J Psycholinguist Res DOI 10.1007/s10936-014-9304-8
Lexical Retrieval of Nouns and Verbs in a Sentence Completion Task Alyson D. Abel · Mandy J. Maguire · Fizza M. Naqvi · Angela Y. Kim
© Springer Science+Business Media New York 2014
Abstract This study explored noun and verb retrieval using a sentence completion task to expand upon previous findings from picture naming tasks. Participants completed sentences missing either a target noun or verb in the final position. Non-target responses were coded for substitution type, imageability and frequency. Like picture naming, nouns and verbs differed in non-target substitution type—within-category substitutions were primarily nouns and outof-category substitutions were primarily verbs. Imageability predicted multiple substitution types for both word classes, whereas frequency predicted noun substitution types but not verbs. Findings support theories of noun and verb differences in semantic retrieval, showing the robustness of this effect across methodologies, and shed new light on the influence of imageability and frequency during semantic retrieval. Keywords
Semantic retrieval · Word class · Sentence completion
Introduction The fact that nouns and verbs differ in semantics, syntax, and phonology is well established. How these differences influence behaviors such as word retrieval remains an area of debate. Studies of patients with aphasia and healthy adults consistently reveal poorer accuracy and slower retrieval of action verbs compared to object nouns (Colombo and Burani 2002; Crepaldi et al. 2012; De Bleser and Kauschke 2003; Druks 2002; Federmeier and Bates 1997; Kauschke and von Frankenberg 2008; Luzzatti et al. 2002; Székely et al. 2005). There is also evidence that object noun and action verb retrieval are differentially influenced by factors like
A. D. Abel (B) · M. J. Maguire · A. Y. Kim Callier Center for Communication Disorders, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 1966 Inwood Rd., Dallas, TX 75235, USA e-mail: [email protected] F. M. Naqvi Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, TX 75080, USA
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word frequency and imageability (Barry et al. 2001; Colombo and Burani 2002; Cuetos and Alija 2003; Kauschke and von Frankenberg 2008; Székely et al. 2005). These studies primarily use picture naming tasks, which are useful for studying aphasic populations with a range of language difficulties but have limitations. For example, object recognition precedes lexical retrieval (Barry et al. 2001), thus any differences between nouns and verbs in recognition could influence the accuracy and speed of word production. Thus, recognition differences may ultimately be incorrectly attributed to retrieval. Further, still image stimuli in picture naming tasks limit the words that can be used, restricting researchers’ ability to effectively manipulate variables like imageability and word frequency that affect word retrieval (Barry et al. 2001; Crepaldi et al. 2006; Székely et al. 2005). In this paper, we expand on picture naming study findings by investigating noun and verb retrieval using a sentence completion task. Sentence completion bypasses some of the potential problems with picture naming and allows a closer examination of word frequency and imageability effects on retrieval. Our goal is to better understand the organization of object noun and action verb semantic categories by studying the errors made with difficult noun and verb retrieval. Picture naming studies clearly show slower and less accurate verb retrieval versus noun retrieval, but these studies are limited in how they can interpret these findings. By examining retrieval errors, responses provided when participants are unable to retrieve the target word, researchers can provide more insight into how differences in noun and verb semantic organization influence retrieval (Budd et al. 2011; Kambanaros et al. 2013; Kauschke et al. 2007; Mätzig et al. 2009). For instance, errors in object noun picture naming are often withincategory members (e.g., lady for nun) while action verb picture naming errors are more likely to be from completely different semantic categories (e.g., yawning for laughing; Mätzig et al. 2009). Within-category noun errors are consistent with the theory that nouns, object nouns in particular, are organized hierarchically based heavily on shared perceptual features (i.e., living objects, non-living objects, tools and body parts; Huttenlocher and Lui 1979; Mätzig et al. 2009). For example, the category animal includes dogs, horses, and wombats, all of whom breathe, move, have fur, etc. Subdividing dog into categories like beagles and dobermans, the number of shared category features increases even more. That noun errors in picture naming come from the same semantic category supports this hierarchical organization (Mätzig et al. 2009). The predominance of out-of-category verb errors during retrieval has been interpreted in two ways. The first interpretation is task-related, namely that action pictures are harder to recognize than object pictures because they have a less direct relationship (Mätzig et al. 2009) or more complex relationship (Federmeier and Bates 1997) with their label. Alternatively, like within-category noun errors, out-of-category verb errors may reveal information about verbs’ semantic organization. Verb organization is believed to be matrix-like without welldefined levels of structure (Masterson et al. 2008; Vigliocco et al. 2011, 2004). As a result, action verbs do not show clear clustering within or across categories, as evidenced by adult similarity ratings of verbs that cross expected category lines (e.g., forms of communication show a close relationship to forms of exchange; Vigliocco et al. 2004). It follows that outof-category verb picture naming errors may be due to retrieval of semantically similar words but that, for verbs, “semantically similar words” are out-of-category or cross-category while “semantically similar words” for nouns are within-category. If this holds true, differences in object noun and action verb errors in picture naming are likely related to noun and verb semantic organization as opposed to task-related demands. Sentence completion tasks remove the element of image recognition and, therefore, may be able to clarify whether retrieval errors are due to task demands or semantic organization. Sentence completion tasks also allow
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more flexibility to study other features that influence word retrieval such as imageability and frequency (Berndt et al. 2002). Imageability and frequency are consistently identified as influencing word retrieval in typical adults, aphasic patients and children. Differences in how these features impact noun and verb retrieval can provide insight into semantic organization (Colombo and Burani 2002; Kauschke and von Frankenberg 2008; Masterson et al. 2008; Mätzig et al. 2009; Székely et al. 2005). Typical adults retrieve high imageability words faster and more accurately than low imageability words regardless of word class (Crepaldi et al. 2012; Kauschke and von Frankenberg 2008). Although, to our knowledge, no one has directly compared the degree of the imageability effect between nouns and verbs, it seems that noun retrieval would benefit more from imageability because, in general, nouns are more imageable than verbs (Bird et al. 2000, 2003; Chiarello et al. 1999). Effects of imageability may be greater for picture naming tasks because words that are highly imageable are likely easier to depict and identify than low imageability words. In this case, imageability differences during picture naming may be due to effects on recognition rather than retrieval. Thus, the influence of imageability on word retrieval may be somewhat different in a sentence completion task without visual aids. Word frequency effects on lexical retrieval are more complex than those of imageability. High frequency nouns are named more quickly and accurately than low frequency nouns (Alario et al. 2002; Almeida et al. 2007; Barry et al. 1997; Bates et al. 2001; Colombo and Burani 2002; Ellis and Morrison 1998; Griffin and Bock 1998; Kauschke and von Frankenberg 2008; Székely et al. 2005) but, for verbs, high frequency has the opposite effect (Colombo and Burani 2002; Székely et al. 2005). Székely et al. (2005) attribute the frequency disadvantage for verb retrieval to counting “light verbs” (i.e., go, do and make), which are very high frequency, as valid responses. Picture naming is slower for light verbs than more specific but less frequent responses (e.g., make dinner vs. cook), increasing the latency for high frequency verbs. Thus, while the imageability effect may be related to the visual aids provided by pictures, it is unlikely that the influence of frequency on lexical retrieval would change across methodologies. Here we used a sentence completion task that requires participants to retrieve a word from their lexicon without the aid of picture stimuli. To our knowledge, the current study is the first to examine noun and verb retrieval in an unaided sentence completion task.1 Stimuli were sentences that were equally likely to elicit target nouns and verbs and analyses focused on non-target responses, the words participants gave when they were unable to retrieve the target. Specifically, we examined types of non-target responses (i.e., withincategory or out-of-category) as well as the frequency and imageability of the non-target responses. Similar to Mätzig et al.’s (2009) picture naming findings, we predicted that nontarget responses for noun sentences would be within-category (e.g., cat for dog) whereas non-target responses for verb sentences would be out-of-category (e.g., see for talk). These findings would suggest that noun and verb retrieval differences here and in previous picture naming tasks relate to differences in semantic organization as opposed to differences in object and action recognition. The second prediction was that effects of frequency and imageability on non-target responses would parallel effects found in picture naming studies. Specifically, we anticipated that imageability would strongly influence noun and verb retrieval but that word frequency would differentially affect the word classes. This finding would strengthen 1 Note that in the sentence completion task used by Crepaldi et al. (2006) the subject is cued to the missing
word by the presence of the target word in a different form in a sentence preceding the open-ended sentence and the sentence completion task used by Griffin and Bock (1998) provides a picture of the target word in the position of the missing word.
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arguments about the importance of imageability in lexical retrieval regardless of word class and help us better understand the role of word frequency in retrieval.
Methods Participants A total of 238 undergraduate students in Language Development courses completed subsets of the total list for extra credit. Stimuli Stimuli included 816 sentences (411 nouns and 405 verbs) that followed these criteria: (a) 6–9 words long, (b) the target word was in the sentence-final position, (c) all words and concepts were appropriate for young children (i.e., target words had an age of acquisition of 30 months or earlier; Fenson et al. 1994), (d) target nouns were preceded by either a determiner (a, the) or a possessive (his, her), (e) target verbs were preceded by either an infinitival to or a modal (would, could, will). Examples include, “When you leave be sure to lock the ______” (target—door) and “The jar of pickles was hard to _____” (target—open). Procedure Each participant received a pseudo-randomized list of 16–25 sentences with the target word removed and replaced with a blank line. No target word appeared more than once on the same list. Participants were instructed to complete each sentence with the first single word that came to mind. If unable to come up with a possible response, they were to leave it blank. Scoring Each response was scored as correct if it was the target word or a variation of the target word (e.g., bike for bicycle) or incorrect if it was not the target word. Pluralizations and tense variations were scored as correct. From the original set of 816 sentences we selected a subset of 252 sentences (129 noun sentences, 123 verb sentences) that were equally likely to elicit the target response [t (128) = 0.34, p = 0.74] and, therefore, did not differ in how well they elicited the target word. Lexical coding To examine whether nouns and verbs differed in the types of words retrieved, responses were coded following the lexical coding system used in D’Amico et al. (2001). Because this system was confined to nouns, we modified it to accommodate verb responses. Five codes were included:
(1) (2) (3) (4) (5)
Correct Target Synonym—same meaning as the target (trip for fall) Hyponym—subordinate or superordinate to the target (pizza for food) Categorical—same category as target (dress for jacket) Out-of-category—unrelated to the word (drink for hear)
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Because each target word had a different number of responses, we calculated the proportion of non-target responses (2–5 above) for each category. Targets were not included in the analysis. Frequency and Imageability Frequency values for target and non-target responses were obtained from the English CELEX Lexical Database (Max Planck Institute for Psycholinguistics 2001). To provide an imageability score, 52 undergraduate students were given lists of target words and non-target responses, identified as nouns or verbs, and asked to rate each on a scale from 1 (not imageable) to 7 (highly imageable). Finally, non-target responses were grouped by lexical code and the average frequency and imageability score for that lexical code was calculated.
Results We identified noun and verb sentences that did not differ significantly in how well they elicited the target word or in the number of different non-target words elicited: number of different non-target words—nouns = 426, verbs = 407, F(1, 250) = 0.00, p = 0.997. Lexical Category The proportion of each lexical code is shown in Fig. 1. A 2 (word class) × 5 (lexical code) Analysis of Variance (ANOVA) revealed a significant interaction, F(4, 247) = 6.23, p < 0.001, and main effect of lexical code, F(4, 247) = 637.79, p < 0.001. Follow up t tests revealed that the interaction was driven by a higher proportion of categorical responses for nouns than verbs, t (250) = 4.35, p < 0.01, and a higher proportion of out-of-category responses for verbs than nouns, t (250) = −3.12, p < 0.01, as predicted.
Fig. 1 Proportion (SE) of each lexical code type
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Imageability and Frequency Recall that, in English, nouns are generally more imageable than verbs (Chiarello et al. 1999) and verbs are more frequent than nouns (Masterson et al. 2008). This pattern was shown in the target words such that nouns were higher in imageability than verbs (Nouns: M = 5.73, S D = 1.97; Verbs: M = 4.43, S D = 1.6; t (76) = 3.2, p < 0.01) and verbs were higher in frequency than nouns (Nouns: M = 161.42, S D = 106.97; Verbs: M = 217.31, S D = 190.62; t (76) = −1.6, p = 0.11), although the latter comparison was not statistically significant. The focus of this study was on how imageability and frequency influence the type of word retrieved when the subject was not able to retrieve the target word. Non-target responses were also consistent with frequency and imageability expectations. Specifically, verb responses (M = 324.9, S D = 507.64) were higher in frequency than nouns (M = 139.45, S D = 152.68), t (249) = −3.95, p < 0.01, and noun responses (M = 5.49, S D = 1.93) were more imageable than verb responses (M = 4.2, S D = 1.62), t (249) = 5.73, p < 0.01. Multiple Regression Analyses A series of multiple regression analyses were conducted to examine whether frequency and imageability differentially influence the types of non-target nouns and verbs provided. This parsed the unique contribution of frequency and imageability on noun and verb responses. For each lexical category, lexical code proportion was entered as the dependent variable with frequency and imageability entered as predictor variables. Results are shown in Table 1. For nouns, imageability significantly predicted categorical responses, R 2 = 0.09, F(1, 125) = 12.43, and frequency significantly predicted synonym responses, R 2 = 0.033, F(1, 125) = 4.3, and out-of-category responses, R 2 = 0.06, F(1, 125) = 8.47. For hyponyms, which were relatively rare, neither frequency nor imageability was predictive. Verb retrieval followed a very different pattern. Frequency did not predict any response type while imageability significantly predicted categorical, R 2 = 0.05, F(1, 120) = 6.45 and out-of-category responses, R 2 = 0.151, F(1, 120) = 21.53. Similar to nouns, neither
Table 1 Multiple regression analysis results Dependent variable
Predictor variables
Nouns B coefficient
Synonym
Frequency Imageability
Hyponym
Frequency Imageability
Verbs Std. error
t value
B coefficient
−0.0007
0.00
−2.07*
−0.001
0.004
0.003
1.53
−0.0004
0.00
0.005
0.004
−0.0004
0.00
−0.85 1.28 −0.24
Std. error
t value
0.00
−0.81
0.006
0.97
−0.0007
0.00
1.19
0.005
0.004
−0.6
−0.0003
0.00
−0.82
0.006
Categorical
Frequency Imageability
0.048
0.014
3.53**
0.25
0.01
2.54*
Out-of Category
Frequency
0.001
0.00
2.91*
0.0005
0.00
0.83
Imageability
0.023
0.016
1.44
0.086
0.019
4.64**
* p < 0.05; ** p < 0.01
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variable predicted hyponym responses but, surprisingly, did not predict synonym responses either.
Discussion Here we report two major findings about the nature of noun and verb semantic retrieval using a sentence completion task. First, there was a greater proportion of out-of-category responses for verbs than nouns and a greater proportion of within-category responses for nouns than verbs. Second, imageability and frequency differentially influenced noun and verb non-target response types. Our findings that noun and verb sentences differed in non-target response types support Mätzig et al.’s (2009) picture naming results showing more within-category errors than outof-category errors for object nouns and more out-of-category than within-category errors for action verbs. Mätzig et al. (2009) proposed that noun errors were related to the semantic organization of object nouns but that verb responses may have been impacted by the picture stimuli. That a sentence completion task, which removes the influence of pictures, reveals a pattern similar to Mätzig et al. (2009) strengthens the argument that patterns of non-target responses in semantic retrieval tasks are indicative of differences in the underlying semantic organization of both nouns and verbs. Specifically, the hierarchical, perceptually-based noun structure primes participants to provide within-category responses, while the less distinct category lines within the verb semantic structure results in out-of-category responses (e.g., Huttenlocher and Lui 1979; Masterson et al. 2008; Mätzig et al. 2009; Vigliocco et al. 2011, 2004). In discussing within- and out-of-category responses, it is important to note that, for nouns, participants gave more out-of-category responses (28.9 %) than within-category responses (22.2 %), a similar pattern as verbs. However, for verbs the disparity between response types was much larger (42.8 % out-of-category and 8.93 % within-category). Looking within response types, within-category responses were more commonly nouns, whereas out-ofcategory responses were more commonly verbs. The high percentage of out-of-category responses for both word classes is not surprising given that our focus was on non-target responses, thus we aimed to elicit “incorrect” responses. That the task elicited so many out-of-category responses is informative and worth further investigation. This study also found that frequency and imageability differed in their effect on noun and verb non-target response types. Previous work reported that frequency and imageability influence how quickly and accurately typical adults name pictures (Alario et al. 2002; Almeida et al. 2007; Barry et al. 1997; Bates et al. 2001; Crepaldi et al. 2012; Ellis and Morrison 1998; Griffin and Bock 1998; Kauschke and von Frankenberg 2008). Here, imageability predicted both noun and verb within-category responses and out-of-category verb responses while frequency only predicted out-of-category and synonym noun responses. These findings support results from picture naming studies in which pictures of high imageability objects and actions resulted in faster and more accurate target naming (Crepaldi et al. 2012; Kauschke and von Frankenberg 2008). Taken together, the evidence highlights the importance of imageability on both noun and verb retrieval, made stronger by the fact that the effect is consistent across methodologies. The relationship between word frequency and noun and verb retrieval was more complex than that of imageability. Previous research found that higher frequencies positively impact noun retrieval but negatively influence verb retrieval. Our data showed a positive relationship between frequency and noun out-of-category and synonym responses; however, frequency
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did not influence verb response types. The previously reported high frequency disadvantage on verb retrieval was attributed in part to light verbs (Colombo and Burani 2002; Székely et al. 2005). In the current study, only 23 of the 2352 responses were light verbs and all of those were instances of go. This is likely because the sentence stimuli had one word missing in the sentence-final position and light verbs are usually followed by a referent, such as “make dinner” or “go to bed”. The small number of light verbs may have eliminated the inverse influence of frequency on verb retrieval, supporting Székely et al.’s (2005) claims. Thus, overall, our results indicate that word frequency more strongly influences noun retrieval than verb retrieval, for which there is no significant effect on response type. As many researchers have noted, studying differences between nouns and verbs is difficult due to the inherent differences between the two word classes. However, the more often similar trends appear across methodologies, the more robust our knowledge of the nature of noun and verb semantic organization becomes. Toward the goal of replicating findings across methodologies, our sentence completion task data focusing on non-target responses essentially parallel those from studies using picture naming tasks to examine target responses. Specifically, we show similar patterns in the types of words retrieved and effects of imageability on noun and verb retrieval. Differences between methods appear when examining the effect of frequency on verb retrieval. The potential influence of light verbs on this effect motivates future work examining the nature of the frequency-verb retrieval relationship.
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Lexical Retrieval of Nouns and Verbs in a Sentence Completion Task Alyson D. Abel · Mandy J. Maguire · Fizza M. Naqvi · Angela Y. Kim
© Springer Science+Business Media New York 2014
Abstract This study explored noun and verb retrieval using a sentence completion task to expand upon previous findings from picture naming tasks. Participants completed sentences missing either a target noun or verb in the final position. Non-target responses were coded for substitution type, imageability and frequency. Like picture naming, nouns and verbs differed in non-target substitution type—within-category substitutions were primarily nouns and outof-category substitutions were primarily verbs. Imageability predicted multiple substitution types for both word classes, whereas frequency predicted noun substitution types but not verbs. Findings support theories of noun and verb differences in semantic retrieval, showing the robustness of this effect across methodologies, and shed new light on the influence of imageability and frequency during semantic retrieval. Keywords
Semantic retrieval · Word class · Sentence completion
Introduction The fact that nouns and verbs differ in semantics, syntax, and phonology is well established. How these differences influence behaviors such as word retrieval remains an area of debate. Studies of patients with aphasia and healthy adults consistently reveal poorer accuracy and slower retrieval of action verbs compared to object nouns (Colombo and Burani 2002; Crepaldi et al. 2012; De Bleser and Kauschke 2003; Druks 2002; Federmeier and Bates 1997; Kauschke and von Frankenberg 2008; Luzzatti et al. 2002; Székely et al. 2005). There is also evidence that object noun and action verb retrieval are differentially influenced by factors like
A. D. Abel (B) · M. J. Maguire · A. Y. Kim Callier Center for Communication Disorders, School of Behavioral and Brain Sciences, The University of Texas at Dallas, 1966 Inwood Rd., Dallas, TX 75235, USA e-mail: [email protected] F. M. Naqvi Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, TX 75080, USA
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word frequency and imageability (Barry et al. 2001; Colombo and Burani 2002; Cuetos and Alija 2003; Kauschke and von Frankenberg 2008; Székely et al. 2005). These studies primarily use picture naming tasks, which are useful for studying aphasic populations with a range of language difficulties but have limitations. For example, object recognition precedes lexical retrieval (Barry et al. 2001), thus any differences between nouns and verbs in recognition could influence the accuracy and speed of word production. Thus, recognition differences may ultimately be incorrectly attributed to retrieval. Further, still image stimuli in picture naming tasks limit the words that can be used, restricting researchers’ ability to effectively manipulate variables like imageability and word frequency that affect word retrieval (Barry et al. 2001; Crepaldi et al. 2006; Székely et al. 2005). In this paper, we expand on picture naming study findings by investigating noun and verb retrieval using a sentence completion task. Sentence completion bypasses some of the potential problems with picture naming and allows a closer examination of word frequency and imageability effects on retrieval. Our goal is to better understand the organization of object noun and action verb semantic categories by studying the errors made with difficult noun and verb retrieval. Picture naming studies clearly show slower and less accurate verb retrieval versus noun retrieval, but these studies are limited in how they can interpret these findings. By examining retrieval errors, responses provided when participants are unable to retrieve the target word, researchers can provide more insight into how differences in noun and verb semantic organization influence retrieval (Budd et al. 2011; Kambanaros et al. 2013; Kauschke et al. 2007; Mätzig et al. 2009). For instance, errors in object noun picture naming are often withincategory members (e.g., lady for nun) while action verb picture naming errors are more likely to be from completely different semantic categories (e.g., yawning for laughing; Mätzig et al. 2009). Within-category noun errors are consistent with the theory that nouns, object nouns in particular, are organized hierarchically based heavily on shared perceptual features (i.e., living objects, non-living objects, tools and body parts; Huttenlocher and Lui 1979; Mätzig et al. 2009). For example, the category animal includes dogs, horses, and wombats, all of whom breathe, move, have fur, etc. Subdividing dog into categories like beagles and dobermans, the number of shared category features increases even more. That noun errors in picture naming come from the same semantic category supports this hierarchical organization (Mätzig et al. 2009). The predominance of out-of-category verb errors during retrieval has been interpreted in two ways. The first interpretation is task-related, namely that action pictures are harder to recognize than object pictures because they have a less direct relationship (Mätzig et al. 2009) or more complex relationship (Federmeier and Bates 1997) with their label. Alternatively, like within-category noun errors, out-of-category verb errors may reveal information about verbs’ semantic organization. Verb organization is believed to be matrix-like without welldefined levels of structure (Masterson et al. 2008; Vigliocco et al. 2011, 2004). As a result, action verbs do not show clear clustering within or across categories, as evidenced by adult similarity ratings of verbs that cross expected category lines (e.g., forms of communication show a close relationship to forms of exchange; Vigliocco et al. 2004). It follows that outof-category verb picture naming errors may be due to retrieval of semantically similar words but that, for verbs, “semantically similar words” are out-of-category or cross-category while “semantically similar words” for nouns are within-category. If this holds true, differences in object noun and action verb errors in picture naming are likely related to noun and verb semantic organization as opposed to task-related demands. Sentence completion tasks remove the element of image recognition and, therefore, may be able to clarify whether retrieval errors are due to task demands or semantic organization. Sentence completion tasks also allow
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more flexibility to study other features that influence word retrieval such as imageability and frequency (Berndt et al. 2002). Imageability and frequency are consistently identified as influencing word retrieval in typical adults, aphasic patients and children. Differences in how these features impact noun and verb retrieval can provide insight into semantic organization (Colombo and Burani 2002; Kauschke and von Frankenberg 2008; Masterson et al. 2008; Mätzig et al. 2009; Székely et al. 2005). Typical adults retrieve high imageability words faster and more accurately than low imageability words regardless of word class (Crepaldi et al. 2012; Kauschke and von Frankenberg 2008). Although, to our knowledge, no one has directly compared the degree of the imageability effect between nouns and verbs, it seems that noun retrieval would benefit more from imageability because, in general, nouns are more imageable than verbs (Bird et al. 2000, 2003; Chiarello et al. 1999). Effects of imageability may be greater for picture naming tasks because words that are highly imageable are likely easier to depict and identify than low imageability words. In this case, imageability differences during picture naming may be due to effects on recognition rather than retrieval. Thus, the influence of imageability on word retrieval may be somewhat different in a sentence completion task without visual aids. Word frequency effects on lexical retrieval are more complex than those of imageability. High frequency nouns are named more quickly and accurately than low frequency nouns (Alario et al. 2002; Almeida et al. 2007; Barry et al. 1997; Bates et al. 2001; Colombo and Burani 2002; Ellis and Morrison 1998; Griffin and Bock 1998; Kauschke and von Frankenberg 2008; Székely et al. 2005) but, for verbs, high frequency has the opposite effect (Colombo and Burani 2002; Székely et al. 2005). Székely et al. (2005) attribute the frequency disadvantage for verb retrieval to counting “light verbs” (i.e., go, do and make), which are very high frequency, as valid responses. Picture naming is slower for light verbs than more specific but less frequent responses (e.g., make dinner vs. cook), increasing the latency for high frequency verbs. Thus, while the imageability effect may be related to the visual aids provided by pictures, it is unlikely that the influence of frequency on lexical retrieval would change across methodologies. Here we used a sentence completion task that requires participants to retrieve a word from their lexicon without the aid of picture stimuli. To our knowledge, the current study is the first to examine noun and verb retrieval in an unaided sentence completion task.1 Stimuli were sentences that were equally likely to elicit target nouns and verbs and analyses focused on non-target responses, the words participants gave when they were unable to retrieve the target. Specifically, we examined types of non-target responses (i.e., withincategory or out-of-category) as well as the frequency and imageability of the non-target responses. Similar to Mätzig et al.’s (2009) picture naming findings, we predicted that nontarget responses for noun sentences would be within-category (e.g., cat for dog) whereas non-target responses for verb sentences would be out-of-category (e.g., see for talk). These findings would suggest that noun and verb retrieval differences here and in previous picture naming tasks relate to differences in semantic organization as opposed to differences in object and action recognition. The second prediction was that effects of frequency and imageability on non-target responses would parallel effects found in picture naming studies. Specifically, we anticipated that imageability would strongly influence noun and verb retrieval but that word frequency would differentially affect the word classes. This finding would strengthen 1 Note that in the sentence completion task used by Crepaldi et al. (2006) the subject is cued to the missing
word by the presence of the target word in a different form in a sentence preceding the open-ended sentence and the sentence completion task used by Griffin and Bock (1998) provides a picture of the target word in the position of the missing word.
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arguments about the importance of imageability in lexical retrieval regardless of word class and help us better understand the role of word frequency in retrieval.
Methods Participants A total of 238 undergraduate students in Language Development courses completed subsets of the total list for extra credit. Stimuli Stimuli included 816 sentences (411 nouns and 405 verbs) that followed these criteria: (a) 6–9 words long, (b) the target word was in the sentence-final position, (c) all words and concepts were appropriate for young children (i.e., target words had an age of acquisition of 30 months or earlier; Fenson et al. 1994), (d) target nouns were preceded by either a determiner (a, the) or a possessive (his, her), (e) target verbs were preceded by either an infinitival to or a modal (would, could, will). Examples include, “When you leave be sure to lock the ______” (target—door) and “The jar of pickles was hard to _____” (target—open). Procedure Each participant received a pseudo-randomized list of 16–25 sentences with the target word removed and replaced with a blank line. No target word appeared more than once on the same list. Participants were instructed to complete each sentence with the first single word that came to mind. If unable to come up with a possible response, they were to leave it blank. Scoring Each response was scored as correct if it was the target word or a variation of the target word (e.g., bike for bicycle) or incorrect if it was not the target word. Pluralizations and tense variations were scored as correct. From the original set of 816 sentences we selected a subset of 252 sentences (129 noun sentences, 123 verb sentences) that were equally likely to elicit the target response [t (128) = 0.34, p = 0.74] and, therefore, did not differ in how well they elicited the target word. Lexical coding To examine whether nouns and verbs differed in the types of words retrieved, responses were coded following the lexical coding system used in D’Amico et al. (2001). Because this system was confined to nouns, we modified it to accommodate verb responses. Five codes were included:
(1) (2) (3) (4) (5)
Correct Target Synonym—same meaning as the target (trip for fall) Hyponym—subordinate or superordinate to the target (pizza for food) Categorical—same category as target (dress for jacket) Out-of-category—unrelated to the word (drink for hear)
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Because each target word had a different number of responses, we calculated the proportion of non-target responses (2–5 above) for each category. Targets were not included in the analysis. Frequency and Imageability Frequency values for target and non-target responses were obtained from the English CELEX Lexical Database (Max Planck Institute for Psycholinguistics 2001). To provide an imageability score, 52 undergraduate students were given lists of target words and non-target responses, identified as nouns or verbs, and asked to rate each on a scale from 1 (not imageable) to 7 (highly imageable). Finally, non-target responses were grouped by lexical code and the average frequency and imageability score for that lexical code was calculated.
Results We identified noun and verb sentences that did not differ significantly in how well they elicited the target word or in the number of different non-target words elicited: number of different non-target words—nouns = 426, verbs = 407, F(1, 250) = 0.00, p = 0.997. Lexical Category The proportion of each lexical code is shown in Fig. 1. A 2 (word class) × 5 (lexical code) Analysis of Variance (ANOVA) revealed a significant interaction, F(4, 247) = 6.23, p < 0.001, and main effect of lexical code, F(4, 247) = 637.79, p < 0.001. Follow up t tests revealed that the interaction was driven by a higher proportion of categorical responses for nouns than verbs, t (250) = 4.35, p < 0.01, and a higher proportion of out-of-category responses for verbs than nouns, t (250) = −3.12, p < 0.01, as predicted.
Fig. 1 Proportion (SE) of each lexical code type
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Imageability and Frequency Recall that, in English, nouns are generally more imageable than verbs (Chiarello et al. 1999) and verbs are more frequent than nouns (Masterson et al. 2008). This pattern was shown in the target words such that nouns were higher in imageability than verbs (Nouns: M = 5.73, S D = 1.97; Verbs: M = 4.43, S D = 1.6; t (76) = 3.2, p < 0.01) and verbs were higher in frequency than nouns (Nouns: M = 161.42, S D = 106.97; Verbs: M = 217.31, S D = 190.62; t (76) = −1.6, p = 0.11), although the latter comparison was not statistically significant. The focus of this study was on how imageability and frequency influence the type of word retrieved when the subject was not able to retrieve the target word. Non-target responses were also consistent with frequency and imageability expectations. Specifically, verb responses (M = 324.9, S D = 507.64) were higher in frequency than nouns (M = 139.45, S D = 152.68), t (249) = −3.95, p < 0.01, and noun responses (M = 5.49, S D = 1.93) were more imageable than verb responses (M = 4.2, S D = 1.62), t (249) = 5.73, p < 0.01. Multiple Regression Analyses A series of multiple regression analyses were conducted to examine whether frequency and imageability differentially influence the types of non-target nouns and verbs provided. This parsed the unique contribution of frequency and imageability on noun and verb responses. For each lexical category, lexical code proportion was entered as the dependent variable with frequency and imageability entered as predictor variables. Results are shown in Table 1. For nouns, imageability significantly predicted categorical responses, R 2 = 0.09, F(1, 125) = 12.43, and frequency significantly predicted synonym responses, R 2 = 0.033, F(1, 125) = 4.3, and out-of-category responses, R 2 = 0.06, F(1, 125) = 8.47. For hyponyms, which were relatively rare, neither frequency nor imageability was predictive. Verb retrieval followed a very different pattern. Frequency did not predict any response type while imageability significantly predicted categorical, R 2 = 0.05, F(1, 120) = 6.45 and out-of-category responses, R 2 = 0.151, F(1, 120) = 21.53. Similar to nouns, neither
Table 1 Multiple regression analysis results Dependent variable
Predictor variables
Nouns B coefficient
Synonym
Frequency Imageability
Hyponym
Frequency Imageability
Verbs Std. error
t value
B coefficient
−0.0007
0.00
−2.07*
−0.001
0.004
0.003
1.53
−0.0004
0.00
0.005
0.004
−0.0004
0.00
−0.85 1.28 −0.24
Std. error
t value
0.00
−0.81
0.006
0.97
−0.0007
0.00
1.19
0.005
0.004
−0.6
−0.0003
0.00
−0.82
0.006
Categorical
Frequency Imageability
0.048
0.014
3.53**
0.25
0.01
2.54*
Out-of Category
Frequency
0.001
0.00
2.91*
0.0005
0.00
0.83
Imageability
0.023
0.016
1.44
0.086
0.019
4.64**
* p < 0.05; ** p < 0.01
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variable predicted hyponym responses but, surprisingly, did not predict synonym responses either.
Discussion Here we report two major findings about the nature of noun and verb semantic retrieval using a sentence completion task. First, there was a greater proportion of out-of-category responses for verbs than nouns and a greater proportion of within-category responses for nouns than verbs. Second, imageability and frequency differentially influenced noun and verb non-target response types. Our findings that noun and verb sentences differed in non-target response types support Mätzig et al.’s (2009) picture naming results showing more within-category errors than outof-category errors for object nouns and more out-of-category than within-category errors for action verbs. Mätzig et al. (2009) proposed that noun errors were related to the semantic organization of object nouns but that verb responses may have been impacted by the picture stimuli. That a sentence completion task, which removes the influence of pictures, reveals a pattern similar to Mätzig et al. (2009) strengthens the argument that patterns of non-target responses in semantic retrieval tasks are indicative of differences in the underlying semantic organization of both nouns and verbs. Specifically, the hierarchical, perceptually-based noun structure primes participants to provide within-category responses, while the less distinct category lines within the verb semantic structure results in out-of-category responses (e.g., Huttenlocher and Lui 1979; Masterson et al. 2008; Mätzig et al. 2009; Vigliocco et al. 2011, 2004). In discussing within- and out-of-category responses, it is important to note that, for nouns, participants gave more out-of-category responses (28.9 %) than within-category responses (22.2 %), a similar pattern as verbs. However, for verbs the disparity between response types was much larger (42.8 % out-of-category and 8.93 % within-category). Looking within response types, within-category responses were more commonly nouns, whereas out-ofcategory responses were more commonly verbs. The high percentage of out-of-category responses for both word classes is not surprising given that our focus was on non-target responses, thus we aimed to elicit “incorrect” responses. That the task elicited so many out-of-category responses is informative and worth further investigation. This study also found that frequency and imageability differed in their effect on noun and verb non-target response types. Previous work reported that frequency and imageability influence how quickly and accurately typical adults name pictures (Alario et al. 2002; Almeida et al. 2007; Barry et al. 1997; Bates et al. 2001; Crepaldi et al. 2012; Ellis and Morrison 1998; Griffin and Bock 1998; Kauschke and von Frankenberg 2008). Here, imageability predicted both noun and verb within-category responses and out-of-category verb responses while frequency only predicted out-of-category and synonym noun responses. These findings support results from picture naming studies in which pictures of high imageability objects and actions resulted in faster and more accurate target naming (Crepaldi et al. 2012; Kauschke and von Frankenberg 2008). Taken together, the evidence highlights the importance of imageability on both noun and verb retrieval, made stronger by the fact that the effect is consistent across methodologies. The relationship between word frequency and noun and verb retrieval was more complex than that of imageability. Previous research found that higher frequencies positively impact noun retrieval but negatively influence verb retrieval. Our data showed a positive relationship between frequency and noun out-of-category and synonym responses; however, frequency
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did not influence verb response types. The previously reported high frequency disadvantage on verb retrieval was attributed in part to light verbs (Colombo and Burani 2002; Székely et al. 2005). In the current study, only 23 of the 2352 responses were light verbs and all of those were instances of go. This is likely because the sentence stimuli had one word missing in the sentence-final position and light verbs are usually followed by a referent, such as “make dinner” or “go to bed”. The small number of light verbs may have eliminated the inverse influence of frequency on verb retrieval, supporting Székely et al.’s (2005) claims. Thus, overall, our results indicate that word frequency more strongly influences noun retrieval than verb retrieval, for which there is no significant effect on response type. As many researchers have noted, studying differences between nouns and verbs is difficult due to the inherent differences between the two word classes. However, the more often similar trends appear across methodologies, the more robust our knowledge of the nature of noun and verb semantic organization becomes. Toward the goal of replicating findings across methodologies, our sentence completion task data focusing on non-target responses essentially parallel those from studies using picture naming tasks to examine target responses. Specifically, we show similar patterns in the types of words retrieved and effects of imageability on noun and verb retrieval. Differences between methods appear when examining the effect of frequency on verb retrieval. The potential influence of light verbs on this effect motivates future work examining the nature of the frequency-verb retrieval relationship.
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