Psychophysiology
Psychophysiology (from
Background
Some people have difficulty distinguishing a psychophysiologist from a physiological psychologist, two very different perspectives. Psychologists are interested in why we may fear spiders and physiologists may be interested in the input/output system of the amygdala. A psychophysiologist will attempt to link the two.[2] Psychophysiologists generally study the psychological/physiological link in intact human subjects. While early psychophysiologists almost always examined the impact of psychological states on physiological system responses, since the 1970s, psychophysiologists also frequently study the impact of physiological states and systems on psychological states and processes. It is this perspective of studying the interface of mind and body that makes psychophysiologists most distinct.[3]
Historically, most psychophysiologists tended to examine the physiological responses and
Psychophysiology is closely related to the field of neuroscience, which primarily concerns itself with relationships between
While psychophysiology was a discipline off the mainstream of psychological and medical science prior to roughly the 1940s, more recently, psychophysiology has found itself positioned at the intersection of psychological and medical science, and its popularity and importance have expanded commensurately with the realization of the inter-relatedness of mind and body.
Measures
Psychophysiology measures exist in multiple domains; reports, electrophysiological studies, studies in
Finally, one can measure overt action or behavior, which involves the observation and recording actual actions, such as running, freezing, eye movement, and facial expression. These are good response measures and easy to record in animals, but they are not as frequently used in human studies.[4]
Uses
Psychophysiological measures are often used to study
Emotions as example of psychophysiological studies
Psychophysiology studies multiple aspects of behavior, and emotions are the most common example. It has long been recognized that emotional episodes are partly constituted by physiological responses.[14] Early work done linking emotions to psychophysiology started with research on mapping consistent autonomic nervous system (ANS) responses to discrete emotional states. For example, anger might be constituted by a certain set of physiological responses, such as increased cardiac output and high diastolic blood pressure, which would allow us to better understand patterns and predict emotional responses. Some studies were able to detect consistent patterns of ANS responses that corresponded to specific emotions under certain contexts, like an early study by Paul Ekman and colleagues in 1983 "Emotion-specific activity in the autonomic nervous system was generated by constructing facial prototypes of emotion muscle by muscle and by reliving past emotional experiences. The autonomic activity produced distinguished not only between positive and negative emotions, but also among negative emotions".[15] However, as more studies were conducted, more variability was found in ANS responses to discrete emotion inductions, not only among individuals but also over time in the same individuals, and greatly between social groups.[16] Some of these differences can be attributed to variables like induction technique, context of the study, or classification of stimuli, which can alter a perceived scenario or emotional response. However it was also found that features of the participant could also alter ANS responses. Factors such as basal level of arousal at the time of experimentation or between test recovery, learned or conditioned responses to certain stimuli, range and maximal level of effect of ANS action, and individual attentiveness can all alter physiological responses in a lab setting.[17] Even supposedly discrete emotional states fail to show specificity. For example, some emotional typologists consider fear to have subtypes, which might involve fleeing or freezing, both of which can have distinct physiological patterns and potentially distinct neural circuitry.[18] As such no definitive correlation can be drawn linking specific autonomic patterns to discrete emotions, causing emotion theorists to rethink classical definitions of emotions.
Psychophysiological inference and physiological computer games
Physiological computing represents a category of affective computing that incorporates real-time software adaption to the psychophysiological activity of the user. The main goal of this is to build a computer that responds to user emotion, cognition and motivation. The approach is to enable implicit and symmetrical human-computer communication by granting the software access to a representation of the user's psychological status.
There are several possible methods to represent the psychological state of the user (discussed in the affective computing page). The advantages of using psychophysiological indices are that their changes are continuous, measures are covert and implicit, and only available data source when the user interacts with the computer without any explicit communication or input device. These systems rely upon an assumption that the psychophysiological measure is an accurate one-to-one representation of a relevant psychological dimension such as mental effort, task engagement and frustration.
Physiological computing systems all contain an element that may be termed as an adaptive controller that may be used to represent the player. This adaptive controller represents the decision-making process underlying software adaptation. In their simplest form, adaptive controllers are expressed in Boolean statements. Adaptive controllers encompass not only the decision-making rules, but also the psychophysiological inference that is implicit in the quantification of those trigger points used to activate the rules. The representation of the player using an adaptive controller can become very complex and often only one-dimensional. The loop used to describe this process is known as the biocybernetic loop. The biocybernetic loop describes the closed loop system that receives psychophysiological data from the player, transforms that data into a computerized response, which then shapes the future psychophysiological response from the player. A positive control loop tends towards instability as player-software loop strives towards a higher standard of desirable performance. The physiological computer game may wish to incorporate both positive and negative loops into the adaptive controller.[19]
See also
- Karl U. Smith
- Vladimir Nebylitsyn
- Jemma B. King
- Physiological psychology
- Search activity concept
- Behavior change
References
Citations
- ^ Psychophysiology at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
- ISSN 1935-990X.
- ISBN 978-0-08-043076-8, retrieved 2023-12-14
- ^ a b c Cacioppo, John; Tassinary, Louis; Berntson, Gary (2007). "25". Handbook of Psychophysiology (3rd ed.). Cambridge University Press. pp. 581–607.
- S2CID 17630161.
- S2CID 14815385.
- S2CID 17805222.
- ISBN 1-4338-3713-7, retrieved 2023-12-21
- PMID 36901016.
- S2CID 1533394.
- ^ Brady, ST; Siegel GJ, Albers RW, Price DL. (2012). Basic Neurochemistry. McGill Press.
{{cite book}}
: CS1 maint: multiple names: authors list (link) - ^ Arroyo, Ivon; Woolf, B; Cooper, D; Burleson, W; Muldner, K; Christopherson, R (2009). "Emotion Sensors Go To School". Artificial Intelligence in Education. 1 (1): 18–37.
- ISBN 978-3-642-21868-2.
- ^ Williams, James (1884). "What is an Emotion?". Mind. 34 (2): 188–205.
- S2CID 15285913.
- ^ Cacioppo, John; Berntson, Gary; Larsen, Jeff; Poehlmann, Kirsten; Ito, Tiffany (2000). "The Psychophysiology of Emotion". Handbook of Emotions. 2: 173–191.
- S2CID 24408358.
- S2CID 5319555.
- ^ Gruszynski, Mike; Stephen H Faircloug. "Psychophysiological Inference and Physiological Computer Games".
{{cite journal}}
: Cite journal requires|journal=
(help)
Bibliography
- Bos, M. W.; Dijksterhuis, A.; Van Baaren, R. (2012). "Food for thought? Trust your unconscious when energy is low". Journal of Neuroscience, Psychology, and Economics. 5 (2): 124–130. doi:10.1037/a0027388.
- Cushman, F.; Gary, K.; Gaffey, A.; Mendes, W. B. (2012). "Simulating murder: The aversion to harmful action". Emotion. 12 (1): 2–7. PMID 21910540.
- Fabiani, M (2012). "It was the best of times, it was the worst of times: A psychophysiologist's view of cognitive aging". Psychophysiology. 49 (3): 283–304. PMID 22220910.
- Greenland, K.; Xenias, D.; Maio, G. (2012). "Intergroup anxiety from the self and other: Evidence from self-report, physiological effects, and real interactions". European Journal of Social Psychology. 42 (2): 150–163. doi:10.1002/ejsp.867.
- Kakarot, N.; Mueller, F.; Bassarak, C. (2012). "Activity–rest schedules in physically demanding work and the variation of responses with age". Ergonomics. 55 (3): 282–294. S2CID 25508336.
- Kircanski, K.; Morazavi, A.; Castriotta, N.; Baker, A. S.; Mystkowski, J. L.; Yi, R.; Craske, M. G. (2012). "Challenges to the traditional exposure paradigm: Variability in exposure therapy for contamination fears". Journal of Behavior Therapy and Experimental Psychiatry. 43 (2): 745–751. PMID 22104655.
- Ong, A. D.; Rothstein, J. D.; Uchino, B. N. (2012). "Loneliness accentuates age differences in cardiovascular responses to social evaluate threat". Psychology and Aging. 27 (1): 190–198. PMID 22004517.
- Pietschnig, J.; Nader, I. W.; Gittler, G. (2012). "Pheromone exposure impairs spatial task performance in young men". Canadian Journal of Behavioural Science. 44 (2): 93–98. doi:10.1037/a0026194.
- Satpute, A. B.; Mumford, J. A.; Naliboff, B. D.; Poldrack, R. A. (2012). "Human anterior and posterior hippocampus respond distinctly to state and trait anxiety". Emotion. 12 (1): 56–68. PMID 22309734.
- Van Dooren, M.; de Vries, J. J. G.; Janssen, J. H. (2012). "Emotional sweating across the body: Comparing 16 different skin conductance measurement locations". Physiology and Behavior. 106 (2): 298–304. S2CID 19704826.
- Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation. 1996:1043-1065.
- Heel-Lancing in Newborns: Behavioral and Spectral Analysis Assessment of Pain Control Methods.
A. Weissman, M. Aranovitch, S. Blazer, and E. Z. Zimmer (2009) Pediatrics 124, e921-e92
- Effects of Low-Intensity Exercise Conditioning on Blood Pressure, Heart Rate, and Autonomic Modulation of Heart Rate in Men and Women with Hypertension.
L. P.T. Hua, C. A. Brown, S. J.M. Hains, M. Godwin, and J. L. Parlow (2009) Biol Res Nurs 11, 129-143
- Malik M, Camm A. Heart Rate Variability. Futura Publishing Company, 1995.
- ISBN 978-0-521-34885-0)
{{citation}}
: CS1 maint: multiple names: authors list (link - )
- Welcome MO, Pereverzeva EV, and Pereverzev VA. A novel psychophysiological model of the effect of alcohol use on academic performance of male medical students of Belarusian State Medical University. IJCRIMPH 2 (6): 183–197, 2010.
External links
- Society for Psychophysiological Research. The primary American professional organization of psychophysiological research.
- British Society for Clinical Psychophysiology (BSCP) Clinical Psychophysiology
- The International Society for the Advancement of Respiratory Psychophysiology (ISARP)
- The Medipsych Institute Clinical Psychophysiology
- Brain, Body and Bytes: Psychophysiological User Interaction CHI 2010 Workshop (10-15, April 2010)