Scar Implicatures: Why Some Professional Athletes Are Rich

Implicatures, in general, refer to meanings in utterances that are not explicitly conveyed by the speaker, but can be inferred by the listener (Grice, 1975). On the other hand, scalar implicatures are those that, on a scale of informativeness, act as the weaker term (some) in which its occurrence is thought to be less informative than the stronger term (all), thus some = not all (Horn, 1972). For example, consider the following sentence:
1) Some professional athletes are rich.
When a speaker produces a sentence such as (1), the listener conveys the idea that in (2):
2) Some professional athletes are rich, but some are not.
The inferred meaning in (2) is not included in the original utterance, yet there seems to be a consensus as to which
In the logical interpretation, the quantifier some has an inherent semantic association with it that leads to the some and possibly all reading. That is, the scenario in which all professional athletes are rich is considered a subset of the scenario where some professional athletes are rich. Because if all of them are rich then some of them must be rich, or else the statement would be false. On the other hand, the reading that some but not all is not assigned semantically/logically, rather it is a rich pragmatic inference that is not part of the original utterance (Grice, 1975; Horn, 1972). A differentiating factor between the two reasons is whether the implicature can be cancelled (Doran et al., 2012). The pragmatic scalar can be cancelled if we were to assume that the following sentence in the same context provided additional information that run against the initially assigned meaning. Assuming that the leading context provided relative information against the inference, the meaning of the entire sentence would still make sense and thus not leading to major sentence reanalysis. On the other hand, the semantic assignment of some does not allow for defeasibility since it would indicate contradicting evidence with regard to the entire context. Examples explained
The idea here is to look at power i.e. amplitude changes within a local functional network. The post synaptic potentials or voltage changes of a large number of neurons will lead to power increase and thus allowing to stipulate a relationship between a particular event and the synchronous activation at a given frequency band. It then follows that power increase in a given recording site gets averaged across a number of trials to ensure the association of the experimental event to the power change. This is due to the fact that presence of power at any frequency band may simply be associated with artefacts, such as eye movements or muscle artefacts. Therefore, different methods can be applied to extract these signals from the raw EEG data. Some of the widely used analyses are Induced band Power (IBP; Klimesch, Russegger, Doppelmayr, & Pachinger, 1998), Event-related Desynchronization (ERD;G Pfurtscheller & Aranibar, 1979; G. Pfurtscheller & Lopes da Silva, 1999), wavelet analysis (Tallon-Baudry, Bertrand, Peronnet, & Pernier, 1998), as well as Fast Fourier