3. El apego en el contexto de la adopción
3.2. Investigación sobre el estilo y la seguridad en las conductas de apego en
biases of different ages into account. More so, to our knowledge, there are to date no studies focusing on the real-time effect of visual emotional cues on language processing in children.
However, in two off-line studies Ruffman, Slade, Rowlandson, Rumsey, and Garnham (2003) investigated in how far language in 3-5-year old children relates to emotion understanding and specifically focused on the correlation between emotion understanding and syntax and semantics. They tested children’s understanding of syntax and semantics using the comprehension of word order and embedded clauses.
The sentence stimuli were carefully designed to permit a clear distinction between syntax and semantics. Moreover, Ruffman et al. (2003) asked the same children to identify emotions (happiness, fear, sadness, anger and surprise) from facial expressions. Additionally, children had to perform false belief and desire tasks indicating what they thought the protagonist of a story thought or wanted. Their results suggest a tight coupling between syntax and semantics, as well as between language use and the ability to attribute and infer mental states and desires from oneself to others, i.e., theory of mind (TOM, Premack & Woodruff, 1978). Moreover, they also found a high correlation between emotion recognition and syntax but not semantics. Correlations between syntax and TOM were expected because syntax (e.g., an embedded sentence) is used to describe and understand another character’s situational representation. On the other hand, Ruffman et al. (2003) did not expect a correlation between emotion recognition and syntax as according to them emotion understanding is not needed for insights into another character’s situational representations.
3.7.2 The Degree of Naturalness in the Depiction of Facial Expressions
Apart from the complexity of different emotional valences and their biases regarding the age of the comprehender, a further aspect that we should consider when examining emotional facial expressions and language is the way the former are used and depicted. Especially in experimental studies, stimuli (e.g., emotional facial expressions) cannot always be natural since they have to be strictly controlled. We must strive to eliminate as many confounds as possible so as to be able to draw clear conclusions based on the obtained results. On the other hand, the less natural our
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stimuli are, the less we can claim that our results are also valid in the real word, i.e., outside of the laboratory. Whether or not natural versions of our stimuli would yield the same results as degraded or simplified stimuli is an empirical question (see also Adolphs, 2006 on this matter).
However, we do not now whether, for instance, schematic emotional facial expressions can be used in the same way, to the same extent and with the same time course as natural emotional facial expressions. Yet, see Section 6.4.4.3 for results of the studies we will present and Section 10.5 for further discussion on this matter (taking our results from Section 6.4.4.3 into account).
Rhodes, Brennan, and Carey (1987), as well as Benson and Perrett (1991) for example investigated the effect of caricaturing on the recognition of line drawings and natural face depictions. Both studies found a recognition advantage (in both recognition speed based on reaction time measures and likeness based on participants’
subjective ratings) for slightly exaggerated faces compared to the original face.
Caricatures accentuate particular details of a face (Brennan, 1985) and thus highlight the most prominent features of an expression, making it easier for participants to recognize the person the face belongs to. Moreover, in studies on human - robot interaction the appearance of the robot plays an important role for self-identification with a robot’s face. It is assumed that the more schematic a robot’s face is, the more humans can identify with it, as the distinction between ones own representation and that of another person becomes less and less pronounced (the more schematic a face is, the more characters it can represent, Blow, Dautenhahn, Appleby, Nehaniv, & Lee, 2006).
On the other hand, most of the time we interact with other human beings and easily attribute mental states, beliefs and feelings to our interaction partners, based on our own mental states (Premack & Woodruff, 1978). That is, we are much more experienced in our interaction with real and natural emotional faces than with schematic facial representations. However, research has also suggested that schematic (computer-generated) faces are recognized as well as natural facial expressions (e.g., Chang, 2006; Öhman, Lundqvist, & Esteves, 2001; Ruffman, Ng, & Jenkin, 2009).
Nevertheless, when it comes to the difference between static and dynamic natural faces, findings indicate that dynamic natural emotions are recognized faster and more accurately and elicit enhanced and prolonged cortical responses compared with their
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natural static counterparts (see e.g., Harwood, Hall, & Shinkfield, 1999 for identification of emotions from moving and static videotaped and photographic displays and Recio, Sommer, & Schacht, 2011 for ERP evidence). Additionally, Sato, Yoshikawa, Kochiyama, and Matsumura (2004) conclude that dynamic in contrast to static emotional facial expressions leads to a facilitation in emotion recognition. They hypothesize that the better recognition of dynamic (vs. static) facial expressions is due to the enhanced activation of brain regions implicated in face perception. Brain regions responsible for face perception were more active when dynamic compared to static faces were presented. Bassili's (1979) study also already suggested that dynamic emotional images were better recognized than their static counterparts, meaning that all emotions displayed indicated more accurate recognition results in the dynamic face condition as opposed to the static face condition. Moreover, their study investigating the role of facial movement demonstrated that even the surface movement of a face serves as information that helps to correctly identify specific emotions (see also Schultz & Pilz, 2009).
However, children face difficulties in identifying emotions and are still in the process of development regarding both emotion and language processing. Hence, displaying stereotypical emotions, e.g., in the form of a smiley, might help them to link emotion to language. Yet, it would also be possible that this stereotypicality confuses or hinders people in making use of a specific cue, as these non-natural cues present only a limited amount of (emotional) information (Gross & Ballif, 1991). Del Giudice and Colle (2007) have for example indicated that both adults and 8-10-year old children found smiles with an open mouth (bared teeth) much more salient than closed smiles. Children judged these strong smiles as more sincere, whereas adults found it easier to judge them as fake smiles.
Hence, even though it seems clear that dynamic facial expressions have a recognition advantage over static faces, there does not seem to be a clear consensus regarding the use of stereotypical schematic versus natural facial expressions in the study of emotion and face processing. In how far and to what extent schematic and natural dynamic facial expressions have an influence on the way emotional language is processed is even less clear.