Reading research has long endorsed the view that words are processed strictly one by one. The primary empirical test of this notion is the search for effects from upcoming words on readers' eye movements during sentence reading. Here we argue that no conclusions can be drawn from the absence of such effects, and that the serial versus parallel processing debate cannot be resolved without treading beyond the methodological scope of tracking eye movements. Recent considerations of how the brain organizes linguistic input have sparked key predictions in-and outside the realm of text reading, with ensuing research revealing phenomena that complicate the serial processing perspective. A case is made for parallelism, along with new methods to infer the cognitive architecture driving reading. The Serial Processing Ideal It is evident that when we read, we adopt a largely serial strategy: texts are processed from left to right and from the top downwards, with the eyes' fixation jumping from one word to the next. Should we cease to impose seriality on the linguistic plane that bombards our retina with so many words at once, we would squander the canonical order of words and so a key ingredient of linguistic communication. Moreover, simultaneous processing of multiple words might conceivably incite word-to-word interference; for instance, through confusion about which letters belong to which word. For these reasons we can safely claim that during text reading we should ideally be able to confine our attention (see Glossary) to single words. But to what extent can we live up to this ideal? In the course towards a full understanding of the reading system, the question of whether readers process multiple words simultaneously has retained much prominence. Precisely a decade ago Reichle, Liversedge, Pollatsek, and Rayner expressed in this journal the opinion that parallel word processing is implausible [1]. At the time, it had already become clear that serial and parallel processing frameworks were equally capable of accounting for eye movement behavior during text reading [2-4]. Therefore, Reichle et al.'s argument for serial processing was not necessarily data driven (although later empirical work was deemed to be in support of serial processing; see the next section), but rather one of parsimony: a serial processing system recognizes words in the intended order and does not mix-up information across words, given that only one word is attended at any time. By contrast, a parallel processing system might recognize words out of order, and 'if one were to simultaneously activate orthographic units for two words, this would produce noisy output corresponding to neither word' ([1], see p. 117). A decade later, we can but agree that a serial processing system would have the simplest time reading, yet recent research has yielded various reasons to believe that the reading process is in fact not so simple. In this Opinion article we show that the serial processing assumption holds only if the word recognition process is treated as a black box-which has been a key aspect of both serial and parallel models of eye movements in text reading. The latest theoretical campaigns, marked by the implementation of word recognition mechanisms in models of text Highlights Whether words are processed serially or in parallel continues to be a major debate in reading research. In the past decade, many researchers have embraced the seriality assumption. This Opinion article shows that empirical findings were incorrectly thought to falsify parallel processing and that the seriality assumption works only if one treats the word recognition process as a black box. The newest model of text reading comprises true word recognition mechanisms , causing it to spark fresh predictions. Successful tests of these predictions cannot be harmonized with serial processing. Reading research is ready for a paradigm shift, both methodologically (treading beyond the measurement of eye movements in sentence reading) and theoretically (abandoning serial processing in favor of parallel processing).