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PY2112 Individual Assignment Experiment Conducted

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Question:

So what is the purpose of the lab report writing exercise?

Ask yourself at every point: "Am I communicating with my reader as clearly as possible?

What is it? It is a presentation of the specific problem under study and describes the research strategy.

What is it for? Its purpose is to give your reader a firm sense of what problem you were trying to solve, and why you chose to investigate it in the manner you did.

It should answer the following questions:

  • What is the point of the study? (what problem is it intended to solve?)
  • How do the hypothesis and the experimental design relate to the problem?
  • How does the study relate to previous work in the area?
  • What did you expect to find, and why?

The discussion is the avenue for you to communicate to your reader:

  • What you believe you have contributed to the field under research
  • How you believe your study has helped to resolve the problem under consideration
  • Any additional information that may be relevant to any subsequent attempts to

Replicate your study (such as methodological issues etc).

  • What conclusions and theoretical implications you draw from your study.
 

Answer:

Introduction

In many cases, people tend to have different abilities to memorize things which are either appealing to the senses, whether they are sound, touch, smell, sight or taste. On the other hand, some people can also memorize different effects which are disgusting. However, each person has a different ability to memorize various scenarios. In face recognition, different aspects can be considered. For instance, the race of the faces which a particular participant is subjected to can create different memory abilities. For instance, most people tend to memorize their own races as compared to other races (Meissner & Brigham, 2001) (Wright, Boyd, & Tredoux, 2001). The memory of own race is as a result of perception which these individuals have, especially due to the fact that they spend most of their times interacting with others people from their own race (Chiroro & Valentine, 1995). Moreover, it is also easier to recognize faces that show happiness rather than sadness. In this experiment, E-prime 2.0 software was used to program the experiment and to make the findings one that can be modeled using mathematical expressions from which a conclusion can be drawn.

 

The main objectives of the study include the following:

  1.  To determine the general effects of own-race bias in Singapore.  
  2.  To explore the outcomes on effect of facial emotional expression on the own-race bias.

In order for the experiment to give the desired results, the following hypotheses are formulated:

H1.  The experiment expected that the participants from Singapore will portray an own-race bias as afar as remembering the faces of the elements to be studied is concerned. As such, they will show a greater memory recognition for the East Asian faces as compared to the Caucasian faces. This is in relation to the literature written by other scholars.

H2.  In line with the research that has been done in the past, it is expected that neutral faces will be remembered less as compared to happy faces.

H3. It is expected that happy East-Asian faces will give a greater recognition as compared to the neutral East-Asians faces. However, there will be a small variation in the recognition of faces happy and neutral faces among the Caucasians.

Signal Detection Theory (SDT) will be used to measure memory recognition.  

In this experiment, the members who participated in the study are presented with images of faces which are not familiar to them. The images are provided in the screen of the computer. When they have been presented with the images of the faces, the participants are asked to memorize the pictures. After a short while, the participants are again presented with other images, including the ones they had been subjected to before. The study aims at finding the ability of the person to memorize things based on the number of images they have correctly identified (Baddeley, 2014).

According to Wright, Boyd, & Tredoux (2001) in a study conducted in South Africa and England, it was found that people have a tendency to recognize the faces of those who come from their races. Since the study employed similar techniques and methods in the study, it was easy for the study to arrive at a conclusion that there are own-race bias when it comes to facial recognition. Moreover, the study added to the fact that there were correlations between accuracy and observer confidence when the participant observed a person of from their race (Valentine, Lewis, & Hills, 2016). The own race bias is attributed to the fact that most people tend to associate with people from their own races, hence their perceptions are always geared towards those who come from the same race (Valentine, 2017). In essence, due to this interaction, these individuals can be thought to be experts who have learned the art of remembering the faces from those with whom they interact most of the time.

Meissner & Brigham (2001) also agrees with the fact that people tend to memorize the others who come from their own race. According to their study, Meissner & Brigham explores the various factors to ascertain, for instance, whether the findings can be true, and how reliable the findings can be especially in witness accounts. Moreover, the study also assessed the influence of racial attitudes and interracial contact and how these two add to the ability of a person to memorize what they observed in a crime scene.

Faces that are happy tend to be recognized more easily and even more accurately as compared to faces that are not happy. On the other hand, it is easier for a person to detect an angry person in terms of visual search as compared to one that is happy (Nummenmaa & Calvo, 2015).These can be explained further. For instance, the speed and accuracy of recognizing happy faces results from stimulus types. However, visual search majorly relies on the photographic faces yielded more accurate result for happy face detection.

 

Methodology

This experiment used data of 110 participants. Among the participants, there were 78 females (71%) 32 males (29%). Of these participants, 96 were right-handed while 16 were left-handed. However, due to technical problems, for instance, due to the constraints of the study, the data used for the analysis was taken from 68 of the participants. This represents 62 percent of the sample. The mean age of the participants was 21.87 years with a stand deviation of 4.61.

Design

To achieve the purpose of this experiment the study used a 2(Race: East-Asian, Caucasian) X 2(Facial Expression: neutral, happy) within-subjects. Also, Signal detection theory was employed to determine the measure of recognition memory discrimination (d’). The recognition memory discrimination (d’) was made useful in the determination of DV in SPSS.

Materials

East-Asian faces. A total of 72 picked from the DFH Database as well as from the CUHK Face Database were used in the experiment. Half of the pictures used were portraited neutral-emotion faces while the other half were portraited happy-emotion faces. The number of pictures of the males and females was the same. The pictures were the taken in a lit-condition environment so that all the features of the face were distinctly visible. The pictures covered the entire face, and was taken in a close-up mode. This mode focused on the face, from the forehead to the chin. The background of the pictures were dark, so that each face appeared as a single object on the screen. The resolution of the images were high and the produced detailed information of the face, including small visible spots and curvature of the skins. Moreover, the photos were enlarged to cover the entire screen of the monitor.

Caucasian faces. Seventy-two images were selected from the Radboud Faces Database. Half of the pictures used were portraited neutral-emotion faces while the other half were portraited happy-emotion faces. The number of pictures of the males and females was the same. The pictures were the taken in a lit-condition environment so that all the features of the face were distinctly visible. The pictures covered the entire face, and was taken in a close-up mode. This mode focused on the face, from the forehead to the chin. The background of the pictures were dark, so that each face appeared as a single object on the screen. The resolution of the images were high and the produced detailed information of the face, including small visible spots and curvature of the skins. Moreover, the photos were enlarged to cover the entire screen of the monitor.

Procedure

Each of the participants was given fifteen seconds to recognize each of the faces on the screen. However, each participant was subjected to a maximum of ten different images of both races as well as with different facial expressions.

Results

 The analysis of variance (ANOVA) for 2(Race: East-Asian, Caucasian) X 2(Facial Expression: neutral, happy) within-subjects was performed the recognition memory with the discrimination as the variable. In the analysis, the values that were significantly greater than 0 indicated that there were elements of discrimination. Values that approached 0 indicated that there was no discrimination between subjects. However, values that are less than zeros indicate a false feedback meaning there were a greater number of false alarms as compared to hit responses. The analysis showed the primary effects of emotion F(1, 66) = 36.09, p < .001, η2p = .35. Happy faces we easily recognized as compared to neutral faces. These two had the mean and standard deviations as Happy faces M= 0.84, Happy face SD = 0.50, Neutral face M = 1.24and Neutral face SD = 0.64.

The primary effect of race was determined as F(1, 66) = 12.67, p = .001, η2p = .16. The mean and standard deviations of the Asians and Caucasians were found to be as follows: Asian M= 0.92, Asian SD= 0.57, Caucasian M= 1.16 and Caucasian SD = 0.58. The major effects as qualified interaction F(1, 66) = 25.38, p < .001, η2p = .28. The interaction was majorly determined and influenced by the revelation that happy Caucasian faces with a probability of p < .001 had a better chance of being recognized as compared with happy Asian faces. However, there was no significant relationship between neutral Caucasian faces and neutral Asian faces with a probability of 0.59. Neutral Asian faces showed a greater recognition with a probability greater than .001happy Asian faces. However, neutral and happy Caucasian faces had a similar recognition index of p = .17.

Recognition memory discrimination

(d’)

Mean

Error

SD

SE

Happy Asian

0.58

0.64

0.08

Happy Caucasian

1.10

0.60

0.07

Neutral Asian

1.27

0.71

0.09

Neutral Caucasian

1.22

0.74

0.09

 
 

Discussion

This experiment had three hypotheses. It predicted that Singaporean participants will show an own-race bias when remembering faces, whereby their memory recognition will be greater for East Asian faces than Caucasian faces (Coolican, 2017). The results revealed that happy Asian faces were recognized significantly worse than happy Caucasian faces with a probability greater than 0 .001, which portrays that there was an element of discrimination. However, the results showed that there were no recognition differences between neutral Asian faces and neutral Caucasian faces, having produced a probability that was greater than 0.

The results agree with the Meissner and Brigham’s study of the own-race bias for memory of faces. Most of the people easily recognize the faces of those who come from their race. Primarily, this is based in the fact that most people tend to spend most of their time with the people with whom they associate with, both in color and origin. As a result, their perception is quite accustomed to these people, making them have an internal mastery of their faces (Meissner & Brigham, 2001).

The study predicted that happy faces had a chance of being remembered more easily as compared to neutral faces. It was easy for the participants to recognize neutral Asian faces as compared to happy faces, since the happy faces were deformed in particular ways which affected the normal face. Since the study was based in Singapore, which comprises of Asians, then the Caucasian element in the study made a difference in terms of the facial expression. For instance, in the experiment, it was realized that Asian neutral faces were recognized much better compared to happy faces. However, the Caucasian faces did not show difference in terms of recognition, whether they were happy or neutral. This element also revealed that there was an element of racial discrimination that played a critical role in the recognizing the faces. With a probability of 0.17, the Singaporean participants were quite accustomed to their Asian faces and could not distinguish the other race irrespective of the facial expression (Calvo, Avero, Fernández-Martín, & Recio, 2016) (Calvo & Nummenmaa, 2016).

Studies indicate that the ease in the recognition of faces based on the facial expression is determined by the time taken for the participants to correctly recognize the image (Nummenmaa & Calvo, 2015). Most of the participants took a shorter time to recognize the faces of those who were from their races as compared with those from the Caucasian race. At the same time, it takes a shorter time to recognize a neutral face as compared to a happy face, because due to the deformities created in the facial expression, different participants tend to have different perceptions of the images (Matthews & Meck, 2016). For instance, a neutral face may not create several impulses in the mind of the observer, which may make the image to be registered in the mind quite fast as compared to a face that shows an expression. In the instance that the face has expressions, the mind of the observer has to process several information to enable it to recognize the face (McKone , et al., 2012).  

 

Conclusion

There is a significant own-race bias effects as well as the effect of facial expression during face recognition. The study has shown that people tend to have better memories of the images of those who hail from similar races as themselves, while a short memory for other races. When expressions are involved, there is no difference in the determination of the facial expression in other races, although there is a significance change in the recognition within a particular race. Facial expressions leads to the deformation of the face, leading to a low memory of the face.

 

References

Baddeley, A. (2014). Applied cognitive and cognitive applied psychology: The case of face recognition. Perspectives on Learning and Memory, 19(5), 367-388.

Calvo, M. G., & Nummenmaa, L. (2016). Perceptual and affective mechanisms in facial expression recognition: An integrative review. Cognition and Emotion, 30(6), 1081-1106.

Calvo, M. G., Avero, P., Fernández-Martín, A., & Recio, G. (2016). Recognition thresholds for static and dynamic emotional faces. Emotion, 16(8), 1186.

Chiroro, P., & Valentine, T. (1995). An investigation of the contact hypothesis of the own-race bias in face recognition. The Quarterly Journal of Experimental Psychology, 48(4), 879-894.

Coolican, H. (2017). Research methods and statistics in psychology. Psychology Press.

Matthews, W. J., & Meck, W. H. (2016). Temporal cognition: Connecting subjective time to perception, attention, and memory. Psychological bulletin, 142(8), 865.

McKone , E., Stokes , S., Liu , J., Cohan , S., Fiorentini , C., Pidcock, M., & Pelleg, M. (2012). A Robust Method of Measuring Other-Race and Other-Ethnicity Effects: The Cambridge Face Memory Test format. PLoS One, 7(10), e47956.

Meissner, C. A., & Brigham, J. C. (2001). THIRTY YEARS OF INVESTIGATING THE OWN-RACE BIAS IN MEMORY FOR FACES- A Meta-Analytic Review. sychology, Public Policy, and Law, 7(1), 3-35.

Nummenmaa, L., & Calvo, M. G. (2015). Dissociation Between Recognition and Detection Advantage for Facial Expressions: A Meta-Analysis. Emotion, 15(2), 243–256.

Valentine, T. (2017). Cognitive and computational aspects of face recognition: Explorations in face space. Routledge.

Valentine, T., Lewis, M. B., & Hills, P. J. (2016). Face-space: A unifying concept in face recognition research. The Quarterly Journal of Experimental Psychology, 69(10), 1996-2019.

Wright, D. B., Boyd, C. E., & Tredoux, C. G. (2001). A field study of own-race bias in outh Africa and England. Psychology, Public Policy, and Law, 7(1), 119-133.

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