  Language decoding studies have identified word representations which can be
used to predict brain activity in response to novel words and sentences
(Anderson et al., 2016; Pereira et al., 2018). The unspoken assumption of these
studies is that, during processing, linguistic information is transformed into
some shared semantic space, and those semantic representations are then used
for a variety of linguistic and non-linguistic tasks. We claim that current
studies vastly underdetermine the content of these representations, the
algorithms which the brain deploys to produce and consume them, and the
computational tasks which they are designed to solve. We illustrate this
indeterminacy with an extension of the sentence-decoding experiment of Pereira
et al. (2018), showing how standard evaluations fail to distinguish between
language processing models which deploy different mechanisms and which are
optimized to solve very different tasks. We conclude by suggesting changes to
the brain decoding paradigm which can support stronger claims of neural
representation.
