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In the present study, the focus lies on accounting for data that reportedly capture the under- lying organisation of concepts in semantic memory. We seek to explore the representations participants use by looking at publicly available datasets of semantic priming (Hutchison et al., 2013) (in the present Chapter) andlexical decisions(in Chapter 4). Semantic priming refers to the facilitative effect of a certain word on processing a semantically related word. For example, processing the word claw will facilitate the processing of the wordcat but processing calendar would not. This facilitation manifests as a faster reaction time to say the wordcathaving seen claw (µ =493.02ms, SE=19.41) compared to when having seen

the word calendar (µ=572.04ms, SE=32.36).†Because –apparent– semantic relatedness

influences this non-semantic task (reading a word aloud) researchers suggest that this effect is due to the underlying organisation of semantic memory.

Subsequent literature has corroborated these early results (Neely, 1977, 1991, for early reviews) and (Hutchison, 2003a; Lucas, 2000, for more recent meta-analyses) highlighting the importance of semantic priming in understanding the structure of semantic memory. Most of the experiments have focused on finding the kind of relations between the prime and the target that are more likely to yield semantic priming effects. For example, researchers have focused on whether the observed effects are due to semantic overlap or association strength between the prime and the target (Chiarello, Burgess, Richards & Pollock, 1990; Moss, Ostrin, Tyler & Marslen-Wilson, 1995; Williams, 1994), whether mediated relations (e.g., see §2.2.1) can yield semantic priming or whether the effects depend on the input modality (Moss, McCormick & Tyler, 1997; Ostrin & Tyler, 1993; Zwitserlood & Schriefers, 1995). In his meta-analysis, Hutchison (2003a) summarises the main conclusions of these studies highlighting that when care has been taken in choosing the type of association between the prime and the target (e.g., synonyms, antonyms) both semantic and associative relations can yield semantic priming effects, as well as that both cross-modal and mediated priming are also possible.

One problem identified early on (Neely, 1977) was that during the experiments participants notice the relations between the word pairs and form conscious strategies that enable them to predict the upcoming words. This anticipation problem masks the overall priming effect as the faster response to the target would be explained from the participants’ predictive strategies rather than from the structure of the semantic memory. There are two main ways of gauging automaticity in such experiments. Firstly, the researcher generates more balanced lists where foils are dispersed alongside critical trials so that the relatedness of some words is not that obvious. Secondly, following Posner & Snyder (1975) and Becker (1980) the researcher can lower the time offset between the prime and the target word. The reasoning behind this decision is that such strategic shifts in attention require time to occur, hence by lowering this

threshold we decrease the chances of participants generating expectancies. While there is no agreed threshold distinguishing between conscious strategies and unconscious processing (Hutchison, Neely & Johnson, 2001), probably because it depends on other variables (e.g., the nature of the task), in the study described below we focus on those trials in the spp where the onset of the target word was 250ms after the start of the trial.³

3.2.1

Prior Work

Very few computational approaches have been attempted to link distributional vectors to semantic priming effects. Lund et al. (1995) correlated the Euclidean distance (Table 2.4) between word vectors generated by hal (§2.2.2) with previously reported priming effects (Chiarello et al., 1990; Shelton & Martin, 1992), as well as from one novel experiment. While hal was unable to model associative relations, it was successful at capturing the priming effects of semantic relations. Given the discussion in §2.2 above, we can attribute this preference to the paradigmatic nature of semantic and taxonomic relations (see, Ex. 2.2.3). Moreover, Lund, Burgess & Audet (1996) attempted a more rigorous examination, separating pure semantic, semantic + associative and pure associative relations. They found again that hal was able to predict similar priming effects for the semantic and semantic + associative relations but not for the purely associative relation. Subsequent studies have focused on whether relations that exert semantic priming are recoverable in dsms. In one instance, Livesay & Burgess (1998) were unable to show mediated priming effects such as those shown in Balota & Lorch (1986) in hal. Conversely, Chwilla & Kolk (2002) were able to demonstrate mediated priming effects in lsa. We attribute this dissociation, again, in the models’ specifications; the dimensionality reduction step in lsa brings forward such higher order co-occurrences whereas hal is unable to account for a similar effect.

The first study to systematically compare the predictions of different dsms with semantic priming effects demonstrated under a variety of conditions comes from Jones et al. (2006). In this study, Jones et al. (2006) train lsa, hal and beagle models using the tasa corpus and examine the effects predicted by the cosine differences in the word vectors against those obtained in the behavioural experiments. The authors report simulation results from nine different experiments attempting to distinguish between semantic and associative relations. In short, they look at the predictions between semantic and associative relations (Chiarello et al., 1990; Ferrand & New, 2003), more fine-grained semantic relations (e.g., script, collocate or instrument) (Moss et al., 1995; Williams, 1996) and mediated priming (Balota & Lorch,

³This number refers to the Stimulus Onset Asynchrony (soa), that is, the time from the start of the trial until the onset of the target word. The inter-stimulus interval, i.e., the time gap between the prime and the target was 50ms.

1986; de Groot, 1983; McKoon & Ratcliff, 1992; McNamara, 1992; McNamara & Altarriba, 1988). The authors found that beagle was able to make the best predictions overall, as either hal or lsa underestimated the priming effect of the associatively and semantically related words, respectively. Conversely, the integrative nature of beagle combining two streams of information (paradigmatic and syntagmatic) into a single representation makes it more successful in modelling these relations.

One shortcoming of this study noted by Hutchison, Balota, Cortese & Watson (2008) is that it ignores performance on the item level. That is, Jones et al. (2006) comparedonlythe priming effects from the studies with the ones obtained by the computational models. The problem is that considering the size of the datasets, even one pair would be able to skew the results. In their study, Hutchison et al. (2008) using hierarchical regression introduced lsa priming scores (similarity of an unrelated pair - similarity of a related pair) as a factor to the model to explain the variance in standardised scores of priming effects. lsa scores did not enter in the hierarchical regression model as a significant factor, nor did they correlate significantly with priming effects, but lsa managed to predict a priming effect found in the data as related words had significantly higher similarity values than unrelated ones. Although these results might suggest that dsms are unable to capture the structure of semantic memory, we note that the above study was based solely on the examination of a single lsa model trained on 300 dimensions.

Using the estimates of a single instantiation of a single model might seem all too biasing against lsa as the results are too dependent on the training parameters. Indeed, both Hutchison et al. (2008) and Jones et al. (2006) did not train the lsa models themselves but used the estimates derived by Landauer & Dumais (1997).4 The appeal of using such pre-trained vectors for this task, on the one hand, ensures that these estimates are ‘proven’ and not a result of spurious correlations. On the contrary, if the choice of the model or its parameters are dependent on the present task, it follows that we need to tune the model parameters accordingly.

Two more studies appeared recently (Ettinger & Linzen, 2016; Mandera, Keuleers & Brysbaert, 2017) that link dsms with priming effects. Ettinger & Linzen (2016) train several neural embeddings models on a few configurations of parameters until they maximise the fit between the computational model estimates and the priming effects obtained in the Semantic Priming Project. The authors equate the two tasks (naming and lexical decision) and do not focus on the results of the former as the dsms do not provide a good fit there. Similar to Hutchison et al. (2008) the dsms do not seem to improve the fit in comparison to a baseline model containing only word frequency as its covariate. Mandera et al. (2017), on the other

hand, looked at directly predicting the rts from the Semantic Priming Project. Using lsa, hal and neural embedding estimates, they report a higher fit for a baseline model that contained

word frequency, thenumber of orthographic neighbours and word length as its covariates (R2 ₌ 0.31). Adding dsm estimates improved the model by a small amount (R2 ₌ 0.33),

however, performing rigorous analyses using Bayes factors it turned out that the model was significantly better. The relatively high results obtained by Mandera et al. (2017) might be explained as a result ofoverfitting. They reportR2values coming from the same set used to

find the best parameters for the models. The problem with this approach is that there is no independent test set used for evaluation. We can conjecture from this that is it possible that the models found a ‘good’ solution regarding the fit but meaningless otherwise.

The aim of this study, therefore, is to find the best representations capturing elements of the semantic memory using vsms as well as other semantic baselines (described in §3.3.1). Contrary to Ettinger & Linzen (2016) we train several different models on the dataset on many parameter settings. Moreover, we improve on Mandera et al. (2017), again by training more models on more settings as well as following a more rigorous data splitting and model selection procedure. The parameter sets provided by each of these models gives endless possibilities as to what might be the optimal tuning for predicting semantic priming effects. In §3.3.1 we outline a general methodology in the hope of isolating some key parameters (such as sentential and vector size, encoding of syntactic relations) which influence the fit of the models in approximating semantic memory representations.

3.2.2

Impact on models of semantic priming

The issue of whether semantic priming is the result of overlapping semantic representations or simply because of some learnt association between the words has had an impact on computa- tional approaches, too. Take the spreading activation theory (Anderson, 1983; Collins & Loftus, 1975; Quillian, 1967, 1968), for example; within this framework, semantic memory is assumed to consist of concept-nodes which are connected to either other concepts or semantic features viarelationallinks. For instance, the conceptscatandanimalare connected via alabelledlink, whereascatandfur are connected via ahasvertex. According to the spreading activation account, when the node of the prime is activated, the activation is spread along its pathways, with which this node is connected. The short reaction time, therefore, is accounted for by the residual partial activation of the target word. The strength of the priming effect is a function of the number of links ‘exiting’ the prime node and of the frequency of the connection between the word pairs.

Semantic overlap accounts, conversely, assume that words can be represented as sets of semantic features (McRae et al., 2005) and that the size of the priming effect grows with the size

of the intersection between the two sets. Researchers in this domain (Cree et al., 1999; Plaut, 1995) have used attractor neural networks to model semantic priming effects by observing that the network requires fewer steps to activate the target word given a related prime than an unrelated one. Given these two paradigms, it is not exactly clear how distributional models of semantics could fit in. While dsms can provide us with a notion of semantic distance between two words they lack concrete semantic features, assumed by both theories, as well as links to a subset of words (they are associated withallthe words in the corpus).

We have already mentioned attractor networks in §1.3.1. Attractor networks can model

memory retrievalprocesses by performingmultivariate linear regressioncomputing the func- tion f(x) =y, wherexis some probe, e.g., a phonological representation, andyis the memory

trace (e.g., the semantic representation). Attractor networks model the memory retrieval

process by computing this function in aseriesof discrete steps. During the first few steps, the input pattern is presented to the network computing the output pattern. In the last few steps, activation continues to propagate to the output layer based on the activations of the previous steps. Concretely, letxbe some input phonological pattern of a word andybe its distributed semantic representation. Also, letTbe the total number of steps required for ˆy(the prediction) to settle intoy(the teacher pattern). The way the network computesf(x) =ˆyis by minimising

the distance of the prediction to the target as we approachT (i.e., limt→Tcos(ˆy,y) =1).5

Given this formulation, it should not be surprising thatsemantic primingnaturally arises in these models. Consider that we feed the pattern associated with a particularprimeto the network, and the output layer has settled on the corresponding semantic representation. At

t=1, when presenting the target, cos(y,ˆ y)will be higher if the target is related to the prime.

Therefore, it should take fewer steps for the network to settle on a stable pattern in the output layer when presenting a related target than when presenting an unrelated one. Within this framework, semantic priming arises because, given a prime word, the semantic memory activates similar features as to when it needs to parse the target word.

Attractor neural networks can accommodate distributional semantic vectors in their output layer without any alterations in their structure. In this context, the semantic vectors are some memory trace (for more on this idea see also, Kintsch & Mangalath, 2011) that is activated given aprime. On the other hand, it is far from trivial to extendspreading activation theoryto accommodate the above representations. Spreading activation assumes that (a) concepts have a limited number of connections to the other concepts and (b) they are also indirectly connected by mutual semantic features. While we can simulate (a) in this context, in Chapter 4 we discuss ways of extracting semantic neighbourhoods from these representations, it is not clear how

5In reality, attractor neural networks use a cost function such as the mean squared error between the predicted and golden outputs. However, it is easy to show that as the error of the network gets smaller, the cosine similarity between the predicted and the golden output is maximised.

we could make (b) work. Distributional models of semantics do not encode concrete features as WordNet does, as there is no single element in their vector that encodes, say,animacy, or the type of the relation to the feature. While we can extract such information from the word vectors (Demeester, Rocktäschel & Riedel, 2016; Rogers & McClelland, 2004; Rumelhart & Todd, 1993, as well as §7.2), the output still needs a substantial amount of processing to be compatible with spreading activation theory.