Content deleted Content added
Jwdietrich2 (talk | contribs) m →Current situation and prospects: Inserting hyperlinks |
No edit summary Tags: Mobile edit Mobile app edit iOS app edit App section source |
||
| (21 intermediate revisions by 15 users not shown) | |||
Line 1:
{{Short description|Psychological theory}}
'''Perceptual control theory''' ('''PCT''') is a model of [[behavior]] based on the properties of [[negative feedback]] control loops. A control loop maintains a sensed variable at or near a reference value by means of the effects of its outputs upon that variable, as mediated by physical properties of the environment. In [[Control theory|engineering control theory]], reference values are set by a user outside the system. An example is a thermostat. In a living organism, reference values for controlled perceptual variables are endogenously maintained. Biological [[homeostasis]] and [[reflex]]es are simple, low-level examples. The discovery of mathematical principles of control introduced a way to model a negative feedback loop closed through the environment (circular causation), which spawned perceptual control theory. It differs fundamentally from
==Principles and differences from other theories==
The perceptual control theory is deeply rooted in [[biological cybernetics]], [[systems biology]] and [[control theory]] and the related concept of feedback loops. Unlike some models in behavioral and cognitive psychology it sets out from the concept of circular causality. It shares, therefore, its theoretical foundation with the concept of [[Classical control theory|plant control]], but it is distinct from it by emphasizing the control of the [[Good regulator|internal representation of the physical world]].<ref name="Floegel_2023">{{cite journal |last1=Floegel |first1=Mareike |last2=Kasper |first2=Johannes |last3=Perrier |first3=Pascal |last4=Kell |first4=Christian A. |title=How the conception of control influences our understanding of actions |journal=Nature Reviews Neuroscience |date=30 March 2023 |volume=24 |issue=5 |pages=313–329 |doi=10.1038/s41583-023-00691-z |pmid=36997716|s2cid=257857085 }}</ref>
The plant control theory focuses on neuro-computational processes of movement generation, once a decision for generating the movement has been taken. PCT spotlights the embeddedness of agents in their environment. Therefore, from the perspective of perceptual control, the central problem of motor control consists in finding a sensory input to the system that matches a desired perception.<ref name="Floegel_2023"/>
==History==
PCT has roots in the 19th-century physiological insights of [[Claude Bernard]] and in
A key insight of PCT is that the controlled variable is not the output of the system (the behavioral actions), but its input, that is, a sensed and transformed function of some state of the environment that the control system's output can affect. Because these sensed and transformed inputs may appear as consciously perceived aspects of the environment, Powers labelled the controlled variable "perception". The theory came to be known as "Perceptual Control Theory" or PCT rather than "Control Theory Applied to Psychology" because control theorists often assert or assume that it is the system's output that is controlled.<ref name=Astrom>{{cite book | last1 =Astrom | first1 =Karl J. | last2 =Murray | first2 =Richard M. | title =Feedback Systems: An Introduction for Scientists and Engineers | publisher =Princeton University Press | date =2008 | url =http://www.cds.caltech.edu/~murray/books/AM08/pdf/am08-complete_28Sep12.pdf | isbn =978-0-691-13576-2 }}</ref> In PCT it is the internal representation of the state of some variable in the environment—a "perception" in everyday language—that is controlled.<ref>For additional information about the history of PCT, see:
Line 9 ⟶ 16:
* [http://www.pctweb.org/BillPowersAudioInterview2.mp3 Interview with William T. Powers on origin and history of PCT (Part Two – 20070728 (57.7M)]</ref> The basic principles of PCT were first published by Powers, Clark, and MacFarland as a "general feedback theory of behavior" in 1960,<ref name=PCM1960>{{cite journal | last1 =Powers | first1 =William T. | last2 =Clark | first2 =R.K. | last3 =McFarland | first3 =R.L. | title =A general feedback theory of human behavior (Part I) | journal =Perceptual and Motor Skills | volume =11 | issue =1 | pages =71–88 | date =1960 | doi = 10.2466/pms.1960.11.1.71| s2cid =145256548 }} and {{cite journal | last1 =Powers | first1 =William T. | last2 =Clark | first2 =R.K. | last3 =McFarland | first3 =R.L. | title =A general feedback theory of human behavior (Part II) | journal =Perceptual and Motor Skills | volume =11 | issue =3 | pages =309–323 | date =1960 | doi = 10.2466/pms.1960.11.3.309| s2cid =220712715 }} [Reprinted in {{citation | last1 =Bertalanffy | first1 =Ludwig von | last2 =Rapoport | first2 =Anatol | title =General Systems: Yearbook of the Society for General Systems Research | place =Ann Arbor, Michigan | publisher =Society for General Systems Research | volume =5 | year =1960 }}, pages 63-73, 75-83. Partial reprint in {{cite book | last =Smith | first =A. G. | title =Communication and Culture | publisher =Holt, Rinehart, and Winston | date =1966 | ___location =New York | url =https://archive.org/details/communicationcul00smit| url-access =registration }}]</ref> with credits to cybernetic authors [[Norbert Wiener|Wiener]] and [[William Ross Ashby|Ashby]]. It has been systematically developed since then in the research community that has gathered around it.<ref>[http://www.pctresources.com/ Archives of the Control Systems Group (CSG)], also in the [http://discourse.iapct.org/ IAPCT Discourse forum].</ref> Initially, it was overshadowed by the [[cognitive revolution]] (later supplanted by [[cognitive science]]), but has now become better known.<ref name="Marken2009rev" /><ref name=Mansell2011>{{cite journal | last =Mansell | first =Warren | title =Control of perception should be operationalized as a fundamental property of the nervous system | journal =Topics in Cognitive Science | volume =3 | issue =2 | pages =257–261 | date =2011 | doi =10.1111/j.1756-8765.2011.01140.x | pmid =25164294 }}</ref><ref name=Mansell.Carey.revolution>{{cite journal | last1 =Mansell | first1 =Warren | last2 =Carey | first2 =Timothy A. | title =A perceptual control revolution? | journal =The Psychologist | publisher =The British Psychological Society | date =28 November 2015 | url =https://thepsychologist.bps.org.uk/volume-28/november-2015/perceptual-control-revolution | access-date =17 July 2016 }}</ref><ref>{{cite book |date=2020 |editor-last=Mansell |editor-first=Warren |title=The Interdisciplinary Handbook of Perceptual Control Theory: Living Control Systems IV |___location=Cambridge |publisher=Academic Press |doi= |isbn=978-0128189481 }}</ref>
Powers and other researchers in the field point to problems of purpose, causation, and teleology at the foundations of psychology which control theory resolves.<ref>{{cite journal |last1=Powers |first1=William T. |date=1978 |title=Quantitative analysis of purposive systems: Some spadework at the foundations of scientific psychology |journal=Psychological Review |volume=85 |issue=5 |pages=417–435 |doi=10.1037/0033-295X.85.5.417 }}</ref> From [[Aristotle]] through [[William James]] and [[John Dewey]] it has been recognized that behavior is purposeful and not merely reactive, but how to account for this has been problematic because the only evidence for intentions was subjective. As Powers pointed out, behaviorists following [[Wilhelm Wundt|Wundt]], [[Edward Thorndike|Thorndike]], [[John B. Watson|Watson]], and others rejected introspective reports as data for an objective science of psychology. Only observable behavior could be admitted as data.<ref>"The behaviorist asks: Why don't we make what we can observe the real field of psychology? Let us limit ourselves to things that can be observed, and formulate laws concerning only those things. Now what can we observe? We can observe behavior—what the organism does or says." Watson, J.B. (1924). ''Behaviorism''. New York: People's Institute Publishing Company.</ref>
Another, more specific reason that Powers observed for psychologists' rejecting notions of purpose or intention was that they could not see how a goal (a state that did not yet exist) could cause the behavior that led to it. PCT resolves these philosophical arguments about [[teleology]] because it provides a model of the functioning of organisms in which purpose has objective status without recourse to [[introspection]], and in which causation is circular around [[feedback loops]].<ref name=Runkel-PLT>{{cite book | last =Runkel | first =Philip J. | title =People as living things | publisher =Living Control Systems Publishing | date =2003 | ___location =Hayward, CA | isbn =978-0-9740155-0-7 }}</ref>
Line 19 ⟶ 26:
If the speed of the car starts to drop below the goal-speed, for example when climbing a hill, the small increase in the error signal, amplified, causes engine output to increase, which keeps the error very nearly at zero. If the speed begins to exceed the goal, e.g. when going down a hill, the engine is throttled back so as to act as a brake, so again the speed is kept from departing more than a barely detectable amount from the goal speed (brakes being needed only if the hill is too steep). The result is that the cruise control system maintains a speed close to the goal as the car goes up and down hills, and as other disturbances such as wind affect the car's speed. This is all done without any planning of specific actions, and without any blind reactions to stimuli. Indeed, the cruise control system does not sense disturbances such as wind pressure at all, it only senses the controlled variable, speed. Nor does it control the power generated by the engine, it uses the 'behavior' of engine power as its means to control the sensed speed.
The same principles of negative feedback control (including the ability to nullify effects of unpredictable external or internal disturbances) apply to living control systems.<ref name=Wiener48/> Implications of these principle are e.g. intensively studied by [[Biological cybernetics|biological]] and [[
The thesis of PCT is that animals and people do not control their behavior; rather, they vary their behavior as their means for controlling their perceptions, with or without external disturbances. This is harmoniously consistent with the historical and still widespread assumption that behavior is the final result of stimulus inputs and cognitive plans.<ref name=Marken2009rev/><ref>{{cite book| last =Miller| first =George| author2 =Galanter, Eugene| author3 =Pribram, Karl| title =Plans and the structure of behavior| publisher =[[Holt, Rinehart and Winston]]| year =1960| ___location =[[New York City|New York]]| isbn =978-0-03-010075-8| url =https://archive.org/details/plansstructureo00mill}}</ref>
Line 50 ⟶ 57:
To illustrate the mathematical calculations employed in a PCT simulation, consider a pursuit tracking task in which the participant keeps a mouse cursor aligned with a moving target on a computer monitor.
The model assumes that a perceptual signal within the participant represents the magnitude of the input quantity ''q<sub>i</sub>''. (This has been demonstrated to be a rate of firing in a neuron, at least at the lowest levels.)<ref name=Powers73>{{cite book| title=Behavior: The Control of Perception| year=1973| first=William T.| last=Powers| isbn=978-0-7045-0092-1| title-link=Behavior: The Control of Perception| publisher=Wildwood House}}</ref><ref name=Yin.BG2014>{{cite journal | last =Yin | first =Henry H. | title =How Basal Ganglia Outputs Generate Behavior | journal =Advances in Neuroscience | volume =2014 | issue =768313 | pages =1–28 | date =18 November 2014 | doi =10.1155/2014/768313 | doi-access =free }}</ref> In the tracking task, the input quantity is the vertical distance between the target position ''T'' and the cursor position ''C'', and the random variation of the target position acts as the disturbance ''d'' of that input quantity. This suggests that the perceptual signal ''p'' quantitatively represents the cursor position ''C'' minus the target position T, as expressed in the equation ''p''=''C''–''T''.
Between the perception of target and cursor and the construction of the signal representing the distance between them there is a delay of ''τ'' milliseconds, so that the working perceptual signal at time ''t'' represents the target-to-cursor distance at a prior time, ''t'' – ''τ''. Consequently, the equation used in the model is
Line 86 ⟶ 93:
Perceptions, in PCT, are constructed and controlled in a hierarchy of levels. For example, visual perception of an object is constructed from differences in light intensity or differences in sensations such as color at its edges. Controlling the shape or ___location of the object requires altering the perceptions of sensations or intensities (which are controlled by lower-level systems). This organizing principle is applied at all levels, up to the most abstract philosophical and theoretical constructs.
The Russian physiologist [[Nikolai Bernstein|Nicolas Bernstein]]<ref>Bernstein, Nicolas. 1967. ''Coordination and regulation of movements''. New York: Pergamon Press.</ref> independently came to the same conclusion that behavior has to be multiordinal—organized hierarchically, in layers. A simple problem led to this conclusion at about the same time both in PCT and in Bernstein's work. The spinal reflexes act to stabilize limbs against disturbances. Why do they not prevent centers higher in the brain from using those limbs to carry out behavior? Since the brain obviously does use the spinal systems in producing behavior, there must be a principle that allows the higher systems to operate by incorporating the reflexes, not just by overcoming them or turning them off. The answer is that the reference value (setpoint) for a spinal reflex is not static; rather, it is varied by higher-level systems as their means of moving the limbs ([[
Whereas an engineered control system has a reference value or [[Setpoint (control system)|setpoint]] adjusted by some external agency, the reference value for a biological control system cannot be set in this way. The setpoint must come from some internal process. If there is a way for behavior to affect it, any perception may be brought to the state momentarily specified by higher levels and then be maintained in that state against unpredictable disturbances. In a hierarchy of control systems, higher levels adjust the goals of lower levels as their means of approaching their own goals set by still-higher systems. This has important consequences for any proposed external control of an autonomous living control system (organism). At the highest level, reference values (goals) are set by heredity or adaptive processes.
Line 98 ⟶ 105:
This "reorganization system" is proposed to be part of the inherited structure of the organism. It changes the underlying parameters and connectivity of the control hierarchy in a random-walk manner. There is a basic continuous rate of change in intrinsic variables which proceeds at a speed set by the total error (and stops at zero error), punctuated by random changes in direction in a hyperspace with as many dimensions as there are critical variables. This is a more or less direct adaptation of Ashby's "[[homeostat]]", first adopted into PCT in the 1960 paper<ref name=PCM1960 /> and then changed to use E. coli's method of navigating up gradients of nutrients, as described by Koshland (1980).<ref>Koshland, Daniel. (1980). ''Bacterial chemotaxis as a model behavioral system''. New York: Raven Press.</ref>
Reorganization may occur at any level when loss of control at that level causes intrinsic (essential) variables to deviate from genetically determined set points. This is the basic mechanism that is involved in trial-and-error learning, which leads to the acquisition of more systematic kinds of learning processes.<ref name="Cziko 1995">{{cite book| title=Without Miracles| year=1995| first=Gary| last=Cziko| isbn=978-0-262-03232-2| title-link=Without Miracles| publisher=MIT Press}}.</ref>
==Psychotherapy: the method of levels (MOL)==
Line 111 ⟶ 118:
Currently, no one theory has been agreed upon to explain the synaptic, neuronal or systemic basis of learning. Prominent since 1973, however, is the idea that [[long-term potentiation]] (LTP) of populations of [[synapse]]s induces learning through both pre- and postsynaptic mechanisms.<ref>{{cite journal | last1=Bliss | first1=T. V. P. | last2=Lømo | first2=T. | title=Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path | journal=The Journal of Physiology | publisher=Wiley | volume=232 | issue=2 | date=1 July 1973 | issn=0022-3751 | doi=10.1113/jphysiol.1973.sp010273 | doi-access=free | pages=331–356| pmid=4727084 | pmc=1350458 }}</ref><ref>{{cite journal |last1=Bliss |first1=T. V. |last2=Gardner-Medwin |first2=A. R. |last3=Lømo |first3=T. |year=1973 |title=Synaptic plasticity in the hippocampal formation |journal=Macromolecules and Behaviour |volume=193}}</ref> LTP is a form of [[Hebbian learning]], which proposed that high-frequency, tonic activation of a circuit of neurones increases the efficacy with which they are activated and the size of their response to a given stimulus as compared to the standard neurone (Hebb, 1949).<ref name=Hebb49>{{cite book | last =Hebb | first =Donald | title =The organization of behavior: A neuropsychological theory | publisher =Wiley & Sons | date =1949 | ___location =New York | url =https://archive.org/details/in.ernet.dli.2015.226341}}</ref> These mechanisms are the principles behind Hebb's famously simple explanation: "Those that fire together, wire together".<ref name=Hebb49/>
LTP has received much support since it was first observed by [[Terje Lømo]] in 1966 and is still the subject of many modern studies and clinical research. However, there are possible alternative mechanisms underlying LTP, as presented by Enoki, Hu, Hamilton and Fine in 2009,<ref name="Enoki et al 2009">{{cite journal | last1=Enoki | first1=Ryosuke | last2=Hu | first2=Yi-ling | last3=Hamilton | first3=David | last4=Fine | first4=Alan | title=Expression of Long-Term Plasticity at Individual Synapses in Hippocampus Is Graded, Bidirectional, and Mainly Presynaptic: Optical Quantal Analysis | journal=Neuron | publisher=Elsevier BV | volume=62 | issue=2 | year=2009 | issn=0896-6273 | doi=10.1016/j.neuron.2009.02.026 | doi-access=free | pages=242–253| pmid=19409269 }}</ref> published in the journal ''[[Neuron (journal)|Neuron]]''. They concede that LTP is the basis of learning. However, they firstly propose that LTP occurs in individual synapses, and this plasticity is graded (as opposed to in a binary mode) and bidirectional.<ref name="Enoki et al 2009"/> Secondly, the group suggest that the synaptic changes are expressed solely presynaptically, via changes in the probability of transmitter release.<ref name="Enoki et al 2009"/> Finally, the team predict that the occurrence of LTP could be age-dependent, as the plasticity of a neonatal brain would be higher than that of a mature one. Therefore, the theories differ, as one proposes an on/off occurrence of LTP by pre- and postsynaptic mechanisms and the other proposes only presynaptic changes, graded ability, and age-dependence.
These theories do agree on one element of LTP, namely, that it must occur through physical changes to the synaptic membrane/s, i.e. synaptic plasticity. Perceptual control theory encompasses both of these views. It proposes the mechanism of [[#Reorganization in evolution, development, and learning|'reorganisation']] as the basis of learning. Reorganisation occurs within the inherent control system of a human or animal by restructuring the inter- and intraconnections of its hierarchical organisation, akin to the neuroscientific phenomenon of neural plasticity. This reorganisation initially allows the trial-and-error form of learning, which is seen in babies, and then progresses to more structured learning through association, apparent in infants, and finally to systematic learning, covering the adult ability to learn from both internally and externally generated stimuli and events. In this way, PCT provides a valid model for learning that combines the biological mechanisms of LTP with an explanation of the progression and change of mechanisms associated with developmental ability.<ref name=Plooij1984>{{cite book | last =Plooij | first =Frans X. | title =The behavioral development of free-living chimpanzee babies and infants. | publisher =Ablex | date =1984 | ___location =Norwood, N.J. }}</ref><ref name=Plooij-Plooij1987>{{cite journal | last1 =van de Rijt-Plooij | first1 =Hetty | last2 =Plooij | first2 =Frans | title =Growing independence, conflict and learning in mother-infant relations in free-ranging chimpanzees | journal =Behaviour | volume =101 | issue = 1–3| pages =1–86 | date =1987 | doi = 10.1163/156853987x00378}}</ref><ref name=Plooij2003>{{Citation | last =Plooij | first =Frans X. | year =2003 | title =The trilogy of mind | editor-last =Heimann | editor-first =M. | volume =Regression periods in human infancy | pages =185–205 | place =Mahwah, New Jersey | publisher =Erlbaum }}</ref><ref name=Plooij-Plooij1990>{{cite journal | last1 =Plooij | first1 =Frans X. | last2 =van de Rijt-Plooij | first2 =Hetty | title =Developmental transitions as successive reorganizations of a control hierarchy | journal =American Behavioral Scientist | volume =34 | pages =67–80 | date =1990 | doi = 10.1177/0002764290034001007| s2cid =144183592 }}</ref><ref name="Plooij-Plooij2013">{{cite book|title=The Wonder Weeks: How to Stimulate Your Baby's Mental Development and Help Him Turn His 10 Predictable, Great, Fussy Phases into Magical Leaps Forward|last1=van de Rijt-Plooij|first1=Hetty|last2=Plooij|first2=Frans|date=October 22, 2013|publisher=Kiddy World Publishing |isbn=978-9491882005 |___location=Arnhem, Netherlands|pages=480|title-link=The Wonder Weeks}}</ref>
Line 128 ⟶ 135:
Hierarchies of perceptual control have been simulated in computer models and have been shown to provide a close match to behavioral data. For example, Marken<ref name=Marken86>{{cite journal | last =Marken | first =Richard S. | title =Perceptual organization of behavior: A hierarchical control model of coordinated action. | journal =Journal of Experimental Psychology: Human Perception and Performance | volume =12 | issue =3 | pages =267–276 | date =Aug 1986 | doi =10.1037/0096-1523.12.3.267 | pmid =2943855 }}</ref> conducted an experiment comparing the behavior of a perceptual control hierarchy computer model with that of six healthy volunteers in three experiments. The participants were required to keep the distance between a left line and a centre line equal to that of the centre line and a right line. They were also instructed to keep both distances equal to 2 cm. They had 2 paddles in their hands, one controlling the left line and one controlling the middle line. To do this, they had to resist random disturbances applied to the positions of the lines. As the participants achieved control, they managed to nullify the expected effect of the disturbances by moving their paddles. The correlation between the behavior of subjects and the model in all the experiments approached 0.99. It is proposed that the organization of models of hierarchical control systems such as this informs us about the organization of the human subjects whose behavior it so closely reproduces.
==Robotics==
PCT has significant implications for Robotics and Artificial Intelligence. W.T. Powers introduced the application of PCT to robotics in 1978, early in the availability of home computers.<ref>{{cite magazine |last=Powers |first=William T. |date=1978 |title=The Nature of Robots: Part I: Defining Behavior |url=http://www.livingcontrolsystems.com/appendixes/byte_june_1979.pdf |url-status=dead |magazine=Byte: The Small Systems Journal |___location=Peterborough, NH |publisher=McGraw-Hill |issue=4 |pages=132–144 |archive-url=https://web.archive.org/web/20070604100935/http://www.livingcontrolsystems.com/appendixes/byte_june_1979.pdf |archive-date=June 4, 2007 |access-date=}}</ref>
<ref>{{cite magazine |last=Powers |first=William T. |date=1978 |title=The Nature of Robots: Part II: Simulated Control Systems |url=http://www.livingcontrolsystems.com/appendixes/byte_july_1979.pdf |url-status=dead |magazine=Byte: The Small Systems Journal |___location=Peterborough, NH |publisher=McGraw-Hill |issue=4 |pages=134–152 |archive-url=https://web.archive.org/web/20070604100935/http://www.livingcontrolsystems.com/appendixes/byte_july_1979.pdf |archive-date=June 4, 2007 |access-date=}}</ref>
<ref>{{cite magazine |last=Powers |first=William T. |date=1978 |title=The Nature of Robots: Part III: A closer look at human behavior |url=http://www.livingcontrolsystems.com/appendixes/byte_aug_1979.pdf |url-status=dead |magazine=Byte: The Small Systems Journal |___location=Peterborough, NH |publisher=McGraw-Hill |issue=4 |pages=94–116 |archive-url=https://web.archive.org/web/20070604100935/http://www.livingcontrolsystems.com/appendixes/byte_aug_1979.pdf" |archive-date=June 4, 2007 |access-date=}}</ref>
<ref>{{cite magazine |last=Powers |first=William T. |date=1978 |title=The Nature of Robots: Part IV: Looking for controlled variables |url=http://www.livingcontrolsystems.com/appendixes/byte_sep_1979.pdf |url-status=dead |magazine=Byte: The Small Systems Journal |___location=Peterborough, NH |publisher=McGraw-Hill |issue=4 |pages=96–112 |archive-url=https://web.archive.org/web/20070604100935/http://www.livingcontrolsystems.com/appendixes/byte_sep_1979.pdf |archive-date=June 4, 2007 |access-date=}}</ref> The comparatively simple architecture,<ref name=Young2017>{{cite journal | last =Young| first =Rupert | author-link = | title =A General Architecture for Robotics Systems: A Perception-Based Approach to Artificial Life. | journal =Artificial Life| volume =23 | issue =2 | pages =236–286 | date =Jun 2017 | access-date = | doi =10.1162/ARTL_a_00229 | pmid =28513206 }}</ref> a hierarchy of perceptual controllers, has no need for complex models of the external world, inverse kinematics, or computation from input-output mappings. Traditional approaches to robotics generally depend upon the computation of actions in a constrained environment. Robots designed this way are inflexible and clumsy, unable to cope with the dynamic nature of the real world. PCT robots inherently resist and counter the chaotic, unpredictable disturbances to their controlled inputs which occur in an unconstrained environment. The PCT robotics architecture has recently been applied to a number of real-world robotic systems including robotic rovers,<ref>
{{cite AV media
| people =Young, Rupert
| date =Feb 27, 2015
| title =HPCT Autonomous Rover (short version)
| medium =YouTube
| url =https://www.youtube.com/watch?v=JM9tSYpeLZA
| publisher =Perceptual Robots
}}
</ref> balancing robot<ref>
{{cite AV media
| people =Young, Rupert
| date =Mar 9, 2016
| title =Robot on a train
| medium =YouTube
| url =https://www.youtube.com/watch?v=FCPDEeosCPU
| publisher =Perceptual Robots
}}
</ref> and robot arms.<ref>{{cite AV media
| people =Young, Rupert
| date =Jul 1, 2016
| title =Dynamic Visual Robot Arm Control
| medium =YouTube
| url =https://www.youtube.com/watch?v=jmwH0AZtGG4
| publisher =Perceptual Robots
}}
</ref> Some commercially available robots which demonstrate good control in a naturalistic environment use a control-theoretic architecture which requires much more intensive computation. For example, Boston Dynamics has said<ref>{{Cite web |url=https://bostondynamics.com/blog/starting-on-the-right-foot-with-reinforcement-learning/#:~:text=The%20legged%20robots%20we've,to%20take%20in%20the%20moment |title=Starting on the Right Foot with Reinforcement Learning |author=<!--Not stated--> |date=March 19, 2024 |website=bostondynamics.com |publisher=Boston Dynamics |access-date=November 1, 2024 |quote=The legged robots we’ve built at Boston Dynamics have historically leveraged Model Predictive Control (MPC), a control strategy that predicts and optimizes the future states of the robot in order to decide what action to take in the moment.}}</ref> that its robots use historically leveraged [[Model_predictive_control |model predictive control]].
==Current situation and prospects==
Line 137 ⟶ 177:
While many computer demonstrations of principles have been developed, the proposed higher levels are difficult to model because too little is known about how the brain works at these levels. Isolated higher-level control processes can be investigated, but models of an extensive hierarchy of control are still only conceptual, or at best rudimentary.
Perceptual control theory has not been widely accepted in mainstream psychology, but has been effectively used in a considerable range of domains<ref>{{Cite web | url=https://thepsychologist.bps.org.uk/volume-28/november-2015/perceptual-control-revolution |title = A perceptual control revolution? |website=The Psychologist}}</ref><ref name="IJHCS">The June 1999 Issue of ''The International Journal of Human-Computer Studies'' contained papers ranging from tracking through cockpit layout to self-image and crowd dynamics.</ref> in human factors,<ref name="CBUT">PCT lies at the foundation of [[Component-Based Usability Testing]].</ref> clinical psychology, and psychotherapy (the "[[Method of Levels]]"), it is the basis for a considerable body of research in sociology,<ref>For example: McClelland, Kent A. and Thomas J. Fararo, eds. 2006, ''Purpose, Meaning and Action: Control Systems Theories in Sociology'', New York: Palgrave Macmillan. (McClelland is co-author of Chapter 1, "Control Systems Thinking in Sociological Theory," and author of Chapter 2, "Understanding Collective Control Processes."). McClelland, Kent, 2004, "Collective Control of Perception: Constructing Order from Conflict", ''International Journal of Human-Computer Studies'' 60:65-99. McPhail, Clark. 1991, ''The myth of the madding crowd'' New York: Aldine de Gruyter.</ref> and it has formed the conceptual foundation for the reference model used by a succession of [[NATO]] research study groups.<ref name="IST">volume-28november-2015 Reports of these groups are available from the [[NATO Research and Technology Administration]] publications page: {{cite web
Recent approaches use principles of perceptual control theory to provide new algorithmic foundations for [[artificial intelligence]] and [[machine learning]].<ref>{{cite journal |last1=Monaco |first1=Joseph D. |last2=Hwang |first2=Grace M. |title=Neurodynamical Computing at the Information Boundaries of Intelligent Systems |journal=Cognitive Computation |date=27 December 2022 |volume=16 |issue=5 |pages=1–13 |doi=10.1007/s12559-022-10081-9|s2cid=255222711 |doi-access=free |pmid=39129840 |pmc=11306504 }}</ref>
==Selected bibliography==
Line 148 ⟶ 188:
* Marken, Richard S. (1992) ''Mind readings: Experimental studies of purpose''. Benchmark Publications: New Canaan, CT.
* Marken, Richard S. (2002) ''More mind readings: Methods and models in the study of purpose''. Chapel Hill, NC: New View. {{ISBN|0-944337-43-0}}
* Pfau, Richard H. (2017). ''Your Behavior: Understanding and Changing the Things You Do.'' St. Paul, MN: Paragon House. {{ISBN|9781557789273}}
* Plooij, F. X. (1984). ''The behavioral development of free-living chimpanzee babies and infants''. Norwood, N.J.: Ablex.
* Plooij, F. X. (2003). "The trilogy of mind". In M. Heimann (Ed.), ''Regression periods in human infancy'' (pp. 185–205). Mahwah, NJ: Erlbaum.
Line 203 ⟶ 244:
[[Category:Cybernetics]]
[[Category:Formal sciences]]
[[Category:Robotics engineering]]
| |||