. This indicates that differences in their initial conditions were amplified to the same extent. Even though large differences in biomass occasionally occurred between replicates for a short time, dynamics returned to the same path at all hierarchical levels. This suggests that internal feedback mechanisms make the spring development of phytoplankton highly predictable.},
  language  = {en}
}