Empirical Evidence for the FUL-Model

While several studies (as outlined above) have shown that what matters in lexical access is the goodness of fit between input and representation not (only) on a phoneme level, but also on a sub-phonemic, featural level (e.g. Connine, Blasko &

Wang, 1994; Marslen-Wilson & Warren, 1994; Connine, Titone, Deelman &

Blasko, 1997; McQueen, Norris & Cutler, 1999; Bölte & Coenen, 2002), some theories (e.g. Lahiri & Reetz, 2002) go even further and state that not all features are equally considered in lexical access and even within a given feature (e.g. place of articulation) the respective expressions of this feature (e.g. coronal vs. dorsal vs.

labial) can play different roles in word recognition. Such models require abstract mental representations, in the sense that the underlying form of a word is not equivalent to its surface form. They assume that not all features that can be extracted from the signal have to be stored in the mental lexicon in order to unambiguously identify a given word. And indeed, a lot of features are either not distinctive in a given language or can be derived by rule. For instance, the feature [NASAL] is not distinctive in English vowels, but instead, nasal vowels are allophones of oral vowels. That is, in English a vowel will simply be nasalized when followed by a nasal consonant, otherwise it is oral. Consequently, nasality does not play an important role in lexical access in English and hence is not specified. Another example is the place feature [CORONAL], which is phonemic but is still not specified. Of the three place feature expressions [LABIAL], [CORONAL] and [DORSAL] only two need to be specified, while in case of an unspecified place of articulation, [CORONAL] is inserted by rule. In contrast to nasality, underspecification of [CORONAL] seems to be a universal phenomenon.

Such a model of underspecified lexical representations was introduced in Chapter 1: the Featurally Underspecified Lexicon (FUL) model. We will shortly summarize its main points here and then review experimental evidence for its hypotheses. It basically assumes that the lexical entry of a word consists of hierarchically ordered features representing the phoneme string. Crucially, not all features that can be extracted form the speech stream are stored in these entries, but only those that are not redundant and not subjected to massive change depending on context or speaker. The underspecification of certain features can lead to asymmetric effects in experiments on lexical access. For instance, a coronal item can be activated not only by coronal input, but also by dorsal and labial information in the signal, because it is not specified for place of articulation and consequently cannot mismatch with non-coronal input. On the contrary, the

features [DORSAL] and [LABIAL] are lexically represented and can be activated only by matching input, while incorrect place information in the signal will lead to a mismatch with the lexical representation. Hence it is not necessarily the amount of deviating information between signal and representation, but also the kind and direction of deviation that affects the goodness of fit in lexical access.

Experimental findings pro and contra these hypotheses will be reviewed in the next sections.

3.4.1.1 Underspecified Representation of Vowels in the Mental Lexicon Underspecification of Nasality in English as Compared to Bengali

One of the first experimental studies on underspecification in the mental lexicon was conducted by Lahiri and Marslen-Wilson (1991), examining the different lexical status of the feature [NASAL] in Bengali and English. In Bengali nasality is a distinctive feature, whereas in English the nasal-oral contrast is purely allophonic.

In English vowels are only nasalized if they directly precede a nasal consonant, due to spreading of the feature [NASAL] onto the preceding oral vowel. Since in this case nasality is not phonemic and can easily be derived by rule, there is no need to store the feature [NASAL] in the mental lexicon. Therefore, the FUL model assumes that English vowels are underspecified for nasality. On the contrary, in Bengali, nasal vowels occur as distinct phonemes between oral consonants and thus have to be specified as such. In comparison, in CVN-words (N stands for a nasal consonant), the vowel is assumed to be underlyingly oral in Bengali also, with vowel-nasality being due to assimilatory processes, as it is the case in English.

When hearing a nasal vowel, Bengali speakers should interpret surface nasality as underlying nasality, and thus access CṼC words (words with a nasal vowel between two non-nasal consonants) instead of CVN words.

These predictions were approved in a gating task. English subjects were tested on CVC and CVN words, Bengali subjects on CVC, CVN and CṼC words.

The nasal vowels in CVN and CṼC stimuli strongly biased Bengali subjects towards CṼC responses. They interpreted the nasalization as a cue for a nasal vowel, not as a cue for nasality in the following consonant. This implies that nasal vowels are stored as such in the Bengali mental lexicon. For the English subjects vowel nasalization in CVNs was an unambiguous cue for a following nasal consonant, suggesting that nasality is interpreted as a property of the consonant, not of the preceding nasal vowel.

Underspecification of the Place Feature [CORONAL]

A design that is frequently used in studies on lexical representations is the so-called Mismatch Negativity paradigm. In this purely auditory design a ‘standard stimulus’

is repeated and creates a central sound representation that corresponds to that string’s representation in the mental lexicon. Another, ‘deviant stimulus’ is presented infrequently. If this stimulus mismatches with the lexical representation of the standard stimulus, a Mismatch Negativity (MMN) is observed in the event related potential (ERP) in form of a negative peak at 100-250ms after stimulus onset (Näätänen, 2001).

The underspecification of the feature [CORONAL] in vowels was tested by Eulitz and Lahiri (2004) and Cornell and colleagues (Cornell, Lahiri & Eulitz, submitted) in mismatch negativity studies. Eulitz and Lahiri (2004) used the dorsal vowel [ο] and the coronal [ø] as standard and deviant. When [ο] was taken as standard, the activated central sound representation was dorsal. From the infrequent deviant stimulus [ø] the feature [CORONAL] was extracted. This mismatched with the feature [DORSAL], evoking an enhanced mismatch negativity, expressed in an earlier peak latency and a higher amplitude in the ERP compared to situations that presumably lacked a conflict between standard and deviant. Such a nonconflict situation according to the FUL model was created as standard and deviant were reversed. With [ø] as standard the activated underlying representation contained no feature for place of articulation because [CORONAL] is assumed to be underspecified. Thus the feature [DORSAL] from the deviant [ο] did not mismatch the central sound representation and consequently did not lead to an MMN. Since the acoustic difference between standard and deviant was exactly the same in both conditions, the asymmetric EEG responses point to mismatch effects at a higher level of representation. These findings were replicated by Cornell and colleagues (Cornell, Lahiri & Eulitz, submitted) using different vowels embedded in real German words.

Underspecification of the Height Feature [MID] in German

Also using the MMN design, Eulitz and Lahiri (2003) gained evidence for the assumption that the Height feature of the vowel [ɔ] is [low] in Bengali, whereas in German [ɔ] is assumed to remain unspecified for Tongue Height. When presenting [ɔ] as the standard stimulus, the FUL model assumes that the Bengali subjects set up a [LOW] central sound representation while the German subjects should not.

Hence, a [HIGH] deviant stimulus, in this case an [u], should elicit a mismatch response in the Bengali speakers but not in the German ones. These expectations

were borne out. This again contradicts the assumption that speech recognition is based in a simple forward manner on phonetic characteristics of the input.

Felder (2006) further investigated the underspecification of the feature [MID] in German vowels using the cross modal fragment priming paradigm with lexical decision. The visual target words were all disyllabic German nouns with an [ɛ] or an [ɪ] in their first syllable. The preceding auditory prime fragment was either the first syllable of the target word (e.g. bech- BECHER and skiz- SKIZZE,

‘mug’ and ‘sketch’, respectively), or the first syllable with the reversed vowel (e.g.

*bich- BECHER and *skez- SKIZZE), or a completely unrelated fragment (e.g.

ham- BECHER and bag- SKIZZE). The two vowels [ɛ] and [ɪ] differ only in the TONGUE HEIGHT feature in that [ɪ] is [HIGH] and also specified as such, while [ɛ]

“is” [MID]. According to the FUL model, the feature [MID] of the [ɛ] is neither specified in the mental lexicon, nor extracted from the signal. Hence, a word like BECHER with unspecified [ɛ] can be activated by a [HIGH] [ɪ], but also a word like SKIZZE with [HIGH] [ɪ] can be activated by a fragment with [ɛ], since there is no height feature in the signal. In the EEG, the P350 amplitudes of both conditions were more negative than the control condition, with no difference in the amplitudes for BECHER preceded by bech- or by bich-; or for SKIZZE preceded by skiz- or skez-.

Interestingly enough, these findings could not be replicated in a purely behavioural study (Felder, Friedrich, Lahiri & Eulitz, 2008). We used the very same stimulus material as in the EEG study reported above, and only changed the design for as to avoid repetitions of prime fragments or target words (similar to Experiments 1 and 2 in this Chapter). Reaction time data revealed facilitated lexical decisions only for the identity priming condition, while the condition with reversed vowels did not differ from the control condition. Note that this is the same finding as in Experiments 1 and 2 reported above in this chapter, showing different result-patterns in ERP as compared to reaction time data.

3.4.1.2 Underspecified Representation of Consonants in the Mental Lexicon Underspecification of the Place Feature [CORONAL]

Evidence from Reaction Time Experiments

Experimental evidence for the underspecification of [CORONAL] in consonants was provided by Lahiri and van Coillie (Lahiri & van Coillie, 1999) in a cross modal semantic priming experiment with lexical decision. In the semantically related condition German words like Bahn (railway) or Lärm (noise) were presented auditorily to the subjects. Right thereafter, semantically related words like Zug

(train) or Krach (bang) showed up on a screen. In the semantically unrelated condition, words like Maus (mouse) or Blatt (leaf) were used as primes for Zug and Krach. In the test condition the final place of articulation of the acoustic primes was altered, thus turning coronal consonants into non-coronals and vice versa. For instance, Bahn (coronal /n/) became *Bahm (dorsal /m/) and Lärm turned into

*Lärn.

Reaction time data show that Bahn as well as *Bahm significantly primed the semantically related target Zug compared to the control prime Maus. In contrast, only Lärm significantly speeded up the reactions to Krach, whereas reaction times in the test condition (*Lärn) did not differ significantly from the control condition. This confirms the hypothesis that the [CORONAL] /n/ in Bahn is underspecified for place. As [LABIAL] was extracted from the acoustic signal of

*Bahm and mapped onto the lexicon, no corresponding entry for a place feature was found and hence the procedure resulted in a nomismatch situation, leaving Bahn activated. On the contrary, [CORONAL] was extracted from the /n/ in *Lärn and mapped onto the [LABIAL] feature of the /m/ in Lärm. This resulted in a mismatch, deactivating Lärm.

Similar results were obtained for word medial variation using the same experimental design. The word Düne (dune) as well as its corresponding pseudoword *Düme primed the semantically related word Sand (sand). However, the word Schramme (a scratch) primed its semantic relative Kratzer (a scratch), while the pseudoword *Schranne did not do so.

Wheeldon and Waksler (2004) investigated the underspecification of the feature [CORONAL] in word final consonants. Furthermore, the prime words were embedded in sentences, so that the prime was either followed by a word that licensed an assimilation of coronal consonants, or that did not license it. This way they also tested the assumption of the FUL model that any coronal consonant can change, irrespective of whether it would usually undergo assimilatory processes and irrespective of the following context. Participants heard sentences that contained a prime word that ended in either a coronal (e.g. wicked) or a non-coronal (e.g. frantic) consonant. At the offset of the prime word, a visual target (WICKED or FRANTIC, respectively) appeared on the screen for lexical decision.

In the identical condition prime words were unchanged (e.g. They heard there was a wicked prince in the castle. / She never had frantic moments with the twins.). In the change condition, the place of articulation was changed, and the prime was followed by either an appropriate context (e.g. They heard there was a wickib prince in the castle. / She never had frantip moments with the twins.) or by

inappropriate context (e.g. They heard there was a wickib ghost in the castle. / She never had frantip days with the twins.). There was also an unrelated control condition (e.g. She never had idle days with the twins.).

Reaction time data showed that a word ending in a coronal consonant (e.g.

wicked) was responded to faster when preceded by either the correct (e.g. wicked) or the changed (e.g. wickib) prime word, with no difference between the two, than when preceded by a control word. The appropriateness of the following context had no effect. On the contrary, a word ending in a non-coronal consonant (e.g.

frantic) was responded to fastest when preceded by the correct prime, slower when preceded by the changed prime (e.g. frantip), and slowest after a control prime.

These results speak in favour of unspecified representations of the feature [CORONAL], and against theories that assume that the context has to license the change (Gaskell & Marslen-Wilson, 1996).

Evidence from EEG Experiments

Friedrich, Eulitz and Lahiri (2006) conducted a pure lexical decision task in the EEG in order to examine underspecification of the feature [CORONAL] in German word medial consonants. Half of the words contained a coronal consonant (e.g.

Horde, ‘horde’), the other half a non-coronal consonant (e.g. Probe, ‘test’). These words were also turned into pseudowords by alternating the place of articulation of the medial consonant, so that a word with a coronal consonant became a pseudoword with a non-coronal consonant (e.g. Horde - *Horbe) and vice versa (e.g. Probe - *Prode). The FUL model predicts that coronal pseudowords (e.g.

*Prode) are classified faster as pseudowords than non-coronal pseudowords (e.g.

*Horbe), because the latter still activate their underspecified base word (e.g.

Horde), while the former mismatch with the non-coronal base word specification (e.g. Probe). Participants responded faster to words than to pseudowords, with no effect of place of articulation. However, the N400 effect averaged on the uniqueness point of words and deviation point of pseudowords revealed that the amplitudes for coronal pseudowords (e.g. *Prode) started to differ from their non-coronal base words (e.g. Probe) approximately 150ms earlier than the non-non-coronal pseudowords (e.g. *Horbe) from their respective coronal base words (e.g. Horde).

This suggests that non-coronal pseudowords were recognized later as pseudowords than coronal pseudowords, because they still caused lexical activation of their base-words.

The same authors (Friedrich, Lahiri & Eulitz, 2008) conducted a similar study examining word onset rather than word medial consonants. Of the words,

half had a coronal consonant as their onset (e.g. Drachen, ‘dragon’), the other half began with a non-coronal consonant (e.g. Grenze, ‘frontier’). The pseudowords differed from the words only with respect to the place feature of the initial segment and were created by changing a coronal word onset into a non-coronal one and vice versa. For instance, the corresponding pseudoword to Drachen was *Brachen and Grenze became *Drenze. The FUL model predicted that coronal pseudowords like

*Drenze are identified faster as pseudowords than non-coronal pseudowords like

*Brachen. *Drenze will not activate a non-coronal word like Grenze due to the mismatch between the feature [DORSAL] in the lexical entry of Grenze and the feature [CORONAL] in the signal of *Drenze. However, the feature [LABIAL] in

*Brachen is matched onto the underlying representation of Drachen, where coronality is not specified. Consequently Drachen is activated to some extent.

Behavioural results show that subjects performed the lexical decisions faster for words than for pseudowords. In the EEG, pseudowords elicited a larger N400 than words. The coronal pseudowords elicited a larger N400 than non-coronal ones, whereas the N400 amplitudes of non-coronal and non-non-coronal words did not differ significantly. The N400 amplitude was enhanced for coronal pseudowords because they could be immediately classified as pseudowords and hence elicited a more marked violation response than the ambiguous non-coronal pseudowords.

Further, they (Friedrich, Lahiri & Eulitz, 2008) conducted a cross-modal fragment priming experiment with the same stimuli as those described above in the N400 study. The visual target words (e.g. Drachen) were preceded auditorily by either their own first syllable (e.g. drach-), by the first syllable of the corresponding pseudoword (e.g. *brach-), or by a syllable of an unrelated word.

The FUL model predicted that a coronal target word was preactivated by a non-coronal pseudoword prime, but not vice versa. The P350 amplitude was most negative for the identity condition (e.g. drach- Drachen, gren-Grenze) and least negative for the control condition. In case of a coronal target, also the condition with the pseudoword prime (e.g. *brach-Drachen) was more negative than the control condition, while this was not the case for non-coronal targets (e.g. *dren-Grenze). Again, in the reaction time data only the identical condition yielded facilitated lexical decisions.