Auswahl der wissenschaftlichen Literatur zum Thema „Psychology Recognition“
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Zeitschriftenartikel zum Thema "Psychology Recognition"
Coburn, William J., und Estelle Shane. „Recognizing Recognition in Self Psychology“. International Journal of Psychoanalytic Self Psychology 3, Nr. 2 (03.04.2008): 153–57. http://dx.doi.org/10.1080/15551020801923029.
Der volle Inhalt der QuelleStrongman, Luke. „The Magic Jacket: Recognition and Organizational Psychology“. International Journal of Psychological Studies 9, Nr. 1 (14.12.2016): 33. http://dx.doi.org/10.5539/ijps.v9n1p33.
Der volle Inhalt der QuelleVislova, Aminat. „The Problem of Face recognition in Psychology and Artificial Intelligence“. Artificial societies 16, Nr. 2 (2021): 0. http://dx.doi.org/10.18254/s207751800015009-8.
Der volle Inhalt der QuelleDeLeon, Patrick H., Patria Forsythe und Gary R. VandenBos. „Federal recognition of psychology in rehabilitation programs.“ Rehabilitation Psychology 31, Nr. 1 (1986): 47–56. http://dx.doi.org/10.1037/0090-5550.31.1.47.
Der volle Inhalt der QuelleDeLeon, Patrick H., Patria Forsythe und Gary R. VandenBos. „Federal recognition of psychology in rehabilitation programs.“ Rehabilitation Psychology 31, Nr. 1 (1986): 47–56. http://dx.doi.org/10.1037/h0091525.
Der volle Inhalt der QuelleZhao, Mengyao. „Emotion Recognition in Psychology of Human-robot Interaction“. Psychomachina 1 (21.11.2023): 1–11. http://dx.doi.org/10.59388/pm00331.
Der volle Inhalt der QuelleOunachad, Khalid, Mohamed Oualla, Abdelalim Sadiq und Abdelghani Sohar. „Face Sketch Recognition: Gender Classification and Recognition“. International Journal of Psychosocial Rehabilitation 24, Nr. 03 (18.02.2020): 1073–85. http://dx.doi.org/10.37200/ijpr/v24i3/pr200860.
Der volle Inhalt der QuelleBunnell, Julie K. „Recognition of Famous Names in Psychology by Students and Staff“. Teaching of Psychology 19, Nr. 1 (Februar 1992): 51–52. http://dx.doi.org/10.1207/s15328023top1901_15.
Der volle Inhalt der QuelleAllinson, N. M., und A. W. Ellis. „Face recognition: combining cognitive psychology and image engineering“. Electronics & Communications Engineering Journal 4, Nr. 5 (1992): 291. http://dx.doi.org/10.1049/ecej:19920050.
Der volle Inhalt der QuellePassmore, Jonathan. „In recognition of the Wiley Organisational Psychology Series“. OP Matters 1, Nr. 37 (März 2018): 9–10. http://dx.doi.org/10.53841/bpsopm.2018.1.37.9.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleBackground: 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.
Der volle Inhalt der QuelleLoneliness – 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.
Der volle Inhalt der QuelleTurnbull, Oliver Hugh. „Spatial transformations and object recognition“. Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364274.
Der volle Inhalt der QuelleValentine, T. R. „Encoding processes in face recognition“. Thesis, University of Nottingham, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.373343.
Der volle Inhalt der QuelleMemon, A. „Context effects in face recognition“. Thesis, University of Nottingham, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355418.
Der volle Inhalt der QuelleKHALIFA, 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.
Der volle Inhalt der QuelleOne 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.
Der volle Inhalt der QuelleGaston, Jeremy R. „The limiting role of backward recognition masking for recognition of speech-like transitions“. Diss., Online access via UMI:, 2005.
Den vollen Inhalt der Quelle findenWhitt, Emma. „Associative processes in recognition memory“. Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12289/.
Der volle Inhalt der QuelleBücher zum Thema "Psychology Recognition"
Barton, G. Michael. Recognition at work. Scottsdale, AZ: WorldatWork, 2002.
Den vollen Inhalt der Quelle findenUttal, William R. A behaviorist looks at form recognition. Mahwah, NJ: L. Erlbaum Associates, 2002.
Den vollen Inhalt der Quelle findenHonneth, Axel. Verdinglichung: Eine anerkennungstheoretische Studie. Frankfurt am Main: Suhrkamp, 2005.
Den vollen Inhalt der Quelle findenRay, Bull, und Milne Rebecca, Hrsg. Witness identification in criminal cases: Psychology and practice. Oxford: Oxford University Press, 2008.
Den vollen Inhalt der Quelle findenPerdue, Charles W. Hazard recognition in mining: A psychological perspective. [Washington, D.C.]: U.S. Dept. of the Interior, Bureau of Mines, 1995.
Den vollen Inhalt der Quelle findenTzenos, Alexandros D. Anagnōrisē tou anthrōpou mesa apo to dikaio kai tēn glōssa. Athēna: A. Tzenos, 1987.
Den vollen Inhalt der Quelle findenManuel, Carreiras, und Grainger Jonathan, Hrsg. Sublexical representations in visual word recognition. Hove: Psychology, 2004.
Den vollen Inhalt der Quelle findenSchaffer, Johanna. Ambivalenzen der Sichtbarkeit: Über die visuellen Strukturen der Anerkennung. Bielefeld: Transcript, 2008.
Den vollen Inhalt der Quelle finden1928-, Swets John A., Hrsg. Signal detection and recognition by human observers: Contemporary readings. Los Altos Hills, Calif: Peninsula Pub., 1988.
Den vollen Inhalt der Quelle findenAgainst Recognition. Polity, 2007.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Psychology Recognition"
Tarr, Michael J. „Pattern recognition.“ In Encyclopedia of psychology, Vol. 6., 66–71. Washington: American Psychological Association, 2000. http://dx.doi.org/10.1037/10521-021.
Der volle Inhalt der QuelleLihan, Chen. „Pattern Recognition“. In 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.
Der volle Inhalt der QuelleArfken, Michael. „Recognition Versus Redistribution“. In 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.
Der volle Inhalt der QuelleHandel, Stephen. „Auditory pattern recognition.“ In Encyclopedia of psychology, Vol. 1., 328–31. Washington: American Psychological Association, 2000. http://dx.doi.org/10.1037/10516-113.
Der volle Inhalt der QuelleMcFarlane, Anna. „Perception in Pattern Recognition“. In Cyberpunk Culture and Psychology, 68–89. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003082477-4.
Der volle Inhalt der QuelleKumar, Naveen, Niraj Kumar Jha, Hrithika Panday, Saurabh Kumar Jha, Ravi Kant Singh und Abhimanyu Kumar Jha. „Altruism: Kin Recognition“. In 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.
Der volle Inhalt der QuelleBate, Sarah. „The Cognitive Psychology of Face Recognition“. In Face Recognition and its Disorders, 3–22. London: Macmillan Education UK, 2013. http://dx.doi.org/10.1007/978-1-137-29277-3_1.
Der volle Inhalt der QuelleJahnke, John C. „Error Factors In Recognition Memory“. In 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.
Der volle Inhalt der QuellePollick, Frank E. „Psychology of Gait and Action Recognition“. In Encyclopedia of Biometrics, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_34-3.
Der volle Inhalt der QuellePollick, Frank E. „Psychology of Gait and Action Recognition“. In Encyclopedia of Biometrics, 1100–1105. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_34.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Psychology Recognition"
Kaernbach, Christian. „On dimensions in emotion psychology“. In Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771350.
Der volle Inhalt der QuellePollick, Frank, und Aina Puce. „Workshop on psychology of face and gesture recognition“. In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813424.
Der volle Inhalt der QuellePike, G. „The psychology of human face recognition“. In IEE Colloquium on Visual Biometrics. IEE, 2000. http://dx.doi.org/10.1049/ic:20000471.
Der volle Inhalt der QuelleXu, Erjia, und Ping Hu. „The Influence of Cultural Background Information on Emotional Body Language Recognition“. In International Association of Cross Cultural Psychology Congress. International Association for Cross-Cultural Psychology, 2024. http://dx.doi.org/10.4087/mrmt8471.
Der volle Inhalt der QuelleHan, Jiaheng, Honggai Li, Jinshi Cui, Qili Lan und Li Wang. „Psychology-Inspired Interaction Process Analysis based on Time Series“. In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956367.
Der volle Inhalt der QuelleDietrich, Manuel, Eugen Berlin und Kristof van Laerhoven. „Assessing activity recognition feedback in long-term psychology trials“. In MUM '15: 14th International Conference on Mobile and Ubiquitous Multimedia. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2836041.2836052.
Der volle Inhalt der Quellede Vries, P. H. „Neural Binding in Letter- and Word-Recognition“. In 14th Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789814699341_0002.
Der volle Inhalt der QuelleHANCOCK, P. J. B., C. D. FROWD, E. BRODIE und C. A. NIVEN. „RECOGNITION OF PAIN EXPRESSIONS“. In Proceedings of the Ninth Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701886_0035.
Der volle Inhalt der QuelleMinoh, Michihiko. „Keynote Talk 1: AI and Psychology“. In 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.
Der volle Inhalt der QuelleMinoh, Michihiko. „Keynote Talk 1: AI and Psychology“. In 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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