Word familiarity and predictive context facilitate visual word processing, leading to faster recognition times and reduced neuronal responses. Previously, models with and without top-down connections, including lexical-semantic, pre-lexical (e.g., orthographic/phonological), and visual processing levels were successful in accounting for these facilitation effects. Here we systematically assessed context-based facilitation with a repetition priming task and explicitly dissociated pre-lexical and lexical processing levels using a pseudoword (PW) familiarization procedure. Experiment 1 investigated the temporal dynamics of neuronal facilitation effects with magnetoencephalography (MEG; N = 38 human participants), while experiment 2 assessed behavioral facilitation effects ( N = 24 human participants). Across all stimulus conditions, MEG demonstrated context-based facilitation across multiple time windows starting at 100 ms, in occipital brain areas. This finding indicates context-based facilitation at an early visual processing level. In both experiments, we furthermore found an interaction of context and lexical familiarity, such that stimuli with associated meaning showed the strongest context-dependent facilitation in brain activation and behavior. Using MEG, this facilitation effect could be localized to the left anterior temporal lobe at around 400 ms, indicating within-level (i.e., exclusively lexical-semantic) facilitation but no top-down effects on earlier processing stages. Increased pre-lexical familiarity (in PWs familiarized utilizing training) did not enhance or reduce context effects significantly. We conclude that context-based facilitation is achieved within visual and lexical processing levels. Finally, by testing alternative hypotheses derived from mechanistic accounts of repetition suppression, we suggest that the facilitatory context effects found here are implemented using a predictive coding mechanism.

To characterize the functional role of the left-ventral occipito-temporal cortex (lvOT) during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filtering out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging described in the literature. In a second evaluation, we empirically demonstrate that quantitative LCM simulations predict lvOT activation better than alternative models across three functional magnetic resonance imaging studies. We found that word-likeness, assumed as input into a lexical categorization process, is represented posteriorly to lvOT, whereas a dichotomous word/non-word output of the LCM could be localized to the downstream frontal brain regions. Finally, training the process of lexical categorization resulted in more efficient reading. In sum, we propose that word recognition in the ventral visual stream involves word-likeness extraction followed by lexical categorization before one can access word meaning.

To a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre‐activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading—visual, orthographic, phonological, and/or lexical‐semantic—contribute to context‐dependent facilitation. To investigate in detail which linguistic representations are pre‐activated in a predictive context and how they affect subsequent stimulus processing, we combined a well‐controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical‐semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical‐semantic representations in the interval before processing the predicted stimulus, suggesting selective pre‐activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical‐semantic representations when processing predictable in contrast to unpredictable letter strings, and pre‐activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre‐activation did not result in “explaining away” predictable stimulus features, but rather in a “sharpening” of brain responses involved in word processing.

To characterize the role of the left ventral occipito-temporal cortex (lvOT) during visual word recognition in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM) according to which lvOT categorizes perceived letter strings into words or non-words. LCM simulations successfully replicate nine benchmark results from human functional brain imaging. Empirically, using functional magnetic resonance imaging and electroencephalography, we demonstrate that quantitative LCM simulations predict lvOT activation and brain activation at an expected time window, respectively. In addition, we found that word-likeness, which is assumed as input to LCM, is represented posterior to lvOT and before the lexical categorization. In contrast, a dichotomous word/non-word contrast, which is the assumed as output of the LCM, could be localized to upstream frontal brain regions. Thus, we propose a ventral-visual-stream processing framework for visual word recognition involving word-likeness extraction followed by lexical categorization, prior to the extraction of word meaning.

Visual word recognition is facilitated by word knowledge (i.e., word familiarity) and predictive context, as reflected in faster reading times and reduced neuronal activation for highly familiar or predictable words. Previous studies could not dissociate whether knowledge-and context-based facilitation requires semantic knowledge or can also stem from prelexical sources of information.Here, we experimentally separate prelexical (i.e., orthographic/phonological) and semantic knowledge in two repetition priming experiments, to investigate their role for knowledge-and context-based facilitation. Experiment 1 investigates repetition suppression effects (i.e., reduced activation for predictable stimuli) in magnetoencephalographic brain responses of human participants (N=38) and Experiment 2 uses response times to investigate behavioral priming effects (N=24). To disentangle prelexical and semantic knowledge, we realized a pseudoword familiarization procedure in both experiments and contrasted familiarized pseudowords with novel pseudowords (unfamiliar, no semantic knowledge) and words (semantics available). In Experiment 2, one further set of pseudowords was additionally associated with semantic information (i.e., objects). We found, in both experiments, a general context effect for all letter strings, which was specifically enhanced when semantic information was available. A knowledge effect for pseudowords was found (familiarized vs. novel pseudowords) but prelexical (i.e., orthographic/phonological) knowledge alone did not enhance context effects. We conclude that knowledge-and context-based facilitation in visual word recognition can be achieved without semantic information processing, i.e., exclusively on the basis of prelexical perceptual knowledge. Semantic knowledge, however, drastically enhances context-based facilitation. Significance StatementThe goal of reading is the extraction of meaning from script. This highly automatized process relies on facilitation based on word familiarity and text context. Here we use repetition priming to show that context-based facilitation is increased when semantic knowledge is present. This was demonstrated by enhanced context effects for letter strings with semantic associations. Still, earlier context effects (~80 ms) and orthographic knowledge effects were found irrespective of semantic processing. Our findings highlight the stronger role of semantic knowledge for achieving facilitated visual word recognition in contrast to semantic-free knowledge. Our findings suggest predictive coding as a potential mechanism that underlies efficient visual word recognition.