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Age and gender effects on the human’s ability to decode posed and naturalistic emotional faces

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Abstract

This paper proposes a systematic approach to investigate the impact of factors such as the gender and age of participants and gender, and age of faces on the decoding accuracy of emotional expressions of disgust, anger, sadness, fear, happiness, and neutrality. The emotional stimuli consisted of 76 posed and 76 naturalistic faces, differently aged (young, middle-aged, and older) selected from FACES and SFEW databases. Either a posed or naturalistic faces’ decoding task was administered. The posed faces’ decoding task involved three differently aged groups (young, middle-aged, and older adults). The naturalistic faces’ decoding task involved two groups of older adults. For the posed decoding task, older adults were found significantly less accurate than middle-aged and young participants, and middle-aged significantly less accurate than young participants. Old faces were significantly less accurately decoded than young and middle-aged faces of disgust, and anger, and young faces of fear, and neutrality. Female faces were significantly more accurately decoded than male faces of anger and sadness, significantly less accurately decoded than male faces of neutrality. For the naturalistic decoding task, older adults were significantly less accurate in decoding naturalistic rather than posed faces of disgust, fear, and neutrality, contradicting an older adults’ emended support from a prior naturalistic emotional experience. Young faces were more accurately decoded than old and middle-aged faces of disgust and anger and old faces of neutrality. Female faces were significantly more accurately decoded than male faces of fear, and significantly less accurately decoded than male faces of anger. Significant effects and significant interdependencies were observed among the age of participants, emotional categories, age, and gender of faces, and type of stimuli (naturalistic vs. posed), not allowing to distinctly isolate the effects of each involved variable. Nevertheless, the data collected in this paper weakens both the assumptions on women enhanced ability to display and decode emotions and participants enhanced ability to decode faces closer to their own age (“own age bias” theory). Considerations are made on how these data would guide the development of assessment tools and preventive interventions and the design of emotionally and socially believable virtual agents and robots to assists and coach emotionally vulnerable people in their daily routines.

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Data availability

The data that support the findings of this study are available from the corresponding author, Anna Esposito, upon reasonable request.

References

  1. Campbell A, Murray JE, Atkinson L, Ruffman T (2015) Face age and eye gaze influence older adults’ emotion recognition. J Gerontol B Psychol Sci Soc Sci 72(4):633–636

    Google Scholar 

  2. Capuano V, Cordasco G, Scibelli F, Maldonato MN, Faundez-Zanuy M, Esposito A (2017) Effects of gender and luminance backgrounds on the recognition of neutral facial expressions. In: Esposito A et al (eds) Multidisciplinary approaches to neural computing, smart innovation, systems and technologies, vol 69. Springer International Publishing, pp 315–325

    Chapter  Google Scholar 

  3. Carstensen LL, DeLiema MT (2018) The positivity effect: a negativity bias in youth fades with age. Curr Opin Behav Sci 19:7–12

    Article  Google Scholar 

  4. Dhall A, Goecke R, Lucey S, Gedeon T (2011) Static facial expression analysis in tough conditions: data, evaluation protocol and benchmark. In: Proceedings of BeFIT’11, IEEE international conference on computer vision workshop, 2011, pp 2106–2112. https://cs.anu.edu.au/few/, last verified September 2, 2020

  5. Demenescu LR, Mathiak KA, Mathiak K (2014) Age- and gender-related variations of emotion recognition in pseudowords and faces. Exp Aging Res 40:187–207

    Article  Google Scholar 

  6. Denkinger B, Kinn M (2018) Own-age bias and positivity effects in facial recognition. Exp Aging Res 44(5):411–426

    Article  Google Scholar 

  7. Desmarais P, Lanctôt KL, Masellis M, Black SE, Herrmann N (2018) Social inappropriateness in neurodegenerative disorders. Int Psychogeriatr 30:197–207

    Article  Google Scholar 

  8. Ebner NC, He Y, Johnson MK (2011) Age and emotion affect how we look at a face: visual scan patterns differ for own age and other-age emotional faces. Cogn Emot 25:983–997

    Article  Google Scholar 

  9. Ebner NC, Riediger M, Lindenberger U (2010) FACES—a database of facial expressions in young, middle-aged, and older women and men: development and validation. Behav Res Methods 42:351–362

    Article  Google Scholar 

  10. Ekman P, FriesenW V (1976) Measuring facial movement. Environ Psychol Nonverbal Behav 1(1):56–75

    Article  Google Scholar 

  11. Esposito A, Esposito AM, Cordasco G, Maldonato M, Vogel C, Bourbakis N (2018) Emotional faces of children and adults: what changes in their perception. In: 9th IEEE international conference on cognitive infocommunications (CogInfoCom 2018), August 22–44, 2018, Budapest, Hungary

  12. Esposito A (2013) The situated multimodal facets of human communication. In: Rojc M, Campbell N (eds) Coverbal synchrony in human–machine interaction, chapter 7, pp 173–202. CRC Press, Taylor & Francis Group, Boca Raton. Print ISBN: 978-1-4665-9825-6

  13. Gonçalves AR, Fernandes C, Pasion R, Ferreira-Santos F, Barbosa F, Marques-Teixeira J (2018) Effects of age on the identification of emotions in facial expressions: a meta-analysis. PeerJ 6:e5278. https://doi.org/10.7717/peerj.5278

    Article  Google Scholar 

  14. Grainger SA, Henry JD, Phillips LH, Vanman EJ, Allen R (2015) Age deficits in facial affect recognition: the influence of dynamic cues. J Gerontol Ser B Psychol Sci Soc Sci. https://doi.org/10.1093/geronb/gbv100

    Article  Google Scholar 

  15. Gregorić B, Barbir L, Ćelić A, Ilakovac V, Hercigonja-Szekeres M, Perković KM, Frencl M, Heffer M (2014) Recognition of facial expressions in men and women. Medicina Fluminensis 50(4):454–461

    Google Scholar 

  16. Harris DA, Hayes-Skelton SA, Ciaramitaro VM (2016) What’s in a face? How face gender and current affect influence perceived emotion. Front Psychol 7:1468

    Google Scholar 

  17. He Y, Ebner NC, Johnson MK (2011) What predicts the own-age bias in face recognition memory? Soc Cogn 29(1):97–109

    Article  Google Scholar 

  18. Isaacowitz DM, Stanley JT (2011) Bringing an ecological perspective to the study of aging and recognition of emotional facial expressions: past, current, and future methods. J Nonverbal Behav 35:261278. https://doi.org/10.1007/s10919-011-0113-6

    Article  Google Scholar 

  19. Ismail Z, Agüera-Ortiz L, Brodaty H, Cieslak A et al (2017) The Mild Behavioral Impairment Checklist (MBI-C): a rating scale for neuropsychiatric symptoms in pre-dementia populations. J Alzheimer’s Dis 2017(56):929–938

    Article  Google Scholar 

  20. Jinhui L, Dexian H, Lingdan Z, Xueru Z, Tingting Z, Wei Z, Xianyou H (2019) The effects of facial attractiveness and familiarity on facial expression recognition. Front Psychol 10:2496. https://doi.org/10.3389/fpsyg.2019.02496

    Article  Google Scholar 

  21. Lambrecht L, Kreifelts B, Wildgruber D (2012) Age-related decrease in recognition of emotional facial and prosodic expressions. Emotion 12:529–539

    Article  Google Scholar 

  22. Reed AE, Carstensen LL (2012) The theory behind the age-related positivity effect. Front Psychol 3, Article 339. https://doi.org/10.3389/fpsyg.2012.00339

  23. Rhodes M, Anastasi J (2012) The own-age bias in face recognition: a meta-analytic and theoretical review. Psychol Bull 138(1):146–174

    Article  Google Scholar 

  24. Ruffman T, Henry JD, Livingstone V, Phillips LH (2008) A meta-analytic review of emotion recognition and aging: implications for neuropsychological models of aging. Neurosci Biobehav Rev 32(863):881

    Google Scholar 

  25. Saylik R, Szameita AJ, Raman E (2018) Sex differences in emotion recognition and working memory tasks. Front Psychol 9:1072

    Article  Google Scholar 

  26. Schaich A, Obermeyer S, Kolling T, Knopf M (2016) An Own-Age Bias in Recognizing Faces with Horizontal Information. Front Aging Neurosci 8:264

    Article  Google Scholar 

  27. Sullivan S, Campbell A, Hutton SB, Ruffman T (2017) What’s good for the goose is not good for the gander: age and gender differences in scanning emotion faces. J Gerontol Ser B 72(3):441–447

    Article  Google Scholar 

  28. Ziaei M, Persson J, Reza BM, Reutens DC, Ebner NC (2019) Amygdala functional network during recognition of own-age vs. other-age faces in younger and older adults. Neuropsychologia 129:10–20

    Article  Google Scholar 

Download references

Acknowledgements

The research leading to these results has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement N. 769872 (EMPATHIC) and N. 823907 (MENHIR), the Ministero dell’Istruzione, dell’Università, e della Ricerca (MIUR), PNR 2015-2020, Decreto Direttoriale 1735 July 13, 2017, Project SIROBOTICS, and Università della Campania “Luigi Vanvitelli”programme V:ALERE 2019, funded with D.R. 906 del 4/10/2019, prot. n. 157264, October 17, 2019, Project ANDROIDS.

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Authors and Affiliations

  1. Dipartimento Di Psicologia, Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito, Terry Amorese, Marialucia Cuciniello, Maria Teresa Riviello & Gennaro Cordasco

  2. International Institute for Advanced Scientific Studies (IIASS), Vietri sul Mare, Italy

    Anna Esposito, Terry Amorese, Marialucia Cuciniello, Maria Teresa Riviello & Gennaro Cordasco

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  1. Anna Esposito
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  2. Terry Amorese
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  3. Marialucia Cuciniello
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  4. Maria Teresa Riviello
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  5. Gennaro Cordasco
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Correspondence to Gennaro Cordasco.

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Appendices

Appendix 1

Detailed description of the statistical analyses performed in the present study.

1.1 Appendix 1.1: Statistical analyses performed for Sect. 3.1 posed faces in general

Results showed a significant age of participants’ effect (F(1,129) = 10.348, p ≪ 0.01) accuracy of Bonferroni’s post hoc tests revealed that older adults (mean = 7.655) were significantly less accurate than young (mean = 9.330, p ≪ 0.01) and middle-aged participants (mean = 9.001, p = 0.002).

Participants’ decoding accuracy significantly differed (F(5645) = 68.844, p ≪ 0.01) among emotional categories. Bonferroni’s post hoc tests revealed that the decoding accuracy of posed faces (mean = 6.182, p ≪ 0.01) of sadness were significantly less accurately decoded than faces of disgust (mean = 8.835), anger (mean = 8.070), fear (mean = 8.688), happiness (mean = 11.274) and neutrality (mean = 8.992). On the other hand, the decoding accuracy of happy posed faces (mean = 11.274, p ≪ 0.01) was significantly more accurate than that of disgust (mean = 8.835), anger (mean = 8.070), sadness (mean = 6.182), fear (mean = 8.688) and neutrality (mean = 8.922).

A significant interaction between emotional categories and participant’s age (F(10,645) = 4.418, p ≪ 0.01) emerged. Bonferroni’s post hoc tests were performed for each single factor (emotional categories and participants’ age).

Concerning emotional categories, Bonferroni’s post hoc tests showed that:

  1. a.

    Older adults (mean = 7.052) were significantly less accurate than young (mean = 9.870, p ≪ 0.01) and middle-aged participants (mean = 9.585, p ≪ 0.01) in decoding faces of disgust. Similarly, older adults (mean = 6.252) were significantly less accurate than young (mean = 9.435, p ≪ 0.01) and middle-aged participants (mean = 8.525, p = 0.001) in decoding faces of anger. In addition, older adults’ decoding accuracy of fear (mean = 7.814) and neutral (mean = 7.806) faces was significantly less accurate than young’ participants decoding accuracy of fear (mean = 9.674, p = 0.015) and neutral (mean = 9.804, p = 0.013) faces.

Concerning participants’ age:

  1. a.

    Young adults were significantly more accurate in decoding faces of happiness (mean = 11.174) rather than faces of disgust (mean = 9.870, p ≪ 0.01), anger (mean = 9.435, p ≪ 0.01), sadness (mean = 6.022, p ≪ 0.01), and fear (mean = 9.674, p = 0.01), and significantly less accurate in decoding faces of sadness than faces of disgust (p ≪ 0.01), anger (p ≪ 0.01), fear (p ≪ 0.01), happiness (p ≪ 0.01) and neutrality (p ≪ 0.01). Young adults were equally accurate in decoding faces of happiness and neutrality.

  2. b.

    Middle-aged adults were significantly more accurate in decoding faces of happiness (mean = 11.385) rather than faces of disgust (mean = 9.585, p ≪ 0.01), anger (mean = 8.525, p ≪ 0.01), sadness (mean = 6.780, p ≪ 0.01), fear (mean = 8.575, p = 0.01) and neutrality (mean = 9.155, p ≪ 0.01), and significantly less accurate in decoding faces of sadness than faces of disgust (p ≪ 0.01), anger (p ≪ 0.01), fear (p ≪ 0.01), happiness (p ≪ 0.01) and neutrality (p ≪ 0.01).

  3. c.

    Older adults were significantly more accurate in decoding faces of happiness (mean = 11.263) rather than faces of disgust (mean = 7.052, p ≪ 0.01), anger (mean = 6.252, p ≪ 0.01), sadness (mean = 5.744, p ≪ 0.01), fear (mean = 7.814, p = 0.01) and neutrality (mean = 7.806, p ≪ 0.01), and significantly less accurate in decoding faces of sadness than faces of fear (p ≪ 0.01), happiness (p ≪ 0.01) and neutrality (p ≪ 0.01). In addition, older adults were less accurate in decoding faces of anger than faces of fear (p = 0.015). Older adults were equally accurate in decoding faces of sadness and anger.

1.2 Appendix 1.2: Statistical analyses performed in Sect. 3.2 assessing posed faces decoding accuracy for each emotion

Repeated measures ANOVA analyses were carried out for each emotional category (disgust, anger, fear, sadness, happiness, and neutrality) considering participants’ age (young, middle-aged, and older adults) and gender as between subjects’ factors, and age and gender of stimuli as within subjects’ factors. The significance level was set at α < 0.05 and differences among means were assessed through Bonferroni’s post hoc tests. Results for each emotional category are reported below.

1.2.1 Appendix 1.2.1: Disgust

Significant effects of participants’ age emerged (F(2,129) = 16.226, p ≪ 0.01). Bonferroni’s post hoc tests revealed that older adults (mean = 1.175) were significantly less accurate than young (mean = 1.645, p ≪ 0.01) and middle-aged (mean = 1.598, p ≪ 0.01) participants in decoding faces of disgust. No effects of the gender of faces were observed (F(1,129) = 3.418, p = 0.067).

Significant effects of the age of faces were observed (F(2,258) = 74.127, p ≪ 0.01). Bonferroni’s post hoc tests revealed that young faces of disgust (mean = 1.683) were significantly more accurately decoded than their middle-aged (mean = 1.544, p = 0.001) and old (mean = 1.191, p ≪ 0.01) versions, and middle-aged faces of disgust (mean = 1.544) were significantly more accurately decoded than their old (mean = 1.191, p ≪ 0.01) versions.

A significant interaction among participants’ age and age and gender of faces (F(4,258) = 4.763, p = 0.001) was found. Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces, and age of participants). These tests revealed that:

  1. a.

    Concerning the age of the faces: young female faces of disgust were more accurately decoded by middle-aged (mean = 1.865) rather than older (mean = 1.521, p = 0.018), and young male posed faces of disgust were more accurately decoded by young (mean = 1.783) rather than older (mean = 1.428, p = 0.026) participants. Middle-aged posed female faces of disgust were more accurately decoded by middle-aged (mean = 1.755, p = 0.003) and young (mean = 1.696, p = 0.010) participants rather than older participants (mean = 1.297) as well as, middle-aged male posed faces of disgust were more accurately decoded by middle-aged (mean = 1.640, p ≪ 0.01) and young (mean = 1.761, p ≪ 0.01) participants rather than older adults (mean = 1.115). Old posed female faces of disgust were more accurately decoded by middle-aged (mean = 1.330, p ≪ 0.01) and young (mean = 1.587, p ≪ 0.01) participants rather than older adults (mean = 0.703).

  2. b.

    Concerning the age of participants, young participants were less accurate in decoding old (mean = 1.239) rather than young (mean = 1.783, p ≪ 0.01) and middle-aged (mean = 1.761, p ≪ 0.01) male posed faces of disgust. Middle-aged participants were less accurate in decoding old (mean = 1.330) rather than young (1.895, p ≪ 0.01) and middle-aged (mean = 1.755, p ≪ 0.01) female posed faces of disgust, as well as, less accurate in decoding old (mean = 1.300) rather than young (mean = 1.695, p = 0.002) and middle-aged (mean = 1.640, p = 0.011) male posed faces of disgust. Older adults were less accurate in decoding old (mean = 0.703) rather than young (mean = 1.521, p ≪ 0.01) and middle-aged (mean = 1.297, p ≪ 0.01), as well ass, less accurate in decoding middle-aged (mean = 1.297) rather than young (mean = 1.521, p = 0.014) female posed faces of disgust. Older adults were also more accurate in decoding young (mean = 1.428) rather than middle-aged (mean = 1.115, p = 0.008) and old (mean = 0.989, p = 0.001) male posed faces of disgust.

  3. c.

    Concerning the gender of stimuli, young participants were less accurate in decoding old males (mean = 1.239) rather than old female faces of disgust (mean = 1.587, p = 0.007), and older adults were less accurate in decoding old female (mean = 0.703) faces rather than old male (mean = 0.989, p = 0.029) faces of disgust.

1.2.2 Appendix 1.2.2: Anger

Significant effects of participants’ age were observed (F(2,129) = 14.369, p ≪ 0.01). Bonferroni’s post hoc tests revealed that older adults (mean = 1.042) were significantly less accurate than young (mean = 1.572, p ≪ 0.01) and middle-aged (mean = 1.421, p = 0.001) participants in decoding posed faces of anger.

Significant effects of the age of the faces (F(2,258) = 55.466, p ≪ 0.01) were observed. Bonferroni’s post hoc tests revealed that young faces (mean = 1.629) were significantly more accurately decoded than middle-aged (mean = 1.230, p ≪ 0.01) and old (mean = 1.176, p ≪ 0.01) posed faces of anger.

Significant effects of the gender of faces (F(1,129) = 14.348, p ≪ 0.01) emerged. Bonferroni’s post hoc tests revealed that female faces (mean = 1.414) were significantly more accurately decoded than male (mean = 1.277, p ≪ 0.01) faces of anger.

A significant interaction emerged between participants’ age and gender of faces (F(2, 129) = 4.643, p = 0.011). Bonferroni’s post hoc tests were performed for each single factor (gender of faces and participants’ age). Tests revealed that:

  1. a.

    Concerning the gender of faces, Bonferroni’s post hoc tests revealed that older (mean = 1.084) were less accurate than young (mean = 1.717, p ≪ 0.01) and middle-aged (mean = 1.440, p = 0.005), and middle-aged (mean = 1.440) were less accurate than young (mean = 1.717, p = 0.037) participants in decoding female faces of anger. Older (mean = 1.000) were also less accurate than young (mean = 1.428, p = 0.001) and middle-aged (mean = 1.402, p = 0.002) participants in decoding male posed male faces of anger.

  2. b.

    Concerning participants’ age, Bonferroni’s post hoc tests revealed that young participants were less accurate in decoding male (mean = 1.428) rather than female (mean = 1.717, p ≪ 0.01) posed faces of anger.

Significant interactions among the age and gender of faces and participants’ age (F(4,258) = 6.520, p ≪ 0.01) were found. Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces and participants’ age).

These tests revealed that:

  1. a.

    Concerning the age of the faces: young female posed faces of anger were less accurately decoded by older (mean = 1.378) rather than middle-aged (mean = 1.750, p = 0.012) and young (mean = 1.870, p ≪ 0.01) participants. Similarly, young male posed faces of anger were less accurately decoded by older (mean = 1.291) rather than middle-aged (mean = 1.725, p = 0.006) and young (mean = 1.761, p = 0.002) participants. Middle-aged posed male faces of anger were less accurately decoded by older (mean = 0.929) rather than young (mean = 1.413, p = 0.009) participants. Old posed female faces of anger were more accurately decoded by young (mean = 1.891) rather than middle-aged (mean = 1.175, p ≪ 0.01) and older (mean = 0.853, p ≪ 0.01) participants. Old posed male faces of anger were more accurately decoded by middle-aged (mean = 1.245) rather than older (mean = 0.782, p = 0.006) participants.

  2. b.

    Concerning the age of participants, young participants were less accurate in decoding middle-aged (mean = 1.391) rather than young (mean = 1.870, p ≪ 0.01) and old (mean = 1.891, p ≪ 0.01) female posed faces of anger. Young participants were also less accurate in decoding old (mean = 1.109) rather than young (1.761, p ≪ 0.01) and middle-aged (mean = 1.413, p = 044) male posed faces of anger, as well as, less accurate in decoding middle-aged (mean = 1.413) rather than young (mean = 1.761, p = 0.011) male posed faces of anger. Middle-aged participants were less accurate in decoding old (mean = 1.175) rather than young (1.750, p ≪ 0.01) and middle-aged (mean = 1.595, p = 0.005) female posed faces of anger, as well as, more accurate in decoding young (mean = 1.725) rather than middle-aged (mean = 1.235, p ≪ 0.01) and old (mean = 1.245, p ≪ 0.01) male posed faces of anger. Older adults were less accurate in decoding old (mean = 0.853) rather than young (mean = 1.378, p ≪ 0.01) and middle-aged (mean = 1.020, p ≪ 0.005) female posed faces of anger, as well as more accurate in decoding young (mean = 1.291) rather than middle-aged (mean = 0.929, p = 0.009) and old (mean = 0.782, p ≪ 0.01) male posed faces of anger.

  3. c.

    Concerning the gender of faces, young participants were less accurate in decoding old male (mean = 1.109) rather than old female (mean = 1.891, p ≪ 0.01) faces of anger.

1.2.3 Appendix 1.2.3: Fear

Significant effects of participants’ age effect were observed (F(2,129) = 4.145, p = 0.018) Bonferroni’s post hoc tests revealed that older adults (mean = 1.302) were significantly less accurate than young participants (mean = 1.612, p = 0.015) in decoding faces of fear.

Significant effects of the age of the faces were observed (F(2,258) = 5.285, p = 0.006). Bonferroni’s post hoc tests revealed that the decoding of old (mean = 1.509) faces of fear was significantly more accurate than the decoding of middle-aged (mean = 1.363, p = 0.010) faces of fear. No effects of the gender of faces were observed (F(1,129) = 1.153, p = 0.285).

Significant interactions emerged between age and gender of faces (F(2,258) = 35.237, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces). Tests revealed that:

  1. a.

    Concerning the gender of faces, young female (mean = 1.256) faces of fear were significantly less accurately decoded than young male faces (mean = 1.687, p ≪ 0.01) of fear, and middle-aged female (mean 1.519) faces of fear were significantly more accurately decoded than middle-aged male (mean = 1.208, p ≪ 0.01) faces of fear.

  2. b.

    Concerning the age of faces, female young faces (mean = 1.256) were less accurately decoded than middle-aged (mean = 1.519, p ≪ 0.01) and old (mean = 1.511, p ≪ 0.01) female faces of fear. Male middle-aged (mean = 1.208) faces were less accurately decoded than young (mean = 1.687, p ≪ 0.01) and old (mean = 1.507, p ≪ 0.01) male faces of fear, and old male (mean = 1.507) faces were less accurately decoded than young male (mean = 1.687, p = 0.006) faces of fear.

Significant interactions emerged among the age and gender of faces, and participants’ age (F(4,258) = 5.394, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces and participants’ age). These tests revealed that:

  1. a.

    Concerning the age of the faces: young female faces of fear were less accurately decoded by older (mean = 0.885) rather than middle-aged (mean = 1.340, p = 0.016) and young (mean = 1.543, p ≪ 0.01) participants. Old posed male faces of fear were more accurately decoded by young (mean = 1.761) rather than older (mean = 1.245, p = 0.003) participants.

  2. b.

    Concerning the age of participants, young participants were less accurate in decoding middle-aged (mean = 1.348) rather than young (mean = 1.826, p ≪ 0.01) and old (mean = 1.761, p = 002) male faces of fear. Middle-aged participants were less accurate in decoding middle-aged (mean = 1.225) rather than young (1.675, p ≪ 0.01) male faces of fear. Older adults were less accurate in decoding young (mean = 0.885) rather than old (mean = 1.568, p ≪ 0.01) and middle-aged (mean = 1.505, p ≪ 0.005) female faces of fear, as well as more accurate in decoding young (mean = 1.560) rather than middle-aged (mean = 1.050, p ≪ 0.01) and old (mean = 1.245, p = 0.006) male faces of fear.

  3. c.

    Concerning the gender of faces, young participants were less accurate in decoding young female (mean = 1.543) rather than young male (mean = 1.826, p = 0.005) faces of fear and less accurate in decoding middle-aged male (mean = 1348) rather than female (mean = 1.630, p = 0.018) faces of fear. Middle-aged participants were less accurate in decoding young female (mean = 1.340) rather than young male (mean = 1.675, p = − 001) faces of fear. Older adults were less accurate in decoding young female (mean = 0.885) rather than young male (mean = 1.560, p ≪ 0.01), middle-aged male (mean = 1.050) rather than middle-aged female (mean = 1.505, p ≪ 0.01) and old male (mean = 1.245) rather than old female (mean = 1.568, p = 0.002) posed faces of fear.

1.2.4 Appendix 1.2.4: Sadness

No significant differences emerged among participants’ age (F(1,129) = 1.771, p = 0.174).

Significant effects of the age of faces were observed (F(2,258) = 10.573, p ≪ 0.01). Bonferroni’s post hoc tests revealed that young faces (mean = 0.913) were significantly less accurately decoded than middle-aged (mean = 1.143, p ≪ 0.01) faces of sadness.

Significant effects of the gender of faces were observed (F(1,128) = 161.084, p ≪ 0.01). Bonferroni’s post hoc tests revealed that female (mean = 1.315) faces were significantly more accurately decoded than male (mean = 0.745, p ≪ 0.01) faces of sadness.

Significant interactions emerged between the gender of faces and participants’ age (F(2,129) = 10.510, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (age of participants and gender of faces). Tests revealed that:

  1. a.

    Concerning the age of participants, young participants were significantly more accurate in decoding female (mean = 1.355) rather than male (mean = 0.652, p ≪ 0.01) faces of sadness. Middle-aged participants were significantly more accurate in decoding female (mean = 1.495) rather than male (mean = 0.765, p ≪ 0.01) faces of sadness. Older adults were significantly more accurate in decoding female (mean = 1.096) rather than males (mean = 0.818, p = 0.001) faces of sadness.

  2. b.

    Concerning the gender of faces, no effects were observed for this interaction.

1.2.5 Appendix 1.2.5: Happiness

No significant differences emerged among participants’ age (F(1,129) = 0.117, p = 0.889).

Significant effects of the age of faces emerged (F(2,258) = 5.996, p = 0.003). Bonferroni’s post hoc tests revealed that young (mean = 1.908) faces were significantly more accurately decoded than old (mean = 1.842, p = 0.015) faces of happiness.

No effects of the gender of faces were observed (F(1, 129) = 0.030, p = 0.864).

Significant interactions emerged between the gender of faces and participants’ age (F (2,129) = 3.662, p = 0.028). Bonferroni’s post hoc tests were performed for each single factor (participants’ age and gender of faces).

  1. a.

    Concerning participants’ age, young participants were more accurate in decoding male (mean = 1.891) rather than female (mean = 1.833, p = 0.033) faces of happiness.

  2. b.

    No effects were observed for the gender of faces.

Significant interactions emerged between the age and gender of faces (F(2,258) = 11.972, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces). Tests revealed that:

  1. a.

    Concerning the gender of faces, young male (mean = 1.918) faces were significantly more accurately decoded than young female (mean = 1.897, p = 0.016) faces of happiness. Middle-aged female (mean = 1.942) faces were significantly more accurately decoded than of middle-aged male (mean = 1.833, p = 0.031) faces of happiness. Old male (mean = 1.890) faces were significantly more accurately decoded than old female (1.794, p = 0.036) faces of happiness.

  2. b.

    Concerning the age of faces, old female (mean = 1.794) faces were significantly less accurately decoded than middle-aged (mean = 1.942, p ≪ 0.01) and young (mean = 1.897, p = 0.009) female faces of happiness. Young male (mean = 1.918) faces were significantly more accurately decoded than middle-aged (mean = 1,833, p = 0.009) male faces of happiness.

1.2.6 Appendix 1.2.6: Neutrality

Significant effects of participants’ age (F(2,129) = 4.401, p = 0.014) were observed. Bonferroni’s post hoc tests revealed that older (mean = 1.301) were significantly less accurate than young (mean = 1.634, p = 0.013) participants in decoding neutral faces.

Significant effects of the age of faces emerged (F(2,258) = 12.804, p ≪ 0.01). Bonferroni’s post hoc tests revealed that young (mean = 1.599) faces of neutrality were significantly more accurately decoded than middle-aged (mean = 1.470, p = 0.004) and old (mean = 1.392, p ≪ 0.01) faces of neutrality.

Significant effects of the gender of faces (F(1, 129) = 6.728, p = 0.011) emerged. Bonferroni’s post hoc tests revealed that male faces (mean = 1.539) were significantly more accurately decoded than female (mean = 1.435, p = 0.011) faces of neutrality.

Significant interactions emerged between the age and gender of faces (F(2,258) = 5.559, p = 0.004). Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces). Tests revealed that:

  1. a.

    Concerning the gender of stimuli, old female (mean = 1.276) faces were significantly less accurately decoded than middle-aged (mean = 1.493, p = 0.004) and young (mean = 1.538, p ≪ 0.01) female faces of neutrality. Young male (mean = 1.660) faces were significantly more accurately decoded than middle-aged (mean = 1,447, p = 0.001) and old (mean = 1.508, p = 0.014) male faces of neutrality.

  2. b.

    Concerning the age of faces, young male (mean = 1.660) faces were more accurately decoded than young female (mean = 1.538, p = 0.022) faces, and old male (mean = 1.508) faces were significantly more accurately decoded than old female (1.276, p = 0.001) faces of neutrality.

1.3 Appendix 1.3: Statistical analyses performed for Sect. 3.3 posed versus naturalistic faces in general

ANOVA repeated measures were performed with participants’ gender and type of stimuli (posed vs. naturalistic stimuli) as between and emotional categories within subjects’ factors.

Significant effects of the type of stimuli emerged (F(1,84) = 39.358, p ≪ 0.01). Bonferroni’s post hoc tests revealed that naturalistic stimuli (mean = 5.848) were significantly less accurately decoded than posed stimuli (mean = 7.655, p ≪ 0.01).

Significant differences emerged among emotional categories (F(5,420) = 77.841, p ≪ 0.01). Bonferroni’s post hoc tests revealed that the decoding of happiness (mean = 11.096) was significantly more accurate than the decoding of disgust (mean = 4.530, p ≪ 0.01), anger (mean = 6.642, p ≪ 0.01), fear (mean = 5.477, p ≪ 0.01), sadness (mean = 5.708, p ≪ 0.01), and neutrality (mean = 6.966, p ≪ 0.01). The decoding of disgust (mean = 4.530) was significantly less accurate than anger (mean = 6.642, p ≪ 0.01) and neutrality (mean = 6.966, p ≪ 0.01). The decoding of fear (mean = 5.477) was significantly less accurate than anger (mean = 6.642, p = 0.027) and neutrality (mean = 6.966, p = 0.004).

Significant interactions emerged between emotional categories and type of stimuli (F(5, 420) = 22.389, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (emotional categories and type of stimuli). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed faces of disgust (mean = 7.052), fear (mean = 7.814) and neutrality (mean = 7.806) were significantly more accurately decoded than naturalistic faces of disgust (mean = 2.008, p ≪ 0.01), fear (mean = 3.141, p ≪ 0.01) and neutrality (mean = 6.126, p = 0.012).

  2. b.

    Concerning the emotional categories, it was found that:

    1. 1.

      Posed faces of happiness (mean = 11.263) were more accurately decoded than posed faces of disgust (mean = 7.052, p ≪ 0.01), anger (mean = 6.252, p ≪ 0.01), sadness (mean = 5.774, p ≪ 0.01), fear (mean = 7.814, p ≪ 0.01), and neutrality (mean = 7.806, p ≪ 0.01). Posed faces of sadness (mean = 5.774) were less accurately decoded than posed faces of fear (mean = 7.814, p ≪ 0.01) neutrality (mean = 7.806, p = 0.019) and happiness (p ≪ 0.01). Posed faces of fear (mean = 7.814,) were less accurately decoded than posed faces of anger (mean = 6.252, p = 0.044)

    2. 2.

      Naturalistic faces of happiness (mean = 10.928) were more accurately decoded than naturalistic faces of disgust (mean = 2.008, p ≪ 0.01), anger (mean = 7.033, p ≪ 0.01), sadness (mean = 5.673, p ≪ 0.01), fear (mean = 3.141, p ≪ 0.01), and neutrality (mean = 6.126, p ≪ 0.01). Naturalistic faces of disgust (mean = 2.008) were less accurately decoded than naturalistic faces of anger (mean = 7.033, p ≪ 0.01), sadness (mean = 5.673, p ≪ 0.01) neutrality (mean = 6.126, p ≪ 0.01), and happiness (mean = 10.928, p ≪ 0.01). Naturalistic faces of fear (mean = 3.141) were less accurately decoded than naturalistic faces of anger (mean = 7.033, p ≪ 0.01), sadness (mean = 5.673, p ≪ 0.01) neutrality (mean = 6.126, p ≪ 0.01), and happiness (mean = 10.928, p ≪ 0.01).

1.4 Appendix 1.4: Statistical analyses performed for Sect. 3.4, posed versus naturalistic for each emotion

Repeated measures ANOVA were carried out for each emotional category (disgust, anger, fear, sadness, happiness, and neutrality) considering participants’ gender and type of stimuli administered (posed vs. naturalistic) as between and age and gender of the stimuli as within factors. The significance level was set at α < 0.05 and differences among marginal means were assessed through Bonferroni’s post hoc tests.

Repeated measures ANOVA were carried out for each emotional category (disgust, anger, fear, sadness, happiness, and neutrality) considering participants’ gender and type of stimuli administered (posed vs. naturalistic) as between and age and gender of the stimuli as within factors. The significance level was set at α < 0.05 and differences among marginal means were assessed through Bonferroni’s post hoc tests.

1.4.1 Appendix 1.4.1: Disgust

Significant effects of the type of stimuli emerged (F(1,84) = 72.029, p ≪ 0.01) Bonferroni’s post hoc tests revealed that naturalistic faces (mean = 0.335) were less accurately decoded than posed faces of disgust (mean = 1.175, p ≪ 0.01).

Significant effects of the age of faces (young, middle-aged, and old faces) emerged (F(2,168) = 28.440, p ≪ 0.01). Bonferroni’s post hoc tests revealed that old faces (mean = 0.529) were significantly less accurately decoded than middle-aged (mean = 0.839, p ≪ 0.01) and young faces (mean = 0.898, p ≪ 0.01) of disgust.

No significant effects of the gender of faces were observed (F(1,84) = 0.005, p = 0.945).

Significant interactions emerged between the age and type of stimuli (F(2,168) = 13.748, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (type of stimuli, and age of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed old (mean = 0.846), middle-aged (mean = 1.206) and young (mean = 1.474) faces of disgust were significantly more accurately decoded than naturalistic old (mean = 0.212, p ≪ 0.01), middle-aged (mean = 0.471, p ≪ 0.01) and young (mean = 0.321, p ≪ 0.01) faces of disgust.

  2. b.

    Concerning the age of faces, old posed (mean = 0.846) faces were significantly less accurately decoded than middle-aged (mean = 1.206, p ≪ 0.01) and young (mean = 1.474, p ≪ 0.01) posed faces of disgust. Middle-aged posed faces (mean = 1.206) were significantly less accurately decoded than young (man = 1.474, p = 0.001) posed faces of disgust. Old naturalistic (mean = 0.212) faces were significantly less accurately decoded than middle-aged (mean = 0.471, p = 0.006) naturalistic faces of disgust.

Significant interactions among age, type of stimuli and participants’ gender (F (2,168) = 3.461, p = 0.034) emerged. Bonferroni’s post hoc tests were performed for each single factor (participants’ gender, age, and type of stimuli). Tests revealed that:

  1. a.

    Concerning participants’ gender, male participants were significantly more accurate in decoding old (mean = 0.952), middle-aged (mean = 1.238), and young (mean = 1.405) posed faces rather than old (mean = 0.184, p ≪ 0.01), middle-aged (mean = 0.341, p ≪ 0.01) and young (mean = 0.342, p ≪ 0.01) naturalistic faces of disgust. Female participants were significantly more accurate in decoding old (mean = 0.739), middle-aged (mean = 1.174) and young (mean = 1.543) posed faces rather than old (mean = 0.240, p ≪ 0.01), middle-aged (mean = 0.600, p = 0.002) and young (mean = 0.300, p ≪ 0.01) naturalistic faces of disgust.

  2. b.

    Concerning the type of stimuli (posed vs. naturalistic), no significant differences emerged.

  3. c.

    Concerning the age of faces, male participants were significantly less accurate in decoding old posed (mean = 0.952) faces rather than middle-aged (mean = 1.238, p = 0.047) and young posed (mean = 1.405, p ≪ 0.01) faces of disgust. Female participants were significantly less accurate in decoding old posed (mean = 0.739) faces rather than middle-aged (mean = 1.174, p = 0.001) and young (mean = 1543, p ≪ 0.01) posed faces of disgust. Female participants were significantly more accurate in decoding middle-aged (mean = 0.600) naturalistic faces rather than old (mean = 240, p = 0.003) and young (mean = 300, p = 0.005) naturalistic faces of disgust.

Significant interactions among age, gender, and type of stimuli (F (2,168) = 4.235, p = 0.016) were found. Bonferroni’s post hoc tests were performed for each single factor (age, gender, and type of stimuli). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed faces of old female (mean = 0.703) and male (mean = 0.989), posed faces of middle-aged female (mean = 1.297) and male (mean = 1.115), and posed faces of young female (mean = 1.521) and male (mean = 1.428) were significantly more accurately decoded than naturalistic faces of old female (mean = 0.272, p = 0.003) and male (mean = 0.153, p ≪ 0.01), middle-aged female (mean = 0.424, p ≪ 0.01) and male (mean = 0.518, p ≪ 0.01), young female (mean = 0.304, p ≪ 0.01) and male (mean = 0.338, p ≪ 0.01) faces of disgust.

  2. b.

    Concerning the age of faces, posed old female (mean = 0.703) faces were significantly less accurately decoded than middle-aged (mean = 1.297, p ≪ 0.01) and young (mean = 1.521, p ≪ 0.01) posed female faces of disgust. Posed middle-aged female (mean = 1.297) faces were significantly less accurately decoded than young (mean = 1.521, p = 0.036) posed female faces of disgust. Posed old male (mean = 0.989) faces were significantly less accurately decoded than middle-aged (mean = 1.115, p = 0.020) and young (mean = 1.428, p ≪ 0.01) posed male faces of disgust. Finally, naturalistic old male (mean = 0.153) faces were significantly less accurately decoded than naturalistic middle-aged (mean = 0.518, p = 0.015) male faces of disgust.

  3. c.

    Concerning the gender of faces, old female (mean = 0.703) posed faces were significantly less accurately decoded than old male (mean = 0.989, p = 0.022) posed faces of disgust.

1.4.2 Appendix 1.4.2: Anger

No significant differences emerged in the decoding accuracy of naturalistic versus posed faces (F(1,84) = 1.838, p = 0.179) of anger. Significant differences emerged concerning the age of faces expressing anger (F(2,168) = 37.406, p ≪ 0.01). Bonferroni’s post hoc tests revealed that old faces (mean = 0.809) were significantly less accurately decoded than middle-aged (mean = 1.270, p ≪ 0.01) and young (mean = 1.242, p ≪ 0.01) faces of anger. Significant effects of the gender of faces were observed (F(1,84) = 34.854, p ≪ 0.01) Bonferroni’s post hoc tests revealed that female faces (mean = 0.965) were significantly less accurately decoded than male (mean = 1.249) faces of anger.

A significant interaction emerged between the age and type of stimuli (F(2,168) = 23.435, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (type of stimuli and age of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, naturalistic middle-aged faces (mean = 1.566) were significantly more accurately decoded than posed middle-aged (mean = 0.974) faces of anger.

  2. b.

    Concerning the age of faces, young posed (mean = 1.334) faces were significantly more accurately decoded than middle-aged (mean = 0.974, p ≪ 0.01) and old (mean = 0.817, p ≪ 0.01) posed faces of anger. Naturalistic middle-aged faces (mean = 1,566) were significantly more accurately decoded than naturalistic old (mean = 0.801, p ≪ 0.01) and young (mean = 1.149, p ≪ 0.01) faces of anger. Naturalistic young faces were significantly more accurately decoded than naturalistic old (p ≪ 0.01) faces of anger.

A significant interaction emerged between gender and type of stimuli (F(1,84) = 58.170, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (type of stimuli and gender of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed female faces (mean = 1.084, p = 0.027) were significantly more accurately decoded than naturalistic female faces of anger (mean = 0.846); and naturalistic male faces (mean = 1.499) were significantly more accurately decoded than posed male faces (mean = 1.000, p ≪ 0.01) of anger.

  2. b.

    Concerning the gender of faces, naturalistic female faces (mean = 0.846) were significantly less accurately decoded than naturalistic male faces of anger (mean = 1.499, p ≪ 0.01).

A significant interaction was observed between gender and age of faces (F(2,168) = 3.688, p = 0.027). Bonferroni’s post hoc tests were performed for each single factor (gender and age of faces). Tests revealed that:

  1. a.

    Concerning the age of faces, old female faces (mean = 0.575) were significantly less accurately decoded than middle-aged (mean = 1.136, p ≪ 0.01) and young (mean = 1.183, p ≪ 0.01) female faces of anger. Old male faces (mean = 1.043) were significantly less accurately decoded than middle-aged (mean = 1.405, p ≪ 0.01) and young (mean = 1.301, p = 0.004) male faces of anger.

  2. b.

    Concerning the gender of faces, old female faces (mean = . 575) were significantly less accurately decoded than old male faces (mean = 1.043, p ≪ 0.01), and middle-aged female faces (mean = 1.136) were significantly less accurately decoded than middle-aged (mean = 1.405, p = 0.005) male faces of anger.

A significant interaction was observed among age, gender, and type of stimuli (F(2,168) = 3.353, p = 0.037) was found. Bonferroni’s post hoc tests were performed for each single factor (type of stimuli and gender, and age of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed old female faces (mean = 0.853) were significantly more accurately decoded than naturalistic old female (mean = 0.298. p ≪ 0.01) faces of anger. Posed old male (mean = 0.972) faces were significantly less accurately decoded than naturalistic old male (mean = 1.304, p = 0.001) faces of anger. Posed middle-aged male faces (mean = 0.929) were significantly less accurately decoded than naturalistic middle-aged male (mean = 1.881, p ≪ 0.01) faces of anger. Posed young female faces (mean = 1.378) were significantly more accurately decoded than naturalistic young female faces (mean = 0.987, p = 0.006) of anger

  2. b.

    Concerning the age of faces, old female posed faces (mean = 0.853) were significantly less accurately decoded than middle-aged (mean = 1.020, p = 0.010) and young female (mean = 1.378, p ≪ 0.01) faces of anger. Old male posed faces (mean = 0.782) were significantly less accurately decoded than posed middle-aged male (mean = 0.929, p = 0.006) and young male (mean = 1.291, p ≪ 0.01) faced of anger. Old female naturalistic faces (mean = 0.298) were significantly less accurately decoded than naturalistic middle-aged (mean = 1.252, p ≪ 0.01) and young female (mean = 0.987, p ≪ 0.01) faces of anger. Middle-aged male naturalistic faces (mean = 1.882) were more accurately decoded than naturalistic old (mean = 1.304, p ≪ 0.01) and young male (mean = 1.311, p ≪ 0.01) faces of anger

  3. c.

    Concerning the gender of faces, old naturalistic female faces (mean = 0.298) were significantly less accurately decoded than old naturalistic male (mean = 1.304, p ≪ 0.01) faces of anger. Middle-aged naturalistic female (mean = 1.252) faces were less accurately decoded than middle-aged naturalistic male (mean = 1.881, p ≪ 0.01) faces of anger. Young naturalistic female (mean = 0.987) faces were less accurately decoded than young naturalistic male (mean = 1.311) faces of anger.

1.4.3 Appendix 1.4.3: Sadness

For sadness, no significant differences emerged neither between posed and naturalistic (F(1,84) = 0.015, p = 0.904) faces, nor for the.

age of faces (F(2,168) = 0.108, p = 0.897) and gender of faces (F(1,84) = 0.373, p = 0.543).

A significant interaction emerged between gender and type of stimuli (F(1,84) = 17.304, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (type of stimuli and gender of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed female faces (mean = 1.096) were significantly more accurately decoded than naturalistic female faces of sadness (mean = 0.842, p = 0.018).

  2. b.

    Concerning the gender of faces, posed female faces (mean = 1.096) were significantly more accurately decoded than posed male (mean = 0.818, p = 0.001) faces of sadness. Naturalistic female faces (mean = 0.842) were less accurately decoded than naturalistic male (mean = 1.049, p = 0.014) faces of sadness

A significant interaction emerged between age and gender of faces (F(2,168) = 8.248, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces). Tests revealed that:

  1. a.

    Concerning the gender of faces, old female faces (mean = 0.834) were significantly less accurately decoded than old male (mean = 1.051, p = 0.025) faces of sadness and young female faces (mean = 1.115) were significantly more accurately decoded than young male faces (mean = 0.767, p = 0.002) of sadness.

  2. b.

    Concerning the age of Faces, young female faces (mean = 1.115) were significantly more accurately decoded than old (mean = 0.834, p = 0.013) female faces of sadness, and young male faces (mean = 0.767) were significantly less accurately decoded than old male faces (mean = 1.051, p = 0.036) of sadness.

A significant interaction emerged among age, gender, and type of stimuli (F(2,168) = 13.678, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (type of stimuli and age, and gender of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed old female faces (mean = 1.208) were significantly more accurately decoded than naturalistic old female (mean = 0.459, p ≪ 0.01) faces of sadness, and posed old male faces (mean = 0.837, p = 0.013) were significantly less accurately decoded than naturalistic old male (mean = 1.265) faces of sadness.

  2. b.

    Concerning the age of faces, female naturalistic young (mean = 1.259) faces were significantly more accurately decoded than middle-aged (mean = 0.808, p = 0.019) and old (mean = 0.459, p ≪ 0.01) female naturalistic faces of sadness. Female naturalistic middle-aged (mean = 0.808) faces were significantly more accurately decoded than female naturalistic old (mean = − 459, p = − 026) faces of sadness. Young naturalistic male (mean = 0.762) faces were significantly less accurately decoded than middle-aged (mean = 1.119, p = 0.025) and old (mean = 1.265, p = 0.006) naturalistic male faces of sadness.

  3. c.

    Concerning the gender of faces, old, posed female (mean = 1.208) faces were significantly more accurately decoded than old, posed male (mean = 0.837, p = 0.007) faces of sadness. Old naturalistic female (mean = 0.459) faces were significantly less accurately decoded than old naturalistic male (mean = 1.265, p ≪ 0.01) faces of sadness. Middle-aged naturalistic female (mean = 0.808) faces were significantly less accurately decoded than middle-aged naturalistic male (mean = 1.119, p = 0.023) faces of sadness. Young naturalistic female (mean = 1.259) faces were significantly more accurately decoded than young naturalistic male (mean = 0.762, p = 0.002) faces of sadness.

1.4.4 Appendix 1.4.4: Fear

Significant differences (F(1,84) = 74.382, p ≪ 0.01) emerged between posed and naturalistic faces of fear. Bonferroni’s post hoc tests revealed that naturalistic faces (mean = 0.524) were significantly less accurately decoded than posed faces (mean = 1.302, p ≪ 0.01) of fear.

No effects of the age of faces (F(2,168) = 1.136, p = 0.324) were observed.

A significant effect of the gender of faces (F (1,84) = 5.782, p = 0.018) was observed. Bonferroni’s post hoc tests revealed that female faces (mean = 0.968) of fear were significantly more accurately decoded than male faces (mean = 0.858) of fear.

A significant interaction emerged between the age and type of stimuli (F(2,168) = 9.821, p ≪ 0.01).

Bonferroni’s post hoc tests were performed for each single factor (type of stimuli and age of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed old (mean = 1.407), middle-aged (mean = 1.277) and young (mean = 1.223) faces of fear were significantly more accurately decoded than naturalistic old (mean = 0.308, p ≪ 0.01), middle-aged (mean = 0.603, p ≪ 0.01) and young (mean = 0.659, p ≪ 0.01) faces of fear.

  2. b.

    Concerning the age of faces, naturalistic old faces (mean = 0.308) were significantly less accurately decoded than naturalistic middle-aged (mean = 0.603, p = 0.004) and young (mean = 0.659, p = 001) faces of fear.

A significant interaction emerged between the age and gender of faces (F(2,84) = 14.950, p ≪ 0.01). Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces). Tests revealed that:

  1. a.

    Concerning the gender of faces, female middle-aged (mean = 1.147) faces were significantly more accurately decoded than male middle-aged (mean.734, p ≪ 0.01) faces of fear. Male young (mean = 1.062) faces were significantly more accurately decoded than female young (mean = 0.820, p = 0.004) faces of fear.

  2. b.

    Concerning the age of faces, middle-aged female (mean = 1.147) faces were significantly more accurately decoded than old (mean = 0.937, p = 0.047) and young (mean = 0.820, p = 0.002) female faces of fear. Young male (mean = 1.062) faces were significantly more accurately decoded than old (mean = 0.778, p = 008) and middle-aged (mean = 0.734, p ≪ 0.01) male faces of fear.

A significant interaction among age, gender, and type of stimuli (F(2,168) = 13.727, p ≪ 0.01) was found. Bonferroni’s post hoc tests were performed for each single factor (type of stimuli and age and gender of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed old female (mean = 1.568) and male (mean = 1.245), middle-aged female (mean = 1.505) and male (mean = 1.050) and young male (mean = 1.560) faces of fear were significantly more accurately decoded than naturalistic old female (mean = 0.305, p ≪ 0.01) and male (mean = 0.311, p ≪ 0.01), middle-aged female (mean = . 788, p ≪ 0.01) and male (mean = 0.418, p ≪ 0.01) and young male (mean = 0.563, p ≪ 0.01) faces of fear.

  2. b.

    Concerning the gender of faces, old (mean = 1.568) and middle-aged (mean = 1.505) female posed faces were significantly more accurately decoded than old (mean = 1.245, p = 0.006) and middle-aged (mean = 1.050, p ≪ 0.01) male posed faces of fear. Female young faces (mean = 0.885) were significantly less accurately decoded than male young (mean = 1.560, p ≪ 0.01) posed faces of fear. Middle-aged naturalistic female (mean = 0.788) faces were significantly more accurately decoded than middle-aged naturalistic male (mean = 0.418, p = 0.003) faces of fear.

  3. c.

    Concerning the age of faces, young, posed female (mean = 0.885) faces were significantly less accurately decoded than old (mean = 1.568, p ≪ 0.01) and middle-aged (mean = 1.505, p ≪ 0.01) posed female faces of fear. Young, posed male (mean = 1.560) faces were significantly more accurately decoded than middle-aged (mean = 1.050, p ≪ 0.01) male faces of fear. Old naturalistic female (mean = 0.305) faces were significantly less accurately decoded than middle-aged (mean = 0.788, p ≪ 0.01) and young (mean = 0.756, p = 0.002) naturalistic female faces of fear.

1.4.5 Appendix 1.4.5: Happiness

No significant differences (F(1,84) = 0.926, p = 0.339) emerged between posed and naturalistic faces of happiness.

No effects of the age (F(2,168) = 0.185, p = 0.831) and gender of faces (F(1,84) = 1.396, p = 0.241) were observed.

A significant interaction between gender and type of stimuli (F(1,84) = 7.864, p = 0.006) was found. Bonferroni’s post hoc tests were performed for each single factor (type of stimuli and gender of faces). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed female faces (mean = 1.900) were significantly more accurately decoded than naturalistic female faces (mean = 1.766, p = 0.043) of happiness.

  2. b.

    Concerning the gender of faces, naturalistic female (mean = 1.766) faces were significantly less accurately decoded than naturalistic male faces (mean = 1.876, p = 0.006). of happiness

A significant interaction emerged among age, gender, and type of stimuli (F(2,168) = 6.714, p = 0.002). Bonferroni’s post hoc tests were performed for each single factor (age, gender, and type of stimuli). Tests revealed that:

  1. a.

    Concerning the type of stimuli, posed middle-aged female (mean = 1.952) faces were significantly more accurately decoded than naturalistic middle-aged female faces (mean = 1.676, p = 0.002) of happiness. Posed young male (mean = 1.929) faces significantly were more accurately decoded than naturalistic young male (mean = 1.762, p = 0.043) faces of happiness

  2. b.

    Concerning the age of faces, naturalistic middle-aged male (mean = 1.960) faces were significantly more accurately decoded than naturalistic young male (mean = 1.762, p = 0.018) faces of happiness

  3. c.

    Concerning the gender of faces, posed middle-aged female (mean = 1.676) faces were significantly less accurately decoded than posed middle-aged male (mean = 1.960, p = 0.043) faces of happiness. Middle-aged naturalistic female (mean = 1.676) faces were significantly less accurately decoded than middle-aged (mean = 1.960, p =  ≪ 0.01) naturalistic male faces of happiness.

1.4.6 Appendix 1.4.6: Neutrality

Significant differences emerged between the type of stimuli (F(1,84) = 6.522, p = 0.012). Bonferroni’s post hoc tests revealed that posed neutral faces (mean = 1.301) were significantly more accurately decoded than naturalistic neutral faces (mean = 1.021, p = 0.012). A significant effect of the age of faces was observed (F(2,84) = 5.564, p = 0.005). Bonferroni’s post hoc tests revealed that old faces (mean = 1.054) were significantly less accurately decoded than young (mean = 1.256, p = 0.006) faces of neutrality.

No effects of the gender of stimuli were observed (F(1,84) = 0.851, p = 0.359).

A significant interaction emerged between age and gender of faces (F(2,168) = 6.059, p = 0.003). Bonferroni’s post hoc tests were performed for each single factor (age and gender of faces). Tests revealed that:

  1. a.

    Concerning the gender of faces, middle-aged female (mean = 1.283) faces were significantly less accurately decoded than middle-aged male (mean = 1.064, p = 0.033) faces of neutrality. Young male (mean = 1.372) faces were significantly less accurately decoded than young female (mean = 1.140, p = 0.015) faces of neutrality.

  2. b.

    Concerning the age of faces, old female (mean = 0.985) faces were significantly less accurately decoded than middle-aged (mean = 1.283, p = 0.10) female faces of neutrality. Young male (mean = 1.373) faces were significantly more accurately decoded than old (mean = 1.123, p = 0.008) and middle-aged (mean = 1.064, p = 0.007) male faces of neutrality.

Appendix 2

 

Happy

Angry

Fearful

Sad

Disgusted

Neutral

Italians

Germans

Italians

Germans

Italians

Germans

Italians

Germans

Italians

Germans

Italians

Germans

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

Young female raters

Young female faces

95

2

99

3

95

2

94

5

89

2

85

14

61

3

85

16

91

2

81

14

95

1

91

11

Young male faces

95

2

99

2

91

2

90

9

95

1

85

16

5

1

80

13

93

2

83

13

91

2

96

6

Middle-aged female faces

95

2

99

3

75

3

90

8

86

3

86

16

66

3

85

12

84

2

83

15

86

3

89

13

Middle-aged male faces

95

2

97

8

75

3

89

11

68

3

86

12

52

3

77

13

91

2

78

16

84

2

90

10

Older female faces

86

3

96

7

95

1

84

12

80

3

83

16

61

4

83

10

82

3

74

14

77

3

87

11

Older male faces

98

1

95

15

70

3

80

14

95

1

84

14

27

3

72

12

64

3

67

20

75

3

83

14

Young male raters

Young female faces

93

2

97

11

96

2

89

16

70

3

85

16

72

3

86

17

93

2

76

19

89

2

93

13

Young male faces

98

2

96

15

89

2

88

16

91

2

86

18

33

2

78

18

89

3

78

18

93

2

94

15

Middle-aged female faces

100

1

96

10

67

3

84

16

80

3

86

17

80

3

82

18

89

2

75

19

85

3

86

17

Middle-aged male faces

96

2

96

10

70

3

84

15

70

2

82

18

46

3

70

18

89

2

70

21

78

3

87

16

Older female faces

91

2

96

12

98

1

78

19

80

3

83

16

74

4

74

18

80

2

65

17

67

3

82

19

Older male faces

98

2

96

10

43

3

75

17

85

3

81

18

37

3

68

19

63

3

59

18

80

3

79

21

Middle-aged female raters

Young female faces

96

1

98

4

90

2

93

8

64

3

79

14

66

3

79

18

82

3

73

18

72

3

96

5

Young male faces

96

1

98

6

80

3

90

9

80

2

81

14

30

2

72

21

94

2

75

19

84

3

95

10

Middle-aged female faces

96

2

98

5

72

3

89

9

72

3

85

12

76

3

71

19

88

2

71

18

72

3

91

9

Middle-aged male faces

86

2

98

4

56

3

92

8

60

3

81

15

58

3

69

19

84

2

64

21

80

3

92

9

Older female faces

88

2

97

7

60

3

75

12

70

4

83

12

74

3

67

16

68

3

62

20

60

4

83

15

Older male faces

94

2

98

4

62

3

77

9

74

3

78

13

34

3

62

18

60

2

53

17

80

3

81

17

Middle-aged male raters

Young female faces

98

1

98

6

85

3

86

12

70

4

81

18

78

3

82

14

93

2

74

17

75

4

87

13

Young male faces

98

1

99

3

93

2

87

12

88

3

78

23

33

3

79

18

88

2

72

18

85

3

93

13

Middle-aged female faces

100

0

99

2

68

4

86

12

70

4

83

17

80

3

75

15

88

3

72

16

73

3

84

16

Middle-aged male faces

95

2

99

2

68

4

90

10

63

4

77

19

38

4

64

21

80

3

64

20

80

3

86

16

Older female faces

93

2

98

3

58

4

73

15

70

4

78

19

75

4

70

15

65

4

68

14

73

4

79

17

Older male faces

100

0

97

6

63

4

81

15

78

3

75

19

38

4

56

22

70

3

57

19

83

3

82

17

Older female raters

Young female faces

96

1

99

2

78

3

81

15

46

3

82

13

54

4

82

17

78

3

70

22

70

3

88

11

Young male faces

100

0

97

5

70

3

77

19

87

3

78

17

39

4

80

17

76

3

68

22

89

3

91

9

Middle-aged female faces

100

0

98

5

54

4

76

14

70

3

83

16

61

3

74

18

63

4

67

16

72

3

81

15

Middle-aged male faces

89

2

99

3

50

3

82

11

48

4

78

13

37

3

71

17

54

3

61

22

63

4

84

12

Older female faces

96

1

95

11

54

3

67

18

78

3

83

12

57

3

75

15

37

3

64

18

57

3

78

19

Older male faces

93

1

97

7

54

3

73

17

67

3

82

15

46

4

62

17

37

3

52

22

78

3

78

18

Older male raters

Young female faces

95

2

93

14

60

4

76

16

43

4

76

19

43

4

72

18

74

4

68

20

64

4

88

15

Young male faces

93

2

92

16

60

4

76

19

69

3

70

25

38

3

71

14

67

4

69

23

60

4

93

13

Middle-aged female faces

95

2

91

16

48

4

70

20

81

3

74

22

50

4

66

20

67

4

64

21

64

4

85

15

Middle-aged male faces

93

2

91

18

43

4

79

13

57

3

72

21

48

3

61

17

57

4

55

22

52

4

87

14

Older female faces

88

3

89

18

31

4

58

21

79

3

69

23

64

3

62

22

33

3

56

20

52

4

80

20

Older male faces

88

3

92

16

24

3

64

21

57

4

68

25

38

4

53

19

62

4

50

23

60

4

80

19

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Esposito, A., Amorese, T., Cuciniello, M. et al. Age and gender effects on the human’s ability to decode posed and naturalistic emotional faces. Pattern Anal Applic 25, 589–617 (2022). https://doi.org/10.1007/s10044-021-01049-w

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