within and between individuals


“Knowledge increases not by the matching of images with the  real world … but by a relentless bias toward the perception of  error.” (Boulding 1980)


Truth is the child of time, not of authority.  Our ignorance is infinite, let’s whittle away just one cubic millimeter.  Why should we still want to be so clever when at long we have a chance of being a little less stupid . . . . The aim of science is not to open the door to everlasting wisdom, but to set a limit on everlasting error (Bertolt Brecht in “Life of Galileo” sc 9)


All connections involve more-or-less intimate relations between two things–apparently attributable to more-or-less shared qualities or one’s influence on the state of the other.  




CELLS must detect error in order to survive (review in Llinas: In I of the Vortex, Rodolfo Llinas, a founding father of modern brain science, presents an original view of the evolution and nature of mind. According to Llinas, the “mindness state” evolved to allow predictive interactions between mobile creatures and their environment. He illustrates the early evolution of mind through a primitive animal called the “sea squirt.” The mobile larval form has a brainlike ganglion that receives sensory information about the surrounding environment. As an adult, the sea squirt attaches itself to a stationary object and then digests most of its own brain. This suggests that the nervous system evolved to allow active movement in animals. To move through the environment safely, a creature must anticipate the outcome of each movement on the basis of incoming sensory data. Thus the capacity to predict is most likely the ultimate brain function. One could even say that Self is the centralization of prediction.

At the heart of Llinas’s theory is the concept of oscillation. Many neurons possess electrical activity, manifested as oscillating variations in the minute voltages across the cell membrane. On the crests of these oscillations occur larger electrical events that are the basis for neuron-to-neuron communication. Like cicadas chirping in unison, a group of neurons oscillating in phase can resonate with a distant group of neurons. This simultaneity of neuronal activity is the neurobiological root of cognition. Although the internal state that we call the mind is guided by the senses, it is also generated by the oscillations within the brain. Thus, in a certain sense, one could say that reality is not all “out there,” but is a kind of virtual reality.”


THE BRAIN is an ORGAN of PREDICTION.   Llinás and Roy (2009) propose that “global brain function is geared towards the implementation of intelligent motricity. Motricity is the only possible external manifestation of nervous system function (other than endocrine and exocrine secretion and the control of vascular tone). // The intelligence component of motricity requires, for its successful wheeling, a prediction imperative to approximate the consequences of the impending motion. // We address how such predictive function may originate from the dynamic properties of neuronal networks.”  (Rodolfo R. Llinás and Sisir Roy  (2009) The ‘prediction imperative’ as the basis for self-awareness Philos Trans R Soc Lond B Biol Sci. 2009 May 12; 364(1521): 1301–1307.  doi: 10.1098/rstb.2008.0309  PMCID: PMC2666709  PMID: 19528011  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2666709/)


As information flows the originating entity monitors the actions it evokes.  When they are between organisms and their environments, they require sensory feedback and thing are a little different:

“One challenge that our brains face in monitoring our actions is the inherently ambiguous information they receive. We experience the world outside our heads through the veil of our sensory systems: the peripheral organs and nervous tissues that pick up and process different physical signals, such as light that hits the eyes or pressure on the skin. Though these circuits are remarkably complex, the sensory wetware of our brain possesses the weaknesses common to many biological systems: the wiring is not perfect, transmission is leaky, and the system is plagued by noise – much like how the crackle of a poorly tuned radio masks the real transmission.

But noise is not the only obstacle. Even if these circuits transmitted with perfect fidelity, our perceptual experience would still be incomplete. This is because the veil of our sensory apparatus picks up only the ‘shadows’ of objects in the outside world.  (https://aeon.co/essays/how-our-brain-sculpts-experience-in-line-with-our-expectations? )[1]  Copied & glossed at C:\Users\Greenberg\Dropbox\A&O\A&O READINGS\A&O – ERROR DETECTION – Illusion  (Yon in Aeon 2019).docx 


AMONGST the critical functions of error detection is the mitigation of BIAS (read the A&O notes on BIAS and its comments on error detection) 


IS ERROR DETECTION the basis for self awareness? (=?= consciousness):


Every living cell must have a means for correcting its “errors.”  And eventually we have “mind” (Llinás)



CULTURALLY anchored: Monday, July 01, 2019Approximate TRUTH by means of ERROR-DETECTION.  Good notes about origin of brain itself and by extension, mind—copied to

  • Cognitive scientist Donald D. Hoffman discusses why neural correlates of consciousness do not cause consciousness.  Good rationale for use of precise mathematics to model precisely where you are wrong https://www.youtube.com/watch?v=Zr7eaE9AUtg&feature=share&fbclid=IwAR17aB-BVU6_-2ZAkllrsY7IusvB1Md3waaEyh08NfaVaQpckfmsE-LFb38
  • [because science is a search for ERRORS:  “Knowledge increases not by the matching of images with the  real world … but by a relentless bias toward the perception of  error.” (Boulding 1980)]
  •  “[I]f in other sciences we should arrive at certainty without doubt and truth without error, it behooves us to place the foundations of knowledge in mathematics, in so far as disposed through it we are able to reach certainty in other sciences and truth by the exclusion of error”—Roger Bacon  (c.1267);   
  • Stephan Jay Gould quotes the economist Vilfredo Pareto with approval:  “give me a fruitful error any time, full of seeds, bursting with its own corrections.  You can keep your sterile truth for yourself” (quoted by H Allen Orr in “The Descent of Gould” in the New Yorker, September 30, 2002 pp 132-138.)
  • Truth is the child of time, not of authority.  Our ignorance is infinite, let’s whittle away just one cubic millimeter.  Why should we still want to be so clever when at long we have a chance of being a little less stupid . . . . The aim of science is not to open the door to everlasting wisdom, but to set a limit on everlasting error.”—Bertolt Brecht in “Life of Galileo” sc 9) 



“One of the more recent proposals [for the orign of laughter] appears in a 2011 book dedicated to an evolutionary explanation of humor, Inside Jokes: Using Humor to Reverse-Engineer the Mind (MIT Press, 2011), by Matthew M. Hurley of Indiana University Bloomington, Daniel C. Dennett (a prominent philosopher at Tufts University) and Reginal Adams, Jr., of Pennsylvania State University. The book grew out of ideas proposed by Hurley.

Hurley was interested, he wrote on his website, in a contradiction. “Humor is related to some kind of mistake. Every pun, joke and comic incident seemed to contain a fool of some sort—the ‘butt’ of the joke,” he explained. And the typical response is enjoyment of the idiocy—which “makes sense when it is your enemy or your competition that is somehow failing but not when it is yourself or your loved ones.” This observation led him to ask, “Why do we enjoy mistakes?” and to propose that it is not the mistakes per se that people enjoy. It is the “emotional reward for discovering and thus undoing mistakes in thought. We don’t enjoy making the mistakes, we enjoy weeding them out.”

Hurley’s thesis is that our mind continuously makes rule-of-thumb conjectures about what will be experienced next and about the intentions of others. The idea is that humor evolved from this constant process of confirmation: people derive amusement from finding discrepancies between expectations and reality when the discrepancies are harmless, and this pleasure keeps us looking for such discrepancies. (To wit: “I was wondering why the Frisbee was getting bigger, and then it hit me.”) Moreover, laughter is a public sign of our ability to recognize discrepancies. It is a sign that elevates our social status and allows us to attract reproductive partners.” (from SA whats-so-funny-the-science-of-why-we-laugh by Giovanni Sabato on June 26, 2019)



 At cellular level, Gamma Motor Neurons innervate intrafusal fibers and provide feedback about the lengthening of  extrafusal muscle fibers (Textbook of clinical Neurology 2007)[2]


Closed loop controlThe classical definition of a closed loop system for human movement comes from Jack A. Adams (1971) :[6] „ A closed loop system has feedback, error detection and error correction as key elements. There is a reference that specifies the desired value for the system, and the output of the system is fed back and compared to the reference for error detection and, if necessary corrected….. A closed loop system is self-regulating by compensating for deviating from the reference.”

Most movements that are carried out during day-to-day activity are formed using a continual process of accessing sensory information and using it to more accurately continue the motion. This type of motor control is called feedback control, as it relies on sensory feedback to control movements. Feedback control is a situated form of motor control, relying on sensory information about performance and specific sensory input from the environment in which the movement is carried out. This sensory input, while processed, does not necessarily cause conscious awareness of the action. Closed loop control[7] is a feedback based mechanism of motor control, where any act on the environment creates some sort of change that affects future performance through feedback. Closed loop motor control is best suited to continuously controlled actions, but does not work quickly enough for ballistic actions. Ballistic actions are actions that continue to the end without thinking about it, even when they no longer are appropriate.[citation needed]Because feedback control relies on sensory information, it is as slow as sensory processing. These movements are subject to a speed/accuracy trade-off, because sensory processing is being used to control the movement, the faster the movement is carried out, the less accurate it becomes.” —https://en.wikipedia.org/wiki/Motor_control#Sensorimotor_feedback


Reafference:  An efference copy or efferent copy is an internal copy of an outflowing (efferent), movement-producing signal generated by the motor system.[1] It can be collated with the (reafferent) sensory input that results from the agent’s movement, enabling a comparison of actual movement with desired movement, and a shielding of perception from particular self-induced effects on the sensory input to achieve perceptual stability.[1] Together with internal models, efference copies can serve to enable the brain to predict the effects of an action.[1]

An equal term with a different history is corollary discharge.[2]

Efference copies are important in enabling motor adaptation such as to enhance gaze stability. They have a role in the perception of self and nonself electric fields in electric fish. They also underlie the phenomenon of tickling.    https://en.wikipedia.org/wiki/Efference_copy


EXAMPLE: central feedback modulating auditory input: 

The dorsal cochlear nucleus (DCN) of the brainstem is a crucial point in the auditory pathway where sensory sharpening, modulation, and gating take place. Descending inputs from higher-order areas of the auditory system project back to auditory pathway nodes, potentially providing attentional and contextual information. The inferior colliculus (IC) in particular sends signals back to the DCN. //  Balmer and Trussell identified the cell types across the entire DCN that receive input from IC and tested their postsynaptic responses. Granule cells, unipolar brush cells, and Golgi cells received direct, glutamatergic input. Numerous other cell types also processed this input, suggesting that the IC is a strong source of top-down control of the first auditory processing region in the brain that may sharpen tuning and improve auditory feature detection.”  J. Neurosci. 42, 3381 (2022). 



2002.  Time course of error detection and correction in humans: neurophysiological evidence.    Rodriguez-Fornells A1, Kurzbuch AR, Münte TF (2002).  J Neurosci. 2002 Nov 15;22(22):9990-6.

AbstractUsing event-related brain potentials, the time course of error detection and correction was studied in healthy human subjects. A feedforward model of error correction was used to predict the timing properties of the error and corrective movements. Analysis of the multichannel recordings focused on (1) the error-related negativity (ERN) seen immediately after errors in response- and stimulus-locked averages and (2) on the lateralized readiness potential (LRP) reflecting motor preparation. Comparison of the onset and time course of the ERN and LRP components showed that the signs of corrective activity preceded the ERN. Thus, error correction was implemented before or at least in parallel with the appearance of the ERN component. Also, the amplitude of the ERN component was increased for errors, followed by fast corrective movements. The results are compatible with recent views considering the ERN component as the output of an evaluative system engaged in monitoring motor conflict.    PMID: 12427856


1994.   Localization of a neural system for error detection and compensation

S Dehaene, MI Posner, DM Tucker – Psychological Science, 1994 – JSTOR  for
a brain mechanism dedicated to monitoring performance and compensating for


Conflict monitoring and anterior cingulate cortex: an update. Botvinick MM1Cohen JDCarter CS. Trends Cogn Sci. 2004 Dec;8(12):539-46.  PMID: 15556023    DOI: 10.1016/j.tics.2004.10.003

Abstract: One hypothesis concerning the human dorsal anterior cingulate cortex (ACC) is that it functions, in part, to signal the occurrence of conflicts in information processing, thereby triggering compensatory adjustments in cognitive control. Since this idea was first proposed, a great deal of relevant empirical evidence has accrued. This evidence has largely corroborated the conflict-monitoring hypothesis, and some very recent work has provided striking new support for the theory. At the same time, other findings have posed specific challenges, especially concerning the way the theory addresses the processing of errors. Recent research has also begun to shed light on the larger function of the ACC, suggesting some new possibilities concerning how conflict monitoring might fit into the cingulate’s overall role in cognition and action.



Anterior cingulate conflict monitoring and adjustments in control.  Kerns JG1Cohen JDMacDonald AW 3rdCho RYStenger VACarter CSScience. 2004 Feb 13;303(5660):1023-6.

AbstractConflict monitoring by the anterior cingulate cortex (ACC) has been posited to signal a need for greater cognitive control, producing neural and behavioral adjustments. However, the very occurrence of behavioral adjustments after conflict has been questioned, along with suggestions that there is no direct evidence of ACC conflict-related activity predicting subsequent neural or behavioral adjustments in control. Using the Stroop color-naming task and controlling for repetition effects, we demonstrate that ACC conflict-related activity predicts both greater prefrontal cortex activity and adjustments in behavior, supporting a role of ACC conflict monitoring in the engagement of cognitive control.

Comment in  Neuroscience. Conflict and cognitive control. [Science. 2004]



2004.  The Neural Basis of Error Detection: Conflict Monitoring and the Error-Related Negativity.

Yeung, Nick; Botvinick, Matthew M.; Cohen, Jonathan D.  Psychological Review, Vol 111(4), Oct 2004, 931-959. http://dx.doi.org/10.1037/0033-295X.111.4.931   Abstract.   According to a recent theory, anterior cingulate cortex is sensitive to response conflict, the coactivation of mutually incompatible responses. The present research develops this theory to provide a new account of the error-related negativity (ERN), a scalp potential observed following errors. Connectionist simulations of response conflict in an attentional task demonstrated that the ERN-its timing and sensitivity to task parameters-can be explained in terms of the conflict theory. A new experiment confirmed predictions of this theory regarding the ERN and a second scalp potential, the N2, that is proposed to reflect conflict monitoring on correct response trials. Further analysis of the simulation data indicated that errors can be detected reliably on the basis of post-error conflict. It is concluded that the ERN can be explained in terms of response conflict and that monitoring for conflict may provide a simple mechanism for detecting errors. (PsycINFO Database Record (c) 2012 APA, all rights reserved)

error detection in information theory: http://en.wikipedia.org/wiki/Error_detection_and_correction

error detection in brainhttp://www.ncbi.nlm.nih.gov/pubmed/15482068 :

Psychol Rev. 2004 Oct;111(4):931-59.  The neural basis of error detection: conflict monitoring and the error-related negativity.  Yeung NBotvinick MMCohen JD

Abstract  According to a recent theory, anterior cingulate cortex is sensitive to response conflict, the coactivation of mutually incompatible responses. The present research develops this theory to provide a new account of the error-related negativity (ERN), a scalp potential observed following errors. Connectionist simulations of response conflict in an attentional task demonstrated that the ERN–its timing and sensitivity to task parameters–can be explained in terms of the conflict theory. A new experiment confirmed predictions of this theory regarding the ERN and a second scalp potential, the N2, that is proposed to reflect conflict monitoring on correct response trials. Further analysis of the simulation data indicated that errors can be detected reliably on the basis of post-error conflict. It is concluded that the ERN can be explained in terms of response conflict and that monitoring for conflict may provide a simple mechanism for detecting errors.  2004 APA  PMID: 15482068  [PubMed – indexed for MEDLINE]  Publication Types, MeSH Terms  LinkOut – more resources


re-afference   http://biomedicalcybernetics.wikia.com/wiki/Reafference_principle 

    • http://en.wikipedia.org/wiki/Efference_copy
    • saved a copy of orig von Holst & Mittlestaedt
    • “To err is autonomic: Error-related brain potentials, ANS activity, and post-error compensatory behavior by Greg Hajcak,  Nicole McDonald,  Robert F. Simons.  Article first published online: 11 SEP 2003  DOI: 10.1111/1469-8986.00107.  Psychophysiology Volume 40, Issue 6, pages 895–903, November 2003  :  A two-component event-related brain potential consisting of an error-related negativity (ERN/Ne) and positivity (Pe) has been associated with response monitoring and error detection. Both the ERN and Pe have been source-localized to the anterior cingulate cortex (ACC)—a frontal structure implicated in both cognitive and affective processing, as well as autonomic nervous system (ANS) modulation. The current study sought to examine the relationships among the ERN, the Pe, two autonomic measures, and behavior. Electroencephalogram (EEG), heart rate (HR), and skin conductance (SC) were recorded while subjects performed a two-choice reaction-time task. In addition to the characteristic ERN-Pe complex, errors were associated with larger SCRs and greater HR deceleration. The ERN correlated with the number of errors, but was unrelated to ANS activity and compensatory behavior. Pe, on the other hand, was correlated significantly with SCR, and both SCR and Pe were significantly correlated with post-error slowing. 


2007Science 7 December 2007:   Vol. 318 no. 5856 p. 1539 … DOI: 10.1126/science.318.5856.1539a

Gene Variant May Influence How People Learn From Their Mistakes    Constance Holden

Related Resources  In Science Magazine

REPORT:  Genetically Determined Differences in Learning from ErrorsTilmann A. Klein et al.  Science 7 December 2007: 16421645.


“Once burned, twice shy” works for most people. But some people are slow to learn from bad experiences. Now, a team of neuroscientists in Germany reports on page 1642 that people with a particular gene variant have more difficulty learning via negative reinforcement.

The research, which combined brain imaging with a task in which participants chose between symbols on a computer screen, centers on the A1 variant, or allele, of the gene encoding the D2 receptor, a protein on the surface of brain cells activated by the neurotransmitter dopamine. Earlier studies have hinted that this variant alters the brain’s reward pathways and thereby makes people more vulnerable to addictions.

The new report, from Tilmann Klein of the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, and colleagues, has earned a mixed reception. Among those impressed is geneticist Bert Vogelstein of the Johns Hopkins University School of Medicine in Baltimore, Maryland, who says that “demonstrating that a single-base-pair difference in the genome is associated with a remarkably different ability to learn from past mistakes is quite an accomplishment.”

Klein’s team enlisted 26 healthy German males, 12 of them with at least one A1 allele. While undergoing functional magnetic resonance imaging (fMRI), the men performed a learning task that involved looking at three pairs of Chinese ideograms and determining which in each pair was the “good” symbol. For one pair, for example, choosing the good symbol elicited a smiley face 80% of the time; the other times the good symbol was chosen, it elicited a frown. For the other two pairs, choosing the good symbol produced positive reinforcement 60% or 70% of the time. The volunteers viewed each pair 140 times during the learning phase, and the researchers at the end saw no significant difference between men with or without A1 alleles in how well they learned to select the good symbols.

Then, the researchers presented the subjects with the six symbols in various new pair combinations and evaluated how well each man had learned to identify good symbols versus how well they had learned to steer clear of a “bad” symbol. The ones with the A1 allele did a significantly poorer job of not choosing a bad symbol, suggesting they have a deficit in “avoidance learning.”

During the initial learning phase, the fMRI scans of subjects with A1 alleles showed less activity in an area of the frontal cortex and the hippocampus—locales involved in negative feedback monitoring and memory—than did those of the controls. A single A1 allele is associated with as much as a 30% reduction in D2 receptor density and means that “the monitoring system seems to respond less to negative feedback,” says co-author Markus Ullsperger. He suggests that this phenomenon could be related to impaired reward systems in addictions.

Figure   View larger version:   In this page   In a new window



Feedback with a smile.

Scientists monitored brain activity (color) as a subject chose between two symbols (inset) and was rewarded with a smiley or frowny face.


The D2 story remains tangled, however. “Everyone realizes [the D2 receptor] is critical for reward and many other behaviors,” says David Goldman of the National Institute on Alcohol Abuse and Alcoholism in Bethesda, Maryland. But, he says, the A1 allele has not been shown to alter how the receptor operates. Geneticist Neil Risch of the University of California, San Francisco, adds that this allele “has been a candidate gene for every imaginable psychiatric phenotype for 18 years now, and to my knowledge none of the originally reported associations has held up.”

Nonetheless, cognitive neuroscientist Michael Frank of the University of Arizona in Tucson says the study shows that differences in responses to negative feedback can be “reliably predicted by genetic factors controlling dopamine D2 receptor density” and that this connection is backed up by relevant patterns of brain activation.

The editors suggest the following Related Resources on Science sites

In Science Magazine   Genetically Determined Differences in Learning from Errors   Tilmann A. Klein,   Jane NeumannMartin Reuter,   Jürgen HennigD. Yves von Cramon,  and Markus Ullsperger     Science 7 December 2007: 16421645.   Abstract  Full Text   Full Text (PDF)   Supporting Online Material

2010.  Brain’s error-detection system demystified

Greg Hickok, cognitive sciences professor and Center for Cognitive Neuroscience director, is quoted by MSN and Healthday News October 28, 2010

UCI mentioned:
A new study provides insight into the brain’s ability to detect and correct errors, such as typos, even when someone is working on “autopilot….” Gregory Hickok, director of the Center for Cognitive Neuroscience at the University of California at Irvine, said such research can indeed lead to advances. Simply reaching for a cup is a fairly complicated process, said Hickok, who’s familiar with the study findings. “Despite all that is going on, our movements are usually effortless, rapid, and fluid even in the face of unexpected changes,” he said.

For the full story, please visit http://health.msn.com/health-topics/articlepage.aspx?cp-documentid=10026….    Friday, October 29, 2010




2014.  Public Release: 10-Sep-2014 Penn research shows how brain can tell magnitude of errors   http://www.eurekalert.org/pub_releases/2014-09/uop-prs091014.php   University of Pennsylvania

                 IMAGE   IMAGE: This is a two-photon microscopy image of a mouse’s Purkinje cells. view more   Credit: Andrea Giovannucci

University of Pennsylvania researchers have made another advance in understanding how the brain detects errors caused by unexpected sensory events. This type of error detection is what allows the brain to learn from its mistakes, which is critical for improving fine motor control.

Their previous work explained how the brain can distinguish true error signals from noise; their new findings show how it can tell the difference between errors of different magnitudes. Fine-tuning a tennis serve, for example, requires that the brain distinguish whether it needs to make a minor correction if the ball barely misses the target or a much bigger correction if it is way off.

The study was led by Javier Medina, an assistant professor in the Department of Psychology in Penn’s School of Arts & Sciences, and Farzaneh Najafi, then a graduate student in the Department of Biology. They collaborated with postdoctoral fellow Andrea Giovannucci and associate professor Samuel S. H. Wang of Princeton University.

It was published in the journal eLife.

Our movements are controlled by neurons known as Purkinje cells. Each muscle receives instructions from a dedicated set of hundreds of these brain cells. The instructions sent by each set of Purkinje cells are constantly fine tuned by climbing fibers, a specialized group of neurons that alert Purkinje cells whenever an unexpected stimulus occurs.

“An unexpected stimulus is often a sign that something has gone wrong,” Medina said, “When this happens, climbing fibers send signals to their related Purkinje cells that an error has occurred. These Purkinje cells can then make changes to avoid the error in the future.”

These error signals are mixed in with random firings of the climbing fibers, however, and researchers were long mystified about how the brain tells the difference between this noise and the useful, error-related information it needs to improve motor control.

Medina and his team showed the mechanism behind this differentiation in a study published earlier this year. By using a non-invasive microscopy technique that could monitor the Purkinje cells of awake and active mice, the researchers could measure the level of calcium within these cells when they received signals from climbing fibers.

The unexpected stimuli in this experiment were random puffs of air to the face, which caused the mice to blink. The researchers located Purkinje cells that control the mice’s eyelids and saw that calcium levels necessary for neuroplasticity, i.e., the brain’s ability to learn, were greater when the mice got an error signal triggered by a puff of air than they were after a random signal.

While being able to make such a distinction is critical to the brain’s ability to improve motor control, more information is needed to fine-tune it.

“We wanted to see if the Purkinje cells could tell the difference not just between random firings and true errors signals but between smaller and bigger errors,” Medina said.

In their new study, the researchers used the same experimental set-up, with one key difference. They used air puffs of different durations: 15 milliseconds and 30 milliseconds.

What they found was that the eyelid-associated Purkinje cells filled with different amounts of calcium depending on the length of the puff; the longer ones produced larger spikes in calcium levels.

In addition, the researchers saw that different percentages of eyelid-related Purkinje cells respond depending on the length of the puff.

“Though there is a large population of climbing fibers that can give error-related information to the relevant Purkinje cells when they encounter something unexpected, not all of them fire each time,” Medina said. “We saw that there is information coded in the number of climbing fibers that fire. The longer puffs corresponded to more climbing fibers sending signals to their Purkinje cells.”

Their study could help explain how practice makes perfect, even when errors are imperceptibly small.

“If you felt a short puff and a long puff, you might not be able to say which one was which, but Purkinje cells can tell the difference,” Medina said. “The difference between a ‘very good’ and an ‘awesome’ tennis serve rests on being able to distinguish errors even as tiny as that.”

ACC[3] involved in error detection:   (Wikipedia)

Many studies attribute specific functions such as error detection, anticipation of tasks, attention,[10] motivation, and modulation of emotional responses to the ACC.[4][5][11]

Error detection and conflict monitoring

The most basic form of ACC theory states that the ACC is involved with error detection.[4] Evidence has been derived from studies involving a Stroop task.[5] However, ACC is also active during correct response, and this has been shown using a letter task, whereby participants had to respond to the letter X after an A was presented and ignore all other letter combinations with some letters more competitive than others.[12] They found that for more competitive stimuli ACC activation was greater.

A similar theory poses that the ACC’s primary function is the monitoring of conflict. In Eriksen flanker task, incompatible trials produce the most conflict and the most activation by the ACC. Upon detection of a conflict, the ACC then provides cues to other areas in the brain to cope with the conflicting control systems.

Evidence from electrical studies

Evidence for ACC as having an error detection function comes from observations of error-related negativity (ERN) uniquely generated within the ACC upon error occurrences.[4][13][14][15] A distinction has been made between an ERP following incorrect responses (response ERN) and a signal after subjects receive feedback after erroneous responses (feedback ERN).

No-one has clearly demonstrated that the ERN comes from the ACC.[citation needed], but patients with lateral PFC damage do show reduced ERNs.[16]

Reinforcement learning ERN theory poses that there is a mismatch between actual response execution and appropriate response execution, which results in an ERN discharge.[4][14] Furthermore, this theory predicts that, when the ACC receives conflicting input from control areas in the brain, it determines and allocates which area should be given control over the motor system. Varying levels of dopamine are believed to influence the optimization of this filter system by providing expectations about the outcomes of an event. The ERN, then, serves as a beacon to highlight the violation of an expectation.[15] Research on the occurrence of the feedback ERN shows evidence that this potential has larger amplitudes when violations of expectancy are large. In other words, if an event is not likely to happen, the feedback ERN will be larger if no error is detected. Other studies have examined whether the ERN is elicited by varying the cost of an error and the evaluation of a response.[14]

In these trials, feedback is given about whether the participant has gained or lost money after a response. Amplitudes of ERN responses with small gains and small losses were similar. No ERN was elicited for any losses as opposed to an ERN for no wins, even though both outcomes are the same. The finding in this paradigm suggests that monitoring for wins and losses is based on the relative expected gains and losses. If you get a different outcome than expected, the ERN will be larger than for expected outcomes. ERN studies have also localized specific functions of the ACC.[15]

The rostral ACC seems to be active after an error commission, indicating an error response function, whereas the dorsal ACC is active after both an error and feedback, suggesting a more evaluative function (for fMRI evidence, see also[17][18][19] ). This evaluation is emotional in nature and highlights the amount of distress associated with a certain error.[4] Summarizing the evidence found by ERN studies, it appears to be the case that ACC receives information about a stimulus, selects an appropriate response, monitors the action, and adapts behavior if there is a violation of expectancy.[15]

Evidence against error detection and conflict monitoring theory

Studies examining task performance related to error and conflict processes in patients with ACC damage cast doubt on the necessity of this region for these functions. The error detection and conflict monitoring theories cannot explain some evidence obtained by electrical studies[11][14][15] that demonstrate the effects of giving feedback after responses because the theory describes the ACC as strictly monitoring conflict, not as having evaluative properties.

It has been stated that “The cognitive consequences of anterior cingulate lesions remain rather equivocal, with a number of case reports of intact general neuropsychological and executive function in the presence of large anterior dorsal cingulate lesions.[20] For an alternative view of anterior cingulate, see Rushworth’s review (2007).[21]



Arguably, knowing one’s self serves biological needs.  For example, what you are or are not capable of physically at a given moment is essential in predation and in predator avoidance.  It is reasonable that most species learn this empirically by the punishing or rewarding effects on maximum effort.  That we change over time with practice or with otherwise programmed physical development (surely they interact) is a valuable bit of insight (something that coaches and social referees must intuit or have learned).  We learn at every level, from cell to sociality by error detection although arguably, we have the vast advantage of being able to consider vicarious or hypothetical information.  We probably know competencies change as we grow—mature or decline from neglect or senescence. 

For example, research at the University of Iowa showed that while “… older adults performed just as well as the younger adults in [an experiment], the younger adults more readily recognized when they had made a mistake — and remained more open to the possibility that they may have unknowingly erred. The older adults, however, were less likely to recognize their own mistakes and more likely to be certain in their answers.”  (Wessel, Dolan, & Hollingworth 2018) showed a diminished phasic autonomic response (pupil dilation) to errors in older age)[4]  


DEGREES or STATES of CONSCIOUSNESS  at a more intimate level of organization involves arousal and selective attention, mediated by specific cerebral modules.  Metacognitive error detection[5] and confidence judgements are well known and probably share some cerebral machinery ( )[6]  Further, self-knowledge is arguably an extreme representation of error-detection—also seen at the even deeper organizational level of neurons.   [copied from CNS-ERROR DETECTION to CONSCIOUSNESS]



Home » News » Older Adults May Be Less Likely to Notice Their Mistakes

Older Adults May Be Less Likely to Notice Their Mistakes

By Traci Pedersen    ~ 2 min read

A new study suggests that as we get older, we become much less likely to notice our mistakes.

The study involved a simple, computerized test designed to determine how readily both younger and older adults were able to detect when they’d made an error.

Although the older adults performed just as well as the younger adults in the actual experiment, the younger adults more readily recognized when they had made a mistake — and remained more open to the possibility that they may have unknowingly erred. The older adults, however, were less likely to recognize their own mistakes and more likely to be certain in their answers.

“The good news is older adults perform the tasks we assigned them just as well as younger adults, albeit more slowly,” said Dr. Jan Wessel, assistant professor in the University of Iowa (UI) Department of Psychological and Brain Sciences and the study’s corresponding author. “But we find there is this impaired ability in older adults to recognize an error when they’ve made one.”

The study offers new insights into how older people perceive their decisions, and especially how they view their performance; whether judging their own ability to drive or how regularly they believe they’ve taken medications.

“Realizing fewer errors can have more severe consequences,” Wessel said, “because you can’t remedy an error that you don’t realize you’ve committed.”

For the study, the researchers recruited 38 younger adults (average age of 22) and 39 older adults (average age of 68) to participate in a series of tests that involved looking away from a circle appearing in a box on one side of a computer screen.

While the test was simple, younger adults couldn’t resist glancing at the circle before shifting their gaze about 20 percent of the time on average. That’s expected, Wessel said, as it’s human nature to focus on something new or unexpected, and the researchers wanted the participants to err.

After each “mistake,” the participants were asked whether they had made an error. They then were asked “how sure” and used a sliding scale from “unsure” to “very sure” to determine how confident they were about whether they had made a mistake in the test.

The findings show that the younger participants were correct 75 percent of the time when it came to acknowledging that they had erred. The older test-takers were correct 63 percent of the time when asked whether they had erred.

This means that in more than one in three instances, the older adults didn’t realize they had made a mistake. In addition, the older adults acted far more certain than the younger participants that they were correct.

“It shows when the younger adults thought they were correct, but in fact had made an error, they still had some inkling that they might have erred,” said Wessel, who is affiliated with the Department of Neurology and the Iowa Neuroscience Institute. “The older adults often have no idea at all that they were wrong.”

The researchers supported these observations by measuring how much participants’ pupils dilated during these experiments. In humans and most animals, pupils dilate when something unexpected occurs — triggered by surprise, fright, and other core emotions. It also happens when people think they’ve made a mistake.

The results show that the younger participants’ pupils dilated when they thought they had made a mistake. This effect was blunted when they made errors they did not recognize. In comparison, older adults showed a strong reduction of this pupil dilation after errors that they recognized and showed no dilation at all when they made a mistake they did not recognize.

“That mirrors what we see in the behavioral observations,” Wessel said, “that more often they don’t know when they’ve made an error.”      Source: University of Iowa




[1] Yon, Daniel (2019) Aeon 4July2019, .  (https://aeon.co/essays/how-our-brain-sculpts-experience-in-line-with-our-expectations? :  Now you see it (Our brains predict the outcomes of our actions, shaping reality into what we expect. That’s why we see what we believe)  Daniel Yon  (is a cognitive neuroscientist and experimental psychologist at Birkbeck, University of London.)  3,600 words Edited by Pam Weintraub Aeon 4July2019.    Glossed version saved at C:\Users\Greenberg\Dropbox\A&O\A&O READINGS\A&O – ERROR DETECTION – Illusion  (Yon in Aeon 2019).docx  

One challenge that our brains face in monitoring our actions is the inherently ambiguous information they receive. …  Though these circuits are remarkably complex, the sensory wetware of our brain possesses the weaknesses common to many biological systems: the wiring is not perfect, transmission is leaky, and the system is plagued by noise – much like how the crackle of a poorly tuned radio masks the real transmission. … But noise is not the only obstacle. Even if these circuits transmitted with perfect fidelity, our perceptual experience would still be incomplete. This is because the veil of our sensory apparatus picks up only the ‘shadows’ of objects in the outside world. … The second challenge we face in effectively monitoring our actions is the problem of pace

Our experience of our actions is biased towards what we expect

In short, our analysis revealed that there was more information about the outcomes that participants had seen when they were consistent with the actions they were performing. A closer look found that these ‘sharper signals’ in visual brain areas were accompanied by some suppressed activity – but only in parts sensitive to unexpected events. In other words, predictions generated during action seemed to edit out unexpected signals, generating sharper representations in the sensory brain that are more strongly weighted towards what we expect.

These findings suggest that our expectations sculpt neural activity, causing our brains to represent the outcomes of our actions as we expect them to unfold. This is consistent with a growing psychological literature suggesting that our experience of our actions is biased towards what we expect.”   See GLOSSED version at C:\Users\Greenberg\Dropbox\A&O\A&O READINGS\A&O – ERROR DETECTION – Illusion  (Yon in Aeon 2019).docx 

[2] “Like the alpha motor neurons, the gamma motor neurons lie in the ventral horn of the spinal cord interspersed among the alpha motor neurons innervating the same muscle. Gamma motor neurons innervate the intrafusal muscle fibers of a specialized sensory organ, the muscle spindle.11 The muscle spindle consists of a small bag of muscle fibers that lie in parallel with the extrafusal skeletal muscle fibers. Therefore, when the muscle lengthens or shortens, the intrafusal muscle spindle fibers are stretched or relaxed correspondingly. The intrafusal fibers are surrounded by sensory nerve endings that become 1a afferents to the dorsal root ganglion. When the intrafusal fibers are stretched as the muscle is lengthened, the sensory fibers are activated, providing sensory feedback about the degree of lengthening that has occurred. When the muscle contracts and shortens, the intrafusal spindle fibers also shorten. If this were a completely passive system, the intrafusal fibers would relax and the sensory endings would become silent, providing no helpful feedback information about the state of the muscle. To prevent this inefficient circumstance, gamma motor neurons are activated, maintaining tension in the intrafusal fibers to continue to provide precise sensory information.” — John P. Hammerstad, in Textbook of Clinical Neurology (Third Edition), 2007. On Gamma Motor Neuron.  “

REFLEX EFFECTS:  “Depolarization of the afferent fibers from the low-threshold articular mechanoreceptors reaches the fusimotor neurons polysynaptically, thus contributing to the gamma feedback loop from the muscle spindle both at rest and during joint motion. “By this means the articular mechanoreceptors exert reciprocally coordinated reflexogenic influences on muscle tone and on the excitability of stretch reflexes in all the striated muscles” (Wyke 1981).” — Diane Lee, in The Pelvic Girdle (Third Edition), 2004.

[3] ACC ANATOMY.   “The anterior cingulate cortex can be divided anatomically based on cognitive (dorsal), and emotional (ventral) components.[4] The dorsal part of the ACC is connected with the prefrontal cortex and parietal cortex as well as the motor system and the frontal eye fields[5] making it a central station for processing top-down and bottom-up stimuli and assigning appropriate control to other areas in the brain. By contrast, the ventral part of the ACC is connected with the amygdala, nucleus accumbens, hypothalamus, and anterior insula, and is involved in assessing the salience of emotion and motivational information. The ACC seems to be especially involved when effort is needed to carry out a task such as in early learning and problem-solving.[6]

On a cellular level, the ACC is unique in its abundance of specialized neurons called spindle cells.[7] These cells are a relatively recent occurrence in evolutionary terms (found only in humans and other great apes, cetaceans, and elephants) and contribute to this brain region’s emphasis on addressing difficult problems, as well as the pathologies related to the ACC.[8]


[4] A blunted phasic autonomic response to errors indexes age-related deficits in error awarenessJan R.Wessel, Kylie A.Dolan, Andrew Hollingworth  (2018)  Neurobiology of Aging  Volume 71, November 2018, Pages 13-20 https://doi.org/10.1016/j.neurobiolaging.2018.06.019Get rights and content

Abstract: Conscious error detection is impaired in older age, yet it is unclear which age-related changes in the nervous system contribute to this deficit. In younger adults, error commission is accompanied by phasic autonomic arousal, which purportedly contributes to conscious error detection. Because aging is associated with declining autonomic reactivity, reduced phasic arousal to errors may therefore contribute to age-related error detection deficits. To test this, we measured pupil dilation in younger (<30 years) and older (60–80 years) healthy adults during an eye movement task.

The task required a subjective assessment of response accuracy, as well as a “meta-judgment” of the certainty underlying that accuracy-assessment. This allowed for a precise quantification of subjective error awareness. Behaviorally, we found reduced error awareness in older adults. Furthermore, while younger adults showed “residual” awareness of error commission on unreported errors (indicated by decreased rating certainty compared with correct responses), this effect was absent in older adults. Notably, pupil dilation correlated with both measures: between subjects, greater pupil dilation to reported errors was correlated with greater subjective certainty of error detection, and greater pupil dilation to unreported errors was correlated with greater “residual” awareness of unreported errors. In line with this association, older adults showed a reduced pupil response to both reported and unreported errors. Notably, older adults showed no pupil dilation to unreported errors, in line with their lack of “residual” error awareness on such trials. Taken together, our results suggest that reduced autonomic reactivity may contribute to age-related error awareness deficits.


[5]  ERROR DETECTION and COMPENSATION.   Gehring et al. 1993 proposed a brain system dedicated to monitoring performance and detecting and compensating for error

Comments at:  Localization of a Neural System for Error Detection and Compensation.  Stanislas Dehaene, Michael I. Posner and Don M. Tucker   Psychological Science   Vol. 5, No. 5 (Sep., 1994), pp. 303-305   Published by: Sage Publications, Inc. on behalf of the Association for Psychological Science   Stable URL: http://www.jstor.org/stable/40063122 Page Count: 3

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TThe anterior cingulate as a conflict monitor: fMRI and ERP studies.  van Veen V, Carter CSPhysiol Behav. 2002 Dec;77(4-5):477-82.  Abstract:  We propose that the anterior cingulate cortex (ACC) contributes to cognition by detecting the presence of conflict during information processing, and to alert systems involved in top-down control to resolve this conflict. Here, we review several functional magnetic resonance imaging (fMRI) and event-related potential (ERP) studies that have used simple response interference tasks, and propose that ACC activity is activated prior to the response during correct conflict trials and reflected in the frontocentral N2, and immediately following error trials and reflected in the error-related negativity (ERN). Furthermore, we suggest that certain disturbances in cognition and behavior in common mental disorders such as schizophrenia and obsessive-compulsive disorder (OCD) can be understood as resulting from alteration in performance monitoring functions associated with this region of the brain. 

[6] Shared Neural Markers of Decision Confidence and Error Detection.  Annika Boldt and Nick Yeung.  The Journal of Neuroscience, 25 February 2015, 35(8): 3478-3484; doi: 10.1523/JNEUROSCI.0797-14.2015.  Abstract. Empirical evidence indicates that people can provide accurate evaluations of their own thoughts and actions by means of both error detection and confidence judgments. … Electroencephalography studies have identified the error positivity (Pe)—an event-related component observed following incorrect choices…. Here we assessed whether the Pe also varies in a graded way with participants’ subjective ratings of decision confidence…. We observed clear, graded modulation of the Pe by confidence, with monotonic reduction in Pe amplitude associated with increasing confidence in the preceding choice. This effect was independent of objective accuracy. ….These results suggest that shared mechanisms underlie two forms of metacognitive evaluation that are often treated separately….” Full Text (PDF)