Características

The current experiment used images selected from the FACES database (Ebner, Riediger, & Lindenberger, 2010) as this database fulfilled the requirements of the experimental design including a degree of ecological validity (e.g., colour images); OAs were represented; expressions included different basic emotions; and the dataset had sufficient images to create the emotion and non-emotion tasks without the need to repeat any individual image. The importance of these criteria is now explained.

Emotion recognition research often uses a variation of the Ekman faces (i.e., Facial Expressions of Emotion: Stimuli and Tests [FEEST]; Young, Perrett, Calder, Sprengelmeyer, & Ekman, 2002; Japanese and Caucasian Facial Expressions of Emotion [JACFEE]; Matsumoto, & Ekman, 1988; Pictures of Facial Affect [POFA]; Ekman & Friesen, 1976; and the Hexagon task consisting of morphed images of one Ekman poser JJ [e.g., Calder et al., 2003; Sullivan & Ruffman, 2004]). Using variations

of the same database provides consistencies within the stimuli across studies and enables informative comparisons (Suzuki, Hoshino, Shigemasu, & Kawamura, 2007).

However, the Ekman and Friesen (1976) dataset consists of black and white or grey scale photographs using photograph imagery of its time, so the images appear dated. Hence the resolution detail may not be as sharp as current photographic technology will allow and this, alongside the black and white format, further reduces ecological validity of the stimuli (Horning, Cornwall, & Davis, 2012). Furthermore, the expressions used in the Ekman and Friesen (1976) faces are based on prototypes that require facial muscle movements to be consistent with the Facial Action Coding System (FACS; Ekman & Friesen, 1978). This system uses action codes that represent a

particular configuration of facial muscle movements considered to typify a specific facial expression. Supposedly when the facial muscle movements map onto the expected emotion specific action codes then there is confidence that the expression represents the target emotion. Results from studies using FACs based stimuli suggest that emotion recognition accuracy differs between emotion types with happiness or surprise being the easiest to recognise (Matsumato & Hwang, 2011). However, these exemplars are criticised as they are seen as extreme caricatures of an emotion

expression and are specifically designed to enable discrimination between emotion types (Barrett, Lindquist, & Gendron, 2007). Consequently, the exemplars lack the range of emotion expressions that are observed in real social interactions (Russell,

Bachorowski, & Fernandez-Dols, 2003). To overcome this issue, instead of only using

emotion prototypes the models in the FACES database went through an emotion induction phase and were trained to produce optimum expressions. Thus, the emotions portrayed in the current experiment are arguably more naturalistic than images using a prototypical approach, such as the faces in Ekman and Friesen (1976).

An alternative dataset to those based on the Ekman faces is the Diagnostic Analysis of Nonverbal Behaviour (DANVA 2; Nowicki, 2004). The images within this dataset are in colour with background information (e.g., set in a classroom

environment). However, these photographs were not used in the current study as OAs tend to have proportionately more age-related emotion recognition deficits in tasks based on images taken from the DANVA 2 than from Ekman and Friesen (e.g., Calder et al., 2003; Circelli, Clark, & Cronin-Golomb, 2013; Krendl & Ambady, 2010; Krendl, Rule, & Ambady, 2014; Stanley & Isaacowitz, 2015). Given that OAs appear to

process contextual cues more than YAs (Isaacowitz & Stanley, 2011) then the background context in the DANVA 2 images may not provide useful information to support OAs’ emotion recognition ability. Thus to avoid potential contextual cues the images used in the current experiment are presented on a plain background.

Another consideration when selecting a suitable database was the age range of the actors portraying the emotion, as encoder’s age may influence emotion recognition accuracy (Anastasi & Rhodes, 2005). OAs and YAs have more experience in social interactions with same age individuals and this may produce an own-age bias in processing faces (Bartlett & Fulton, 1991; Isaacowitz & Stanley, 2011; Phillips & Slessor, 2011). However, the extent of an own-age bias is unclear as there are

inconstancies as to its existence. For example, one study provides evidence for an own age bias as OAs had higher emotion recognition accuracy when expressions were portrayed by OA actors than YA actors; the findings were vice versa for YAs (Ebner, He, & Johnson, 2011c). In contrast, facial expressions displayed by OAs can be difficult to read for all adults regardless of age (Riediger, Voelke, Ebner, &

Lindenberger, 2011); possibly due to the physical changes in the face that occur with age, such as wrinkles and lower expression intensity (Fölster, Hess, & Werheid, 2014; Hess, Adams, Simard, Stevenson, & Kleck, 2012; Porcheron, Mauger, & Russell, 2013;

Riediger et al., 2011). Moreover, the age of the actor might differentially affect

processing of different emotions, as evidence suggests that when emotions are presented by YAs the categorisation of happy faces is faster than angry or sad faces but there was no emotion specific differences when emotion were presented by OAs (Craig & Lipp, 2018). Despite the possible own-age bias the majority of research investigating age- related emotion recognition ability have only used YA actors to portray facial expressions (e.g., Calder et al., 2003; Isaacowitz et al., 2007; Sullivan & Ruffman, 2004) and this may disadvantage task performance in OAs (Ebner et al., 2010). Given the potential of an own-age processing bias it is good practice to include facial

expressions of actors representing a spread of ages (Phillips & Slessor, 2011).

Therefore, one advantage of the FACES dataset is that it includes models across a spread of ages. Thus in the current study the selected models represented YAs (age range 19-31 years), middle-aged adult (age range 39-55 years) and OAs (age 69-80 years).

A further advantage of the FACES dataset is that it comprises 2,052 high quality digital coloured images of 171 models displaying 5 discrete emotion states of

(happiness, sad, fear, anger, and disgust, plus neutral expressions); thus contains sufficient images required to create the current emotion and non-emotion tasks. Furthermore, the database has good validity of the target expression with disgust

recognition having the lowest accuracy (68%) and fear recognition the highest accuracy (96%) (Ebner et al., 2010). Taken together, the images in the FACES database

provided the most suitable stimuli for the current research as it is a dataset with: high quality photographs, it uses an induction technique to increase the ecological validity of the expressions posed by the models, holds numerous examples, and it includes images of OA models.

Regarding the design of the emotion recognition task several factors that may compromise task validity were considered, including familiarity effects and encoder effects. First, repeated exposure to a stimulus may produce familiarity effects that can alter the perceiver’s judgement of the stimuli (Zajonc, 1968) and may reduce the validity of a task. To avoid the risk of familiarity effects in the current experiment six different experimental versions were created with each model appearing only once in a given version. Thus, participants only had a single exposure to any given model. Second, the ability to accurately recognise an emotion can depend on the skill a particular model has in accurately portraying the target emotion (i.e., the encoder's ability) (Brunswick, 1956). To address the possibility of variations in encoding ability across actors each model presented each of the six emotion types only once across the six experimental versions. In this manner if a model were less able than other models to accurately portray emotions then this encoder effect would be minimised.

Each emotion recognition task consisted of 36 experimental trials and eight practice trials (see Appendix 3.2). Thirty-six photographs of models were used in the experimental trials with each model portraying one of the six emotion states (happy, sad, fear, anger, disgust and neutral), as such a total of 224 images were used (36 models × 6 emotion states + 8 practice trials). Of the 36 models in the experimental trials 12 were YAs (age range between 19 and 31 years), 12 middle-aged adults (age range between 39 and 55 years) and 12 OAs (age range between 70 and 77 years) and each age group had six male models and six female models. Finally, within each of the experimental versions there were six trials for each emotion type (6 trials × 6 emotion types). The models in the practice task did not appear in the experimental task and the practice trials measured each emotion once and neutral expressions three times.

The stimuli were head and shoulder colour images of a model portraying one of six facial expressions (see Figure 3.3.1). Each trial had the same layout but the model

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and emotion expression varied. Each photograph was presented in the centre of the computer screen and the emotion labels appeared under the image in Arial and font size 18. The numbers 1 to 6 were presented as a guide to participants as to which numerical key they should press on the response box.

Figure 3.3.1. An example of the emotion recognition task using static faces. In this instance the correct response is 5 (disgust). Image reproduced with permission as per FACES database agreement section 7