Difference between real time and old information processing in the brain

Difference between real time and old information processing in the brain

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Can we differentiate between the following two processes?

  • Brain receives information via senses, such as eye, ear, nose, skin and tongue. That is, vision, hearing, smell, touch and taste.

  • Brain stops receiving information via senses; but continuously keeps processing previously accumulated information from the mind: for instance, death of a loved one.

Is it possible?

Of course. You've differentiated the two already by describing two mutually exclusive processes. If you're looking for terminological differences that delineate theory along this distinction, it seems you're looking to distinguish perception from cognition as it exists and operates independently from the direct influence of sensation. Plenty of cognition does not involve sensation or perception directly. Metacognition is a sort of self-perceptive process that operates apart from sensation in the traditional "five senses" sense, but not all cognition even involves self-perception or conscious awareness of the cognitive processes in question. This may be debatable, but I would call perception a special case of cognition - i.e., cognition is the broader domain of mental processes that subsumes perception, but does not always involve it.

One might also argue that you've only described sensation in your first sentence, not perception. Perception refers more directly to the processing of sensory information that the brain has received, whereas sensation refers to all processes leading up to sensory information processing. Thus sensation may include the process of delivering sensory information to the brain, depending on whose definition you subscribe to. Cognitive processes only enter the equation once the information has arrived, because cognition refers to the processes carried out by the brain - mostly the higher processes, so (again, maybe this is debatable) not even what happens in the rest of the central nervous system. E.g., the optic nerves feed cognitive processes, but do not carry out cognitive processes themselves.

Summary: If your first sentence only refers to raw sensory information being conveyed to the brain before the brain does any work to process this information, then I don't think it describes cognition at all, whereas the second sentence describes cognition directly. If you mean to include the brain's interpretive process in the "receives information" step of the first sentence, then it describes perception, which is only one kind of cognition (or maybe also distinct from cognition by some definitions).

A gift of memory!?

Everybody knows that memory is a very important element to success either in work and life. For most of the people, even the most important moments can be faded of our lives with time. People who hyperthymesia, will not experience this “washed way” or “let it go” moment. Is it a gift with a blessing or in a totally contrast way?

Individuals who have hyperthymesia can remember every detail in their lives. Once they encountered the date, it is like playing a recall movie in their head, without any hesitation, days in the past come alive. AJ(Real name Jill price), the first documented hyperthymestic, she can remember every detail of her life since she was fourteen years old. “Starting on February 5 th , 1980, I remember everything. That was a Tuesday.” Her brain was subject to the hippocampus and prefrontal cortex were reportedly normal. What a dream gift that everybody wants, however it not fully true. “I still feel bad about stuff that happened 30 years ago,” Price said. “And I really live it and feel it.”

What more astonished to me is, in fact, AJ was not good at the memory at all, according to the study published in Neurocase.

In our lesson, we learned about long-term memory. There are two kinds of long-term memories, Episodic memories and semantic memory I am wondering if it tied to her memory system and brain function.

Nowadays, since it is a rarely people diagnosed with hyperthymesia, the scientist still doesn’t know the reason of it. Hopefully our science can find out the reason and cure them if they do not take them as a blessing gift.

Shafy, Samiha. “An Infinite Loop in the Brain”. The Science of Memory. Spiegel Online. Retrieved 6 December 2011.

Parker ES, Cahill L, McGaugh JL (February 2006). “A case of unusual autobiographical remembering.”. Neurocase. 12 (1): 35–49

Early Childhood Cognitive Development: Information Processing

The Information Processing model is another way of examining and understanding how children develop cognitively. This model, developed in the 1960's and 1970's, conceptualizes children's mental processes through the metaphor of a computer processing, encoding, storing, and decoding data.

By ages 2 to 5 years, most children have developed the skills to focus attention for extended periods, recognize previously encountered information, recall old information, and reconstruct it in the present. For example, a 4-year-old can remember what she did at Christmas and tell her friend about it when she returns to preschool after the holiday. Between the ages of 2 and 5, long-term memory also begins to form, which is why most people cannot remember anything in their childhood prior to age 2 or 3.

Part of long-term memory involves storing information about the sequence of events during familiar situations as "scripts". Scripts help children understand, interpret, and predict what will happen in future scenarios. For example, children understand that a visit to the grocery store involves a specific sequences of steps: Dad walks into the store, gets a grocery cart, selects items from the shelves, waits in the check-out line, pays for the groceries, and then loads them into the car. Children ages 2 through 5 also start to recognize that are often multiple ways to solve a problem and can brainstorm different (though sometimes primitive) solutions.

Between the ages of 5 and 7, children learn how to focus and use their cognitive abilities for specific purposes. For example, children can learn to pay attention to and memorize lists of words or facts. This skill is obviously crucial for children starting school who need to learn new information, retain it and produce it for tests and other academic activities. Children this age have also developed a larger overall capacity to process information. This expanding information processing capacity allows young children to make connections between old and new information. For example, children can use their knowledge of the alphabet and letter sounds (phonics) to start sounding out and reading words. During this age, children's knowledge base also continues to grow and become better organized.

Auditory Processing and Language Processing: What’s the Difference?

Language comprehension…language processing…auditory processing… what does it all mean? The various terminology used to describe a child’s difficulty with listening can be overwhelming to say the least. A first encounter with these terms might feel perplexing as parents search for the best possible help to meet their child’s needs.

A recent surge in public awareness of auditory processing disorders has led to many misconceptions about what this disorder really is (and what it is not). The term “auditory processing disorder” is frequently applied loosely, and often incorrectly, to any individuals having trouble with listening and processing spoken language. However, there are several possible underlying causes for listening difficulty.

Auditory Processing and Language Processing: What’s the Difference?

Central Auditory Processing Disorder (CAPD) refers to how the central nervous system takes in auditory information. It is an auditory deficit, not to be confused with other higher-order cognitive, language or related disorders. A child with an auditory processing disorder is able to hear sounds, but their brain interprets these sounds atypically. Although gathering information across many disciplines is very helpful in diagnosing CAPD, the actual diagnosis must be made by an audiologist.

Language processing refers to the ability to attach meaning to auditory information and formulate an expressive response. It is an extremely important skill that affects many areas of a child’s life, so it is critical that it be correctly identified and effectively addressed.

Symptoms of a language processing disorder might include behaviors such as:

• Using generic language instead of a specific word (e.g. saying “the thing” instead of “the notebook”)

• Taking a long time to respond to a question

• Experiencing difficulty following long or complicated directions

• Naming a general category instead of a specific word (e.g. saying “food” instead of “cake”)

• Using descriptions instead of the intended word (e.g. saying “the yellow thing for writing” instead of “pencil”)

• Being quick to say “I don’t know” in response to a question

• Having difficulty understanding humor or idioms

• Feeling lost when listening to stories with lots of events and characters

Why we need to understand the difference:

In order to effectively address a child’s difficulty with listening, it is critical to correctly understand where the breakdown is occurring. For example, some children with ADHD might have trouble comprehending or remembering verbal information, even though their neural processing of auditory information is intact. Their ability to process the information is actually impacted by difficulty with attention, as opposed to the suspected auditory deficit.

Because the behaviors of CAPD, ADHD and language processing disorders can look similar at times, children are at risk of being misdiagnosed. Treatment approaches for each of these disorders vary significantly,so a correct understanding of the underlying deficit will ultimately result in the most effective treatment approach for a child’s specific needs.

How parents can help their child with language processing difficulties:

• Use visual support to supplement auditorally presented information

• Present new information in a multi-modality and context-rich environment to tap into the other senses

• Allow more “thinking time” to prevent unnecessary pressure during moments of difficulty

• Encourage your child to request repetition or help, rather than simply saying “I don’t know” during moments of difficulty

• Encourage your child to seek out a “study buddy” to check information during class assignments

• Make sure your child is ready to listen before you begin speaking

• Explain idioms or figurative language to your child. For example, don’t assume your child knows what you mean when you say “keep a lid on it”

• Use a tape recorder to record class lectures

• Increase your child’s awareness of his or her strengths by providing frequent positive encouragement


To account for the fact that we were unable to match participant groups on education, we entered educational level as a covariate in all analyses. 4 4 Note that the assumption of independence between the covariate and the independent variable only applies to independent variables that are directly manipulated by the experimenter (Grace-Martin, 2012 ). Because Participant Background (African, European) was not, and cannot be, directly manipulated, the fact that it was related to Educational Level is irrelevant for the analysis of covariance. Crucially, there was independence between Educational Level and the independent variable that was directly manipulated: Recognition Format. Visual inspection of scatterplots for each dependent variable (see Figure 5 for an example) revealed that the data patterns for the group of African participants with low levels of education (none or primary school)—which could not be matched to European participants because nearly all of them had higher levels of education—did not diverge from the data patterns for the other participants, rendering the data suitable for analysis of covariance (ANCOVA). Prior to the analyses, we checked all relevant assumptions. When assumptions of normality or homogeneity of variance were not met, we double-checked the parametric findings with nonparametric tests and provide medians in addition to means and standard deviations. All reported p values are two-tailed.

3.1 Depth perception

Scores on the depth perception test ranged from 0 to 17. Because the data violated assumptions of normality (significant negative skew: z = 3.01) and homogeneity of variance, F(1, 90) = 37.74, p < .001, we checked the findings with nonparametric tests, which confirmed all parametric results. To examine differences in depth perception scores between African and European participants, we conducted an ANCOVA with educational level as a covariate. We found a significant effect of educational level, F(1, 89) = 6.91, p = .010, η 2 = .07. Unsurprisingly, participants with a higher level of education performed better on the depth perception test (see Figure 5). After controlling for educational level, there was still a significant and large difference between African participants (M = 7.57, SD = 5.51, Mdn = 6.50) and European participants (M = 15.89, SD = 2.49, Mdn = 17.00), F(1, 89) = 68.31, p < .001, η 2 = .43.

Whereas Europeans performed close to ceiling, Africans showed much greater variance in performance (see Figure 5). We explored three potential explanations for this variance: urban/rural background, exposure to TV/computer screens in their home country, and time in Europe. African participants from a city performed marginally better (M = 9.00, SD = 5.40, Mdn = 9.00) than African participants from a village (M = 6.13, SD = 5.35, Mdn = 6.00), t(44) = 1.81, p = .077, d = 0.53, 95% CI [−0.06, 1.12]. Similarly, African participants who had owned a screen in their home country performed marginally better (M = 8.38, SD = 5.31, Mdn = 8.00) than African participants who had not owned a screen (M = 5.25, SD = 5.63, Mdn = 4.00), t(44) = 1.73, p = .091, d = 0.57, 95% CI [−0.09, 1.25]. Neither of these trends was statistically significant though, possibly due to a lack of statistical power (e.g., only 12 African participants had not owned a screen in their home country). Finally, a simple linear regression revealed that time in Europe did not significantly predict performance on the depth perception test, R 2 = .05, F(1, 43) = 2.50, p = .121. 5 5 The data for time in Europe contained one outlier that was more than two standard deviations higher than the mean: a participant who had been in Europe for 96 months (Z = 5.64). That participant was removed from all analyses involving time in Europe.

3.2 Visuospatial processing

Scores on the visuospatial test ranged from 0 to 4. The data violated assumptions of normality (significant platykurtosis: z = 2.80) and homogeneity of variance, F(1, 90) = 5.29, p = .024, but nonparametric tests confirmed all parametric results reported below. An ANCOVA with educational level as a covariate revealed a significant effect of education, F(1, 89) = 12.31, p < .001, η 2 = .12. Participants with a higher level of education performed better on the test (no education: Mdn = 0, primary school: Mdn = 1, high school: Mdn = 2, higher vocational and university education: Mdn = 4). After controlling for educational level, there was still a significant and large difference between groups, F(1, 88) = 34.55, p < .001, η 2 = .28. European participants achieved significantly higher scores (M = 3.30, SD = 0.94, Mdn = 4.00) than African participants (M = 1.63, SD = 1.32, Mdn = 1.00).

Again, European participants performed close to ceiling, whereas African participants showed much greater variance in performance on the test. We therefore explored potential explanatory variables. We found no significant difference between African participants originating from a city (M = 1.83, SD = 1.53, Mdn = 1.00) versus a village (M = 1.43, SD = 1.08, Mdn = 1.00), t(39.60) = 1.00, p = .321, d = 0.29, 95% CI [−0.29, 0.88]. Similarly, we found no significant difference between African participants who had owned a screen (M = 1.79, SD = 1.39, Mdn = 1.00) versus not owned a screen (M = 1.17, SD = 1.03, Mdn = 1.00) in their home country, t(44) = 1.43, p = .160, d = 0.47, 95% CI [−0.19, 1.14]. It should be noted again that nonsignificant results may be due to low power. Finally, a simple linear regression revealed that time in Europe did not significantly predict visuospatial processing performance, R 2 = .04, F(1, 43) = 1.64, p = .208.

3.3 Object recognition accuracy

The proportion of correct responses on the object recognition test (i.e., overall recognition accuracy see Figure 6) was subjected to a 2 (Participant Background: African, European) × 2 (Recognition Format: 2D, 3D) × 2 (Type of Vase: African, European) mixed ANCOVA with type of vase as a within-participant factor and Educational Level as a covariate. There was a significant effect of Educational Level, F(1, 87) = 4.48, p = .037, η 2 = .05: participants with some form of higher education achieved higher recognition accuracy than participants who had not completed any higher education (no education: M = .66, SD = .13 primary school: M = .63, SD = .10 high school: M = .64, SD = .13 higher vocational education: M = .72, SD = .07 university: M = .72, SD = .11). The ANCOVA revealed no significant main effects of Participant Background, F(1, 87) = 0.24, p = .628, η 2 = .00, or Type of Vase, F(1, 87) = 0.41, p = .526, η 2 = .00, but there was a significant effect of Recognition Format, F(1, 87) = 5.64, p = .020, η 2 = .06. Participants in the 3D condition achieved significantly higher recognition accuracy (M = .70, SD = .13) than participants in the 2D condition (M = .65, SD = .09).

Our prediction that African participants would have more difficulty with the 2D recognition test than European participants was not confirmed: the interaction between Participant Background and Recognition Format was not significant, F(1, 87) = .04, p = .848, η 2 = .00 (see Figure 6). Our prediction that participants would be better at recognizing vases from their own continent was not confirmed either: the interaction between Participant Background and Type of Vase was not significant, F(1, 87) = 2.13, p = .148, η 2 = .02. None of the other interactions were significant (all Fs < 3.04, all ps > .086).

Within the African sample, we explored potential explanatory variables for differences in performance. We found no significant difference between African participants originating from a city (M = .65, SD = .14) versus a village (M = .66, SD = .12), t(44) = −0.11, p = .911, d = −0.03, 95% CI [−0.61, 0.55]. However, African participants who had owned a screen in their home country achieved significantly higher recognition accuracy (M = .69, SD = .13) than those who had not owned a screen (M = .57, SD = .08), t(44) = 3.05, p = .004, d = 1.01, 95% CI [0.33, 1.71]. It should again be noted that interpretation of these findings is limited by the fact that our sample included only 12 participants who had not owned a screen in their home country. Finally, a simple linear regression revealed that time in Europe did not significantly predict recognition accuracy, R 2 = .00, F(1, 43) = 0.14, p = .708.

3.4 Signal detection analysis

To assess the extent to which the recognition accuracy data were driven by true discrimination between old and new vases, as opposed to a tendency to respond liberally or conservatively on the recognition test, we conducted signal detection analysis. Correct-positive and false-positive responses on the object recognition test were used to calculate discrimination accuracy (d′) and response criterion (c). Prior to calculation, proportions of 0 and 1 were converted to 1/(2 N) = .05 and 1–1/(2 N) = .95, respectively (MacMillan & Creelman, 1991 ).

Across the total sample, d′ ranged from −0.51 to 2.93, with higher positive values indicating better discrimination accuracy. A 2 × 2 × 2 mixed ANCOVA on d′ revealed a significant effect of Educational Level, F(1, 88) = 4.93, p = .029, η 2 = .05. Participants with some form of higher education tended to discriminate better between old and new vases (no education: M = 0.95, SD = 0.81 primary school: M = 0.79, SD = 0.66 high school: M = 0.83, SD = 0.77 higher vocational education: M = 1.22, SD = 0.44 university: M = 1.28, SD = 0.70). The ANCOVA revealed no significant main effects of Participant Background, F(1, 87) = 0.37, p = .547, η 2 = .00, or Type of Vase, F(1, 87) = 0.40, p = .841, η 2 = .00, but a marginally significant effect of Recognition Format, F(1, 87) = 3.85, p = .053, η 2 = .04. Participants tended to achieve higher discrimination accuracy on the 3D test (M = 1.17, SD = 0.82) than on the 2D test (M = 0.87, SD = 0.57). There were no significant interactions (all Fs < 2.13, all ps > .148).

Across the total sample, response criterion c ranged from −1.61 (more liberal) to 0.64 (more conservative). Because the data violated the assumption of homogeneity of variance, F(1, 90) = 11.47, p = .001, we checked the findings with nonparametric tests. 6 6 The nonparametric tests confirmed all parametric results except one: a Wilcoxon signed-rank test showed that participants responded significantly more liberally to African vases (M = −0.31, SD = 0.56, Mdn = −0.29) than to European vases (M = −0.11, SD = 0.56, Mdn = 0.00), T = 1011.00, p < .001. A 2 × 2 × 2 mixed ANCOVA on c revealed no significant effect of Educational Level, F(1, 87) = 0.04, p = .847, η 2 = .00. There was also no significant effect of Type of Vase, F(1, 87) = 0.21, p = .649, η 2 = .00, but there were significant effects of both Participant Background, F(1, 87) = 16.42, p < .001, η 2 = .16, and Recognition Format, F(1, 87) = 6.05, p = .016, η 2 = .06. African participants were significantly more likely to respond liberally (i.e., say “yes”) on the recognition test (M = −.039, SD = 0.55, Mdn = −0.40) than European participants (M = −0.01, SD = 0.29, Mdn = 0.00). Further, participants were significantly more likely to respond liberally on the 2D recognition test (M = −0.31, SD = 0.46, Mdn = −0.21) than on the 3D test (M = −0.10, SD = 0.47, Mdn = 0.00). There were no significant interactions (all Fs < 1.45, all ps > .232).

A technique in which a continuous measure of some aspect of a person's biology is presented to that person for the purpose of training them to control the measure and, thereby, the corresponding biological function.

A chemical agent that, when injected into a person, increases the measured contrast (the difference in image intensity) between different types of tissue. For example, a Gadolinium-based dye is sometimes used in MRI.

(Electroencephalography). A method for measuring the fast electrical activity in the brain that is associated with neuronal activation.

A medical state in which a patient has very limited or no ability to communicate with the world, often owing to extensive paralysis.

(Magnetoencephalography). A method for measuring the fast magnetic activity in the brain that is associated with neuronal activation.

A method used for measuring brain blood flow and oxygenation near the cranial surface by shining near-infrared light through the skull and measuring the resulting emitted light spectrum, which is indicative of blood properties.

A computer-modelling method for classifying statistical patterns in complex multi-parameter data. For example, pattern-classification algorithms have been built that will classify spatial patterns of fMRI data (2D images or 3D volumes) by estimating what task a subject was undertaking when each particular fMRI pattern was measured.

Persistent vegetative state

A medical condition in which a patient shows sustained unresponsiveness and does not show evidence of awareness.

Region of interest (ROI) analysis

A method for measuring the time course of activation from a selected volume of the brain. This method can be used to infer the average activation in a region of a person's brain that has been caused by a stimulus or task, or conversely a level of ROI activation can be used to attempt to infer what task is being undertaken by a person.

Spatial-point spread function

The amount of spread, through space, of the measured signal that arises from an idealized single point in space. Spatial spread is caused by noise and imperfections in the measurement technique, for instance MRI.

A 3D volume element of measurement (for example, a cube). Voxels are the 3D volume equivalent of a pixel in a 2D image.

Sensation and perception are elements that balance and complement one another. They work together for us to be able to identify and create meaning from stimuli-related information. Without sensation, perception will not be possible, except for people who believe in extrasensory perception or ESP. And without perception, our sensations would remain to be "unknown" to us since there is no mental processing of what we sense.

Sensation and perception are two completely different elements in terms of how they process information. In sensation, the physical stimulus, together with its physical properties, is registered by sensory organs. Then, the organs decode this information, and transform them into neural impulses or signals. These signals are transmitted to the sensory cortices of the brain. The line of difference between sensation and perception is now drawn perception follows sensation. In the brain, the nerve impulses go through a series of organization, translation and interpretation. Once perception is finished, a person is able to "make sense" out of the sensations. For instance, seeing the light (sensation) is different from determining its color (perception). Another example is that feeling the coldness of the environment is different from perceiving that winter is coming. Also, hearing a sound is different from perceiving the music being played.

Most psychologists believe that sensation is an important part of bottom-up processing. This means that sensation occurs when the sensory organs transmit information towards the brain. On the other hand, perception is a part of top-down processing. In this case, perception happens when the brain interprets the sensory information and sends corresponding signals to sensory organs for response to the physical stimuli.

Content: Information Vs Knowledge

Comparison Chart

Basis for ComparisonInformationKnowledge
MeaningWhen the facts obtained are systematically presented in a given context it is known as information.Knowledge refers to the relevant and objective information gained through experience.
What is it?Refined dataUseful information
Combination ofData and contextInformation, experience and intuition
ProcessingImproves representationIncreases concisousness
TransferEasily transferableRequires learning
ReproducibilityCan be reproduced.Identical reproduction is not possible.
Prediction Information alone is not sufficient to make predictionsPrediction is possible if one possess required knowledge.
One in otherAll information need not be knowledge.All knowledge is information.

Definition of Information

The term ‘information’ is described as the structured, organised and processed data, presented within context, which makes it relevant and useful to the person who wants it. Data means raw facts and figures concerning people, places, or any other thing, which is expressed in the form of numbers, letters or symbols.

Information is the data which is transformed and classified into an intelligible form, which can be used in the process of decision making. In short, when data turn out to be meaningful after conversion, it is known as information. It is something that informs, in essence, it gives an answer to a particular question.

The main characteristics of information are accuracy, relevance, completeness and availability. It can be communicated in the form of content of a message or through observation and can be obtained from various sources such as newspaper, television, internet, people, books, and so on.

Definition of Knowledge

Knowledge means the familiarity and awareness of a person, place, events, ideas, issues, ways of doing things or anything else, which is gathered through learning, perceiving or discovering. It is the state of knowing something with cognizance through the understanding of concepts, study and experience.

In a nutshell, knowledge connotes the confident theoretical or practical understanding of an entity along with the capability of using it for a specific purpose. Combination of information, experience and intuition leads to knowledge which has the potential to draw inferences and develop insights, based on our experience and thus it can assist in decision making and taking actions.

Why Is Critical Period Important

Critical periods are important because many crucial functions of our body are established during those periods, and some only during those periods.

Studies have found that the following functions are best developed during their critical periods.

Emotional regulation

Emotional self regulation is the ability to monitor and modulate emotions. Learning to self-regulate is a key milestone in a child&rsquos development. It can significantly impact a child&rsquos relationships, academic performance, mental health and long-term well-being.

In a study in a Romanian orphanage, only orphans who were adopted by foster families before the age of 2 were able to develop emotional regulation skills comparable to those of the never institutionalized children 𔁴​ . Those who remained in the orphanage suffered from deprivation of social contact or maternal care, and grew up lacking emotional regulation later in life.

The sensitive period of emotional self regulation is therefore believed to be from birth to age 2.

Vision System

There are different critical periods for different visual functions of the visual system. They usually fall between eye-opening and puberty 𔁵​ .

For example, research results show that visual acuity usually develops from birth to around age 5 and the period between ages 3 and 5 shows the most growth. On the other hand, stereopsis, the perception of depth, has a critical period that ends at 2 years of age.

Susceptibility to damage in visual development also has its own critical period. For instance, amblyopia, the condition where one of the eyes has reduced vision because the eye and brain are not working together properly, can result between several months old and 7 or 8 years of age.

Absolute Pitch in Music Listening

Absolute pitch is the ability to identify and produce the pitch of a musical sound without an external sounds as reference points 𔁶​ .

Children who started musical training between ages 4 and 6 are most likely to reach the absolute pitch.

But trainings that occur after the age of 9 rarely leads to that level of proficiency 𔁷​ in adult.

Auditory Processing

For children who are born with congenital deafness, the absence of auditory input from birth can affect the normal growth of a functional auditory system, severely affecting their ability to learn to speak.

Scientists have found that when cochlear implants are installed to bypass the non-functional inner ears in these children before age 3.5, they can most likely learn to speak successfully, especially if they are also exposed to language-rich environments 󈑖​ .

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