Factors underlying masked priming effects in competitive network models of visual word recognition

Many models of reading assume that visual word recognition is driven by a competitive activation process. In these models, the effect of a masked prime is to manipulate the competitive process by shifting the balance between the target stimulus and its competitors. Computational modelling makes it possible to explore these effects in detail. To this end, the present chapter develops a precise description of the factors underlying masked priming effects in a specific competitive network model the interactive activation (IA) model (McClelland & Rumelhart, 1981). Because the resulting expression is formulated in terms of standard psycholinguistic variables, the analysis presented here helps to bridge the divide between purely computational accounts and verbal theories of visual word recognition and priming. This approach is assisted by a framework for partitioning the set of competitors of a target stimulus, and a graphical technique for depicting the course of the competitive process in competitive network models of visual word recognition. The development of simple regression models that are able to fully capture the effects of priming within a complex (interactive, nonlinear, and dynamic) network model is a valuable outcome that has broader implications for the computational modelling of cognition. The analysis of priming effects in the model also leads to a number of predictions that can be tested empirically. The use of masked priming techniques to investigate basic questions in visual word recognition has become increasingly common in recent years (e.g., Andrews, 1996; Bodner & Masson, 1997; Bowers, Vigliocco, & Hans, 1998; Castles, C. Davis, & Letcher (1999); C. Davis & Forster, 1994; C. J. Davis, 2001; de Moor & Brysbeart, 2000; Forster, 1998; Forster & C. Davis, 1984, 1991; Grainger & Jacobs, 1993, 1994; Hinton, Liversedge, & Underwood, 1998; Humphreys, Besner, & Quinlan, 1988; Humphreys, Evett, & Quinlan, 1990; Kinoshita, 2000; Lukatela & Turvey, 1990; Perea & Rosa, 2000; Rastle, M. Davis, Marslen-Wilson, & Tyler, 2000; Segui & Grainger, 1990; Sereno, 1991; Van Heuven, Dijkstra, Grainger, & Schriefers, 2001). This methodological trend intensifies the need for detailed theoretical accounts of masked priming. The present chapter analyses the account of masked priming effects offered by competitive network models of visual word recognition. The chief goal is not to report simulations of specific experiments, but rather to develop a precise description of the factors underlying masked priming effects in a competitive network model. As such, most of the chapter is concerned with the development of a general formula for predicting masked priming effects in a specific competitive network model the interactive activation (IA) model (McClelland & Rumelhart, 1981). Because the resulting expression is formulated in terms of standard psycholinguistic variables, the analysis presented here helps to bridge the divide between purely computational accounts and verbal theories of visual word recognition and priming. The analysis of masked priming effects in the model will be developed in stages, as a function of the nature of the relationship between the prime and the target. Thus, the analysis commences with the relatively straightforward case of identity primes, and then proceeds to different types of orthographic form primes: partial word form primes, Masked priming effects in competitive network models 2 nonword form primes and word form primes. The main emphasis throughout is on the development of a precise description of priming effects in the IA model, although the correspondence between these predictions and empirical findings is also discussed, where relevant. A number of novel predictions, which await experimental investigation, will also be discussed. Prior to this analysis, I make some brief introductory remarks concerning competitive network models in general, and the IA model in particular, and describe the assumptions that are required to enable this model to simulate masked priming in the lexical decision task. COMPETITIVE NETWORK MODELS OF VISUAL WORD RECOGNITION Many computational models of visual word recognition embody the hypothesis that word recognition is driven by a competitive process (e.g., Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; C. J. Davis, 1999; Grainger & Jacobs, 1996; McClelland & Rumelhart, 1981). According to these models, presentation of a target word results in the activation of not only the target’s representation, but also the representations of orthographically similar words. Competition amongst these representations is assumed to result in the selection of the target and the suppression of competing words. Grainger and Jacobs (1993) refer to this hypothesis as the lexical inhibition hypothesis, which they contrast with the lexical independence hypothesis. According to the latter hypothesis, the recognition process selects the learned representation that best matches the input stimulus by employing a relative goodness rule, rather than a competitive process. Some evidence for the lexical inhibition hypothesis has been assembled. Several experiments have investigated this issue by manipulating neighbourhood frequency in particular, whether or not a word has higher frequency neighbours (where two words of equal length are orthographic neighbours, following the definition of Coltheart, Davelaar, Jonasson, & Besner, 1977, if they are identical save for the substitution of one letter at a single letter position). According to competitive models, the presence of a higher frequency neighbour should inhibit target recognition. In keeping with this prediction, inhibitory effects of neighbourhood frequency have often been observed in lexical decision (Carreiras, Perea, & Grainger, 1997; Grainger, O’Regan, Jacobs, & Segui, 1989; Grainger, 1990; Grainger & Jacobs, 1996; Huntsman & Lima, 1996; Perea & Pollatsek, 1998), although some experiments have failed to replicate this effect (Forster & Shen, 1996; Sears, Hino, & Lupker, 1995; Siakaluk, Sears, & Lupker, 2002). Inhibitory effects of neighbourhood frequency have also been observed in perceptual identification (Carreiras et al., 1997; Grainger & Jacobs, 1996; Grainger & Segui, 1990; although see Sears, Lupker, & Hino, 1999) and in the Reicher-Wheeler task (Grainger & Jacobs, 1994; Paap, Johansen, Chun, & Vonnahme, 2000). These effects are consistent with the hypothesis that selection of a specific word representation involves a competitive process. Activity trajectories One way to visualise this competitive process is to plot the activity of the target representation and the total activity of competing representations on separate axes. Each point plotted in this way represents the state of the competition at a particular instant; joining these points results in an activity trajectory that illustrates the competitive process associated with recognition of a particular target. Figure 1 shows an example of two such activity trajectories. The variable plotted on the bottom axis – total competitor activity – is calculated by summing the activities across each of the Masked priming effects in competitive network models 3 non-target nodes with positive activities. Although these trajectories are representative of competitive network models in general, the particular values are derived from a specific competitive network model – the IA model of McClelland and Rumelhart (1981). The two trajectories represent the words EXIT and ANEW respectively. Both of these words have a single orthographic neighbour, but the frequency of these neighbours differs enormously: EXIT’s neighbour is the low frequency word EDIT, whereas the neighbour of ANEW is the very high frequency word KNEW. Consider first the trajectory for the word EXIT. The activity of the target word node is assumed to start from a resting level that is slightly below zero, where resting activity is a function of log word frequency. As can be seen, the initial direction of movement is a straight vertical line, indicating that the target is able to become slightly active before any of its competitors start to activate. Following this, the trajectory moves upwards and to the right, reflecting the simultaneous rise in the activities of both the target and its competitors; this is the period of greatest competition between the target and its neighbour (EDIT). Once the target node has reached a certain activity (about .45 in this case), it starts to actively suppress its competitors – that is, the lateral inhibitory signals that the EXIT node sends to the EDIT node overwhelm the positive bottom-up input signals received by the latter node. This causes the trajectory to shift back to the left, until the activity of competing word nodes has been completely suppressed. Target node activity then reaches equilibrium (at .76).