Littérature scientifique sur le sujet « Psychology Recognition »
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Articles de revues sur le sujet "Psychology Recognition"
Coburn, William J., et Estelle Shane. « Recognizing Recognition in Self Psychology ». International Journal of Psychoanalytic Self Psychology 3, no 2 (3 avril 2008) : 153–57. http://dx.doi.org/10.1080/15551020801923029.
Texte intégralStrongman, Luke. « The Magic Jacket : Recognition and Organizational Psychology ». International Journal of Psychological Studies 9, no 1 (14 décembre 2016) : 33. http://dx.doi.org/10.5539/ijps.v9n1p33.
Texte intégralVislova, Aminat. « The Problem of Face recognition in Psychology and Artificial Intelligence ». Artificial societies 16, no 2 (2021) : 0. http://dx.doi.org/10.18254/s207751800015009-8.
Texte intégralDeLeon, Patrick H., Patria Forsythe et Gary R. VandenBos. « Federal recognition of psychology in rehabilitation programs. » Rehabilitation Psychology 31, no 1 (1986) : 47–56. http://dx.doi.org/10.1037/0090-5550.31.1.47.
Texte intégralDeLeon, Patrick H., Patria Forsythe et Gary R. VandenBos. « Federal recognition of psychology in rehabilitation programs. » Rehabilitation Psychology 31, no 1 (1986) : 47–56. http://dx.doi.org/10.1037/h0091525.
Texte intégralZhao, Mengyao. « Emotion Recognition in Psychology of Human-robot Interaction ». Psychomachina 1 (21 novembre 2023) : 1–11. http://dx.doi.org/10.59388/pm00331.
Texte intégralOunachad, Khalid, Mohamed Oualla, Abdelalim Sadiq et Abdelghani Sohar. « Face Sketch Recognition : Gender Classification and Recognition ». International Journal of Psychosocial Rehabilitation 24, no 03 (18 février 2020) : 1073–85. http://dx.doi.org/10.37200/ijpr/v24i3/pr200860.
Texte intégralBunnell, Julie K. « Recognition of Famous Names in Psychology by Students and Staff ». Teaching of Psychology 19, no 1 (février 1992) : 51–52. http://dx.doi.org/10.1207/s15328023top1901_15.
Texte intégralAllinson, N. M., et A. W. Ellis. « Face recognition : combining cognitive psychology and image engineering ». Electronics & ; Communications Engineering Journal 4, no 5 (1992) : 291. http://dx.doi.org/10.1049/ecej:19920050.
Texte intégralPassmore, Jonathan. « In recognition of the Wiley Organisational Psychology Series ». OP Matters 1, no 37 (mars 2018) : 9–10. http://dx.doi.org/10.53841/bpsopm.2018.1.37.9.
Texte intégralThèses sur le sujet "Psychology Recognition"
Strowger, Megan E. « Interoceptive sounds and emotion recognition ». Thesis, University of the Sciences in Philadelphia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10294821.
Texte intégralBackground: Perception of changes in physiological arousal is theorized to form the basis for which the brain labels emotional states. Interoception is a process by which individuals become aware of physiological sensations. Lowered emotional awareness has been found to be associated with lower interoceptive awareness. Alexithymia is a personality trait associated with lowered emotion recognition ability which affects 10-20% of the university student population in Western countries. Research suggests that being made aware of one’s heartbeat may enhance emotional awareness. Objective(s): The present study attempted to enhance emotion recognition abilities directly via an experimental interoceptive manipulation in order to decrease levels of alexithymia. It had three aims: 1) To examine whether exposing individuals to the interoceptive sound of their own heart beat could illicit changes in their emotion recognition abilities,2) To examine whether higher emotion recognition abilities as a result of listening to one’s own heartbeat differed by alexithymia group, and 3) if higher interoceptive awareness was associated with higher RME scores during the own heartbeat sound condition. Methods: 36 participants were recruited from an introductory psychology class at the University of the Sciences in Philadelphia. Participants completed lab-based tests of emotion recognition followed by questionnaires assessing alexithymia and interoceptive abilities. During the lab-based test of emotion recognition, participants were subjected to an interoceptive manipulation by listening to three sounds (in random order): own heartbeat, another person’s heartbeat, and footsteps. To test aim 1, a repeated-measures ANOVA examined differences in emotion recognition scores during the various sound conditions (i.e., no sound, own heartbeat, other heartbeat, footsteps). For evaluating aim 2, a two way 3 x 4 RM ANOVA tested for differences in RME scores by sound condition when individuals were alexithymic, possibly alexithymic and not alexithymic. Aim 3 was examined using correlations between the attention to body and emotion awareness subscale scores separately with RME score for own heartbeat. Results: Contrary to predictions, RME performance did not vary according to body sound condition, F (3, 105) =.53, p = .67, η² = .02. A significant interaction was seen between alexithymia category and RME scores during the interoceptive sound conditions, F (6, 99) = 2.27, p = .04, η ² = .12. However, post-hoc analyses did not reveal significant differences between specific alexithymia categories and RME scores. A significant positive relationship was seen between RME during own heartbeat and being able to pay attention to the body (r (36) = .34, p = .05, R² = .11). Discussion: Our results suggest that more attention was directed toward facial emotions when subjects listened to their own heartbeat but this increase did not result in measurable changes in RME performance. Limitations: Although using a within-subjects design potentially increased statistical power, a between-subjects design with random assignment could have eliminated the effects of repeated measurement and condition order. Implications: The most novel of these findings was that individuals paid more attention to the emotional stimuli when hearing their own heartbeat. More research is needed to understand if the interoceptive sound manipulation may aide in improving other cognitive functions or earlier steps in the emotion process. Future research using other measures of interoception and attention are necessary to confirm the result.
Stoehr, Michele. « Loneliness and Emotion Recognition| A Dynamical Description ». Thesis, Florida Atlantic University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10610509.
Texte intégralLoneliness – the feeling that manifests when one perceives one’s social needs are not being met by the quantity or especially the quality of one’s social relationships – is a common but typically short-lived and fairly harmless experience. However, recent research continues to uncover a variety of alarming health effects associated with longterm loneliness. The present study examines the psychological mechanisms underlying how persons scoring high in trait loneliness perceive their social environments. Evaluations of transient facial expression morphs are analyzed in R using dynamical systems methods. We hypothesize that, consistent with Cacioppo and Hawkley’s socio-cognitive model, subjects scoring high in loneliness will exhibit hypervigilance in their evaluations of cold and neutral emotions and hypovigilance in their evaluations of warm emotions. Results partially support the socio-cognitive model but point to a relationship between loneliness and a global dampening in evaluations of emotions.
Bingham, Charles W. « Theorizing recognition in education / ». Thesis, Connect to this title online ; UW restricted, 1999. http://hdl.handle.net/1773/7802.
Texte intégralTurnbull, Oliver Hugh. « Spatial transformations and object recognition ». Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364274.
Texte intégralValentine, T. R. « Encoding processes in face recognition ». Thesis, University of Nottingham, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.373343.
Texte intégralMemon, A. « Context effects in face recognition ». Thesis, University of Nottingham, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355418.
Texte intégralKHALIFA, INTISSAR. « Deep psychology recognition based on automatic analysis of non-verbal behaviors ». Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/314920.
Texte intégralOne highly crucial aspect in the domain of human-human interaction is the communication of emotions. Being able to deduce emotional states through non-verbal behaviors allows humans to understand and reason about each others’ underlying goals and intents. Affective Computing is the branch of computer science that aims to profit from the power of emotions to facilitate a more efficient human-machine interaction. The goal is to give the machines the ability to express, recognize, and regulate emotions. In this dissertation, we look in detail at the role of visual and auditory expressions for communicating emotions and we develop computational models for automatic emotion recognition which is an active research area over the last decade. In general, communication of emotions through body cues is less understood than other modalities. Social psychology that has inspired many computational approaches has traditionally focused on facial cues. However, body gestures are a significant source of information especially when other channels are hidden or there is a subtle nuance of expressions. In this context, we propose our approaches for emotional body gesture recognition using two different models. For the part-based model, we develop a hybrid approach that incorporates two techniques of motion estimation and temporal normalization for hand motion modeling, then we move to present our deep-spatio temporal approach for body motion modeling to have finally the person’s emotional state. In this part, we demonstrate that our deep learning technique outperforms traditional machine learning techniques. For the kinematic-based model, we combine human pose estimation for skeleton detection and emotion classification to propose a new deep multi-stage architecture able to deal with both tasks by exploiting the strong points of models pre-trained. We demonstrate that transfer learning techniques outperform traditional machine learning techniques. As another modality, speech is the fastest normal way to communicate among humans. This reality motivates us to identify the emotional conditions of the uttering person by utilizing his/her voice automatically. We propose a deep temporal-cepstrum representation based on the concatenation of spectral features, temporal derivatives features, and a deep learning classifier for speech emotion recognition. The results obtained for both modalities using our suggested methods are very promising and competitive over existing methods in the state of the art. We believe that our work is pertinent to both social computing and organizational psychology. Taking the example of job interviews, which is well studied by social psychologists, our study may provide insights for how non-verbal cues could be used by the companies for the hiring decision. In fact, our dissertation shows the feasibility of using automatically extracted cues to analyze the psychological states as an attractive alternative to manual annotations of behavioral cues.
Shriver, Edwin R. « Stereotypicality Moderates Face Recognition : Expectancy Violation Reverses the Cross-Race Effect in Face Recognition ». Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1310067080.
Texte intégralGaston, Jeremy R. « The limiting role of backward recognition masking for recognition of speech-like transitions ». Diss., Online access via UMI:, 2005.
Trouver le texte intégralWhitt, Emma. « Associative processes in recognition memory ». Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12289/.
Texte intégralLivres sur le sujet "Psychology Recognition"
Barton, G. Michael. Recognition at work. Scottsdale, AZ : WorldatWork, 2002.
Trouver le texte intégralUttal, William R. A behaviorist looks at form recognition. Mahwah, NJ : L. Erlbaum Associates, 2002.
Trouver le texte intégralHonneth, Axel. Verdinglichung : Eine anerkennungstheoretische Studie. Frankfurt am Main : Suhrkamp, 2005.
Trouver le texte intégralRay, Bull, et Milne Rebecca, dir. Witness identification in criminal cases : Psychology and practice. Oxford : Oxford University Press, 2008.
Trouver le texte intégralPerdue, Charles W. Hazard recognition in mining : A psychological perspective. [Washington, D.C.] : U.S. Dept. of the Interior, Bureau of Mines, 1995.
Trouver le texte intégralTzenos, Alexandros D. Anagnōrisē tou anthrōpou mesa apo to dikaio kai tēn glōssa. Athēna : A. Tzenos, 1987.
Trouver le texte intégralManuel, Carreiras, et Grainger Jonathan, dir. Sublexical representations in visual word recognition. Hove : Psychology, 2004.
Trouver le texte intégralSchaffer, Johanna. Ambivalenzen der Sichtbarkeit : Über die visuellen Strukturen der Anerkennung. Bielefeld : Transcript, 2008.
Trouver le texte intégral1928-, Swets John A., dir. Signal detection and recognition by human observers : Contemporary readings. Los Altos Hills, Calif : Peninsula Pub., 1988.
Trouver le texte intégralAgainst Recognition. Polity, 2007.
Trouver le texte intégralChapitres de livres sur le sujet "Psychology Recognition"
Tarr, Michael J. « Pattern recognition. » Dans Encyclopedia of psychology, Vol. 6., 66–71. Washington : American Psychological Association, 2000. http://dx.doi.org/10.1037/10521-021.
Texte intégralLihan, Chen. « Pattern Recognition ». Dans The ECPH Encyclopedia of Psychology, 1–2. Singapore : Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6000-2_990-1.
Texte intégralArfken, Michael. « Recognition Versus Redistribution ». Dans Encyclopedia of Critical Psychology, 1643–49. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-5583-7_633.
Texte intégralHandel, Stephen. « Auditory pattern recognition. » Dans Encyclopedia of psychology, Vol. 1., 328–31. Washington : American Psychological Association, 2000. http://dx.doi.org/10.1037/10516-113.
Texte intégralMcFarlane, Anna. « Perception in Pattern Recognition ». Dans Cyberpunk Culture and Psychology, 68–89. London : Routledge, 2021. http://dx.doi.org/10.4324/9781003082477-4.
Texte intégralKumar, Naveen, Niraj Kumar Jha, Hrithika Panday, Saurabh Kumar Jha, Ravi Kant Singh et Abhimanyu Kumar Jha. « Altruism : Kin Recognition ». Dans Encyclopedia of Sexual Psychology and Behavior, 1–9. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08956-5_3-1.
Texte intégralBate, Sarah. « The Cognitive Psychology of Face Recognition ». Dans Face Recognition and its Disorders, 3–22. London : Macmillan Education UK, 2013. http://dx.doi.org/10.1007/978-1-137-29277-3_1.
Texte intégralJahnke, John C. « Error Factors In Recognition Memory ». Dans Recent Research in Psychology, 79–84. New York, NY : Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4756-2_6.
Texte intégralPollick, Frank E. « Psychology of Gait and Action Recognition ». Dans Encyclopedia of Biometrics, 1–7. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_34-3.
Texte intégralPollick, Frank E. « Psychology of Gait and Action Recognition ». Dans Encyclopedia of Biometrics, 1100–1105. Boston, MA : Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_34.
Texte intégralActes de conférences sur le sujet "Psychology Recognition"
Kaernbach, Christian. « On dimensions in emotion psychology ». Dans Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771350.
Texte intégralPollick, Frank, et Aina Puce. « Workshop on psychology of face and gesture recognition ». Dans Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813424.
Texte intégralPike, G. « The psychology of human face recognition ». Dans IEE Colloquium on Visual Biometrics. IEE, 2000. http://dx.doi.org/10.1049/ic:20000471.
Texte intégralXu, Erjia, et Ping Hu. « The Influence of Cultural Background Information on Emotional Body Language Recognition ». Dans International Association of Cross Cultural Psychology Congress. International Association for Cross-Cultural Psychology, 2024. http://dx.doi.org/10.4087/mrmt8471.
Texte intégralHan, Jiaheng, Honggai Li, Jinshi Cui, Qili Lan et Li Wang. « Psychology-Inspired Interaction Process Analysis based on Time Series ». Dans 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956367.
Texte intégralDietrich, Manuel, Eugen Berlin et Kristof van Laerhoven. « Assessing activity recognition feedback in long-term psychology trials ». Dans MUM '15 : 14th International Conference on Mobile and Ubiquitous Multimedia. New York, NY, USA : ACM, 2015. http://dx.doi.org/10.1145/2836041.2836052.
Texte intégralde Vries, P. H. « Neural Binding in Letter- and Word-Recognition ». Dans 14th Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789814699341_0002.
Texte intégralHANCOCK, P. J. B., C. D. FROWD, E. BRODIE et C. A. NIVEN. « RECOGNITION OF PAIN EXPRESSIONS ». Dans Proceedings of the Ninth Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701886_0035.
Texte intégralMinoh, Michihiko. « Keynote Talk 1 : AI and Psychology ». Dans 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR). IEEE, 2020. http://dx.doi.org/10.1109/icievicivpr48672.2020.9306582.
Texte intégralMinoh, Michihiko. « Keynote Talk 1 : AI and Psychology ». Dans 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR). IEEE, 2020. http://dx.doi.org/10.1109/icievicivpr48672.2020.9306582.
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