Готові списки джерел за темами / Recognition (Psychology)

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Добірка наукової літератури з теми "Recognition (Psychology)"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 1 серпня 2024

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Статті в журналах з теми "Recognition (Psychology)"

Coburn, William J., and Estelle Shane. "Recognizing Recognition in Self Psychology." International Journal of Psychoanalytic Self Psychology 3, no. 2 (April 3, 2008): 153–57. http://dx.doi.org/10.1080/15551020801923029.

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Strongman, Luke. "The Magic Jacket: Recognition and Organizational Psychology." International Journal of Psychological Studies 9, no. 1 (December 14, 2016): 33. http://dx.doi.org/10.5539/ijps.v9n1p33.

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Recognition is essential in human social life. It is also critical in the workplace as one of the central communication activities that provides social cohesion, meaning and direction amongst colleagues and clients. Without expressions of recognition to others-formal and informal, high-context and low-context, social and structural, from a simple greeting to an affirmation for competent achievement, the workplace and the human behavior in it may become less than optimal and even dysfunctional. Expressions of recognition promote social cohesion. Based on a literature review and qualitative analysis, this article will provide an understanding of recognition in the workplace from a variety of viewpoints. It will explain recognition as central to the rationale of productivity, identify characteristics of its use and prevalence, discuss recognition as forms of behavioral guidance and social capital and exchange, before concluding to emphasize the role of recognition in the social and regulative functions of the modern workplace.

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Vislova, 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.

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The article deals with the problem of face recognition in artificial intelligence (AI) and in psychology. The possibility of using data from the psychology of perception in the interpretation of artificial face recognition systems is analyzed. The emphasis is made on the psychophysiological mechanisms of face recognition / recognition. The available methods for solving the problem in the field of AI, trends in improving face recognition technologies in the context of the digitalization of the social and economic life of society are described. Considering the vast scope of application of face recognition technologies and the insufficient development of the problem of the relationship between AI and psychology in solving this problem, the need for a more detailed study of this phenomenon from an interdisciplinary perspective is stated.

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DeLeon, Patrick H., Patria Forsythe, and 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.

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Zhao, Mengyao. "Emotion Recognition in Psychology of Human-robot Interaction." Psychomachina 1 (November 21, 2023): 1–11. http://dx.doi.org/10.59388/pm00331.

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The field of Human-Robot Interaction (HRI) has garnered significant attention in recent years, with researchers and practitioners seeking to understand the psychological aspects underlying the interactions between humans and robots. One crucial area of focus within HRI is the psychology of emotion recognition, which plays a fundamental role in shaping the dynamics of human-robot interaction. This paper provides an overview of the background of psychology in the context of human-robot interaction, emphasizing the significance of understanding human emotions in this domain. The concept of emotion recognition, a key component of human psychology, is explored in detail, highlighting its relevance in the context of human-robot interaction. Emotion recognition allows robots to perceive and interpret human emotions, enabling them to respond appropriately and enhance the quality of interaction. The role of emotion recognition in HRI is examined from a psychological standpoint, shedding light on its implications for the design and development of effective human-robot interfaces. Furthermore, this paper delves into the application of machine learning techniques for emotion recognition in the context of human-robot interaction. Machine learning algorithms have shown promise in enabling robots to recognize and respond to human emotions, thereby contributing to more natural and intuitive interactions. The utilization of machine learning in emotion recognition reflects the intersection of psychology and technological advancements in the field of HRI. Finally, the challenges associated with emotion recognition in HRI are discussed, encompassing issues such as cross-cultural variations in emotional expression, individual differences, and the ethical implications of emotion detection. Addressing these challenges is pivotal in advancing the understanding and implementation of emotion recognition in human-robot interaction, underscoring the interdisciplinary nature of this endeavor. In conclusion, this paper underscores the critical role of emotion recognition in the psychology of human-robot interaction, emphasizing its potential to revolutionize the way humans and robots engage with each other. By integrating insights from psychology, machine learning, and technology, advancements in emotion recognition have the potential to pave the way for more empathetic and responsive human-robot interactions, offering new avenues for research and practical applications in this burgeoning field.

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Ounachad, Khalid, Mohamed Oualla, Abdelalim Sadiq, and Abdelghani Sohar. "Face Sketch Recognition: Gender Classification and Recognition." International Journal of Psychosocial Rehabilitation 24, no. 03 (February 18, 2020): 1073–85. http://dx.doi.org/10.37200/ijpr/v24i3/pr200860.

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Bunnell, Julie K. "Recognition of Famous Names in Psychology by Students and Staff." Teaching of Psychology 19, no. 1 (February 1992): 51–52. http://dx.doi.org/10.1207/s15328023top1901_15.

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Historical awareness of psychology majors and faculty members was assessed using a name recognition questionnaire, which included the names of 53 eminent contributors in the history of psychology. Before taking a course in the history of psychology, students showed a low level of name recognition, which was markedly inferior to that of faculty members. It appears that, without explicit instruction, students acquire little knowledge of the history of their discipline.

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Allinson, N. M., and 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.

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Passmore, Jonathan. "In recognition of the Wiley Organisational Psychology Series." OP Matters 1, no. 37 (March 2018): 9–10. http://dx.doi.org/10.53841/bpsopm.2018.1.37.9.

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Дисертації з теми "Recognition (Psychology)"

Strowger, Megan E. "Interoceptive sounds and emotion recognition." Thesis, University of the Sciences in Philadelphia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10294821.

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Background: 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.

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Stoehr, Michele. "Loneliness and Emotion Recognition| A Dynamical Description." Thesis, Florida Atlantic University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10610509.

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Loneliness – 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.

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Bingham, Charles W. "Theorizing recognition in education /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/7802.

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Turnbull, Oliver Hugh. "Spatial transformations and object recognition." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364274.

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Valentine, T. R. "Encoding processes in face recognition." Thesis, University of Nottingham, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.373343.

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Memon, A. "Context effects in face recognition." Thesis, University of Nottingham, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355418.

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KHALIFA, 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.

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Un aspetto estremamente cruciale nel dominio dell’interazione uomo-uomo è la comunicazione delle emozioni. Essere in grado di dedurre gli stati emotivi attraverso comportamenti non-verbali consente agli esseri umani di comprendere e ragionare su obiettivi ed intenti altrui. L’Affective Computing è una branca dell’informatica che mira a trarre vantaggio dal potere delle emozioni per facilitare un’interazione uomo-macchina più efficiente. L’obiettivo è dare alle macchine la capacità di esprimere, riconoscere e regolare le emozioni. In questa tesi, esamineremo in dettaglio il ruolo delle espressioni visive ed uditive nel comunicare emozioni, e svilupperemo modelli computazionali per il riconoscimento automatico delle emozioni: un’area di ricerca molto attiva nell’ultimo decennio. In generale, la comunicazione delle emozioni attraverso i segnali del corpo è compresa in misura minore rispetto a altre modalità. La psicologia sociale che ha ispirato molti approcci computazionali si è tradizionalmente concentrata sui segnali facciali. Tuttavia, la gestualità del corpo è una fonte significativa di informazioni, soprattutto quando altri canali sono nascosti o in presenza di sottili sfumature di espressioni. In questo contesto, proporremo diversi approcci per il riconoscimento di gesti con applicazione alle emozioni, utilizzando due modelli. Per il modello basato su parti, svilupperemo un approccio ibrido che incorpora due tecniche di stima del movimento e di normalizzazione temporale per la modellazione del movimento della mano. Passeremo poi a presentare il nostro approccio spazio-temporale profondo (deep) per modellare il movimento del corpo, ed infine ottenere lo stato emotivo della persona. In questa parte, dimostreremo che la nostra tecnica basata sul deep learning supera le tradizionali tecniche di machine learning. Per il modello basato sulla cinematica, combineremo la stima della posa del soggetto (con applicazione al rilevamento dello scheletro) e la classificazione delle emozioni per proporre una nuova architettura profonda a più stadi in grado di affrontare entrambi i compiti sfruttando i punti di forza dei modelli pre-addestrati. Dimostreremo che le tecniche di transfer learning superano le tradizionali tecniche di apprendimento automatico. Come ulteriore modalità, il parlato è la forma più comune e veloce per comunicare tra esseri umani. Questa realtà ci ha spinti a riconoscere le condizioni emotive del soggetto parlante in maniera automatica tramite la sua voce. Proporremo una rappresentazione profonda di tipo temporale e basata sul cepstrum, che sfrutta la concatenazione di feature spettrali, feature di basate su derivate temporali, ed un classificatore basato sul deep learning per il riconoscimento delle emozioni del parlato. I risultati ottenuti per entrambe le modalità utilizzando i nostri metodi sono molto promettenti e competitivi rispetto ai metodi esistenti nello stato dell’arte. Riteniamo che il nostro lavoro sia pertinente sia per il social computing che per la psicologia organizzativa. Prendendo come esempio i colloqui di lavoro, un ambito ben studiato dagli psicologi sociali, il nostro studio può fornire informazioni utili su come sfruttare i segnali non verbali per supportare le aziende nel processo di assunzione. Questa tesi descrive la fattibilità di usare indizi estratti automaticamente per analizzare gli stati psicologici, come interessante alternativa alle annotazioni manuali dei segnali comportamentali.
One 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.

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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.

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Gaston, Jeremy R. "The limiting role of backward recognition masking for recognition of speech-like transitions." Diss., Online access via UMI:, 2005.

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Whitt, Emma. "Associative processes in recognition memory." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12289/.

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Recognition memory, or the discrimination between novelty and familiarity, is well predicted by an associative model of memory (Wagner’s SOP). In this thesis I examined predictions from this model concerning priming of stimuli, and stimulus spacing, in rats’ object recognition. Priming of an object resulted in a bias in behaviour towards the non-primed object. This may be due to associative processes, as described by the SOP model. Spacing stimuli in a sample stage of an object recognition task resulted in longer-lasting or better discrimination in a test of familiar versus novel object, as predicted by the model. Incorporating a short or long delay between sample and test led to better discrimination after a short delay, though differences in stimulus spacing conditions at each delay were not significant. I also examined recognition using stimulus generalisation. Generalisation of a conditioned response occurred between stimuli that shared elements of familiarity. Although not significant, familiarity generalisation may have been less apparent in animals with lesions to perirhinal cortex, providing some support for the suggestion that perirhinal cortex has a role in novelty/familiarity discrimination. The main conclusion was that recognition memory, as measured by the object recognition and generalisation tasks, might involve associative processes.

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Книги з теми "Recognition (Psychology)"

Barton, G. Michael. Recognition at work. Scottsdale, AZ: WorldatWork, 2002.

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Uttal, William R. A behaviorist looks at form recognition. Mahwah, NJ: L. Erlbaum Associates, 2002.

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Honneth, Axel. Verdinglichung: Eine anerkennungstheoretische Studie. Frankfurt am Main: Suhrkamp, 2005.

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Ray, Bull, and Milne Rebecca, eds. Witness identification in criminal cases: Psychology and practice. Oxford: Oxford University Press, 2008.

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Perdue, Charles W. Hazard recognition in mining: A psychological perspective. [Washington, D.C.]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Tzenos, Alexandros D. Anagnōrisē tou anthrōpou mesa apo to dikaio kai tēn glōssa. Athēna: A. Tzenos, 1987.

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Manuel, Carreiras, and Grainger Jonathan, eds. Sublexical representations in visual word recognition. Hove: Psychology, 2004.

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Schaffer, Johanna. Ambivalenzen der Sichtbarkeit: Über die visuellen Strukturen der Anerkennung. Bielefeld: Transcript, 2008.

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1928-, Swets John A., ed. Signal detection and recognition by human observers: Contemporary readings. Los Altos Hills, Calif: Peninsula Pub., 1988.

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Against Recognition. Polity, 2007.

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Частини книг з теми "Recognition (Psychology)"

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.

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Lihan, 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.

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Arfken, 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.

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Handel, 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.

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McFarlane, Anna. "Perception in Pattern Recognition." In Cyberpunk Culture and Psychology, 68–89. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003082477-4.

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Kumar, Naveen, Niraj Kumar Jha, Hrithika Panday, Saurabh Kumar Jha, Ravi Kant Singh, and 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.

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Bate, 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.

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Jahnke, 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.

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Pollick, 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.

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Pollick, 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.

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Тези доповідей конференцій з теми "Recognition (Psychology)"

Kaernbach, Christian. "On dimensions in emotion psychology." In Gesture Recognition (FG 2011). IEEE, 2011. http://dx.doi.org/10.1109/fg.2011.5771350.

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Pollick, Frank, and 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.

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Pike, G. "The psychology of human face recognition." In IEE Colloquium on Visual Biometrics. IEE, 2000. http://dx.doi.org/10.1049/ic:20000471.

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Xu, Erjia, and 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.

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Culture shapes how people express and feel emotions in specific situations and certain aspects of emotions vary across cultures. Body expressions are as powerful as facial expressions in conveying emotions, with North Americans tending to exhibit more exaggerated emotional body language (EBL) than East Asians (Scherer et al., 2018). Our study used two experiments to explore whether individuals' emotion recognition of EBL was affected by cultural background information. Experiment 1 recruited fifty Chinese participants to explored whether individual recognition of emotions was affected by the cultural background of the expresser. We found that participants were more likely to perceive the expresser as an American for high-arousal emotions and to perceive the expresser as a Chinese for low-arousal emotions. Thiry-eight Chinese participants were recruited in Experiment 2a and Experiment 2b respectively. The results (2a) showed that when the expressers were contextualized within an American cultural environment, participants demonstrated faster reaction times and higher accuracy in recognizing happiness, anger, and fear EBL as opposed to a Chinese cultural environment. However, when the identity of the expressers was ‘American’ or ‘Chinese’, there were no significant difference in the participants' recognition of the expressers's emotions (2b). In conclusion, cultural background information plays a significant role in emotion recognition and cross-cultural communication.

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Han, Jiaheng, Honggai Li, Jinshi Cui, Qili Lan, and 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.

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Dietrich, Manuel, Eugen Berlin, and 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.

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de 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.

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HANCOCK, P. J. B., C. D. FROWD, E. BRODIE, and 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.

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Minoh, 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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