Predictors of picture naming and picture categorization in Spanish
Goals and objectives of correlation analysis. Research of psycholinguistic predictor variables. Determination of lexical frequency and H index when naming images. Optimization of the task of categorizing and classifying images of high complexity.
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| Язык | английский |
| Дата добавления | 30.10.2020 |
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1Federico Gonzalo Soriano Institute of Cognitive and Translational Neuroscience
2Buenos Aires University
3Institute of Cognitive and Translational Neuroscience
4Institute for Reasearch in Psychology, Buenos Aires University
Predictors of picture naming and picture categorization in Spanish
1Macarena Martrnez Cuitino
2Virginia Jaichenco
3Brenda Steeb
4Juan Pablo Barreyro
Argentina
Abstract
The aim of this paper was to identify which psycholinguistic variables are better predictors of performance for healthy participants in a picture naming task and in a picture categorization task. A correlation analysis and a Path analysis were carried out. The correlation analysis showed that naming accuracy and naming latency are significant and positively correlated with lexical frequency and conceptual familiarity variables, whereas they are negatively correlated with H index. Reaction times in the categorization task were negatively correlated with lexical frequency and conceptual familiarity variables and positively correlated with visual complexity variable. The Path analysis showed that subjective lexical frequency and H index are the better predictors for picture naming task. In picture categorization task, for reaction times, the better predictor variables were subjective lexical frequency, conceptual familiarity and visual complexity. These findings are discussed considering previous works on the field.
Keywords: psycholinguistic variables, predictors variables, picture naming, picture categorization, accuracy, reaction times.
Анотація
Предиктори найменування зображень та їхньої ка- тегоризації в іспанській мові. Квітіньйо Макарена Мартінез, Соріано Федеріко Ґонзало, Яйченко Вірджинія, Стіб Бренда, Барейро Хуан Пабло.
Мета статті - визначити, які психолінгвістичні змінні є кращими предикторами продуктивності найменування зображень та їх категоризації серед здорових учасників. У дослідженні застосовано кореляційний аналіз та аналіз віднаходження шляхів. Кореляційний аналіз доводить, що точність та латентність назв є високими, і вони позитивно корелюють з лексичною частотою та концептуальною схожістю змінних, проте негативно співвідносяться з індексом H. Кількість реакцій у завданні на категоризацію негативно співвідносилася з лексичною частотою та концептуальною схожістю змінних, проте позитивно корелювала зі змінною візуальної складності. Аналіз віднаходження шляхів показав, що суб'єктивна лексична частота і індекс H - кращі предиктори у завданні найменування зображень. У завданні класифікації зображень на кількість реакцій, предикторними змінними були суб'єктивна лексична частота, концептуальна схожість та візуальна складність. Отримані результати обговорюються в поданій статті, з огляду на попередні наукові праці у цій сфері.
Ключові слова: психолінгвістичні змінні, предикторні змінні, найменування зображень, категоризація зображень, точність, кількість реакцій.
Introduction
The picture naming task (PNT) is one of the most used paradigms in psycholinguistics, cognitive psychology and neuropsychology. The aim of thi s task is to evoke the first name that comes to mind when a picture (e.g. object or action) is showed. For this task, the dependent variable is the elapsed time, measured in milliseconds, since the picture is presented until the subject begins to name it. Despite being a task that appears to be very simple, many successive cognitive processes, which under normal conditions are performed very quickly and automatically, are necessary. This is the most commonly used task to decide how mental representations could be retrieved from memory (Carroll & White, 1973; Humphreys, Riddoch, & Quinlan, 1988; Oldfield & Wingfield, 1965; Snodgrass & Yuditsky, 1996).
Previous studies have assessed the impact of psycholinguistic factors in PNT. Many factors could influence different stages, such as visual recognition, concept access or word retrieval. Characterize and quantify these factors in some variables would aid to identify which is the best predictor of accuracy and speed in healthy subjects' performance (Alario et al., 2004; Barry, Morrison, & Ellis, 1997; Cuetos, Ellis, & Alvarez, 1999; Manoiloff, Artstein, Canavoso, Fernandez, & Segrn, 2010). These factors should be similar across different languages but could be different depending on the material used (Khwaileh, Mustafawi, Herbert, & Howard, 2018).
A less frequently employed but equally useful way to assess conceptual retrieval is picture categorization task (PCT). In PCT, subjects must classify stimuli into one of a set of categories (for example, dog as an ANIMAL or hammer as a TOOL). Categorization implies deciding whether an item belongs to certain classification (e.g. a semantic category or a semantic domain). In this task, it is possible to use pictures or words. PCT should appear to be easier than word categorization task since perceptual attributes provide information about the membership of the item to some semantic categories (e.g. the face or the legs in an animal and a handle or a blade in a tool).
The main variables identified which affect PNT are: visual complexity, conceptual familiarity, lexical frequency, name agreement, length, typicality and age of acquisition (Bakhtiar & Weekes, 2015; Balota, Pilotti, & Cortese, 2001; Barry, et al., 1997; Bates, Burani, D'amico, & Barca, 2001; Cuetos, et al., 1999; Cycowicz,
Friedman, Rothstein, & Snodgrass, 1997; Khwaileh, et al., 2018; Laws, 1999; Snodgrass & Vanderwart, 1980; Snodgrass & Yuditsky, 1996; Szekely & Bates, 2000; Szekely et al., 2003). In a PCT, the most frequently variables which affect subjects performance are: visual complexity, conceptual familiar, lexical frequency, typicality and age of acquisition (Barbon & Cuetos, 2006; Morrison, Ellis, & Quinlan, 1992). Until today, the best predictors for successful performance in picture naming and picture categorization are discussed.
Lexical frequency is a measure that denotes the degree of activation of a word. This variable refers to how frequent a word is activated in a specific language. It is associated with accuracy and speed in PNT. A word with higher frequency will be more accurately and quickly recovered (Humphreys, et al., 1988; Martein, 1995; Oldfield & Wingfield, 1965). Oldfield and Wingfield (1965) identified, using a PNT with 26 pictures, a linear relationship between naming and latency times. They found a negatively correlation between naming latency and lexical frequency. This means that the names of pictures represented by words more frequently used are more available than the names that belong to words which are not so frequently used.
Age of acquisition (AoA) refers to the age at which a word is learned. The earlier a word is learned, the faster it is recovered and the higher accuracy in PNT is (Akinina et al., 2015; Bonin, Chalard, Meot, & Fayol, 2002; Bonin, Peereman, Malardier, Meot, & Chalard, 2003; Cameirao & Vicente, 2010). AoA is frequently reported as highly correlated with lexical frequency. Words that are acquired earlier tend to be high frequency (Meschyan & Hernandez, 2002). Carroll and White (1973) considered this variable a better predictor in PNT than lexical frequency. Moreover, AoA was the only significant variable in their multiple regression analysis to explain naming latencies times. Similar findings were reported by Morrison et al. (1992) using a multiple regression analysis with a PNT. In this study, the only variables that had significant effect in latencies times were AoA and the number of phonemes. Barry, Morrison and Ellis (1997) identified that speed naming was predicted by lexical frequency, the interaction between AoA and lexical frequency and name agreement. Also, Iyer et al. (2001) identified that the AoA was a slightly better predictor of latencies times than lexical frequency and also, by another variable, conceptual familiarity. They found a high correlation between AoA and latency times and also between latency times and lexical frequency. Moreover, in the same study, using a Stepwise Regression analysis, the authors identified that AoA and lexical frequency as independent variables. Some evidence suggests that the lexical frequency effect could also be partiality explained by the age of acquisition (Carroll & White, 1973). The AoA effect was also present in pictures categorization task (Barbon & Cuetos, 2006; Carroll & White, 1973). Pictures that represent concepts learned earlier are categorized faster.
Visual complexity is one of the variables which could affect accuracy and also latency times (Ellis & Morrison, 1998; Szekely & Bates, 2000). This variable refers to the numbers of lines and details included in the pictures. Nevertheless, some studies assessing picture naming in adults fail to identify visual complexity as a predictor variable in latency times (Barry, et al., 1997; Bonin, et al., 2002; Bonin, et al., 2003; Cuetos, et al., 1999; Khwaileh, Body, & Herbert, 2014; Snodgrass & Yuditsky, 1996), while others were able to identify it (D'amico, Devescovi, & Bates, 2001; Shao, Roelofs, & Meyer, 2012). The major problem is that visual complexity is frequently mistaken for another variables associated with the picture representation. In addition, it has been reported that visual complexity is negatively correlated with lexical frequency and conceptual familiarity and positively with AoA (Barry, et al., 1997; Cycowicz, et al., 1997; Ellis & Morrison, 1998; Morrison, Chappell, & Ellis, 1997; Sanfeliu & Fernandez, 1996; Snodgrass & Vanderwart, 1980; Snodgrass & Yuditsky, 1996). This means that pictures that are visually more complex are denoted by less frequent words and represented by less familiar concepts. Moreover, concepts representing more complex pictures are acquired later in life. Also, Cycowicz et al. (1997) identified that visual complexity ratings measured in young children for a set of 400 pictures did not differ from the adult's rating. This means that the conceptual familiarity or lexical frequency does not play a role when visual complexity ratings are measured.
Name agreement is another variable that predicts the performance in a PNT. This refers to the degree to which a concept is associated only to a specific name. Pictures with high name agreement were named with shorter latencies (Alario & Ferrand, 1999; Barry, et al., 1997; Boukadi, Zouaidi, & Wilson, 2016; Martinez - Cuitino & Vivas, In press).This variable is frequently negatively correlated with visual complexity. Snodgrass and Vanderwart (1980) identified that more visually complex pictures produce more alternative names (i.e. H value). This value is another measure regarding name agreement. When H is equal to 0, a perfect name agreement is reached. When H value increases, the name agreement decreases. This correlation was replicated by Cycowicz et al. (1997) but other studies did not find it (Barry, et al., 1997; Bonin, et al., 2002; Bonin, et al., 2003; Cuetos, et al., 1999; Ellis & Morrison, 1998; Sanfeliu & Fernandez, 1996; Snodgrass & Yuditsky, 1996).
Conceptual familiarity refers to daily contact with an object or a concept within a specific language or culture. This variable predicts reaction times in PNT. Familiar concepts are retrieved faster (Akinina, et al., 2015; Boukadi, et al., 2016; Ellis & Morrison, 1998; Snodgrass & Yuditsky, 1996). Conceptual familiarity is strongly correlated with visual complexity and also with lexical frequency (Snodgrass & Vanderwart, 1980). Because of the high correlation, Bates et al (2003) assumed that the frequency effect is a conceptual effect like conceptual accessibility. Almeida et al. (2007) considered that this measure affects the lexical level since it was presented in a PNT but it did not affect the semantic level, since it was absent in the categorization task.
Laws (1999) postulated that conceptual familiarity is a construct which includes a variety of concepts: conceptual, visual and functional familiarity. Laws and Neve (1999) identified that only visual familiarity is a good predictor in restricted picture naming tasks. But, until now, these findings have not been replicated.
Another variable that could be a possible predictor in both tasks is the semantic domain. The pictures used in naming and categorization tasks represent living things (LT) and inanimate objects (IO). Differences in performance with both types of stimuli have been reported with neurological patients and control subjects (Albanese, Capitani, Barbarotto, & Laiacona, 2000; Capitani, Laiacona, Barbarotto, & Trivelli, 1994; Gaffan & Heywood, 1993; Laws, 2000; Laws & Neve, 1999; Lloyd-Jones & Humphreys, 1997). Picture representations of LT and IO significantly vary in complexity: LT are visually more complex than IO (Laws, 2000), words representing LT are less frequent than the ones referring to IO (Warrington & McCarthy, 1983), conceptual familiarity is higher for IO(Laws, 2000), and AoA between both domains is dissimilar.
Typicality is another variable to take into consideration. Typicality measures if an exemplar is representative of the other exemplars included in the same semantic category (e.g. tomato or lemon for the fruit category). More typical exemplars of a category are named and categorized faster (Martmez-Cuitino & Vivas, In press). Using a regression analysis, Morrison et al. (1992) identified that typicality was the only variable that predicted responses times.
The purpose of this work is to identify which variables are best predictors of the performance (accuracy and speed) of healthy participants in two tasks (naming and categorizing linear black-and-white pictures), employing a wide corpus of stimuli that belong to different semantic categories.
1. Methods
Participants. For this study, 48 participants were assessed in the picture naming task (30 % male and 70 % female), with a mean age of 25.96 years (SD = 5.78). In the categorization task, 35 participants were evaluated (45.7 % male and 54.3 % female), with a mean age of 27.68 years (SD = 6.06).
All participants were undergraduate students, Spanish native speakers, and none of them suffered from alcoholism, drugs abuse, psychiatric or neurological diseases. All participants were right handed. They did not present visual impairments (or they had it corrected) by the time of the assessment (e.g. glasses or contact lenses).
Participants took part of the study voluntarily and signed an informed consent to participate. They did not receive any money retribution for their collaboration. The study was conducted in agreement with the Declaration of Helsinki and approved by the institutional ethics' committee.
Materials. The 400 pictures from Cycowicz et al. (Cycowicz, et al., 1997) were used to design both tasks In this material are included 260 black and white, simple pictures designed by Snodgrass and Vanderwart (1980) an also the pictures taken from Berman et al. (1989). This set of stimuli has normative data for Argentine population (Manoiloff, et al., 2010; Martmez-Cuitino, Barreyro, Wilson, & Jaichenco, 2015). This set of pictures includes 108 LT and 292 IO.
Procedure. Participants were assessed during a 40-minute individual session for naming task, and a 20-minute session for categorization task. Both tasks were designed using the DMDX software (Forster & Forster, 2003) and were administered in a 15- inch-screen TOSHIBA laptop computer.
Stimuli were presented in a pseudo-random way, in four stages of 100 items each, with three breaks in between. For the naming task, items were counterbalanced according to initial phonemes (Szekely, et al., 2003).
Picture naming. A practice with 10 items was presented before the task. A fixation point (*) appeared during 400 milliseconds (ms) on the screen. Then one picture was presented for 800 ms. and, finally, a blank screen to name the picture was presented during 4000 ms. This screen remained even if the subjects named the picture before the 800 ms had passed. No feedback was given regarding correct or wrong responses. Responses were subsequently analyzed with the Check Vocal program (Protopapas, 2007).
Categorization task. A practice with 10 items was presented before the task. A fixation point (*) appeared during 400 ms on the screen, then one picture was presented for 800 ms and, finally, a blank screen appeared during 2000 ms. Subjects were instructed to press the S key on the keyboard if the item belonged to the LT domain or the N key if it did not. If the participant pressed the key before the provided time was over, the next item was automatically presented. No feedback was given regarding correct or wrong responses.
Data analysis. A Pearson's r correlation analysis was performed for picture naming and picture categorization considering the following variables: lexical frequency, conceptual familiarity, visual complexity, age of acquisition, H index, correct answers and reaction times.
In order to identify predictive variables, a Path analysis was performed, using structural equation modeling. The analysis was executed by employing maximum likelihood estimate between measures as an input for the data analysis (Arbuckle, 2003).
The model proposed and tested had two dependent variables: a) a categorization latent factor created from correct categorization answer and categorization reaction times and, b) a naming latent factor created from correct naming answer and naming latency times. The independent variables in the model were lexical frequency, conceptual familiarity, visual complexity, age of acquisition and H index. The independent variables were correlated between them.
2. Results
First, the descriptive statistics of the variables are presented en Table 1.
Table 1 Descriptive statistics
|
M |
SD |
Min |
Max |
Sk |
Ku |
||
|
CNA |
0,84 |
0,20 |
0,00 |
1,00 |
-1,61 |
2,21 |
|
|
NLT |
1127,02 |
287,75 |
682,97 |
2289,91 |
0,75 |
0,31 |
|
|
CCA |
0,96 |
0,06 |
0,63 |
1,00 |
-2,03 |
4,54 |
|
|
CRT |
498,73 |
39,99 |
370,60 |
683,22 |
0,85 |
1,57 |
|
|
LF |
2,59 |
1,22 |
1,03 |
5,00 |
0,49 |
-1,15 |
|
|
H |
0,79 |
0,72 |
0,00 |
2,66 |
0,67 |
-0,71 |
|
|
CF |
2,86 |
1,17 |
1,14 |
5,00 |
0,28 |
-1,27 |
|
|
AoA |
2,52 |
0,69 |
1,14 |
4,72 |
0,43 |
-0,12 |
|
|
VC |
3,14 |
0,96 |
1,00 |
4,94 |
-0,23 |
-0,79 |
CNA = Correct naming answer, NLT = Naming latency times, CCA = Correct categorization answer, CRT = Categorization reaction times; LF = Lexical Frequency, H = H Index, CF = Conceptual Familiarity, AoA = Age of Acquisition; VC = Visual Complexity.
The results of correlation analysis between the variables and the results of the naming and categorization tasks are presented in Table 2.
Accuracy in naming task (CNA) is significantly and positively correlated with lexical frequency (LF) and conceptual familiarity (CF), and negatively with H index.
The same results were found for reaction times (NLT). This implies that words with higher lexical frequency require less time to be retrieved, while words that were acquired later in life require more time. Also, words with higher conceptual familiarity and words with lower values in H index (higher name agreement) require less time.
Table 2 Correlations between variables and results in the naming and categorization tasks
*p < .05, **p < .01, ***p < .001
CNA = Correct naming answer, NLT = Naming latency times, CCA = Correct categorization answer, CRT = Categorization reaction times; LF = Lexical Frequency, H = H Index, CF = Conceptual Familiarity, AoA = Age of Acquisition; VC = Visual Complexity.
Reaction times in categorization task (CRT) are negatively correlated with lexical frequency and conceptual familiarity, and positively with visual complexity (VC). In the categorization task, however, pictures with higher visual complexity, lower familiarity and lower lexical frequency required longer times to be categorized.
Subsequently, and with the aim of identify variables affecting naming and categorization of simple drawings, a path analysis was carried out (Arbuckle, 2003).
The model showed a good adjustment of the data to the model (x2(14) = 21.24, p = .S0, x2/df = 1.52, CFI = .99; TLI = .98, AGFI = .96; RMSEA = .04). The model is presented in Figure 1.
Figure 1. Path Analysis Model proposed
The path analysis showed that the picture naming task is predicted by H index (P = -.30; p < .001) and lexical frequency (P = -.17; p < .05), and picture categorization is only predicted by semantic domain (P = -.99;p < .01).
3. Discussion
The aim of this paper was to identify which psycholinguistic variables are the best predictors of performance (accuracy and speed) in naming and categorization tasks with healthy participants. We employed a wide corpus of stimuli (Cycowicz, et al., 1997) that belongs to different semantic categories. This material has normative data for Argentinian population. A correlation analysis was carried out, at first, to study relation among the variables, and a Path analysis (Arbuckle, 2003) was performed, consequently, to identify the better predictors.
The correlation analysis showed for both, accuracy and latency times in picture naming task, that the related variables are lexical frequency, conceptual familiarity and H index (name agreement). These results accord with the various previous findings in which the lexical frequency is associated with the accuracy and the reaction times in picture naming task (Alario, et al., 2004; Barry, et al., 1997; Cuetos, et al., 1999; Ellis & Morrison, 1998; Khwaileh, et al., 2018; Martein, 1995; Oldfield & Wingfield, 1965; Snodgrass & Yuditsky, 1996). That means that the names of pictures represented by words more frequently used are more available than words which are not so frequently used. Also, these results accord with the findings reported about conceptual familiarity. Words that refer to more familiar concepts are retrieved faster than those related to less familiar concepts (Akinina, et al., 2015; Barca, Burani, & Arduino, 2002; Boukadi, et al., 2016; Cuetos, et al., 1999; Ellis & Morrison, 1998; Khwaileh, et al., 2018; Snodgrass & Yuditsky, 1996).
The relation between H index and the accuracy and the latency time of picture naming was also identified in various previous studies (Alario, et al., 2004; Barry, et al., 1997; Bonin, et al., 2002; Bonin, et al., 2003; Boukadi, et al., 2016; Cuetos, et al., 1999; Cycowicz, et al., 1997; DelTAcqua, Lotto, & Job, 2000; Khwaileh, et al., 2014; Khwaileh, et al., 2018; Martmez-Cuitino & Vivas, In press; Snodgrass & Yuditsky, 1996; Vitkovitch & Tyrrell, 1995). The H index is a measure of name agreement, and that means that a word with low values of H index correlates to a high accuracy and fast latency time in naming. A picture with higher name agreement has less competition and this influences accuracy and naming latency. correlation categorizing image predictor lexical
In our study, age of acquisition shows no relation to accuracy and latency time in the picture naming task. These results are not in agreement with various of previous studies (Akinina, et al., 2015; Alario, et al., 2004; Barry, et al., 1997; Bonin, et al., 2002; Bonin, et al., 2003; Cameirao & Vicente, 2010; Cuetos, et al., 1999; DelTAcqua, et al., 2000; Khwaileh, et al., 2018; Meschyan & Her nandez, 2002; Snodgrass & Yuditsky, 1996) that classified the age of acquisition as a strong predictor variable. Words learned earlier have robust lexical representation. However, a possible explanation could be that in this study only 108 stimuli were LT. The OI are learned earlier. Perhaps this difference in the material could explain the absence of the age of acquisition effect because most lexical items in this set are acquired at earlier age therefore no AoA effect is found. Our results also failed to detect a relation between visual complexity, accuracy and latency time in picture naming task. This accords with a major group of previous research (Barry, et al., 1997; Bonin, et al., 2002; Bonin, et al., 2003; Cuetos & Barbon, 2006; Cuetos, et al., 1999; Snodgrass & Yuditsky, 1996). Only some studies found this relation in French (Alario, et al., 2004) and in British English (Ellis & Morrison, 1998). The Path analysis showed that lexical frequency and H index are the best predictors for the picture naming task, but excluded the conceptual familiarity. Previous reports have suggested that conceptual familiarity is included within lexical frequency, so only the most robust variable of the two would show up in the analysis.
That means only the most frequent words and with the lower H index are named more accurately and faster. In a previous research in French language (Alario, et al., 2004) that performed multiple regressions analyses, it was found that lexical frequency, age of acquisition, name agreement, image agreement, imageability and visual complexity are predictors for a picture naming task using the same pictures, but they did not find conceptual familiarity as a predictor. In Peninsular Spanish (Cuetos, et al., 1999), they spotted lexical frequency, age of acquisition, name agreement, image agreement, number of syllables, number of phonemes and conceptual familiarity as predictor variables.
According to previous studies, our results confirm that lexical frequency and name agreement are the best predictors for picture naming task. Conceptual familiarity and lexical frequency are independent measures although they are high related (Barry, et al., 1997; Bonin, Boyer, Meot, Fayol, & Droit, 2004). Age of acquisition did not appear as a predictor. This is a rare result, since all previous studies identified the contribution of this variable to the naming times. Also, the impact of this variable had been identified across different languages (Bonin, et al., 2004; Brysbaert, Van Wijnendaele, & De Deyne, 2000; Cameirao & Vicente, 2010; Cuetos & Barbon, 2006; Khwaileh, et al., 2018). In the picture categorization task, the correlation analysis showed, only for reaction times, that the related variables are lexical frequency, conceptual familiarity and visual complexity. This means that the pictures with higher visual complexity, lower familiarity and lower lexical frequency require longer times to be categorized. In a previous study, Barbon & Cuetos (2006) identified that the variables related to reaction times were lexical frequency, familiarity, age of acquisition and others not included in this study, such as imaginability and availability.
The Path analysis showed that semantic domain is the only predictor of the reaction times in the picture categorization task. Semantic domain (LT vs IO) predicts the speed of participants' performance. This variable, with the exception of Laws' study (2000), has not been taken as a possible predictor. Our data show that it is important to consider this variable, because IO domain is categorized more accurately and faster than LT. Categorization differs from naming because the subjects only have to recognize an item as belonging to certain category. This is not enough to respond in the naming task, where the subjects need to access the particular identity of an item and retrieve the exact name for a picture.
In both tasks, different variables affect the performance of participants. This means that both tasks are not similar and because of that, the variables that have an impact on a task are not the same that do so on the other. Considering this, it is possible to think that the cognitive processes involved in both tasks are different. A more thorough study could be necessary to detect the specific components and mechanisms involved in each task.
A limitation of this study is that all the results allow knowing in greater depth the psycholinguistic factors that predict the performance of young adults only. In a near future, it would be important to replicate these analyzes considering the performance of older adults in order to know if the same psycholinguistic variables are the predictors.
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