Material

The present work owes a lot to the study realized by Luzzatti and De Bleser (1996) on aphasic speakers. Luzzatti and De Bleser (1996) evaluated the performance of two Italian agrammatic speakers on a series of tasks concerning GG retrieval. The eight tasks in use differentiated according to the kind of expected performance (phrase production, pluralization, etc.), and the lexical material in use (simple nouns, derived forms, compounds). Much of the original material for simple and derived nouns is in use also in the present task in order to allow for a comparison between PADs and agrammatic speakers; I made a few changes though, in order to adjust the task in the light of my own research questions, and to conform the material to the needs of PADs (i.e., reduced length of execution).

First, I intentionally excluded compounds from the present investigation in order to avoid inserting a further source of complexity. Previous findings from Chiarelli, Menichelli & Semenza (2007) pointed out that PADs are impaired at retrieving compound nouns in a picture-naming task and usually compensate their weakness by producing simple words or by applying the most productive structure among those available for Italian compounds, namely Verb-Noun compounds. Moreover, retrieval of the second element in the compound is much more impaired in comparison to the first one: authors explained the data in the view of a problem of information overload. Thus, given the intrinsic difficulties brought up by compounds, I decided to start my investigation on GG by focusing only on simple and derived forms.

The task includes 100 Italian nouns, subdivided across four main classes – regular, opaque, irregular and derived nouns – and a number of subclasses.

I set aside derived nouns for a moment, and start by illustrating the first three classes of simple nouns. The baseline categorization takes into account the transparency of final

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word markers, this leads to the distinction between regular, opaque and irregular nouns.

A further subcategorization takes into account natural gender (NG) and distinguishes between nouns referring either to animate (with masculine or feminine natural gender) or to inanimate referents. Moreover, words are counterbalanced across masculine and feminine gender. The results of the operation are presented in Table 4.1 for regular nouns, in Table 4.2 for opaque nouns and in Table 4.3 for irregular nouns.

Table 4.1. Regular nouns (1) include four subclasses.

Example GG NG Marker Class

a. marito 'husband'

masculine masculine -o I (transparent)

b. mamma 'mum'

feminine feminine -a II (transparent)

c. mondo 'world'

masculine inanimate -o I (transparent)

d. musica 'music'

feminine inanimate -a II (transparent)

Words in class (1a) and (1b) are regular nouns endowed with natural gender, while (1c) and (1d) are regular nouns with inanimate referents. In this latter case, regularity stems from the transparency of the final word markers (-o for masculine and -a for feminine).

In the former case, (1a) and (1b), regularity is reinforced by matching natural gender too (in the sense that grammatical gender and natural gender coincide). Each subclass includes five items.

The nouns in the four subclasses of regular nouns ((1a) to (1d)) were then paired to equivalent opaque nouns, which share similar characteristics with respect to natural gender (masculine, feminine or inanimate) and grammatical gender (masculine and feminine). Table 4.2 provides examples.

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Table 4.2. Opaque nouns (2) include four subclasses.

Example GG NG Marker Class

a. padre 'father'

masculine masculine -e III (opaque)

b. madre 'mother'

feminine feminine -e III (opaque)

c. cuore 'heart'

masculine inanimate -e III (opaque)

d. voce 'voice'

feminine inanimate -e III (opaque)

Table 4.3 exemplifies irregular nouns included in the experimental material. The irregularity exemplified in (3a) stems from information clash between the masculine GG (matching with masculine NG in (3c)) and the final marker -a, otherwise characterizing regular feminine nouns. Subclass (3b) includes nouns characterized by masculine natural gender and final marker -a; in this case, GG assignment regularizes according to the final marker: nouns have feminine GG. In this case the information clash is caused by the mismatch between masculine natural gender and feminine grammatical gender. Nouns in (3c) present the pattern already illustrated for (3a) (masculine GG despite -a as final marker), except for being inanimate and therefore not specified for natural gender. The reverse pattern is exemplified in (3d): inanimate referents, feminine GG and -o final markers.

Table 4.3. Irregular nouns (3) include four subclasses.

Example GG NG Marker Class

a. poeta 'poet'

masculine masculine -a V (irregular)

b. guardia²⁹ 'guard'

feminine masculine -a II (regular)

c. problema 'problem'

masculine inanimate -a V (irregular)

d. mano 'hand'

feminine inanimate -o VII (irregular)

29 The selected names for this class refer to army-related jobs and roles (guard, recruit, etc.), which traditionally were carried out only by men and have been practised only more recently by women. Based on this consideration, they are classified as masculine for natural gender.

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In order to balance factors across the items, each noun referring to an entity endowed with natural gender was paired to a noun sharing the same characteristics for GG, and declension class, but crucially inanimate³⁰.

In the experiment, the role of derivational morphology has been taken into account too: in order to test the relevance of derivational morphology with respect to GG retrieval, I included nouns from seven further classes. The derivational suffixes in the selected nouns all carry a specific feature for GG and can therefore be considered transparent, despite ending in -e. Table 4.4 provides examples.

Table 4.4. Derived nouns (4) included in the experimental material are divided into 7 subclasses.

Example GG NG Marker Class accordance with the natural gender; the derivational suffixes in use are -iere and -tore for masculine items, and -trice for feminine nouns. The last four subclasses include nouns with no NG, characterized by four different derivational suffixes: two for masculine inanimate nouns (ore and iere), and two for feminine inanimate nouns (aggine and -udine). In order to counterbalance the number of feminine and masculine nouns, class

30 Nouns in classes (3b) were paired with nouns in class (3d). This represents an exception in the design, in that the two classes do not differ only with respect to natural gender. In this case, the opposition between the two classes is built on the fact that class (3b) is irregular in what concerns the clash between natural gender and final marker on one side, and natural gender on the other. In contrast, nouns in class (3d) are irregular in what concerns the relationship between feminine GG and -o final markers, otherwise representative for masculine nouns.

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(4c) includes 10 items, rather than 5 (like all other classes do). This allows for an even number of masculine and feminine derivational nouns with either masculine or feminine NG, as well as for an even number of feminine nouns with feminine NG and feminine nouns with no NG. Derived nouns are characterized by lower frequency, with respect to the other classes in the study³¹.

Overall, the five items included in each subclass³² make a total of 100 nouns: 20 regular nouns, 20 opaque nouns, 20 irregular nouns and 40 derived nouns.

No word starting with a vowel was included in the task because, in that case, definite articles neutralize their phonetic difference between masculine and feminine grammatical gender, preventing the possibility to record whether participants retrieved the proper GG or not.