Running head: Orthographic Neighbourhood, Lexical Access, and Semantic Categorization THE TURPLE EFFECT IS MODULATED BY BASEWORD FREQUENCY:

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1 In Press: Psychonomic Bulletin and Review Running head: Orthographic Neighbourhood, Lexical Access, and Semantic Categorization THE TURPLE EFFECT IS MODULATED BY BASEWORD FREQUENCY: IMPLICATIONS FOR MODELS OF LEXICAL AND SEMANTIC ACCESS Claudio Mulatti 1, Veronica Cembrani 2, Francesca Peressotti 1, & Remo Job 2 1 DPSS Università degli Studi di Padova, Italy 2 DiSCoF Università degli Studi di Trento, Italy Word count: 3982 Corresponding Author: Claudio Mulatti Dipartimento di Psicologia dello Sviluppo e della Socializzazione Università degli Studi di Padova Via Venezia, Padova ITALIA tel: claudio.mulatti@unipd.it

2 Abstract People asked to classify whether or not visually presented words belong to the category of animals respond to nonwords derived from animal names more slowly than to nonwords derived from non-animal names (the turple effect; Forster & Hector, 2002; Forster, 2006). In the present paper we show that the turple effect is modulated by the frequency of the animal names the nonwords are derived from: from high frequency animal names are rejected faster than nonwords derived from low frequency animal names. The implications of this result for two approaches to the modelling of lexical and semantic access are discussed

3 The aim of the studies reported here is to evaluate two models of lexical and semantic access from printed words, Pecher, Zeelenberg, & Wagenmakers s (2005) model (PZW) and the Links model (Forster & Hector, 2002; Forster, 2006). Those two models can be seen as instances of two classes of models, cascaded models (e.g. Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001) and staged models (e.g. Morton, 1969) respectively. In models operating in a cascaded fashion, activation in an early module flows on continuously to later modules, i.e. there are not thresholds between modules. In staged models, processing at each level must be completed before activation passes to the next level. Which of these two approaches to modelling cognition best fits the empirical evidence is a crucial issue in theories of word processing (e.g. Navarrete & Costa, 2005). To achieve our goal we made use of the turple effect (Forster & Hector, 2002; Forster, 2006). In a semantic decision task, with animals as exemplars, nonwords derived from animal names are rejected more slowly than both nonwords derived from nonexemplar names and nonwords without neighbours. Responses to the latter two types of stimuli do not differ. How cascaded models account for the turple effect. Carreiras, Perea, & Grainger (1997) proposed a cascaded model of semantic decision (CPG) as an extension to the MROM model of visual word recognition (Grainger & Jacobs, 1996). Within the CPG model, decisions about whether the stimuli belong to the category of animals are based on the activation level in an animalness feature unit. If the activation level reaches a given criterion, a yes decision is generated. In contrast, no decisions are produced when both a yes decision has not been made and a given amount of time has elapsed, i.e. when a deadline expires. However, such a model is unable to account for the turple effect, because no responses are based on a deadline irrespective of the type of stimulus (nonexemplar words, nonwords derived from animal names, or nonwords derived from non-animal names). This runs against the empirical evidence showing that the neighbours of nonwords are relevant to the decision. The CPG model comprises a mechanism that solves this problem: If low levels of activation (i.e. below the criterion) are detected in the - 3 -

4 animalness unit, the deadline is extended because the stimulus in input could be a low frequency animal name. Through their basewords, nonwords such as turple activate the animalness units enough to cause an extension of the deadline, although not to the criterion. This extension leads to slower responses. The problem with this model is that it does not account for a well known effect: high frequency nonexemplar words are rejected faster than low frequency nonexemplar words (e.g. Monsell, Doyle, & Haggard, 1989). If no decisions are generated when a deadline expires, as posited by the CPG model, the decision time would be the same for all nonexemplar words, i.e. the frequency of the nonexemplar words should be irrelevant. Pecher et al. (2005) proposed a cascaded model (PZW) that accounts for the frequency effect of nonexemplar words. The PZW model could be described in the following way: while it has no deadline it does have two units, one that collects evidence for a yes decision, and one that collects evidence for a no decision (footnote 1). If activation in those two units rises more quickly for high frequency words with respect to low frequency words, decisions are generated earlier for high than for low frequency words, regardless of whether the response is yes or no. However, the PZW model has no means to reject nonwords, especially nonwords without neighbours, since those stimuli do not provide evidence in favour of neither yes nor no decisions. A possibility is that no responses to nonwords are based on the evaluation of orthographic features rather than semantic features. This being the case, no responses to nonwords should be sensitive to the orthographic neighbourhood size (Cotheart, Davelaar, Jonasson, & Besner, 1977). However, Forster & Hector (2002; Experiment 1) showed that semantic decision times to nonwords are insensitive to the size of the orthographic neighbourhood, thus this possibility has to be rejected. Assuming that, in the context of a semantic decision, responses to nonwords are made on the grounds of semantic processing, one way to rescue the PZW model could be to add a deadline, so that if neither a yes nor a no decision is generated within a given time interval, then a no decision is produced. If the deadline is controlled by the amount of activation in the animalness unit - 4 -

5 early in the processing - so that the higher the level of activation the longer the deadline is extended - then the modified PZW model would correctly account for the turple effect. How the Links model accounts for the turple effect. The orthographic lexicon of the Links model consists of a list of entries. Between lexical entries and semantic fields there are links. These links can be thought of as indicators of category membership but do not provide detailed semantic information. These links originate from co-occurrences of words in texts and serve as heuristic devices: thus, they are lexical in nature, all that can be inferred from a link between the entry for turtle and the animal field is that these entities are related in some way (p. 1115, Forster & Hector, 2002). Lexical entries are organized into subsets called bins (e.g. Murray & Forster, 2004). The members of a given bin have similar orthographic properties: however, the size of the bins remains underspecified. Upon the presentation of a stimulus, a hash-code function addresses the input letter string to the appropriate bin. Each entry listed in the bin is compared with the stimulus: closely matching entries are flagged. Within each bin, entries are ordered in terms of frequency and hence the search process begins with the most frequent entry and moves toward the less frequent entries. If an entry possesses a link to the relevant semantic category, as soon as it is flagged it is subjected to a form-check procedure to assess whether it actually matches the stimulus. The serial-search and the form-check are independent procedures, and thus can work in parallel. Also, whereas the serialsearch is fast, the form-check is relatively slow. Suppose the stimulus in input is pitten, a nonword with kitten as a neighbouring animal name. The initial search process would identify the entries for the words kitten, mitten, and bitten, since those entries closely resemble the input. As soon as an entry is flagged, its associative link becomes active. The activated links indicate that whereas kitten could potentially be an animal name (because it is linked to the relevant semantic field), mitten and bitten could not. The entries mitten and bitten are not considered further. A form-check is then performed to decide whether the entry - 5 -

6 kitten matches the input, and because it does not, a no decision is generated (Forster & Hector, 2002). The Links model predicts that a decision about a nonword is delayed until every entry if any that is linked with the relevant semantic field has been eliminated by the form check procedure. Turple will be rejected only after the entry for turtle has been eliminated by the form check procedure. A nonword such as insate, which has no animal names as neighbours, is rejected on the grounds that its neighbours (insane, inmate, and innate) have no links to the animal semantic field, i.e. for such a nonword there is no need to wait for the form check to take place. Two predictions. Each of the above sketched models is sensitive to the frequency of the words: in the PZW model, frequency determines the strength of activation propagation in the net, i.e. the activation of high frequency words rises more quickly than the activation of low frequency words (e.g. the interactive-activation model of McClelland & Rumelhart, 1981; the dual route cascaded model of Coltheart et al., 2001); in the Links model, lexical entries are ordered by frequency, and the most frequent ones are searched first. Now, suppose having to assemble two types of nonwords, nonwords derived from high frequency animal names (hereafter, high frequency nonwords) and nonwords derived from low frequency animal names (hereafter, low frequency nonwords), and to present them mixed with animal and non-animal names in a semantic decision task. With respect to the pattern of results of such an experiment, the two models make different predictions. The PZW model predicts that high frequency nonwords are rejected more slowly than low frequency nonwords. The assumption here is that the activation has to reach a certain level in order to extend the deadline: high frequency nonwords would activate through their basewords the yes unit more strongly than low frequency nonwords, and thus would further extend the deadline. The rationale underlying this mechanism parallels that underlying the lexical decision within the parallel-activation approach (Coltheart et al., 1977; Grainger & Jacobs, 1996; Coltheart et al., 2001) where the value of the deadline is variable, with the variation being controlled by the activation in - 6 -

7 the orthographic lexicon early on in processing: when the activation is high, the likelihood that the stimulus requires a positive answer is high, so it is prudent to set a long deadline to avoid an untimely no decision; when the activation is low, the likelihood that the stimulus requires a positive response is low, so it is quite safe to set the deadline to some low value, allowing the no response to be generated quickly. The Links model predicts that high frequency nonwords are rejected faster than low frequency nonwords. The lexical entries of the basewords of high frequency nonwords are flagged, checked and rejected earlier than the entries of the basewords of low frequency nonwords, since the initial process that locates the entries (which closely match the input) performs a frequency-ordered search: a major component of the decision time will be the time taken to locate the matching entry, which is controlled by frequency (p. 1115, Forster & Hector, 2002). The experiment reported below tested these predictions. EXPERIMENT Method Participants. Twenty students at the Università degli Studi di Trento participated as volunteers. Materials. A total of 259 stimuli were used. These consisted of 122 exemplars (i.e. animal names), and 137 nonexemplars. Nonexemplars consisted of 40 high frequency non-animal names (81 occurrences per million; frequency count from corpus of the Istituto di Linguistica Computazionale CNR di Pisa, 1988), 40 low frequency non-animal names (8 per million), 19 nonwords derived from high frequency animal names (basewords mean frequency: 19), 19 nonwords derived from low frequency animal names (basewords mean frequency: 0.3), and 19 nonwords without orthographic neighbours (hereafter, zero N nonwords). High and low frequency nonwords were derived by changing one letter in the basewords. Since the position of the letter changed influences stimulus processing (Mulatti, Peressotti, & Job, 2007), high and low frequency - 7 -

8 nonwords were balanced with respect to that variable (4.1 vs. 4.2, t<1). High and low frequency nonexemplar words were balanced in terms of letter length (6.2 vs. 6.4, t<1) and neighbourhood size (5.1 vs. 4.7, t<1). High frequency, low frequency, and zero N nonwords were all pronounceable and were balanced in terms of letter length (7.1, 7.2, and 7.2). Also, High and low frequency nonwords were balanced in terms of neighbourhood size (1.3 vs. 1.1, t<1), and each was a neighbour of only one exemplar. Apparatus. The experiment took place in a sound attenuated and dim lit room. Participants were tested individually. Stimuli presentation and data recording were controlled by software developed in E-prime and running on a personal computer. Procedure. The instruction given to the participants was to decide whether the letter string presented was the name of an animal or not; they were told about the presence of nonwords and instructed to respond no to such items. The display consisted of white characters on a black background. The stimuli were presented in a different random order for each participant. Each trial consisted of the following sequence of events. After a fixation point (+) was presented for 500 msec the display went blank for 80 msec. The stimulus, in lowercase letters, then appeared and remained on the display until the participant responded or 3 sec elapsed. Participants responded yes by pressing X on the keyboard and no by pressing N. After the response, a feedback message was presented reporting the speed and accuracy of the response. A practice session preceded the experimental session and consisted of 26 items. Results Correct reaction times (RTs) were submitted to the Van Selst & Jolicoeur s (1994) outlier removal procedure. Outliers (2.7%) were removed prior to RT analysis. Mean RTs, according to conditions and percentages of error, are reported in Table 1. Nonexemplar words. ANOVAs, with frequency (high vs. low) as a repeated factor for the participant analysis (F 1 ) and as an independent factor for the item analysis (F 2 ), were conducted on - 8 -

9 RTs and accuracy. High frequency nonexemplar words were rejected faster, F 1 (1, 19) = 36.4, MSE = 429, p <.001, F 2 (1, 78) = 34.4, MSE = 1024, p <.001, and more accurately, F 1 (1, 19) = 13.2, MSE =.001, p < 005, F 2 (1, 78) = 4.3, MSE =.006, p <.05, than low frequency nonexemplar words. Nonwords. ANOVAs, with type of nonword (high frequency, low frequency, and zero nonwords) as a repeated factor for the participant analysis and as an independent factor for the item analysis, were conducted on mean latencies and accuracy. Latencies. The main effect of type of nonword was significant, F 1 (2, 38) = 23, MSE = 1366, p <.001, F 2 (2, 54) = 14.5, MSE = 3088, p <.001. Paired comparisons revealed that zero N nonwords were rejected faster than the high frequency nonwords, t 1 (19) = 6.4, p <.001, t 2 (36) = 4.5, p <.05, and, crucially, that high frequency nonwords were rejected faster than low frequency nonwords, t 1 (19) = 2.9, p <.01, t 2 (36) = 2.4, p <.05. Errors. The main effect of type of nonword proved significant, F 1 (2, 38) = 34.1, MSE =.006, p <.001, F 2 (2, 54) = 8.8, MSE =.021, p <.001. Paired comparisons revealed that zero N nonwords were rejected more accurately than both high t 1 (19) = 8.4, p <.001, t 2 (36) = 4.2, p <.001 and low t 1 (19) = 8.5, p <.001, t 2 (36) = 4.0, p <.001 frequency nonwords; high frequency nonwords were responded to as accurately as low frequency nonwords, ts < 1. DISCUSSION The first result worth considering is the frequency effect observed for nonexemplar words: high frequency words are rejected faster than low frequency words (e.g. Monsell, Doyle, & Haggard, 1989). This finding is consistent with both PZW and Links models. Having described in the introduction how the PZW model explains the effect, let us now turn to the Links model. The explanation the Links model provides for the frequency effect of the nonexemplar words rests on the same mechanism that predicts the frequency effect for the nonwords derived from animal names. The frequency-ordered search process looks for a matching entry. When this is found, it is immediately checked, and since it has no links to the relevant semantic field, it is rejected. Given - 9 -

10 that the process locating the matching entry is controlled by frequency, frequency constitutes a major component of the decision time. A second, central, finding is that nonwords derived from high frequency animal names are rejected faster than nonwords derived from low frequency animal names. This clearly indicates that the frequency of the basewords influences nonword processing. Whereas this pattern is consistent with the predictions of the Links model, it is inconsistent with the predictions of the PZW model: according to the latter model high frequency nonwords would activate the yes unit more strongly than low frequency nonwords, which would lengthen the deadline and lead to slower decision times. Thus, a deadline at the semantic level is not enough to account for the current result within the PZW model. Is there a way to modify the cascade model to account for our result? Before undertaking such an attempt, let us consider another finding. Nonwords derived from nonexemplar words are rejected as fast as nonwords without neighbours (Forster, 2006, Experiment 2). In Forster s study, the nonexemplar words the nonwords were derived from had an average frequency of 16 (Kucera & Francis, 1967). Of course, nonwords without neighbours had an average neighbourhood frequency of zero. Thus, comparing nonwords without neighbours and nonwords with a nonexemplar neighbour may be interpreted as a frequency manipulation. If this interpretation is plausible, then this finding suggests that either frequency does not affect the processing of nonwords derived from nonexemplar words, or the presence of neighbours is ignored unless they are animal names, no matter what their frequency may be. If frequency does not affect processing of nonwords derived from nonexemplars, then whatever the mechanism responsible for the frequency effect of nonwords derived from the exemplar is, it is called into play only if activation in the animalness unit is detected. Suppose that nonwords derived from animal names (i.e. exemplars) are rejected because they are nonwords, not because a deadline expires. The core mechanism could be the following: if activation in the animalness unit is detected, then a form-check is performed on the stimulus. Let us postulate

11 that this form-check procedure operates serially on the string of letters (for example, by comparing the most active lexical unit with the letter units, from left to right). If that is the case, then there should be a relation between the length of the stimulus (in terms of the number of letters) and the time to process both animal names and nonwords derived from animal names: the longer the stimulus, the longer the reaction time. A length effect is predicted for both animal names and nonwords derived from animal names: since the system has no means to know whether the input stimulus is a word or not until it has performed the form-check, the form-check must also be operative when the stimulus is an animal name. Given that the form-check procedure is invoked only when activation in the animalness unit is detected, nonexemplar words, nonwords derived from nonexemplar names, and nonwords without neighbours should show no relation between letter length and decision time. To test these predictions, we performed an analysis of correlations between length and RTs. The length of nonexemplar words does not correlate with RTs, r =.084, p >.45. The length of the nonwords without neighbours does not correlate with RTs, r =.327, p >.17. The length of the animal names significantly correlates with RTs, r =.398, p <.001. The length of the nonwords derived from high frequency animal names significantly correlates with RTs, r =.674, p <.005, as does the length of the nonwords derived from low frequency animal names, r =.681, p <.005. Thus, the pattern of correlations is consistent with the hypothesis sketched above, and clearly indicates that animal names and nonwords derived from animal names are dealt with differently from the other types of stimuli. Let us now turn back to the PZW cascaded model and see how it can be modified. In this model, nonwords that do not have neighbours or that have a nonexemplar as a neighbour are rejected when a deadline expires. Nonexemplar words are rejected when activation in the no unit reaches criterion and, since activation grows faster for high frequency words than for low frequency words, high frequency nonexemplar words are rejected faster than low frequency nonexemplar words. If activation in the animalness unit is detected, then the system triggers a serial form-check procedure to be performed on the input string. If the form-check procedure yields a positive result, then a yes

12 response is produced; if it yields a negative result, a no decision is taken. Since the rate of activation in the animalness unit depends on the frequency of the word (or the frequency of the baseword the nonword is derived from), then the form-check procedure is triggered earlier when the input is a high frequency word (or a nonword derived from a high frequency animal name) than when the input is a low frequency word (or a nonword derived from a low frequency animal name). As already noted, the Links model posits a form-check procedure that operates when a link to the relevant semantic field is found. All that has to be done in order for this model to explain the pattern of correlations reported above is to postulate that this form-check procedure operates serially on the stimulus. Since a relevant link is found only when the stimulus is an exemplar or a nonword derived from an exemplar, the Links model equipped with a serial form-check mechanism correctly predicts a length effect only for those classes of stimuli. To conclude, we have shown that the frequency of the animal names the nonwords are derived from affects semantic decision time. The Links model straightforwardly accounts for this pattern, since it assumes a mechanism of serial search based on frequency while existing cascaded processing models do not. However, it is possible to modify models of the latter class in order to account for the data we reported by adding a form-check procedure sensitive to the frequency of the lexical representations of exemplars

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15 Footnotes 1. Within a cascaded processing approach, what constitutes evidence for yes or no responses is still under debate (see discussions in Forster & Hector, 2002; Forster, 2006; Pecher et al. 2005)

16 Authors Note The authors wish to thank Derek Besner, Kenneth Forster, and Diane Pecher. This research was supported by grants from MURST and from the Università degli Studi di Padova

17 Table 1. Mean reaction times (RTs) and percentages of error (PE) according to conditions. Exemplars Nonexemplars Words Nonwords High Freq. Low Freq. Zero N High Freq. Low Freq. RTs PE