120 K. Rastle, M. Brysbaert / Cognitive Psychology 53 (2006) 97–145pr<strong>in</strong>tFeatureAnalysisLetter AnalysisOrthographic <strong>in</strong>putlexiconPhonological outputlexiconGPCtranslationPhoneme systemspeechFig. 1. The DRC model of Coltheart et al. (2001).is <strong>in</strong>fluenced by <strong>in</strong><strong>for</strong>mation (i.e., excitation and <strong>in</strong>hibition) from both letter units and<strong>phonological</strong> units. Phonological units can be activated via feedback from the phonemeunits, which are activated via nonlexical grapheme-to-phoneme (GPC) translation ofthe stimulus. It is this <strong>in</strong>put from the <strong>phonological</strong> assembly procedure to orthographiclexical entries that may provide an avenue <strong>for</strong> simulat<strong>in</strong>g fast <strong>phonological</strong> <strong>prim<strong>in</strong>g</strong> <strong>effects</strong>on lexical decision (Coltheart & Rastle, 1994).On the lexical decision rule described by Coltheart et al. (2001), the ‘‘YES’’ response <strong>in</strong>the lexical decision task is made if (a) the activation of any s<strong>in</strong>gle unit <strong>in</strong> the orthographiclexicon reaches a critical value, A; or (b) if the summed activation of every unit <strong>in</strong> that lexiconreaches a critical value, S (i.e., the ‘‘fast guess’’ mechanism of Gra<strong>in</strong>ger & Jacobs,1996). In a <strong>prim<strong>in</strong>g</strong> situation, there<strong>for</strong>e, primes can affect target lexical decisions by (a)<strong>in</strong>fluenc<strong>in</strong>g the activation of any s<strong>in</strong>gle unit <strong>in</strong> the orthographic lexicon (e.g., the unit represent<strong>in</strong>gthe target); or (b) <strong>in</strong>fluenc<strong>in</strong>g the total activation of the orthographic lexicon.In an ideal world, DRC simulations would thus comprise the follow<strong>in</strong>g approach:Primes would be presented <strong>for</strong> some number of cycles consistent with the 58-ms exposureduration used <strong>in</strong> the experiments; targets would then be presented (without resett<strong>in</strong>g activations<strong>in</strong> the model) until a lexical decision was made; lexical decision latencies would becompared across <strong>phonological</strong> <strong>prim<strong>in</strong>g</strong> and graphemic control <strong>prim<strong>in</strong>g</strong> conditions; andf<strong>in</strong>ally, <strong>prim<strong>in</strong>g</strong> data from the model would be compared with <strong>prim<strong>in</strong>g</strong> data from humanreaders. However, two ma<strong>in</strong> issues preclude this approach. First, the account of masked<strong>prim<strong>in</strong>g</strong> offered by the DRC model is not well developed. Coltheart, Woollams, K<strong>in</strong>oshita,and Perry (1999) conducted a prelim<strong>in</strong>ary exploration of masked <strong>prim<strong>in</strong>g</strong> us<strong>in</strong>g a 15-cycleprime exposure duration followed immediately by presentation of the target. However, theauthors of the DRC model (Coltheart et al., 2001, p. 250) emphasise that the work ofColtheart et al. (1999) provides only a very coarse approximation to an adequate
simulation of masked <strong>prim<strong>in</strong>g</strong>. It is not yet clear whether masked <strong>prim<strong>in</strong>g</strong> should be modelledsimply as residual activation due to a briefly exposed prime, or whether additionalmechanisms are needed to simulate the mask<strong>in</strong>g effect of the target. Second, the visual lexicaldecision rule currently employed by the DRC model is crude (e.g., it allows only twovalues of the ‘‘NO’’ response) and requires detailed re<strong>for</strong>mulation.We wanted to ensure that the success or failure of the model to reproduce the pattern ofhuman data did not reflect these more peripheral characteristics. Thus, we sought to identifythe conditions under which there may be scope <strong>in</strong> the DRC model <strong>for</strong> a simulation of a‘‘fast’’ <strong>phonological</strong> <strong>prim<strong>in</strong>g</strong> effect (i.e., an effect that arises with only limited exposure tothe prime), if, <strong>in</strong>deed, there are any such conditions. Our specific approach was to presentthe 112 <strong>phonological</strong> and 112 graphemic control primes used <strong>in</strong> our experiments to theDRC model under several different parameterizations, and to monitor their <strong>in</strong>fluence overa 100-cycle period on (a) the activation of target units <strong>in</strong> the orthographic lexicon (e.g.,activation of the unit RIP on presentation of RYP and ROP) and (b) the total activationof units <strong>in</strong> the orthographic lexicon. We then compared the <strong>in</strong>fluence of the <strong>phonological</strong>primes and the graphemic controls on these two activation measures at three time po<strong>in</strong>ts <strong>in</strong>the 100-cycle period (25, 50, and 100 cycles). Our reason<strong>in</strong>g was that if <strong>phonological</strong>primes yielded greater target activation or total activation than did graphemic controls,then this would <strong>in</strong>dicate scope <strong>in</strong> the DRC model <strong>for</strong> a simulation of <strong>phonological</strong> <strong>prim<strong>in</strong>g</strong><strong>effects</strong> on visual lexical decision. Further, if this pattern of activations could be obta<strong>in</strong>edearly <strong>in</strong> process<strong>in</strong>g (e.g., 25 cycles), then this would <strong>in</strong>dicate scope <strong>in</strong> the DRC model <strong>for</strong> asimulation of fast <strong>phonological</strong> <strong>prim<strong>in</strong>g</strong> <strong>effects</strong>. We <strong>for</strong>eshadow that such a pattern of activationis obta<strong>in</strong>ed follow<strong>in</strong>g a number of major modifications to the model. Our f<strong>in</strong>al simulationthus explores the implications of these modifications <strong>for</strong> other aspects of themodel’s per<strong>for</strong>mance.6.2. Simulation 1K. Rastle, M. Brysbaert / Cognitive Psychology 53 (2006) 97–145 121Prelim<strong>in</strong>ary study of the DRC model yielded optimism regard<strong>in</strong>g its ability to capturefast <strong>phonological</strong> <strong>prim<strong>in</strong>g</strong> <strong>effects</strong> on visual lexical decision: Coltheart and Rastle(1994) demonstrated that the orthographic unit COAT was activated more stronglyand more rapidly by presentation of the pseudohomophone KOAT than by presentationof the graphemic control stimulus FOAT. However, Coltheart and Rastle’s simulationstrategy dur<strong>in</strong>g the development of the DRC model was less rigid than would bedesirable at the current stage of evaluation. They wrote, ‘‘These simulations are doneby vary<strong>in</strong>g the parameters of the model until a parameter set is found (if one can befound) under which the model’s behavior exhibits the effect that human subjects havebeen found to exhibit.’’ (p. 1200). A more desirable strategy is, of course, to ascerta<strong>in</strong>whether a s<strong>in</strong>gle set of parameters can simulate a wide range of human data. This isexactly the approach advocated by Coltheart et al. (2001): ‘‘...we would <strong>in</strong> any casenot be <strong>in</strong>terested <strong>in</strong> an approach <strong>in</strong> which each set of human data is simulated witha different set of DRC parameters. Our aim <strong>in</strong>stead...has been to f<strong>in</strong>d just one setof parameters that, unchanged, simulate a wide variety of sets of human data.’’(p. 218). Thus, the purpose of Simulation 1 was to discover whether there is scope<strong>in</strong> the DRC model to simulate a fast <strong>phonological</strong> <strong>prim<strong>in</strong>g</strong> effect, when the model iscontrolled by the set of parameters already shown to simulate other benchmark <strong>effects</strong><strong>in</strong> visual word recognition.
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