Local Associations and Semantic Ties in Overt and Masked Semantic Priming
Authors: Andrea Nadalini, Marco Marelli, Roberto Bottini, Davide Crepaldi (SISSA, Università di Milano Bicocca, CIMEC, 2018)
Topic: Distributional Semantics

Abstract
Distributional semantic models English. Distributional semantic models (DSM) are widely used in psycholinguistic research to automatically assess the degree of semantic relatedness between words. Model estimates strongly correlate with human similarity judgements and offer a tool to successfully predict a wide range of language-related phenomena. In the present study, we compare the state-of-art model with pointwise mutual information (PMI), a measure of local association between words based on their surface cooccurrence. In particular, we test how the two indexes perform on a dataset of sematic priming data, showing how PMI outperforms DSM in the fit to the behavioral data. According to our result, what has been traditionally thought of as semantic effects may mostly rely on local associations based on word cooccurrence.

Introduction
Over the past two decades, computational semantics has made a lot of progress in the strive for developing techniques that are able to provide human-like estimates of the semantic relatedness between lexical items. Distributional Semantic Models (DSM; Baroni and Lenci, 2010) assume that it is possible to represent lexical meaning based on statistical analyses of the way words are used in large text corpora. Words are modeled as vectors and populate a highdimensionsional space where similar words tend to cluster together. Meaning relatedness between two words corresponds to the proximity of their vectors; for example, one can approximate relatedness as the cosine of the angle formed by two word-vectors: cosθ = !∙! | ! |∙| ! | DSMs have been proposed as a psychologically plausible models of semantic memory, with particular emphasis on how meaning representations are achieved and structured (e.g. LSA, Landauer and Dumais, 1997; HAL, Lund and Burgess, 1996). So, they can be pitted against human behavior, in search for psychological validation of this modeling. For example, the model’s estimates have been used to make reliable predictions about the processing time associated with the stimuli (Baroni et al., 2014; Mandera et al., 2017). The technique most commonly used to explore semantic processing is the priming paradigm (McNamara, 2005), according to which the recognition of a given word (the target) is easier if preceded by a related word (the prime; e.g., cat–dog). Interestingly, facilitation can be observed both when the prime word is fully visible and when it is kept outside of participants’ awareness through visual masking (Forster and Davis, 1984; de Wit and Kinoshita, 2015). In this technique, the prime stimulus is displayed shortly, embedded between a forward and a backward string (Figure 1). Figure 1: exemplar trial in a masked priming experiment. The prime stimulus is briefly presented (<= 50 ms), between the two masks, before the onset of the target stimulus. Beside words’ distribution, one can be interested in the local association strength between lexical items, starting from the assumption that two words that are often used close to each other, tend to become associated. Yet, a given pair may be often attested only because the two components are in turn highly frequent. Therefore, raw frequency counts are often transformed into some kinds of association measure which can determine if the pair is attested above chance (Evert, 2008). A common method is to compute pointwise mutual information (PMI) between two words, according to the formula: PMI(w1,w2) = log2 !(!₁,!₂) !(!₁)!(!₂) where p(w1,w2) corresponds to the probability of the word pair, while p(w1) and p(w2) to the individual probabilities of the two components (Church and Hanks, 1990). PMI has been used to model a wide range of psycholinguistics phenomena, from similarity judgements (Recchia and Jones, 2009) to reading speed (Ellis and Simpson-Vlach, 2009). Moreover, PMI has also been shown to successfully generalize to non-linguistic fields as epistemology and psychology of reasoning (Tentori et al., 2014). On the other hand, PMI has the limit of over-estimating the importance of rare items (Manning and Schütze, 1999). Despite many DSMs use measures of local association between words like PMI to build contingency matrices, the information conveyed by two similar word-vectors is different from the information conveyed by two highly recurrent words. Cosine similarity is based on “higher order” cooccurrences: two words are similar in the way they are used together with all the other words in the vocabulary. Local measures as PMI instead rely only on the effective co-presence of two given words. Two synonyms like the words car and automobile are not likely to often appear close to each other in a given text, still they represent the same referent, and therefore expected to be used in similar contexts. Based on these considerations, PMI and DSMs can be pitted against human behavior, in search for psychological validation of this modeling. In particular, we tested how PMI and cosine proximity predicts priming in a set of data encompassing different prime visibility conditions (masked vs unmasked) and prime durations (33, 50, 200, 1200 ms).

Conclusion
Thanks to the help of computational methods, we provided new insights on the nature of the processing that supports semantic priming. Overall, effects seem to be primarily driven by local word associations as tracked by Pointwise Mutual Information—when semantic priming emerged, PMI effects were consistently stronger and more solid than those related to DSM estimates. This would be in line with previous literature suggesting that the behavior of the human cognitive system may be effectively described by Information Theory principles. For example, Paperno and colleagues (Paperno et al., 2014) showed that PMI is a significant predictor of human judgements of word co–occurrence. The results from masked priming offer another important insight—some kind of prime visibility may be required for semantic/associative priming to emerge. Other studies have shown genuine semantic effects with subliminally presented stimuli (Bottini et al., 2016). However, they typically used words from small/closed classes (e.g., spatial words, planet names). Conversely, we drew stimuli across the lexicon, and sampled form very large category such as animals and tools; this may point to an effect of target predictability. In general, our data cast some doubts on a wide–across–the–lexicon processing of semantic information outside of awareness.