On the Speed of Unconscious Processing

On the Speed of Unconscious Processing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article On the Speed of Unconscious Processing Daniel Rodrigo Serbena, Francisco Carlos Serbena, Juliana Sartori Bonini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7402915/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A theoretical estimation was conducted to explore the potential speed of the unconscious mind. This estimation was based on five reflexes. A series of reflexes were analyzed, including the Achilles, pupillary contraction, triceps, blink, and knee patellar reflex. The analysis entailed the calculation of their bits through differential entropy and standardized discretization. Subsequently, the value obtained from this calculation was divided by their onset latency. This calculation took into account the time of stimulus nerve travel under equiprobable probabilities. In an effort to incorporate biological factors into the analysis, the pupillary contraction reflex was examined through the lens of an adaptation of the Shanon-Hartley theorem. Subsequently, the processing speeds were analyzed in relation to the action's primary roots' gray matter volume and synaptic protein SV2A concentration. This analysis was conducted through the utilization of two-dimensional graphs and a three-dimensional combined graph. Monosynaptic reflexes are theorized to project a line higher in graph space when compared to polysynaptic reflexes and cortical actions. This phenomenon is attributed to the brain's greater optimization of the former compared to the latter. Consequently, the theoretical speed limit of the central nervous system would be contingent upon parameters such as neuron count, connectivity, and gray matter volume fraction. Furthermore, we explore different processing speeds across different gray matter volume fractions, modularization as a computational consequence and central nervous system filtering. Computational Neuroscience Entropy Gray Matter Information theory Latent computation Neuron Computation Predictive coding Unconsciousness White Matter Figures Figure 1 Figure 2 1. Introduction The human mind's processing speed has long been unmeasured. Despite the absence of a universally accepted definition of consciousness (Sattin et al., 2021 ; Seth & Bayne, 2022 ), it is widely accepted that the conscious part of the mind functions more slowly than the unconscious (Ren et al., 2021 ; Watanabe & Haruno, 2015 ). The former has recently been calculated to be around 10 bits per second (Zheng & Meister, 2025 ). However, the latter remains unknown. The objective of this article is to explore and discuss the processing speed of the unconscious mind by calculating the processing speed of medullary and sub-medullary reflexes. In this article, the primary focus will be on the presentation of two fundamental equations: the adoption of the Shannon-Hartley theorem to biological systems and the discrete entropy equation. Following an in-depth examination of Reflexes' findings, a comprehensive analysis of the results and connectivity is conducted through the lens of SV2A synaptic density. Additionally, a thorough investigation of brain structure is undertaken, encompassing the analysis of gray matter volume fraction. One of the equations treats the eye as a channel and calculates its capacity in the experiments' conditions, and the other equation calculates the movement's entropy through its amplitude and a fixed resolution. The final time considered the fact that the stimulus traveled through the body's nerves. It is imperative to reiterate that this estimation is of a theoretical nature. 2. Methods A total of five published articles were subjected to analysis through the application of discrete entropy (Shannon, 1948 ) to ascertain the total bits encountered in the output data. The articles in question were as follows: Aboubacar Nahantchi et al. ( 2022 ); Bergamin & Kardon ( 2003 ); Ellrich & Hopf ( 1996a ); Karam ( 1977a ); Abrams ( 2007 ); and Vickery & Smith ( 2012 ). Furthermore, the pupillary contraction reflex was analyzed using Shannon's information theory. This analysis treated the reflex as a channel and calculated its capacity for the experiment's conditions. This approach was taken to approximate calculation to reality and compare it to the other equation. Subsequently, these results were divided by the reflexes' onset latencies, taking into account the time of stimulus travel, which yielded the processing times of the reflexes. In instances where a variable necessary for the calculation was absent from the primary article under analysis, the median or equivalent value was sought in the extant literature. 2.1. Shanon-Hartley theorem adaptation For input data, biological mechanisms were modeled as additive white Gaussian noise (AWGN) channels. Then, its capacity was calculated as (Forney Jr., 2005 ): $$\:C\left[\frac{b}{s}\right]=Wlog(1+SNR)$$ 1.0 where C is the total capacity of the channel in Bits/Seconds, W is the bandwidth (or sampling rate) in Hertz (Hz) and SNR is the signal-to-noise ratio. SNR was adapted and divided to better represent the biological processes that compose the pupillary contraction reflex's pathway to be used in Eq. ( 1.0 ). SNR was composed of signal power ( SgP ) and noise power, while the latter was divided into three terms: shot noise ( SN ), biological noise ( BN ), and retinal minimal sensitivity, as represented by equitation by (1.1). The final adaptation of the Shanon-Hartley theorem is given by equation $$\:C\left[\frac{b}{s}\right]=W\ast\:log2(1+(\frac{SgP}{(}SN+BN\left)\right))$$ 1.1 Further details and characterizations are addressed in Supplementary_Material_1. In summary, C was calculated for the experiment conditions and multiplied by the stimulus´ duration, then the quantity of Bits was divided by onset latency minus the time of stimulus traveling through nerves. 2.2. Entropy adaptation Entropy ( H ) is traditionally defined by its discrete form (Borda, 2011 ; Gray, 2011 ; Shannon, 1948 ). Movements are typically defined as continuous variables by Lagrangial integral (Huys et al., 2008 ) and measured using differential entropy, which can have units. However, as the analysis done in this speculative article was done utilizing Bits, which is unitless, a resolution to exclude the units would have to be done. Therefore, its discrete form with a small resolution was utilized as an approximation. As all calculations were done utilizing two decimal cases in rounding, resolution was adopted as 10 (−2) , however a sensitivity analysis of this variable was done. Furthermore it´s unit was matched to the numerator of the log, to make it unitless. The final equation is given by 2.0. $$\:H=log2\left(\theta\:\frac{range}{\theta\:res}\right)$$ 2.0 Where \(\:\theta\:range\) is total movement range, \(\:\theta\:res\) is the resolution. It was considered for the entropy calculations that all outcomes are equally probable; this was done to facilitate calculations. And has an important consequence of overestimating entropy (Cover, 1991 ), therefore our calculations should be interpreted as an upper bound theoretical. All equations were set to base 2 to define the result in bits (Schneider, 2013 ). 2.3 Sensitivity analysis A sensitivity analysis was also done with the monosynaptic reflexes and one cortical action with resolution varying from 10 (−2) to 10 (2) in which the same process to obtain the processing speed was simply repeated but with different resolutions. Results are presented in Fig. 1. The findings suggest that monosynaptic reflexes exhibit heightened sensitivity to alterations in resolution when compared to cortical actions, which demonstrate minimal fluctuations in bit values. This phenomenon may be attributable to the inherent characteristics of the actions in question. Many cortical actions, such as the solution of a Rubik's Cube, are characterized by distinct and clearly separate states. Conversely, reflexes, as previously cited, are defined by Lagrangean integrals, which are associated with differential entropy. However, due to the previously cited rationale, a simplification was adopted with discrete entropy. This simplification, in conjunction with the omission of biological variables that influence reflex occurrence, may contribute to the observed heightened sensitivity. A resolution that is reduced in scale captures a greater quantity of information, which consequently engenders heightened sensitivity to minute biological variation. Therefore, a resolution of 10 − 2 was adapted to align with the number of decimal places that were rounded. The adoption of a resolution of 1 was a conceivable possibility, given its equivalence to differential entropy. However, this possibility was precluded by the presence of units in differential entropy, a phenomenon that is absent in its discrete counterpart. 3. Results The results of Shanon-Hartley theorem adaptation are presented in Table 1 . For the highest and lowest light intensity group, respectively, C is 1459.91and 1327.02 bits per second, and Qb (quantity of stimulus´ bits) is 73 and 66.35bits per second. Ps for the fastest reaction time is 294.34 bits per second, while the slowest is 206.06 bits per second. Table 1 Results of the Shannon-Hartley theorem adaptation for pupillary light contraction reflex subdivided into light intensity group. C - total capacity of the channel; Ps - Processing speed; Qb - Quantity of bits. To calculate the processing speed of the knee patellar reflex and the other reflexes, the formula 2.0 was used, and the results are shown in Table 2 . Type of data C (bits/seconds) Qb (bits) Ps (bits/seconds) Highest light intensity 1459.91 73 294.34 Lowest light intensity 1327.02 66.35 206.06 Table 2 Data of entropy calculation and processing speed of other reflexes. Reflex Nerve length Time of travel Onset latency Movement and its Total range Processing speed (Bits per second) Achilles reflex 65.26 ± 14.42 cm in males and 64.79 ± 67.61 cm in females + 29 cm (sciatic) (Elbarrany & Altaf, 2017 ) 28 ms 42,5 ms (Abrams, 2007 ) Plantar flexion, 111 o (Friis, 2017 ) 926.777 Triceps reflex 6.51 cm (Nair et al., 2020 ) 2,16 ms for 60m/s 11 ms (Karam, 1977b ) Forearm extension, 90 o (Armstrong et al., 1998 ) 1 459.556 Blink reflex Same as PCR Same as PCR R1–10.7 ms R2–33.3 ms (ABOUBACAR NAHANTCHI et al., 2022 ). R3–84 ms (Ellrich & Hopf, 1996a ) Fissure closure, 11,5cm (Vasanthakumar et al., 2013 ) R1–922.882 R2–279.661 R3–109.867 Knee patellar reflex 28.56 cm (Kegoye et al., 2023 ) (the averages of both right and left legs and sexes) 8.3 ms 20 ms (Dixon et al., 2004 ; Frijns et al., 1997 ) 90° (Tham et al., 2013 )** 1 094.667 **The article analyzed showcases an onset latency from 9 ms to 15 ms (Tham et al., 2013 ), which is significantly lower than the results of other studies (Frijns et al., 1997 ; Husemann & Behse, 1993 ; Stam & van Crevel, 1989 ). This could be explained by the increased time required for initial myofibrillar contraction to translate into shortening of the muscle's length and, consequently, extension of the knee (Tham et al., 2013 ). As other studies' baseline for onset latency of the knee patellar reflex was at least 20 ms (Dixon et al., 2004 ; Frijns et al., 1997 ), this value was adopted as the onset latency. 4. Discussion The discrepancy in processing speed between input (Shanon-Heartley theorem adaptation) and output (discrete entropy) may be attributable to the nervous system filtering information system (Cabral et al., 2024 ; Histed, 2025 ; Wyss et al., 2024 ). This system was not incorporated into our adaptation due to the reasons elaborated upon in the text. A comparison of the results for the two different reflexes, as presented in Table 3, indicates that the knee patellar reflex (KPR) processing time is significantly faster than that of the pupillary contraction reflex (PCR) or even conscious thought (Zheng & Meister, 2025 ). Within the framework of ITT, proposed explanations for this phenomenon include the hypothesis that simpler actions exhibit faster processing speeds due to their reliance on localized, minimally integrated circuits that require fewer computational steps and minimal coordination between regions (Balduzzi & Tononi, 2008 ; Oizumi et al., 2014 ; Tononi et al., 2016 ). These systems generate low integrated information (Φ), as their limited causal interactions bypass higher-order integration, allowing for rapid and automatic responses. Conversely, complex actions involve distributed networks across cortical and subcortical regions, which necessitate extensive integration of information. This higher level of synchronization necessitates the implementation of complex processes, including iterative feedback loops and the resolution of competing signals. These processes inherently introduce delays, which can compromise the efficiency of processing. Therefore, the trade-off between speed and complexity reflects a fundamental principle: Systems that are optimized for integration and flexibility sacrifice speed, while streamlined, localized systems prioritize rapidity at the expense of adaptability. An alternative explanation for this phenomenon involves the number of pathways involved in each action. The fundamental architecture of reflexes is known as the reflex arc, which involves neural pathways that respond to an impulse before it reaches the brain. Instead of traveling directly to the brain, sensory neurons of a reflex arc synapse in the spinal cord (Derderian et al., 2025 ). The nervous system is composed of five basic units: receptors, sensory neurons, integration centers, motor neurons, and effectors. The initial category comprises the anatomical structures through which external stimuli is received. The subsequent category encompasses the neurons that facilitate the transformation of sensory information within the central nervous system. The third category corresponds to the regions where diverse neural pathways converge. The fourth category involves the neurons responsible for transmitting the response to external stimuli. Finally, the fifth category refers to the agents that execute the response to external stimuli (Clarac et al., 2000 ; Moini et al., 2021 ). Furthermore, interneurons, a specific type of intermediary neuron, are located in either the spinal cord or brain stem and modulate the responses of reflexes (Deska-Gauthier & Zhang, 2019 ; Jankowska et al., 1981 ). However, KPR does not demonstrate modulation from interneurons (Derderian et al., 2025 ). Furthermore, the study of reflexes reveals a taxonomy based on the number of synapses that converge on their integration center, categorizing reflexes as monosynaptic (or simple) or polysynaptic (or complex). While the former is characterized by a smaller number of synapses converging on it, the latter involves a larger number of synapses (Moini & Piran, 2020 ). Consequently, polysynaptic reflexes facilitate the integration and coordination of a greater number of pathways, resulting in more complex motor actions. Such a discrepancy in magnitude could serve as a potential explanation for the observed variation in processing speed among the different actions. The nervous system would possess an inherent baseline capacity for processing, which would undergo a decline with each additional amount of information incorporated into the process. KPR is processed independently from the higher center (Johns, 2014 ), depending entirely on the spinal cord for processing, despite information traveling to higher centers (Callaghan et al., 2012 ). The primary site of PCR processing is the midbrain; however, other regions of the cerebellum (Yoo & Mihaila, 2025 ) and even the hypothalamus (Belliveau et al., 2025 ) have been shown to contribute to this process. Table 3 - Processing speed values of different activities. Action Processing Speed (bits/s) Triceps reflex 1 459.556 Knee Patellar Reflex 1 094.667 Achilles reflex 926.777 Blink reflex R1 922.882 Blink reflex R2 279.661 PRC (fastest reaction time)* 113.202 Blink reflex R3 109.867 PRC (slowest reaction time)* 87.106 Speech** 39 Object recognition** 30–50 Reading (English)** 28–45 Rubik’s cube*** 21.18 Speed card** 17.7 Listening comprehension (English)** 13 Optimal performance in the laboratory motor tasks** 10–12 Typing (English)** 10 Binary digit memorization** 4.9 Choice-reaction experiment** 4 * Values derived from Shannon-Hartley adaptation; ** Data extracted from (Zheng & Meister, 2025 , p. 10); *** This data is calculated further down the article. A salient question pertains to the relationship between processing speed and other cerebral functions or characteristics. At first glance, one might hypothesize that this nature is logarithmic, as is the case with many brain mechanisms. Some examples include neuron densities within most areas of the marmoset cortex (Morales-Gregorio et al., The Weber-Fechner law, which stipulates that the response to a sensory stimulus is proportional to the logarithm of the stimulus amplitude, has been demonstrated to affect light, sound, and even decision-making and short-term memory error accumulation (Gold & Shadlen, 2000 ). Another lognormal distribution has been identified in the mental organization of numbers (Dehaene, 2011 ), and others (Buzsáki & Mizuseki, 2014 ). Furthermore, this phenomenon is not exclusive to the brain; the stationary distribution of spine sizes of individual neurons was also found to follow a lognormal scale (Loewenstein et al., 2011). In order to investigate the nature of this relationship, processing speed was correlated with the primary region of the central nervous system in which its corresponding activity is processed, as well as the local concentration of SV2A protein density. Synaptic vesicle glycoprotein 2A (SV2A) is a transmembrane protein of synaptic vesicles, present in all synaptic terminals, irrespective of neurotransmitter content. It is involved in key functions of neurons, with a focus on the regulation of neurotransmitter release (R. Rossi et al., 2022 ). Consequently, this protein can be utilized to assess the connectivity strength between synapses. The estimated gray matter density for each of the three monosynaptic reflexes was calculated according to their roots from Henmar et al. ( 2020 ). This study measured gray matter and white volumes from each vertebra. The calculation of brain stem gray matter density was derived from two sources: Wang et al. ( 2022 ), which provides the brain stem's white matter volume, and Fujimoto et al. ( 2023 ), which offers the total brain stem volume. As stated in the work of Gennatas et al. ( 2017 ), the density of gray matter in the neocortex is determined by... It has been demonstrated that actions that are processed in the neocortex generally span more than one area, which have different gray matter fractions. Speech, for instance, has been analyzed in the article (Zheng & Meister, 2025 ) and is controlled by the basal ganglia, left anterior insula, lateral premotor cortex, and Broca's area (Wise et al., 1999 ). However, additional areas, such as the supplementary motor area, medial cingulate, thalamus, caudate, pallidum, inferior frontal gyrus (right pars opercularis region), and the cerebellum (right cerebellar crus I region), are involved in this process through minor roles (Lorca-Puls et al., 2021 ; Ludlow, 2015 ). Consequently, the neocortex was regarded as having a predominantly gray matter composition, with an estimated 80% of its volume consisting of this substance. As demonstrated in the study by Johansen et al. ( 2024 ), the neocortex and the pons, a subregion of the brain stem, exhibit a synaptic concentration of SV2A density. Despite the lack of direct measurements of this synaptic protein in the spinal cord, extant literature offers estimates suggesting that its concentration is from two to fivefold lower than that observed in the neocortex (see Rossano et al., 2022 ). Therefore, the following calculation will determine the SV2A concentration for each respective group: The spinal cord (63.3-158.25), the pons, which was utilized for all polysynaptic reflexes (95), the neocortex (538.7 [170–631]), and the gray matter volume fraction: The values for the C7-C8, L2-L5, S1-S2, and brain stem regions were determined to be 0.204, 0.35, 0.487, and 0.22, respectively. The range for the brain stem region was specified as 0.161 to 0.426. The results of these analyses are present in Fig. 2. Group 1 is composed of monosynaptic reflexes that are processed in the spinal cord, Group 2 is composed of polysynaptic reflexes that are processed in the brainstem, and Group 3 is composed of actions that are processed in the cortex. When the data are graphed on a log-log scale, plotting processing speed per gray matter density fraction, a linear line between the variables of monosynaptic reflexes is observed (see Fig. 2A).This line can be considered the optimization limit of the central nervous system (see Fig. 2D). These actions are among the bodies most optimized due to the constant usage and occurrence of these reflexes. They are also among the earliest movements in neonatal motor command (Kuban et al., 1986 ). It has been demonstrated that actions of a more complex nature, or those that have not been optimized, would fall below this threshold, yet they tend to gravitate towards it, as illustrated by the red plane in Fig. 2.D. Consequently, the realistic distribution of activities would align with the purple line of Fig. 2.D. The brain operates at a low energy baseline, with spikes in energy usage that vary according to the stimulus (Saberi et al., 2024 ). In order to maintain this baseline, a variety of mechanisms are utilized (Padamsey & Rochefort, 2023 ; Vergara et al., 2019 ; Watts et al., 2018 ), while others change the energy usage (Habibollahi et al., 2023 ; Sugimoto et al., 2024 ; Zeraati et al., 2021 ). The green line signifies the theoretical upper limit of the brain's processing speed, which is attained when an action is optimized. The brain has been shown to optimize actions according to the specific stimuli received. Consequently, a considerable number of actions would be found to be in a state of incomplete optimization, with many exhibiting only a partial optimization. It is further noted that a significant proportion of actions would not have undergone the formation of their neural circuitry. 4.1. Gray Matter density and Processing speed The enhanced synaptic connectivity observed in the neocortex may be attributed to the capacity to surpass the physical constraints imposed by gray matter volume fraction saturation, as illustrated in Fig. 1.B. Given the role of gray matter in optimizing predictive coding and minimizing free energy, a plausible hypothesis suggests that larger models may possess a greater relative processing capacity while maintaining a high degree of abstraction, potentially attributable to modularization. As the size of the cortex (or model) increases, modularization emerges as a strategy to maintain predictive efficiency and free-energy minimization while overcoming physical or computational limits on connectivity density. It has been demonstrated that minor prediction errors are resolved within modules. This results in the conservation of inter-modular bandwidth and energy. Conversely, major errors trigger broader network coordination. This phenomenon has been previously suggested in experimental studies (Greco et al., 2024 ), which indicates that large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas, thereby enhancing sensory processing. In addition, an analysis of 103 distinct mammalian species' connectomes revealed that as brain volume increases, modular structures become more spatially compact, denser internally, and more segregated. Furthermore, the increased size of larger brains imposes stronger spatial constraints on connectivity, thereby favoring modular organization for efficient communication (Greco et al., 2024 ). Such connections are more frequently observed in conjunction with other regions, collectively constituting a more complex system. For instance, gray matter volume in the hippocampus exhibits strong correlations with that of other regions implicated in the memory system, such as the amygdala and various cortices, including the parahippocampal, perirhinal, entorhinal, and orbitofrontal cortices (Alexander-Bloch et al., 2013 ). A recent study of the brains of 14 primate species revealed a correlation between brain size and the degree of connectedness within the brain. Specifically, the study found that larger brains tend to exhibit reduced levels of overall connectedness, as indicated by sparser long-range connectivity, longer communication paths, higher local network clustering, and higher levels of asymmetry in connectivity patterns between homologous areas across the left and right hemispheres (Ardesch et al., 2022 ). A thorough investigation was conducted into the role of neural feedback in sensorimotor processes. To this end, convolutional neural network (CNN) models augmented with predictive feedback were utilized. These models were trained to compute grasp positions for real-world objects. The study found that, under adverse conditions, the best performance occurred with medium-range asymmetric feedback that originated from a level of representational abstraction closer to the input layer, rather than from more distal layers (Khan et al., 2025 ). The relative importance of gray matter and white matter in determining computational capacity remains a subject of debate. A plethora of clinical studies have demonstrated a positive correlation between intelligence and processing speed in patients with various conditions (Fineschi et al., 2024 ; Kuznetsova et al., 2016 ; Magistro et al., 2015 ; Oschwald et al., 2019 ; Papp et al., 2014 ). However, a direct comparison between different compositions has not yet been conducted. Moreover, further research is necessary to quantify brainstem gray matter volume and spinal cord SV2A density. It is also important to note that factors affecting the intraneuronal level also impact the Ps of the larger integrated system. For example, the presence of differing hormones has been identified as a factor that can influence these levels. Adrenaline, for instance, has been shown to enhance neuron firing by binding to adrenergic receptors, activating second messenger systems (cAMP, PKA, IP3, DAG, PKC), and modulating ion channels and synaptic transmission (Xing et al., 2016 ). Additionally, it has been demonstrated to promote the liberation of calcium through bone resorption (Barritt et al., 1981 ), which results in a neuronal decrease in polarization. The collective impact of these factors results in a reduction of the threshold for action potentials, an augmentation of neurotransmitter release, and an enhancement of neuronal excitability. This, in turn, facilitates more expeditious signal propagation. This external influence on neurons implies a rate of Ps, which can fluctuate according to other factors, increasing with excitatory factors and decreasing with inhibitory. 4.2. Modularization One aspect that favors a continuous interpretation over a discrete one is modular specialization, which is the natural tendency of organizations to develop specialized cores (Meunier et al., 2010 ; Nicolini & Bifone, 2016 ). This phenomenon occurs continuously and does not necessitate the presence of specific thresholds for its occurrence, as evidenced by Alcalá-Corona et al. ( 2021 ). This characteristic could elucidate the reason why the curve assumes the form of a continuous exponential curve, rather than a series of slopes separated by critical thresholds. It is important to note that not all modularities necessarily lead to specialization (Béna & Goodman, 2025 ), and this relationship is not straightforward. It is conceivable that, while modularity has demonstrated its efficacy, there may be a limit to it (Fang & Kim, 2018 ) that could be overcome if a specific critical point or threshold is exceeded. At first glance, one might hypothesize that interhemispheric connections are more significant than intrahemispheric ones for cognitive functions and processing speed, representing the capacity for modularization in performing most daily tasks. However, recent evidence (Deco et al., 2025 ) suggests that computation is primarily a long-range network effect rather than a local one. Consequently, intrahemispheric communication can be instrumental in addressing subdivisions of larger problems. However, interhemispheric communication has the potential to link various subdivisions, thereby facilitating the resolution of the problem. Another salient feature of larger models is the capacity of modules to compensate for the malfunction of others. The impact of gray matter volume and age-related factors on emotion recognition does not stem from localized changes in the brain. Alternatively, these phenomena may have a more diffuse origin, originating from regions throughout the brain. However, some localized changes were also observed, which had small effects on emotional recognition (Karl & Rohe, 2023 ). A study that analyzed magnetic resonance images and test scores found that, compared with global cortical morphology measures, the white matter exhibited a stronger association with cognition. However, the cortical surface areas of the left orbitofrontal cortex, the right posterior-dorsal part of the cingulate gyrus, and the left central sulcus have been found to be positively correlated with cognition (Li et al., 2023 ). This finding suggests that the communication between specialized modules for problem-solving, in later stages of modularization, may be more significant than raw processing power. It has been established that the human brain does not employ its full neuronal capacity during a single action (Roland, 2023 ). Consequently, the neuronal excess that accompanies scaling could be utilized for the further refinement of existing pathways, thereby reducing the neuron count involved in these pathways. This, in turn, could lead to a positive feedback loop between the optimization of existing pathways. Specialized regions exhibit a greater degree of interconnectedness within the broader network infrastructure. Forebrain subregions demonstrate a heightened degree of interconnectedness when their cortical destinations exhibit a higher degree of integration (Zaborszky et al., 2015 ). It is imperative to investigate the numerous stages of the blink reflex to comprehend the correlation between the precipitous slopes of Ps and the quantity of neurons. The blink reflex is comprised of three components: an early (R1), late (R2), and late (R3) component. The R1 component is characterized by an oligosynaptic pathway involving the principal sensory nucleus of the trigeminal nerve and the intermediate subnucleus of the facial nerve (Peterson & Hamel, 2025 ). The R2 component is observed less frequently, and the R3 component is sporadically observed (Ellrich & Hopf, 1996b ; Kofler et al., 2024 ). The second response, R2, involves a pathway of descent to the spinal trigeminal tract. The contralateral response, R2c, has been demonstrated to reflect the crossing of the brainstem in the medulla and progresses through the reticular formation to elicit a response at the contralateral facial nucleus (Brooks & Fragoso, 2013 ). R2 and R3 can initially appear similar because they are both nociceptive (D'Aleo et al., 1999; Giffin et al., 2004 ) and are transported by cutaneous thick myelinated Aβ and Aδ fibers (Ellrich et al., 2001 ; Marin et al., 2015 ). However, R3 exhibits a higher onset latency compared to R2. As indicated by the research of B. Rossi et al. ( 1989 ), the subject in question demonstrated heightened sensitivity to anesthesia. This finding suggests that the R3 response is predominantly influenced by A-delta fibers, which exhibit a greater sensitivity to anesthesia compared to A-beta fibers, as reported by Rosenberg and Heinonen ( 1983 ). As demonstrated in the research by Carmichael et al. ( 2022 ), fibers exhibit a higher conduction velocity in comparison to A-delta fibers. This observation contributes to the explanation of their higher latency, which is further compounded by the ongoing debate surrounding the precise pathway of these fibers (Ellrich et al., 2001 ). R1 is relayed through an oligosynaptic arc, which is likely located in close proximity to the primary sensory nucleus of the trigeminal nerve (Ongerboer de Visser & Cruccu, 1993 ; Romaniello et al., 2002 ). R2 is mediated by a polysynaptic chain of interneurons belonging to the lateral reticular formation in the lower medulla (Ongerboer de Visser & Cruccu, 1993 ; Romaniello et al., 2002 ), while also being processed in the brain stem (Thoma et al., 2022 ). It is hypothesized that R2 and R3 are processed within the brainstem, with pathways analogous to those of R2 (Ellrich et al., 2001 ). While there is evidence of clinical application of R2 (Gunduz et al., 2024 ), the nature of R3 remains to be investigated, and its clinical applications are still under evaluation (Ellrich, 2000 ). The following observations were made: R3 demonstrates a heightened response to modulation under typical conditions (Gabrielli et al., 2002 ) and in the context of attention deficit disorder (Gabrielli et al., 2002 ; B. Rossi et al., 1989 ). The basal forebrain, a region implicated in vigilance, attention, and emotional processing (Chen et al., As demonstrated in the studies by Pessoa ( 2017 ) and Douglas et al. ( 2004 ), the R3 response is already known to interact with the regulation of breathing. Therefore, it would be a significant departure from the existing body of knowledge to consider R3 as an R2-specific response that is primarily related, albeit not exclusively, to nociceptive stimuli arising in particular circumstances. A practical example of this is the solving of a Rubik’s Cube, which has a Qb of 65.22 bits, according to the application of the entropy formula with equiprobable outcomes. Some timestamps of its solving can be given as: a first-ever time solve of a month by its creator ( Erno Rubik | Cube Creator, Puzzle Master, Architect | Britannica , n.d.), a non-scientific measurement of first time solve between a couple of minutes and a few days with the utilization of pre-established guides and strategies (DizzyGiraffe01, 2024 ; Not!, n.d.), and a world record of 3.08 seconds ( YiHeng Wang Breaks World Record 3x3 Single with 3.08 , n.d.), yielding Ps of 2.52 * 10 − 5 and 21.18 bits per second for the creator and world record respectively. While this data lacks scientific rigor, it serves to showcase the brain's capacity to optimize tasks; in this case, the brain optimized to an order of magnitude 10 ^6 . 4.3. Filtering In the study by Zheng and Meister ( 2025 ), the authors propose a novel definition of the sifting number (Si) as the optic nerve's capacity for filtering data prior to its transmission to the central nervous system. This calculation involves the division of sensory information rate by behavior throughput, both measured in bits per second (bps), yielding a rate of 10^8. However, this formula fails to consider information that is captured yet not utilized in conscious and coordinated action. While this oversight is not problematic for conscious actions, it becomes problematic for unconscious acts, such as those addressed in this article. The latter information is not encompassed by the former's behavior throughput. This problem prompts the following inquiry: (1) Is it possible for Si to vary according to the nature of the act? Secondly, the appropriateness of the Si calculation must be examined. The response to (1) remains uncharted territory, as despite the perception and filtration of luminous data occurring in an a priori state (Hegel, 2010 ) prior to its processing by the central nervous system, leading to the formulation and execution of responses, substantial neuronal population filtering scales have yet to be actualized. Consequently, this factor may exert variable ramifications on the filtering process. One potential response to (2) is the calculation of Si based on biological processes of noise reduction, such as: Dynamic range compression is a signal processing technique that has been demonstrated to either reduce the volume of high-intensity stimuli or amplify low-intensity stimuli (May et al.). As demonstrated in biological systems, the stapedic reflex serves to protect from high-audio stimuli (Brask, 1978 ). This reflex has been shown to decrease stimuli by up to 15 dB. Additionally, the saturation of cones and rods outside their optimal range can be considered a form of redundancy, defined as the addition of information neurons' firing output that exceeds the information conveyed by the stimulus (Crumiller et al.). In our calculations, redundancy was not taken into account. We calculated the quantity of information that passed through the channel and assumed, under idealized conditions, that this information was fully transmitted to the central nervous system. We did not try to simulate or estimate this variable. As simulated rates vary substantially from experimental data, which is itself based on a small neuron population (Crumiller et al.), we were unable to do so. In 2011, the concept of "generality" for a significant population was called into question. This notion pertains to the notion of "synergy," which is defined as the process through which neurons exchange information, thereby augmenting the collective information capacity of the population (Crumiller et al., 2011 ). In order to achieve a higher degree of generality with regard to other nerves and actions, it is imperative to implement this approach in lieu of utilizing behavior throughput. Nevertheless, if Si is added to η ((as in 10 ^ (− 8) ) instead of a positive value to represent that the retinal filters this magnitude of data from signal power), the processing speed of data input becomes much closer to that of output, although it should be lower as the latter is an upper limit estimate, as shown in Table 4 . Table 4 Processing speed of the pupillary light contraction reflex according to the Shanon Hartley theorem adaptation taking Si into consideration. C - total capacity of the channel; Ps - Processing speed; Qb - Quantity of bits. Type of data (input) C (bits/seconds) Qb (bits) Ps (bits/seconds) Unfiltered [1459.91; 1327.02] [73; 66.35] [294.34; 206.06] Filtered [396.89; 264.01] [19.84; 13.2] [80.02; 41.00] The present article is subject to three principal limitations. Firstly, certain measurements, principally SV2A spinal cord synaptic concentrations, were not precise, and thus estimations were utilized. Secondly, given the assumption of equiprobable probabilities of events, their bits were overestimated in comparison to normal probabilities. Therefore, the values presented in this article should be regarded as an upper theoretical bound limit. (3) The central nervous filtering is a key component absent from the Shannon-Hartley theorem adaptation that significantly impacts the results. This underscores the necessity for the study of information filtering in large neuronal populations for more reliable and credible estimates. Abbreviations C - Total capacity of the channel; W - Bandwidth; SNR - Signal-to-noise ratio; SgP - Signal power; SN - Shot noise; BN - Biological noise; H - Entropy θrange - Total movement range, θres - Fixed sensorialmotor resolution Ps - Processing speed; Qb - Quantity of bits; KPR - Knee patellar reflex; PCR - Pupillary contraction reflex Si - Sifting number Declarations 6. Conflicts of Interest and Funding We declare no conflicts of interest. This study did not have any funding. References ABOUBACAR NAHANTCHI A, SODA SECKLBDIAGNESN, BASSE M, A. M., MOURABIT, S., BUGUME, M., DIOP GA (2022) AJNS – African Journal of Neurological Sciences | » NORMATIVE VALUES OF THE BLINK REFLEX. Afr J Neurol Sci, 41(1). https://ajns.paans.org/normative-values-of-the-blink-reflex/ Abrams BM (2007) Electromyography and Nerve Conduction Velocity . https://doi.org/10.1016/b978-0-7216-0334-6.50020-0 Alcalá-Corona SA, Sandoval-Motta S, Espinal-Enríquez J, Hernández-Lemus E (2021) Modularity in Biological Networks. Front Genet 12:701331. https://doi.org/10.3389/fgene.2021.701331 Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14(5):322–336. https://doi.org/10.1038/nrn3465 Ardesch DJ, Scholtens LH, de Lange SC, Roumazeilles L, Khrapitchev AA, Preuss TM, Rilling JK, Mars RB, van den Heuvel MP (2022) Scaling Principles of White Matter Connectivity in the Human and Nonhuman Primate Brain. Cereb Cortex 32(13):2831–2842. https://doi.org/10.1093/cercor/bhab384 Armstrong AD, MacDermid JC, Chinchalkar S, Stevens RS, King GJW (1998) Reliability of range-of-motion measurement in the elbow and forearm. J Shoulder Elbow Surg 7(6):573–580. https://doi.org/10.1016/S1058–2746(98)90003–9 Balduzzi D, Tononi G (2008) Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework. PLoS Comput Biol 4(6):e1000091. https://doi.org/10.1371/journal.pcbi.1000091 Barritt GJ, Parker JC, Wadsworth JC (1981) A kinetic analysis of the effects of adrenaline on calcium distribution in isolated rat liver parenchymal cells. J Physiol 312(1):29–55. https://doi.org/10.1113/jphysiol.1981.sp013614 Belliveau AP, Somani AN, Dossani RH (2025) Pupillary Light Reflex. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK537180/ Béna G, Goodman DFM (2025) Dynamics of specialization in neural modules under resource constraints. Nat Commun 16(1). https://doi.org/10.1038/s41467-024-55188–9 Bergamin O, Kardon RH (2003) Latency of the Pupil Light Reflex: Sample Rate, Stimulus Intensity, and Variation in Normal Subjects. Invest Opthalmology Visual Sci 44(4):1546. https://doi.org/10.1167/iovs.02–0468 Borda M (2011) Fundamentals in information theory and coding. Springer Brask T (1978) The Noise Protection Effect of the Stapedius Reflex. Acta Otolaryngol 86(sup360):116–117. https://doi.org/10.3109/00016487809123490 Brooks JBB, Fragoso YD (2013) The blink reflex test does not show abnormalities in a large group of patients with chronic migraine. Arq Neuropsiquiatr 71:862–865. https://doi.org/10.1590/0004–282X20130139 Buzsáki G, Mizuseki K (2014) The log-dynamic brain: How skewed distributions affect network operations. Nat Rev Neurosci 15(4):264–278. https://doi.org/10.1038/nrn3687 Cabral HV, Cudicio A, Bonardi A, Del Vecchio A, Falciati L, Orizio C, Martinez-Valdes E, Negro F (2024) Neural Filtering of Physiological Tremor Oscillations to Spinal Motor Neurons Mediates Short-Term Acquisition of a Skill Learning Task. Eneuro, 11(7), ENEURO.0043–24.2024. https://doi.org/10.1523/ENEURO.0043–24.2024 Callaghan MJ, McKie S, Richardson P, Oldham JA (2012) Effects of Patellar Taping on Brain Activity During Knee Joint Proprioception Tests Using Functional Magnetic Resonance Imaging. Phys Ther 92(6):821–830. https://doi.org/10.2522/ptj.20110209 Carmichael J, Fadavi H, Ishibashi F, Shore AC, Tavakoli M (2022) Chapter 4—Advances in screening, early diagnosis, and accurate staging of diabetic neuropathy. In M. Tavakoli (Ed.), Diabetic Neuropathy (pp. 47–78). Elsevier. https://doi.org/10.1016/B978-0-12–820669–0.00018–9 Chen L, Yin D, Wang T-X, Guo W, Dong H, Xu Q, Luo Y-J, Cherasse Y, Lazarus M, Qiu Z, Lu J, Qu W-M, Huang Z-L (2016) Basal Forebrain Cholinergic Neurons Primarily Contribute to Inhibition of Electroencephalogram Delta Activity, Rather Than Inducing Behavioral Wakefulness in Mice. Neuropsychopharmacology 41(8):2133–2146. https://doi.org/10.1038/npp.2016.13 Clarac F, Cattaert D, Le Ray D (2000) Central control components of a ‘simple’ stretch reflex. Trends Neurosci 23(5):199–208. https://doi.org/10.1016/S0166–2236(99)01535–0 Cover TM (1991) Elements of information theory. Wiley Crumiller M, Knight B, Yu Y, Kaplan E (2011) Estimating the Amount of Information Conveyed by a Population of Neurons. Front NeuroSci. 5 https://doi.org/10.3389/fnins.2011.00090 D’Aleo G, Sessa E, D’Aleo P, Rifici C, Di Bella P, Petix M, Bramanti P (1999) Nociceptive R3 reflex in relapsing-remitting multiple sclerosis patients. Funct Neurol 14(1):43–47 Deco G, Sanz Perl Y, Kringelbach ML (2025) Complex harmonics reveal low-dimensional manifolds of critical brain dynamics. Phys Rev E 111(1):014410. https://doi.org/10.1103/PhysRevE.111.014410 Dehaene S (2011) The number sense: How the mind creates mathematics (Rev. and updated ed). Oxford university press Derderian C, Shumway KR, Tadi P (2025) Physiology, Withdrawal Response. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK544292/ Deska-Gauthier D, Zhang Y (2019) The functional diversity of spinal interneurons and locomotor control. Curr Opin Physiol 8:99–108. https://doi.org/10.1016/j.cophys.2019.01.005 Dixon J, Howe TE, Kent JR, Whittaker VJ (2004) VMO-VL reflex latency difference in osteoarthritic knees and controls. Adv Physiotherapy 6(4):166–172. https://doi.org/10.1080/14038190410020809 DizzyGiraffe01 (2024), August 26 Cubers, how long did you take to solve your first cube? [Reddit Post]. R/Cubers. https://www.reddit.com/r/Cubers/comments/1f1kaaa/cubers_how_long_did_you_take_to_solve_your_first/ Douglas CL, DeMarco GJ, Baghdoyan HA, Lydic R (2004) Pontine and basal forebrain cholinergic interaction: Implications for sleep and breathing. Respir Physiol Neurobiol 143(2):251–262. https://doi.org/10.1016/j.resp.2004.02.011 Elbarrany WG, Altaf FM (2017) The Tibial Nerve and Its Vasculature: An Anatomical Evaluation. Int J Morphology 35(3):812–819. https://doi.org/10.4067/S0717–95022017000300004 Ellrich J (2000) R3 component of the blink reflex is not a suitable model to investigate trigeminal nociception. Comment on Jääskeläinen PAIN 80 (1999) 191–200: PAIN 4051. PAIN, 84(2), 440. https://doi.org/10.1016/S0304–3959(99)00188–8 Ellrich J, Hopf HC (1996a) The R3 component of the blink reflex: Normative data and application in spinal lesions. Electroencephalogr Clin Neurophysiology/Electromyography Motor Control 101(4):349–354. https://doi.org/10.1016/0924–980x(96)95684–2 Ellrich J, Hopf HC (1996b) The R3 component of the blink reflex: Normative data and application in spinal lesions. Electroencephalogr Clin Neurophysiology/Electromyography Motor Control 101(4):349–354. https://doi.org/10.1016/0924–980X(96)95684–2 Ellrich J, Katsarava Z, Przywara S, Kaube H (2001) Is the R3 component of the human blink reflex nociceptive in origin? Pain 91(3):389–395. https://doi.org/10.1016/S0304–3959(00)00465–6 Erno Rubik | Cube Creator, Master P (2025) Architect | Britannica. (n.d.). Retrieved August 3, from https://www.britannica.com/biography/Erno-Rubik Fang C, Kim J (2018) (Jason). The power and limits of modularity: A replication and reconciliation. Strategic Management Journal, 39(9), 2547–2565. https://doi.org/10.1002/smj.2918 Fineschi S, Fahlström M, Fällmar D, Haller S, Wikström J (2024) Comprehensive MRI assessment reveals subtle brain findings in non-hospitalized post-COVID patients with cognitive impairment. Front NeuroSci 18. https://doi.org/10.3389/fnins.2024.1435218 Forney D Jr. (2005) Discrete-time and continuous-time AWGN channels. In Principles of Digital Communication II (p. Chapter 2) Friis EA (ed) (2017) Mechanical testing of orthopaedic implants. Woodhead Publishing, an imprint of Elsevier Frijns CJM, Laman DM, van Duijn MAJ, van Duijn H (1997) Normal values of patellar and ankle tendon reflex latencies. Clin Neurol Neurosurg 99(1):31–36. https://doi.org/10.1016/S0303–8467(96)00593–8 Fujimoto G, Ubukata S, Sugihara G, Oishi N, Aso T, Murai T, Ueda K (2023) A model for estimating the brainstem volume in normal healthy individuals and its application to diffuse axonal injury patients. Sci Rep 13:33. https://doi.org/10.1038/s41598-022-27202-x Gabrielli L, Nocita P, Pasetti C, Bonuccelli U, Dell’Agnello G, Carboncini C, M., Rossi B (2002) Impaired attention modulation of the blink reflex R3 component in Parkinson’s disease: A non-task warning paradigm study. Int J Psychophysiol 44(1):83–91. https://doi.org/10.1016/S0167–8760(01)00195–7 Gennatas ED, Avants BB, Wolf DH, Satterthwaite TD, Ruparel K, Ciric R, Hakonarson H, Gur RE, Gur RC (2017) Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood. J Neurosci 37(20):5065–5073. https://doi.org/10.1523/JNEUROSCI.3550–16.2017 Giffin NJ, Katsarava Z, Pfundstein A, Ellrich J, Kaube H (2004) The effect of multiple stimuli on the modulation of the nociceptive blink reflex. Pain 108(1–2):124–128. https://doi.org/10.1016/j.pain.2003.12.014 Gold JI, Shadlen MN (2000) Representation of a perceptual decision in developing oculomotor commands. Nature 404(6776):390–394. https://doi.org/10.1038/35006062 Gray RM (2011) Entropy and Information Theory, 2nd edn. Springer Science + Business Media, LLC Greco A, Moser J, Preissl H, Siegel M (2024) Predictive learning shapes the representational geometry of the human brain. Nat Commun 15(1):9670. https://doi.org/10.1038/s41467-024-54032–4 Gunduz A, Valls-Solé J, Serranová T, Coppola G, Kofler M, Jääskeläinen SK (2024) The blink reflex and its modulation – Part 2: Pathophysiology and clinical utility. Clin Neurophysiol 160:75–94. https://doi.org/10.1016/j.clinph.2024.02.006 Habibollahi F, Kagan BJ, Burkitt AN, French C (2023) Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks. Nat Commun 14(1):5287. https://doi.org/10.1038/s41467-023-41020–3 Hegel GWF (2010) In: Brinkmann K, Dahlstrom DO (eds) Georg Wilhelm Friedrich Hegel: Encyclopedia of the Philosophical Sciences in Basic Outline. Cambridge University Press. https://doi.org/10.1017/9780511780226 Henmar S, Simonsen EB, Berg RW (2020) What are the gray and white matter volumes of the human spinal cord? J Neurophysiol 124(6):1792–1797. https://doi.org/10.1152/jn.00413.2020 Histed MH (2025) Active filtering: A predictive function of recurrent circuits of sensory cortex (No. arXiv:2501.10521). https://doi.org/10.48550/arXiv.2501.10521 . arXiv Husemann U, Behse F (1993) Reflex time of the knee jerk in controls, spasticity, radiculopathy, and neuropathy. EEG-EMG Zeitschrift Für Elektroenzephalographie, Elektromyographie Und Verwandte Gebiete, 24(4), 280–285 Huys R, Studenka BE, Rheaume NL, Zelaznik HN, Jirsa VK (2008) Distinct Timing Mechanisms Produce Discrete and Continuous Movements. PLoS Comput Biol 4(4):e1000061. https://doi.org/10.1371/journal.pcbi.1000061 Jankowska E, Johannisson T, Lipski J (1981) Common interneurones in reflex pathways from group 1a and 1b afferents of ankle extensors in the cat. J Physiol 310(1):381–402. https://doi.org/10.1113/jphysiol.1981.sp013556 Johansen A, Beliveau V, Colliander E, Raval NR, Dam VH, Gillings N, Aznar S, Svarer C, Plavén-Sigray P, Knudsen GM (2024) An In Vivo High-Resolution Human Brain Atlas of Synaptic Density. J Neurosci 44(33). https://doi.org/10.1523/JNEUROSCI.1750–23.2024 Johns P (2014) Chapter 4—Sensory and motor pathways. In P. Johns (Ed.), Clinical Neuroscience (pp. 49–59). Churchill Livingstone. https://doi.org/10.1016/B978-0-443– 10321–6.00004–7 Karam DB (1977a) Tendon reflex latencies in the upper extremity. Arch Phys Med Rehabil 58(7):306–308 Karam DB (1977b) Tendon reflex latencies in the upper extremity. Arch Phys Med Rehabil 58(7):306–308 Karl V, Rohe T (2023) Structural brain changes in emotion recognition across the adult lifespan. Soc Cognit Affect Neurosci 18(1):nsad052. https://doi.org/10.1093/scan/nsad052 Kegoye ES, Ojewale AO, Ezekiel W, Usman IM, Aigbogun EO, Adewale AO, Fernandez EM, Kasozi KI, Nalugo H, Echoru I, Afodun AM, Kenganzi R, Segun OO, Ssempijja F (2023) Morphologic and morphometric bilateral analysis and sexual dimorphism in sciatic nerves of adults: An anatomic investigation of cadaveric specimens in Uganda with implications on sciatic nerve neuropathologies. Rev. https://doi.org/10.21203/rs.3.rs–3245048/v1 Khan R, Zhong H, Das S, Cai J, Niemeier M (2025) Predictive Coding Explains Asymmetric Connectivity in the Brain: A Neural Network Study (p. 2025.02.27.640572). https://doi.org/10.1101/2025.02.27.640572 . bioRxiv Kofler M, Hallett M, Iannetti GD, Versace V, Ellrich J, Téllez MJ, Valls-Solé J (2024) The blink reflex and its modulation – Part 1: Physiological mechanisms. Clin Neurophysiol 160:130–152. https://doi.org/10.1016/j.clinph.2023.11.015 Kuban KC, Skouteli HN, Urion DK, Lawhon GA (1986) Deep tendon reflexes in premature infants. Pediatr Neurol 2(5):266–271. https://doi.org/10.1016/0887–8994(86)90017–2 Kuznetsova KA, Maniega SM, Ritchie SJ, Cox SR, Storkey AJ, Starr JM, Wardlaw JM, Deary IJ, Bastin ME (2016) Brain white matter structure and information processing speed in healthy older age. Brain Struct Function 221:3223–3235. https://doi.org/10.1007/s00429-015-1097–5 Li W-X, Yuan J, Han F, Zhou L-X, Ni J, Yao M, Zhang S-Y, Jin Z-Y, Cui L-Y, Zhai F-F, Zhu Y-C (2023) White matter and gray matter changes related to cognition in community populations. Front Aging Neurosci 15. https://doi.org/10.3389/fnagi.2023.1065245 Lorca-Puls DL, Gajardo-Vidal A, Team PLORAS, Oberhuber M, Prejawa S, Hope TMH, Leff AP, Green DW, Price CJ (2021) Brain regions that support accurate speech production after damage to Broca’s area. Brain Commun 3(4):fcab230. https://doi.org/10.1093/braincomms/fcab230 Ludlow CL (2015) Central Nervous System Control of Voice and Swallowing. J Clin Neurophysiol 32(4):294–303. https://doi.org/10.1097/WNP.0000000000000186 Magistro D, Takeuchi H, Nejad KK, Taki Y, Sekiguchi A, Nouchi R, Kotozaki Y, Nakagawa S, Miyauchi CM, Iizuka K, Yokoyama R, Shinada T, Yamamoto Y, Hanawa S, Araki T, Hashizume H, Sassa Y, Kawashima R (2015) The Relationship between Processing Speed and Regional White Matter Volume in Healthy Young People. PLoS ONE 10(9):e0136386. https://doi.org/10.1371/journal.pone.0136386 Marin J, Gantenbein A, Paemeleire K, Kaube H, Goadsby P (2015) Nociception-specific blink reflex: Pharmacology in healthy volunteers. J Headache Pain 16:87. https://doi.org/10.1186/s10194-015-0568–7 May T, Kowalewski B, Dau T (2018) Signal-to-Noise-Ratio-Aware Dynamic Range Compression in Hearing Aids. Trends Hear 22:2331216518790903. https://doi.org/10.1177/2331216518790903 Meunier D, Lambiotte R, Bullmore ET (2010) Modular and Hierarchically Modular Organization of Brain Networks. Front NeuroSci 4:200. https://doi.org/10.3389/fnins.2010.00200 Moini J, Avgeropoulos NG, Samsam M (2021) Chapter 1—Anatomy and physiology. In J. Moini, N. G. Avgeropoulos, & M. Samsam (Eds.), Epidemiology of Brain and Spinal Tumors (pp. 3–40). Academic Press. https://doi.org/10.1016/B978-0-12–821736–8.00002–9 Moini J, Piran P (2020) Chapter 19—Spinal cord. In J. Moini & P. Piran (Eds.), Functional and Clinical Neuroanatomy (pp. 585–616). Academic Press. https://doi.org/10.1016/B978-0-12–817424–1.00019–7 Morales-Gregorio A, van Meegen A, van Albada SJ (2023) Ubiquitous lognormal distribution of neuron densities in mammalian cerebral cortex (p. 2022.03.17.480842). bioRxiv. https://doi.org/10.1101/2022.03.17.480842 Nair S, Ankolekar VH, Hosapatna M, DSouza A (2020) A morphologic and histologic study of the radial nerve and its branches at potential compression sites. J Taibah Univ Med Sci 15(5):358–362. https://doi.org/10.1016/j.jtumed.2020.07.009 Nicolini C, Bifone A (2016) Modular structure of brain functional networks: Breaking the resolution limit by Surprise. Sci Rep 6(1):19250. https://doi.org/10.1038/srep19250 Not! R (2025) B. I. or. (n.d.). Spinning With Rubik’s Cube Speedcubers. EverythingGP. Retrieved August 3, from https://everythinggp.com/2020/07/29/spinning-with-rubiks-cube-speedcubers/ Oizumi M, Albantakis L, Tononi G (2014) From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0. PLoS Comput Biol 10(5):e1003588. https://doi.org/10.1371/journal.pcbi.1003588 Ongerboer de Visser DW, Cruccu G (1993) Neurophysiologic examination of the trigeminal, facial, hypoglossal, and spinal accessory nerves in cranial neuropathies and brain stem disorders. Clinical Electromyography, 2nd edn. Butterworth-Heinemann}, pp 61–92 Oschwald J, Guye S, Liem F, Rast P, Willis S, Röcke C, Jäncke L, Martin M, Mérillat S (2019) Brain structure and cognitive ability in healthy aging: A review on longitudinal correlated change. Rev Neurosci 31(1):1–57. https://doi.org/10.1515/revneuro–2018–0096 Padamsey Z, Rochefort NL (2023) Paying the brain’s energy bill. Curr Opin Neurobiol 78:102668. https://doi.org/10.1016/j.conb.2022.102668 Papp KV, Kaplan RF, Springate B, Moscufo N, Wakefield DB, Guttmann CRG, Wolfson L (2014) Processing Speed in Normal Aging: Effects of White Matter Hyperintensities and Hippocampal Volume Loss. Neuropsychology, Development, and Cognition. Section B. Aging Neuropsychol Cognition 21(2):197–213. https://doi.org/10.1080/13825585.2013.795513 Pessoa L (2017) Chapter 29—The Emotional Brain. In P. M. Conn (Ed.), Conn’s Translational Neuroscience (pp. 635–656). Academic Press. https://doi.org/10.1016/B978-0-12–802381–5.00046–4 Peterson DC, Hamel RN (2025) Corneal Reflex. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK534247/ Ren S, Shao H, He S (2021) Interaction Between Conscious and Unconscious Information-Processing of Faces and Words. Neurosci Bull 37(11):1583–1594. https://doi.org/10.1007/s12264-021-00738–0 Roland PE (2023) How far neuroscience is from understanding brains. Front Syst Neurosci 17:1147896. https://doi.org/10.3389/fnsys.2023.1147896 Romaniello A, Valls-Solé J, Iannetti GD, Truini A, Manfredi M, Cruccu G (2002) Nociceptive Quality of the Laser-Evoked Blink Reflex in Humans. J Neurophysiol 87(3):1386–1394. https://doi.org/10.1152/jn.00041.2001 Rosenberg PH, Heinonen E (1983) Differential sensitivity of A and C nerve fibres to long-acting amide local anaesthetics. Br J Anaesth 55(2):163–167. https://doi.org/10.1093/bja/55.2.163 Rossano S, Toyonaga T, Bini J, Nabulsi N, Ropchan J, Cai Z, Huang Y, Carson RE (2022) Feasibility of imaging synaptic density in the human spinal cord using [11C]UCB-J PET. EJNMMI Phys 9:32. https://doi.org/10.1186/s40658-022-00464–0 Rossi B, Risaliti R, Rossi A (1989) The R3 component of the blink reflex in man: A reflex response induced by activation of high threshold cutaneous afferents. Electroencephalogr Clin Neurophysiol 73(4):334–340. https://doi.org/10.1016/0013–4694(89)90111–9 Rossi R, Arjmand S, Bærentzen SL, Gjedde A, Landau AM (2022) Synaptic Vesicle Glycoprotein 2A: Features and Functions. Front NeuroSci 16:864514. https://doi.org/10.3389/fnins.2022.864514 Saberi M, Rieck JR, Golafshan S, Grady CL, Misic B, Dunkley BT, Khatibi A (2024) The brain selectively allocates energy to functional brain networks under cognitive control. Sci Rep 14(1):32032. https://doi.org/10.1038/s41598-024-83696–7 Sattin D, Magnani FG, Bartesaghi L, Caputo M, Fittipaldo AV, Cacciatore M, Picozzi M, Leonardi M (2021) Theoretical Models of Consciousness: A Scoping Review. Brain Sci 11(5):535. https://doi.org/10.3390/brainsci11050535 Schneider TD (2013) Information Theory Primer With an Appendix on Logarithms PDF version. Unpublished. https://doi.org/10.13140/2.1.2607.2000 Seth AK, Bayne T (2022) Theories of consciousness. Nat Rev Neurosci 23(7):439–452. https://doi.org/10.1038/s41583-022-00587–4 Shannon CE (1948) A Mathematical Theory of Communication. Bell Syst Tech J 27(3):379–423. https://doi.org/10.1002/j.1538–7305.1948.tb01338.x Stam J, van Crevel H (1989) Measurement of tendon reflexes by surface electromyography in normal subjects. J Neurol 236(4):231–237. https://doi.org/10.1007/BF00314505 Sugimoto YA, Yadohisa H, Abe MS (2024) How neural network structure alters the brain’s self-organized criticality (p. 2024.09.24.614702). bioRxiv. https://doi.org/10.1101/2024.09.24.614702 Tham LK, Osman NAA, Abas WABW, Lim KS (2013) Motion Analysis of Normal Patellar Tendon Reflex. Can J Neurol Sci / J Canadien Des Sci Neurologiques 40(6):836–841. https://doi.org/10.1017/S0317167100015985 Thoma P, Drämel N, Grothe M, Lotze M, Fleischmann R, Strauss S (2022) Impaired Pain Processing at a Brainstem Level Is Involved in Maladaptive Neuroplasticity in Patients with Chronic Complex Regional Pain Syndrome. Int J Mol Sci 23(23):15368. https://doi.org/10.3390/ijms232315368 Tononi G, Boly M, Massimini M, Koch C (2016) Integrated information theory: From consciousness to its physical substrate. Nat Rev Neurosci 17(7):450–461. https://doi.org/10.1038/nrn.2016.44 Vasanthakumar P, Kumar P, Rao M (2013) Anthropometric Analysis of Palpebral Fissure Dimensions and its Position in South Indian Ethnic Adults. Oman Med J 28(1):26–32. https://doi.org/10.5001/omj.2013.06 Vergara RC, Jaramillo-Riveri S, Luarte A, Moënne-Loccoz C, Fuentes R, Couve A, Maldonado PE (2019) The Energy Homeostasis Principle: Neuronal Energy Regulation Drives Local Network Dynamics Generating Behavior. Front Comput Neurosci 13:49. https://doi.org/10.3389/fncom.2019.00049 Vickery HSF, Smith PA (2012) Surface electromyography reveals males have a slower patellar reflex than females. J Electromyogr Kinesiol 22(6):990–996. https://doi.org/10.1016/j.jelekin.2012.06.001 Wang S, Friedman JM, Suppa P, Buchert R, Mautner V-F (2022) White matter is increased in the brains of adults with neurofibromatosis 1. Orphanet J Rare Dis 17(1):115. https://doi.org/10.1186/s13023-022-02273–1 Watanabe N, Haruno M (2015) Effects of subconscious and conscious emotions on human cue–reward association learning. Sci Rep 5(1):8478. https://doi.org/10.1038/srep08478 Watts ME, Pocock R, Claudianos C (2018) Brain Energy and Oxygen Metabolism: Emerging Role in Normal Function and Disease. Front Mol Neurosci 11:216. https://doi.org/10.3389/fnmol.2018.00216 Wise R, Greene J, Büchel C, Scott S (1999) Brain regions involved in articulation. Lancet 353(9158):1057–1061. https://doi.org/10.1016/S0140–6736(98)07491–1 Wyss LS, Bray SR, Wang B (2024) Neuropeptide-mediated temporal sensory filtering in a primordial nervous system (p. 2024.12.17.628859). bioRxiv. https://doi.org/10.1101/2024.12.17.628859 Xing B, Li Y-C, Gao W-J (2016) Norepinephrine versus dopamine and their interaction in modulating synaptic function in the prefrontal cortex. Brain Res 1641:217–233. https://doi.org/10.1016/j.brainres.2016.01.005 YiHeng (2025) Wang breaks world record 3x3 single with 3.08. (n.d.). Speedcubing.Org. Retrieved August 3, from https://speedcubing.org/blogs/news/yiheng-wang-breaks-world-record–3x3-single-with–3–08 Yoo H, Mihaila DM (2025) Neuroanatomy, Pupillary Light Reflexes and Pathway. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK553169/ Zaborszky L, Csordas A, Mosca K, Kim J, Gielow MR, Vadasz C, Nadasdy Z (2015) Neurons in the Basal Forebrain Project to the Cortex in a Complex Topographic Organization that Reflects Corticocortical Connectivity Patterns: An Experimental Study Based on Retrograde Tracing and 3D Reconstruction. Cerebral Cortex (New York, NY), 25(1), 118–137. https://doi.org/10.1093/cercor/bht210 Zeraati R, Priesemann V, Levina A (2021) Self-organization toward criticality by synaptic plasticity. Front Phys 9:619661. https://doi.org/10.3389/fphy.2021.619661 Zheng J, Meister M (2025) The unbearable slowness of being: Why do we live at 10 bits/s? Neuron 113(2):192–204. https://doi.org/10.1016/j.neuron.2024.11.008 Additional Declarations The authors declare no competing interests. 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Resolution and their respective colour: 10\u003csup\u003e-2\u003c/sup\u003e (darkgreen), 10\u003csup\u003e-1\u003c/sup\u003e (blue), 10\u003csup\u003e0\u003c/sup\u003e (red), 10\u003csup\u003e1\u003c/sup\u003e (purple) and 10\u003csup\u003e2\u003c/sup\u003e (yellow).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7402915/v1/9a4d2ba2a83502a4edb6d52a.png"},{"id":89472628,"identity":"e08c7b52-5bee-443d-b26e-c57ac1c31222","added_by":"auto","created_at":"2025-08-20 09:45:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162502,"visible":true,"origin":"","legend":"\u003cp\u003eLog Log plots of (A) Processing Speed in bits per second and Gray Matter volume fraction; (B) SV2A concentrations and gray matter volume fraction; (C) Processing speed and SV2A concentration; (D) Tridimensional log log plot of processing speed, gray matter volume and SV2A concentration, the green line represents the theoretical optimization limit of the brain, the purple curve represents actual brain function distribution, and the red plane represents the tendency of actions to become more optimized.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7402915/v1/e063f62e568fd012447ba6ff.png"},{"id":89474666,"identity":"14acd3fd-4854-4d7b-b06c-d100bba6d568","added_by":"auto","created_at":"2025-08-20 10:17:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1041339,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7402915/v1/9d4f9c62-b109-42b2-b2f5-cc6fc883abbb.pdf"},{"id":89472630,"identity":"cf4ce946-8d52-4923-ae1f-caadc6c78697","added_by":"auto","created_at":"2025-08-20 09:45:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":572548,"visible":true,"origin":"","legend":"","description":"","filename":"SupplmentaryMaterial11.docx","url":"https://assets-eu.researchsquare.com/files/rs-7402915/v1/56cd21a95e7610628f3ee5d2.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eOn the Speed of Unconscious Processing\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe human mind's processing speed has long been unmeasured. Despite the absence of a universally accepted definition of consciousness (Sattin et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Seth \u0026amp; Bayne, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it is widely accepted that the conscious part of the mind functions more slowly than the unconscious (Ren et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Watanabe \u0026amp; Haruno, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The former has recently been calculated to be around 10 bits per second (Zheng \u0026amp; Meister, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the latter remains unknown. The objective of this article is to explore and discuss the processing speed of the unconscious mind by calculating the processing speed of medullary and sub-medullary reflexes.\u003c/p\u003e\u003cp\u003eIn this article, the primary focus will be on the presentation of two fundamental equations: the adoption of the Shannon-Hartley theorem to biological systems and the discrete entropy equation. Following an in-depth examination of Reflexes' findings, a comprehensive analysis of the results and connectivity is conducted through the lens of SV2A synaptic density. Additionally, a thorough investigation of brain structure is undertaken, encompassing the analysis of gray matter volume fraction. One of the equations treats the eye as a channel and calculates its capacity in the experiments' conditions, and the other equation calculates the movement's entropy through its amplitude and a fixed resolution. The final time considered the fact that the stimulus traveled through the body's nerves. It is imperative to reiterate that this estimation is of a theoretical nature.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eA total of five published articles were subjected to analysis through the application of discrete entropy (Shannon, \u003cspan class=\"CitationRef\"\u003e1948\u003c/span\u003e) to ascertain the total bits encountered in the output data. The articles in question were as follows: Aboubacar Nahantchi et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); Bergamin \u0026amp; Kardon (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e); Ellrich \u0026amp; Hopf (\u003cspan class=\"CitationRef\"\u003e1996a\u003c/span\u003e); Karam (\u003cspan class=\"CitationRef\"\u003e1977a\u003c/span\u003e); Abrams (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e); and Vickery \u0026amp; Smith (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, the pupillary contraction reflex was analyzed using Shannon's information theory. This analysis treated the reflex as a channel and calculated its capacity for the experiment's conditions. This approach was taken to approximate calculation to reality and compare it to the other equation. Subsequently, these results were divided by the reflexes' onset latencies, taking into account the time of stimulus travel, which yielded the processing times of the reflexes.\u003c/p\u003e\n\u003cp\u003eIn instances where a variable necessary for the calculation was absent from the primary article under analysis, the median or equivalent value was sought in the extant literature.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Shanon-Hartley theorem adaptation\u003c/h2\u003e\n\u003cp\u003eFor input data, biological mechanisms were modeled as additive white Gaussian noise (AWGN) channels. Then, its capacity was calculated as (Forney Jr., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:C\\left[\\frac{b}{s}\\right]=Wlog(1+SNR)$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1.0\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003ewhere\u003c/em\u003e \u003cstrong\u003eC\u003c/strong\u003e is the total capacity of the channel in Bits/Seconds, \u003cstrong\u003eW\u003c/strong\u003e is the bandwidth (or sampling rate) in Hertz (Hz) and \u003cstrong\u003eSNR\u003c/strong\u003e is the signal-to-noise ratio. \u003cstrong\u003eSNR\u003c/strong\u003e was adapted and divided to better represent the biological processes that compose the pupillary contraction reflex's pathway to be used in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1.0\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNR\u003c/strong\u003e was composed of signal power (\u003cstrong\u003eSgP\u003c/strong\u003e) and noise power, while the latter was divided into three terms: shot noise (\u003cstrong\u003eSN\u003c/strong\u003e), biological noise (\u003cstrong\u003eBN\u003c/strong\u003e), and retinal minimal sensitivity, as represented by equitation by (1.1). The final adaptation of the Shanon-Hartley theorem is given by equation\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\:C\\left[\\frac{b}{s}\\right]=W\\ast\\:log2(1+(\\frac{SgP}{(}SN+BN\\left)\\right))$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1.1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eFurther details and characterizations are addressed in Supplementary_Material_1. In summary, \u003cstrong\u003eC\u003c/strong\u003e was calculated for the experiment conditions and multiplied by the stimulus\u0026acute; duration, then the quantity of Bits was divided by onset latency minus the time of stimulus traveling through nerves.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Entropy adaptation\u003c/h2\u003e\n\u003cp\u003eEntropy (\u003cstrong\u003eH\u003c/strong\u003e\u003cem\u003e)\u003c/em\u003e is traditionally defined by its discrete form (Borda, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gray, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Shannon, \u003cspan class=\"CitationRef\"\u003e1948\u003c/span\u003e). Movements are typically defined as continuous variables by Lagrangial integral (Huys et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) and measured using differential entropy, which can have units. However, as the analysis done in this speculative article was done utilizing Bits, which is unitless, a resolution to exclude the units would have to be done. Therefore, its discrete form with a small resolution was utilized as an approximation. As all calculations were done utilizing two decimal cases in rounding, resolution was adopted as 10\u003csup\u003e(\u0026minus;2)\u003c/sup\u003e, however a sensitivity analysis of this variable was done. Furthermore it\u0026acute;s unit was matched to the numerator of the log, to make it unitless. The final equation is given by 2.0.\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:H=log2\\left(\\theta\\:\\frac{range}{\\theta\\:res}\\right)$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2.0\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:range\\)\u003c/span\u003e\u003c/span\u003e is total movement range, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:res\\)\u003c/span\u003e\u003c/span\u003e is the resolution. It was considered for the entropy calculations that all outcomes are equally probable; this was done to facilitate calculations. And has an important consequence of overestimating entropy (Cover, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e), therefore our calculations should be interpreted as an upper bound theoretical. All equations were set to base 2 to define the result in bits (Schneider, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Sensitivity analysis\u003c/h2\u003e\n\u003cp\u003eA sensitivity analysis was also done with the monosynaptic reflexes and one cortical action with resolution varying from 10\u003csup\u003e(\u0026minus;2)\u003c/sup\u003e to 10\u003csup\u003e(2)\u003c/sup\u003e in which the same process to obtain the processing speed was simply repeated but with different resolutions. Results are presented in Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cp\u003eThe findings suggest that monosynaptic reflexes exhibit heightened sensitivity to alterations in resolution when compared to cortical actions, which demonstrate minimal fluctuations in bit values. This phenomenon may be attributable to the inherent characteristics of the actions in question. Many cortical actions, such as the solution of a Rubik's Cube, are characterized by distinct and clearly separate states. Conversely, reflexes, as previously cited, are defined by Lagrangean integrals, which are associated with differential entropy.\u003c/p\u003e\n\u003cp\u003eHowever, due to the previously cited rationale, a simplification was adopted with discrete entropy. This simplification, in conjunction with the omission of biological variables that influence reflex occurrence, may contribute to the observed heightened sensitivity. A resolution that is reduced in scale captures a greater quantity of information, which consequently engenders heightened sensitivity to minute biological variation. Therefore, a resolution of 10\u0026thinsp;\u0026minus;\u0026thinsp;2 was adapted to align with the number of decimal places that were rounded. The adoption of a resolution of 1 was a conceivable possibility, given its equivalence to differential entropy. However, this possibility was precluded by the presence of units in differential entropy, a phenomenon that is absent in its discrete counterpart.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe results of Shanon-Hartley theorem adaptation are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For the highest and lowest light intensity group, respectively, \u003cb\u003eC\u003c/b\u003e is 1459.91and 1327.02 bits per second, and \u003cb\u003eQb\u003c/b\u003e (quantity of stimulus\u0026acute; bits) is 73 and 66.35bits per second. \u003cb\u003ePs\u003c/b\u003e for the fastest reaction time is 294.34 bits per second, while the slowest is 206.06 bits per second.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the Shannon-Hartley theorem adaptation for pupillary light contraction reflex subdivided into light intensity group. C - total capacity of the channel; Ps - Processing speed; Qb - Quantity of bits. To calculate the processing speed of the knee patellar reflex and the other reflexes, the formula 2.0 was used, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of data\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e (bits/seconds)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eQb\u003c/em\u003e (bits)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePs\u003c/em\u003e (bits/seconds)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHighest light intensity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1459.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e294.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLowest light intensity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1327.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e206.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData of entropy calculation and processing speed of other reflexes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReflex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNerve length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTime of travel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOnset latency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMovement and its Total range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProcessing speed (Bits per second)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAchilles reflex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.42 cm in males and 64.79\u0026thinsp;\u0026plusmn;\u0026thinsp;67.61 cm in females\u0026thinsp;+\u0026thinsp;29 cm (sciatic) (Elbarrany \u0026amp; Altaf, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42,5 ms (Abrams, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePlantar flexion, 111\u003csup\u003eo\u003c/sup\u003e (Friis, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e926.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriceps reflex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.51 cm (Nair et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,16 ms for 60m/s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 ms (Karam, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1977b\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eForearm extension, 90\u003csup\u003eo\u003c/sup\u003e (Armstrong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 459.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlink reflex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSame as PCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSame as PCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR1\u0026ndash;10.7 ms\u003c/p\u003e\u003cp\u003eR2\u0026ndash;33.3 ms (ABOUBACAR NAHANTCHI et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eR3\u0026ndash;84 ms (Ellrich \u0026amp; Hopf, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1996a\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFissure closure, 11,5cm (Vasanthakumar et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR1\u0026ndash;922.882\u003c/p\u003e\u003cp\u003eR2\u0026ndash;279.661\u003c/p\u003e\u003cp\u003eR3\u0026ndash;109.867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnee patellar reflex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.56 cm (Kegoye et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (the averages of both right and left legs and sexes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.3 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 ms (Dixon et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Frijns et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90\u0026deg; (Tham et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 094.667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e**The article analyzed showcases an onset latency from 9 ms to 15 ms (Tham et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which is significantly lower than the results of other studies (Frijns et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Husemann \u0026amp; Behse, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Stam \u0026amp; van Crevel, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). This could be explained by the increased time required for initial myofibrillar contraction to translate into shortening of the muscle's length and, consequently, extension of the knee (Tham et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As other studies' baseline for onset latency of the knee patellar reflex was at least 20 ms (Dixon et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Frijns et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), this value was adopted as the onset latency.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe discrepancy in processing speed between input (Shanon-Heartley theorem adaptation) and output (discrete entropy) may be attributable to the nervous system filtering information system (Cabral et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Histed, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wyss et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). This system was not incorporated into our adaptation due to the reasons elaborated upon in the text.\u003c/p\u003e\n\u003cp\u003eA comparison of the results for the two different reflexes, as presented in Table\u0026nbsp;3, indicates that the knee patellar reflex (KPR) processing time is significantly faster than that of the pupillary contraction reflex (PCR) or even conscious thought (Zheng \u0026amp; Meister, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWithin the framework of ITT, proposed explanations for this phenomenon include the hypothesis that simpler actions exhibit faster processing speeds due to their reliance on localized, minimally integrated circuits that require fewer computational steps and minimal coordination between regions (Balduzzi \u0026amp; Tononi, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Oizumi et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tononi et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). These systems generate low integrated information (\u0026Phi;), as their limited causal interactions bypass higher-order integration, allowing for rapid and automatic responses. Conversely, complex actions involve distributed networks across cortical and subcortical regions, which necessitate extensive integration of information. This higher level of synchronization necessitates the implementation of complex processes, including iterative feedback loops and the resolution of competing signals. These processes inherently introduce delays, which can compromise the efficiency of processing. Therefore, the trade-off between speed and complexity reflects a fundamental principle: Systems that are optimized for integration and flexibility sacrifice speed, while streamlined, localized systems prioritize rapidity at the expense of adaptability.\u003c/p\u003e\n\u003cp\u003eAn alternative explanation for this phenomenon involves the number of pathways involved in each action. The fundamental architecture of reflexes is known as the reflex arc, which involves neural pathways that respond to an impulse before it reaches the brain. Instead of traveling directly to the brain, sensory neurons of a reflex arc synapse in the spinal cord (Derderian et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe nervous system is composed of five basic units: receptors, sensory neurons, integration centers, motor neurons, and effectors. The initial category comprises the anatomical structures through which external stimuli is received. The subsequent category encompasses the neurons that facilitate the transformation of sensory information within the central nervous system. The third category corresponds to the regions where diverse neural pathways converge. The fourth category involves the neurons responsible for transmitting the response to external stimuli. Finally, the fifth category refers to the agents that execute the response to external stimuli (Clarac et al., \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Moini et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFurthermore, interneurons, a specific type of intermediary neuron, are located in either the spinal cord or brain stem and modulate the responses of reflexes (Deska-Gauthier \u0026amp; Zhang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jankowska et al., \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e). However, KPR does not demonstrate modulation from interneurons (Derderian et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFurthermore, the study of reflexes reveals a taxonomy based on the number of synapses that converge on their integration center, categorizing reflexes as monosynaptic (or simple) or polysynaptic (or complex). While the former is characterized by a smaller number of synapses converging on it, the latter involves a larger number of synapses (Moini \u0026amp; Piran, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, polysynaptic reflexes facilitate the integration and coordination of a greater number of pathways, resulting in more complex motor actions. Such a discrepancy in magnitude could serve as a potential explanation for the observed variation in processing speed among the different actions. The nervous system would possess an inherent baseline capacity for processing, which would undergo a decline with each additional amount of information incorporated into the process.\u003c/p\u003e\n\u003cp\u003eKPR is processed independently from the higher center (Johns, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), depending entirely on the spinal cord for processing, despite information traveling to higher centers (Callaghan et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The primary site of PCR processing is the midbrain; however, other regions of the cerebellum (Yoo \u0026amp; Mihaila, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) and even the hypothalamus (Belliveau et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) have been shown to contribute to this process.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;Table\u0026nbsp;3 - Processing speed values of different activities.\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAction\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eProcessing Speed (bits/s)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTriceps reflex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 459.556\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKnee Patellar Reflex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 094.667\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAchilles reflex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e926.777\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlink reflex R1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e922.882\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlink reflex R2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e279.661\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePRC (fastest reaction time)*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113.202\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlink reflex R3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109.867\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePRC (slowest reaction time)*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpeech**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObject recognition**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u0026ndash;50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReading (English)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u0026ndash;45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRubik\u0026rsquo;s cube***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpeed card**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eListening comprehension (English)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOptimal performance in the laboratory motor tasks**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u0026ndash;12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTyping (English)**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBinary digit memorization**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChoice-reaction experiment**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003e* Values derived from Shannon-Hartley adaptation;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003e** Data extracted from (Zheng \u0026amp; Meister, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e, p. 10);\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*** This data is calculated further down the article.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA salient question pertains to the relationship between processing speed and other cerebral functions or characteristics. At first glance, one might hypothesize that this nature is logarithmic, as is the case with many brain mechanisms. Some examples include neuron densities within most areas of the marmoset cortex (Morales-Gregorio et al., The Weber-Fechner law, which stipulates that the response to a sensory stimulus is proportional to the logarithm of the stimulus amplitude, has been demonstrated to affect light, sound, and even decision-making and short-term memory error accumulation (Gold \u0026amp; Shadlen, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). Another lognormal distribution has been identified in the mental organization of numbers (Dehaene, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), and others (Buzs\u0026aacute;ki \u0026amp; Mizuseki, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, this phenomenon is not exclusive to the brain; the stationary distribution of spine sizes of individual neurons was also found to follow a lognormal scale (Loewenstein et al., 2011).\u003c/p\u003e\n\u003cp\u003eIn order to investigate the nature of this relationship, processing speed was correlated with the primary region of the central nervous system in which its corresponding activity is processed, as well as the local concentration of SV2A protein density.\u003c/p\u003e\n\u003cp\u003eSynaptic vesicle glycoprotein 2A (SV2A) is a transmembrane protein of synaptic vesicles, present in all synaptic terminals, irrespective of neurotransmitter content. It is involved in key functions of neurons, with a focus on the regulation of neurotransmitter release (R. Rossi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, this protein can be utilized to assess the connectivity strength between synapses.\u003c/p\u003e\n\u003cp\u003eThe estimated gray matter density for each of the three monosynaptic reflexes was calculated according to their roots from Henmar et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study measured gray matter and white volumes from each vertebra.\u003c/p\u003e\n\u003cp\u003eThe calculation of brain stem gray matter density was derived from two sources: Wang et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), which provides the brain stem's white matter volume, and Fujimoto et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), which offers the total brain stem volume. As stated in the work of Gennatas et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), the density of gray matter in the neocortex is determined by... It has been demonstrated that actions that are processed in the neocortex generally span more than one area, which have different gray matter fractions. Speech, for instance, has been analyzed in the article (Zheng \u0026amp; Meister, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) and is controlled by the basal ganglia, left anterior insula, lateral premotor cortex, and Broca's area (Wise et al., \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). However, additional areas, such as the supplementary motor area, medial cingulate, thalamus, caudate, pallidum, inferior frontal gyrus (right pars opercularis region), and the cerebellum (right cerebellar crus I region), are involved in this process through minor roles (Lorca-Puls et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ludlow, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, the neocortex was regarded as having a predominantly gray matter composition, with an estimated 80% of its volume consisting of this substance.\u003c/p\u003e\n\u003cp\u003eAs demonstrated in the study by Johansen et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), the neocortex and the pons, a subregion of the brain stem, exhibit a synaptic concentration of SV2A density. Despite the lack of direct measurements of this synaptic protein in the spinal cord, extant literature offers estimates suggesting that its concentration is from two to fivefold lower than that observed in the neocortex (see Rossano et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTherefore, the following calculation will determine the SV2A concentration for each respective group: The spinal cord (63.3-158.25), the pons, which was utilized for all polysynaptic reflexes (95), the neocortex (538.7 [170\u0026ndash;631]), and the gray matter volume fraction: The values for the C7-C8, L2-L5, S1-S2, and brain stem regions were determined to be 0.204, 0.35, 0.487, and 0.22, respectively. The range for the brain stem region was specified as 0.161 to 0.426.\u003c/p\u003e\n\u003cp\u003eThe results of these analyses are present in Fig.\u0026nbsp;2. Group 1 is composed of monosynaptic reflexes that are processed in the spinal cord, Group 2 is composed of polysynaptic reflexes that are processed in the brainstem, and Group 3 is composed of actions that are processed in the cortex.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cp\u003eWhen the data are graphed on a log-log scale, plotting processing speed per gray matter density fraction, a linear line between the variables of monosynaptic reflexes is observed (see Fig.\u0026nbsp;2A).This line can be considered the optimization limit of the central nervous system (see Fig.\u0026nbsp;2D). These actions are among the bodies most optimized due to the constant usage and occurrence of these reflexes. They are also among the earliest movements in neonatal motor command (Kuban et al., \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e). It has been demonstrated that actions of a more complex nature, or those that have not been optimized, would fall below this threshold, yet they tend to gravitate towards it, as illustrated by the red plane in Fig.\u0026nbsp;2.D. Consequently, the realistic distribution of activities would align with the purple line of Fig.\u0026nbsp;2.D.\u003c/p\u003e\n\u003cp\u003eThe brain operates at a low energy baseline, with spikes in energy usage that vary according to the stimulus (Saberi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In order to maintain this baseline, a variety of mechanisms are utilized (Padamsey \u0026amp; Rochefort, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vergara et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Watts et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), while others change the energy usage (Habibollahi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sugimoto et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zeraati et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The green line signifies the theoretical upper limit of the brain's processing speed, which is attained when an action is optimized. The brain has been shown to optimize actions according to the specific stimuli received. Consequently, a considerable number of actions would be found to be in a state of incomplete optimization, with many exhibiting only a partial optimization. It is further noted that a significant proportion of actions would not have undergone the formation of their neural circuitry.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1. Gray Matter density and Processing speed\u003c/h2\u003e\n\u003cp\u003eThe enhanced synaptic connectivity observed in the neocortex may be attributed to the capacity to surpass the physical constraints imposed by gray matter volume fraction saturation, as illustrated in Fig.\u0026nbsp;1.B. Given the role of gray matter in optimizing predictive coding and minimizing free energy, a plausible hypothesis suggests that larger models may possess a greater relative processing capacity while maintaining a high degree of abstraction, potentially attributable to modularization.\u003c/p\u003e\n\u003cp\u003eAs the size of the cortex (or model) increases, modularization emerges as a strategy to maintain predictive efficiency and free-energy minimization while overcoming physical or computational limits on connectivity density. It has been demonstrated that minor prediction errors are resolved within modules. This results in the conservation of inter-modular bandwidth and energy. Conversely, major errors trigger broader network coordination.\u003c/p\u003e\n\u003cp\u003eThis phenomenon has been previously suggested in experimental studies (Greco et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), which indicates that large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas, thereby enhancing sensory processing.\u003c/p\u003e\n\u003cp\u003eIn addition, an analysis of 103 distinct mammalian species' connectomes revealed that as brain volume increases, modular structures become more spatially compact, denser internally, and more segregated. Furthermore, the increased size of larger brains imposes stronger spatial constraints on connectivity, thereby favoring modular organization for efficient communication (Greco et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSuch connections are more frequently observed in conjunction with other regions, collectively constituting a more complex system. For instance, gray matter volume in the hippocampus exhibits strong correlations with that of other regions implicated in the memory system, such as the amygdala and various cortices, including the parahippocampal, perirhinal, entorhinal, and orbitofrontal cortices (Alexander-Bloch et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA recent study of the brains of 14 primate species revealed a correlation between brain size and the degree of connectedness within the brain. Specifically, the study found that larger brains tend to exhibit reduced levels of overall connectedness, as indicated by sparser long-range connectivity, longer communication paths, higher local network clustering, and higher levels of asymmetry in connectivity patterns between homologous areas across the left and right hemispheres (Ardesch et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA thorough investigation was conducted into the role of neural feedback in sensorimotor processes. To this end, convolutional neural network (CNN) models augmented with predictive feedback were utilized. These models were trained to compute grasp positions for real-world objects. The study found that, under adverse conditions, the best performance occurred with medium-range asymmetric feedback that originated from a level of representational abstraction closer to the input layer, rather than from more distal layers (Khan et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe relative importance of gray matter and white matter in determining computational capacity remains a subject of debate. A plethora of clinical studies have demonstrated a positive correlation between intelligence and processing speed in patients with various conditions (Fineschi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kuznetsova et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Magistro et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Oschwald et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Papp et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, a direct comparison between different compositions has not yet been conducted. Moreover, further research is necessary to quantify brainstem gray matter volume and spinal cord SV2A density.\u003c/p\u003e\n\u003cp\u003eIt is also important to note that factors affecting the intraneuronal level also impact the Ps of the larger integrated system. For example, the presence of differing hormones has been identified as a factor that can influence these levels. Adrenaline, for instance, has been shown to enhance neuron firing by binding to adrenergic receptors, activating second messenger systems (cAMP, PKA, IP3, DAG, PKC), and modulating ion channels and synaptic transmission (Xing et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, it has been demonstrated to promote the liberation of calcium through bone resorption (Barritt et al., \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e), which results in a neuronal decrease in polarization. The collective impact of these factors results in a reduction of the threshold for action potentials, an augmentation of neurotransmitter release, and an enhancement of neuronal excitability. This, in turn, facilitates more expeditious signal propagation. This external influence on neurons implies a rate of Ps, which can fluctuate according to other factors, increasing with excitatory factors and decreasing with inhibitory.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2. Modularization\u003c/h2\u003e\n\u003cp\u003eOne aspect that favors a continuous interpretation over a discrete one is modular specialization, which is the natural tendency of organizations to develop specialized cores (Meunier et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nicolini \u0026amp; Bifone, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). This phenomenon occurs continuously and does not necessitate the presence of specific thresholds for its occurrence, as evidenced by Alcal\u0026aacute;-Corona et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This characteristic could elucidate the reason why the curve assumes the form of a continuous exponential curve, rather than a series of slopes separated by critical thresholds. It is important to note that not all modularities necessarily lead to specialization (B\u0026eacute;na \u0026amp; Goodman, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), and this relationship is not straightforward. It is conceivable that, while modularity has demonstrated its efficacy, there may be a limit to it (Fang \u0026amp; Kim, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) that could be overcome if a specific critical point or threshold is exceeded.\u003c/p\u003e\n\u003cp\u003eAt first glance, one might hypothesize that interhemispheric connections are more significant than intrahemispheric ones for cognitive functions and processing speed, representing the capacity for modularization in performing most daily tasks. However, recent evidence (Deco et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) suggests that computation is primarily a long-range network effect rather than a local one. Consequently, intrahemispheric communication can be instrumental in addressing subdivisions of larger problems. However, interhemispheric communication has the potential to link various subdivisions, thereby facilitating the resolution of the problem.\u003c/p\u003e\n\u003cp\u003eAnother salient feature of larger models is the capacity of modules to compensate for the malfunction of others. The impact of gray matter volume and age-related factors on emotion recognition does not stem from localized changes in the brain. Alternatively, these phenomena may have a more diffuse origin, originating from regions throughout the brain. However, some localized changes were also observed, which had small effects on emotional recognition (Karl \u0026amp; Rohe, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA study that analyzed magnetic resonance images and test scores found that, compared with global cortical morphology measures, the white matter exhibited a stronger association with cognition. However, the cortical surface areas of the left orbitofrontal cortex, the right posterior-dorsal part of the cingulate gyrus, and the left central sulcus have been found to be positively correlated with cognition (Li et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This finding suggests that the communication between specialized modules for problem-solving, in later stages of modularization, may be more significant than raw processing power.\u003c/p\u003e\n\u003cp\u003eIt has been established that the human brain does not employ its full neuronal capacity during a single action (Roland, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, the neuronal excess that accompanies scaling could be utilized for the further refinement of existing pathways, thereby reducing the neuron count involved in these pathways. This, in turn, could lead to a positive feedback loop between the optimization of existing pathways.\u003c/p\u003e\n\u003cp\u003eSpecialized regions exhibit a greater degree of interconnectedness within the broader network infrastructure. Forebrain subregions demonstrate a heightened degree of interconnectedness when their cortical destinations exhibit a higher degree of integration (Zaborszky et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIt is imperative to investigate the numerous stages of the blink reflex to comprehend the correlation between the precipitous slopes of Ps and the quantity of neurons. The blink reflex is comprised of three components: an early (R1), late (R2), and late (R3) component. The R1 component is characterized by an oligosynaptic pathway involving the principal sensory nucleus of the trigeminal nerve and the intermediate subnucleus of the facial nerve (Peterson \u0026amp; Hamel, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The R2 component is observed less frequently, and the R3 component is sporadically observed (Ellrich \u0026amp; Hopf, \u003cspan class=\"CitationRef\"\u003e1996b\u003c/span\u003e; Kofler et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The second response, R2, involves a pathway of descent to the spinal trigeminal tract. The contralateral response, R2c, has been demonstrated to reflect the crossing of the brainstem in the medulla and progresses through the reticular formation to elicit a response at the contralateral facial nucleus (Brooks \u0026amp; Fragoso, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eR2 and R3 can initially appear similar because they are both nociceptive (D'Aleo et al., 1999; Giffin et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e) and are transported by cutaneous thick myelinated A\u0026beta; and A\u0026delta; fibers (Ellrich et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Marin et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, R3 exhibits a higher onset latency compared to R2. As indicated by the research of B. Rossi et al. (\u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e), the subject in question demonstrated heightened sensitivity to anesthesia. This finding suggests that the R3 response is predominantly influenced by A-delta fibers, which exhibit a greater sensitivity to anesthesia compared to A-beta fibers, as reported by Rosenberg and Heinonen (\u003cspan class=\"CitationRef\"\u003e1983\u003c/span\u003e). As demonstrated in the research by Carmichael et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), fibers exhibit a higher conduction velocity in comparison to A-delta fibers. This observation contributes to the explanation of their higher latency, which is further compounded by the ongoing debate surrounding the precise pathway of these fibers (Ellrich et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eR1 is relayed through an oligosynaptic arc, which is likely located in close proximity to the primary sensory nucleus of the trigeminal nerve (Ongerboer de Visser \u0026amp; Cruccu, \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e; Romaniello et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). R2 is mediated by a polysynaptic chain of interneurons belonging to the lateral reticular formation in the lower medulla (Ongerboer de Visser \u0026amp; Cruccu, \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e; Romaniello et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), while also being processed in the brain stem (Thoma et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is hypothesized that R2 and R3 are processed within the brainstem, with pathways analogous to those of R2 (Ellrich et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWhile there is evidence of clinical application of R2 (Gunduz et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), the nature of R3 remains to be investigated, and its clinical applications are still under evaluation (Ellrich, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). The following observations were made: R3 demonstrates a heightened response to modulation under typical conditions (Gabrielli et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) and in the context of attention deficit disorder (Gabrielli et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; B. Rossi et al., \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e). The basal forebrain, a region implicated in vigilance, attention, and emotional processing (Chen et al., As demonstrated in the studies by Pessoa (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Douglas et al. (\u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), the R3 response is already known to interact with the regulation of breathing. Therefore, it would be a significant departure from the existing body of knowledge to consider R3 as an R2-specific response that is primarily related, albeit not exclusively, to nociceptive stimuli arising in particular circumstances.\u003c/p\u003e\n\u003cp\u003eA practical example of this is the solving of a Rubik\u0026rsquo;s Cube, which has a \u003cstrong\u003eQb\u003c/strong\u003e of 65.22 bits, according to the application of the entropy formula with equiprobable outcomes. Some timestamps of its solving can be given as: a first-ever time solve of a month by its creator (\u003cem\u003eErno Rubik | Cube Creator, Puzzle Master, Architect | Britannica\u003c/em\u003e, n.d.), a non-scientific measurement of first time solve between a couple of minutes and a few days with the utilization of pre-established guides and strategies (DizzyGiraffe01, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Not!, n.d.), and a world record of 3.08 seconds (\u003cem\u003eYiHeng Wang Breaks World Record 3x3 Single with 3.08\u003c/em\u003e, n.d.), yielding \u003cstrong\u003ePs\u003c/strong\u003e of 2.52 * 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e and 21.18 bits per second for the creator and world record respectively. While this data lacks scientific rigor, it serves to showcase the brain's capacity to optimize tasks; in this case, the brain optimized to an order of magnitude 10\u003csup\u003e^6\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3. Filtering\u003c/h2\u003e\n\u003cp\u003eIn the study by Zheng and Meister (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), the authors propose a novel definition of the sifting number (Si) as the optic nerve's capacity for filtering data prior to its transmission to the central nervous system. This calculation involves the division of sensory information rate by behavior throughput, both measured in bits per second (bps), yielding a rate of 10^8. However, this formula fails to consider information that is captured yet not utilized in conscious and coordinated action. While this oversight is not problematic for conscious actions, it becomes problematic for unconscious acts, such as those addressed in this article. The latter information is not encompassed by the former's behavior throughput.\u003c/p\u003e\n\u003cp\u003eThis problem prompts the following inquiry: (1) Is it possible for Si to vary according to the nature of the act? Secondly, the appropriateness of the Si calculation must be examined.\u003c/p\u003e\n\u003cp\u003eThe response to (1) remains uncharted territory, as despite the perception and filtration of luminous data occurring in an a priori state (Hegel, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) prior to its processing by the central nervous system, leading to the formulation and execution of responses, substantial neuronal population filtering scales have yet to be actualized. Consequently, this factor may exert variable ramifications on the filtering process.\u003c/p\u003e\n\u003cp\u003eOne potential response to (2) is the calculation of \u003cstrong\u003eSi\u003c/strong\u003e based on biological processes of noise reduction, such as: Dynamic range compression is a signal processing technique that has been demonstrated to either reduce the volume of high-intensity stimuli or amplify low-intensity stimuli (May et al.). As demonstrated in biological systems, the stapedic reflex serves to protect from high-audio stimuli (Brask, \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e). This reflex has been shown to decrease stimuli by up to 15 dB. Additionally, the saturation of cones and rods outside their optimal range can be considered a form of redundancy, defined as the addition of information neurons' firing output that exceeds the information conveyed by the stimulus (Crumiller et al.). In our calculations, redundancy was not taken into account. We calculated the quantity of information that passed through the channel and assumed, under idealized conditions, that this information was fully transmitted to the central nervous system. We did not try to simulate or estimate this variable. As simulated rates vary substantially from experimental data, which is itself based on a small neuron population (Crumiller et al.), we were unable to do so. In 2011, the concept of \"generality\" for a significant population was called into question. This notion pertains to the notion of \"synergy,\" which is defined as the process through which neurons exchange information, thereby augmenting the collective information capacity of the population (Crumiller et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). In order to achieve a higher degree of generality with regard to other nerves and actions, it is imperative to implement this approach in lieu of utilizing behavior throughput.\u003c/p\u003e\n\u003cp\u003eNevertheless, if \u003cstrong\u003eSi\u003c/strong\u003e is added to \u003cstrong\u003e\u0026eta;\u003c/strong\u003e ((as in 10\u003csup\u003e^ (\u0026minus; 8)\u003c/sup\u003e) instead of a positive value to represent that the retinal filters this magnitude of data from signal power), the processing speed of data input becomes much closer to that of output, although it should be lower as the latter is an upper limit estimate, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eProcessing speed of the pupillary light contraction reflex according to the Shanon Hartley theorem adaptation taking \u003cstrong\u003eSi\u003c/strong\u003e into consideration. C - total capacity of the channel; Ps - Processing speed; Qb - Quantity of bits.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eType of data (input)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eC\u003c/em\u003e (bits/seconds)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eQb\u003c/em\u003e (bits)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePs\u003c/em\u003e (bits/seconds)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnfiltered\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e[1459.91; 1327.02]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e[73; 66.35]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e[294.34; 206.06]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFiltered\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e[396.89; 264.01]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e[19.84; 13.2]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e[80.02; 41.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe present article is subject to three principal limitations. Firstly, certain measurements, principally SV2A spinal cord synaptic concentrations, were not precise, and thus estimations were utilized. Secondly, given the assumption of equiprobable probabilities of events, their bits were overestimated in comparison to normal probabilities. Therefore, the values presented in this article should be regarded as an upper theoretical bound limit. (3) The central nervous filtering is a key component absent from the Shannon-Hartley theorem adaptation that significantly impacts the results. This underscores the necessity for the study of information filtering in large neuronal populations for more reliable and credible estimates.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eC\u003c/em\u003e\u003c/strong\u003e - Total capacity of the channel;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eW\u003c/em\u003e\u003c/strong\u003e - Bandwidth;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSNR\u003c/em\u003e\u003c/strong\u003e - Signal-to-noise ratio;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSgP\u003c/em\u003e\u003c/strong\u003e - Signal power;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSN\u003c/em\u003e\u003c/strong\u003e - Shot noise;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBN\u003c/em\u003e\u003c/strong\u003e - Biological noise;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e - Entropy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eθrange\u003c/em\u003e\u003c/strong\u003e - Total movement range,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eθres\u003c/em\u003e\u003c/strong\u003e - Fixed sensorialmotor resolution\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePs\u003c/em\u003e\u003c/strong\u003e - Processing speed;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQb\u003c/em\u003e\u003c/strong\u003e - Quantity of bits;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKPR\u003c/strong\u003e - Knee patellar reflex;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR\u003c/strong\u003e - Pupillary contraction reflex\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSi\u003c/em\u003e\u003c/strong\u003e - Sifting number\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Conflicts of Interest and Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no conflicts of interest. This study did not have any funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eABOUBACAR NAHANTCHI A, SODA SECKLBDIAGNESN, BASSE M, A. M., MOURABIT, S., BUGUME, M., DIOP GA (2022) AJNS \u0026ndash; African Journal of Neurological Sciences | \u0026raquo; NORMATIVE VALUES OF THE BLINK REFLEX. Afr J Neurol Sci, 41(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ajns.paans.org/normative-values-of-the-blink-reflex/\u003c/span\u003e\u003cspan address=\"https://ajns.paans.org/normative-values-of-the-blink-reflex/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbrams BM (2007) \u003cem\u003eElectromyography and Nerve Conduction Velocity\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/b978-0-7216-0334-6.50020-0\u003c/span\u003e\u003cspan address=\"10.1016/b978-0-7216-0334-6.50020-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlcal\u0026aacute;-Corona SA, Sandoval-Motta S, Espinal-Enr\u0026iacute;quez J, Hern\u0026aacute;ndez-Lemus E (2021) Modularity in Biological Networks. Front Genet 12:701331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2021.701331\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2021.701331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14(5):322\u0026ndash;336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrn3465\u003c/span\u003e\u003cspan address=\"10.1038/nrn3465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArdesch DJ, Scholtens LH, de Lange SC, Roumazeilles L, Khrapitchev AA, Preuss TM, Rilling JK, Mars RB, van den Heuvel MP (2022) Scaling Principles of White Matter Connectivity in the Human and Nonhuman Primate Brain. Cereb Cortex 32(13):2831\u0026ndash;2842. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cercor/bhab384\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhab384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmstrong AD, MacDermid JC, Chinchalkar S, Stevens RS, King GJW (1998) Reliability of range-of-motion measurement in the elbow and forearm. J Shoulder Elbow Surg 7(6):573\u0026ndash;580. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1058\u0026ndash;2746(98)90003\u0026ndash;9\u003c/span\u003e\u003cspan address=\"10.1016/S1058\u0026ndash;2746(98)90003\u0026ndash;9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalduzzi D, Tononi G (2008) Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework. PLoS Comput Biol 4(6):e1000091. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pcbi.1000091\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1000091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarritt GJ, Parker JC, Wadsworth JC (1981) A kinetic analysis of the effects of adrenaline on calcium distribution in isolated rat liver parenchymal cells. J Physiol 312(1):29\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1113/jphysiol.1981.sp013614\u003c/span\u003e\u003cspan address=\"10.1113/jphysiol.1981.sp013614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelliveau AP, Somani AN, Dossani RH (2025) Pupillary Light Reflex. In StatPearls. StatPearls Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/books/NBK537180/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/books/NBK537180/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026eacute;na G, Goodman DFM (2025) Dynamics of specialization in neural modules under resource constraints. Nat Commun 16(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-024-55188\u0026ndash;9\u003c/span\u003e\u003cspan address=\"10.1038/s41467-024-55188\u0026ndash;9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBergamin O, Kardon RH (2003) Latency of the Pupil Light Reflex: Sample Rate, Stimulus Intensity, and Variation in Normal Subjects. Invest Opthalmology Visual Sci 44(4):1546. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1167/iovs.02\u0026ndash;0468\u003c/span\u003e\u003cspan address=\"10.1167/iovs.02\u0026ndash;0468\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorda M (2011) Fundamentals in information theory and coding. Springer\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrask T (1978) The Noise Protection Effect of the Stapedius Reflex. Acta Otolaryngol 86(sup360):116\u0026ndash;117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/00016487809123490\u003c/span\u003e\u003cspan address=\"10.3109/00016487809123490\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrooks JBB, Fragoso YD (2013) The blink reflex test does not show abnormalities in a large group of patients with chronic migraine. Arq Neuropsiquiatr 71:862\u0026ndash;865. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/0004\u0026ndash;282X20130139\u003c/span\u003e\u003cspan address=\"10.1590/0004\u0026ndash;282X20130139\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuzs\u0026aacute;ki G, Mizuseki K (2014) The log-dynamic brain: How skewed distributions affect network operations. Nat Rev Neurosci 15(4):264\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrn3687\u003c/span\u003e\u003cspan address=\"10.1038/nrn3687\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCabral HV, Cudicio A, Bonardi A, Del Vecchio A, Falciati L, Orizio C, Martinez-Valdes E, Negro F (2024) Neural Filtering of Physiological Tremor Oscillations to Spinal Motor Neurons Mediates Short-Term Acquisition of a Skill Learning Task. Eneuro, 11(7), ENEURO.0043\u0026ndash;24.2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/ENEURO.0043\u0026ndash;24.2024\u003c/span\u003e\u003cspan address=\"10.1523/ENEURO.0043\u0026ndash;24.2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCallaghan MJ, McKie S, Richardson P, Oldham JA (2012) Effects of Patellar Taping on Brain Activity During Knee Joint Proprioception Tests Using Functional Magnetic Resonance Imaging. Phys Ther 92(6):821\u0026ndash;830. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2522/ptj.20110209\u003c/span\u003e\u003cspan address=\"10.2522/ptj.20110209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarmichael J, Fadavi H, Ishibashi F, Shore AC, Tavakoli M (2022) Chapter 4\u0026mdash;Advances in screening, early diagnosis, and accurate staging of diabetic neuropathy. In M. Tavakoli (Ed.), Diabetic Neuropathy (pp. 47\u0026ndash;78). Elsevier. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12\u0026ndash;820669\u0026ndash;0.00018\u0026ndash;9\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12\u0026ndash;820669\u0026ndash;0.00018\u0026ndash;9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen L, Yin D, Wang T-X, Guo W, Dong H, Xu Q, Luo Y-J, Cherasse Y, Lazarus M, Qiu Z, Lu J, Qu W-M, Huang Z-L (2016) Basal Forebrain Cholinergic Neurons Primarily Contribute to Inhibition of Electroencephalogram Delta Activity, Rather Than Inducing Behavioral Wakefulness in Mice. Neuropsychopharmacology 41(8):2133\u0026ndash;2146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/npp.2016.13\u003c/span\u003e\u003cspan address=\"10.1038/npp.2016.13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClarac F, Cattaert D, Le Ray D (2000) Central control components of a \u0026lsquo;simple\u0026rsquo; stretch reflex. Trends Neurosci 23(5):199\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0166\u0026ndash;2236(99)01535\u0026ndash;0\u003c/span\u003e\u003cspan address=\"10.1016/S0166\u0026ndash;2236(99)01535\u0026ndash;0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCover TM (1991) Elements of information theory. Wiley\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrumiller M, Knight B, Yu Y, Kaplan E (2011) Estimating the Amount of Information Conveyed by a Population of Neurons. Front NeuroSci. 5\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2011.00090\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2011.00090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD\u0026rsquo;Aleo G, Sessa E, D\u0026rsquo;Aleo P, Rifici C, Di Bella P, Petix M, Bramanti P (1999) Nociceptive R3 reflex in relapsing-remitting multiple sclerosis patients. Funct Neurol 14(1):43\u0026ndash;47\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeco G, Sanz Perl Y, Kringelbach ML (2025) Complex harmonics reveal low-dimensional manifolds of critical brain dynamics. Phys Rev E 111(1):014410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1103/PhysRevE.111.014410\u003c/span\u003e\u003cspan address=\"10.1103/PhysRevE.111.014410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDehaene S (2011) The number sense: How the mind creates mathematics (Rev. and updated ed). Oxford university press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDerderian C, Shumway KR, Tadi P (2025) Physiology, Withdrawal Response. In StatPearls. StatPearls Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/books/NBK544292/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/books/NBK544292/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeska-Gauthier D, Zhang Y (2019) The functional diversity of spinal interneurons and locomotor control. Curr Opin Physiol 8:99\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cophys.2019.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.cophys.2019.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDixon J, Howe TE, Kent JR, Whittaker VJ (2004) VMO-VL reflex latency difference in osteoarthritic knees and controls. Adv Physiotherapy 6(4):166\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14038190410020809\u003c/span\u003e\u003cspan address=\"10.1080/14038190410020809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDizzyGiraffe01 (2024), August 26 Cubers, how long did you take to solve your first cube? [Reddit Post]. R/Cubers. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.reddit.com/r/Cubers/comments/1f1kaaa/cubers_how_long_did_you_take_to_solve_your_first/\u003c/span\u003e\u003cspan address=\"https://www.reddit.com/r/Cubers/comments/1f1kaaa/cubers_how_long_did_you_take_to_solve_your_first/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDouglas CL, DeMarco GJ, Baghdoyan HA, Lydic R (2004) Pontine and basal forebrain cholinergic interaction: Implications for sleep and breathing. Respir Physiol Neurobiol 143(2):251\u0026ndash;262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.resp.2004.02.011\u003c/span\u003e\u003cspan address=\"10.1016/j.resp.2004.02.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElbarrany WG, Altaf FM (2017) The Tibial Nerve and Its Vasculature: An Anatomical Evaluation. Int J Morphology 35(3):812\u0026ndash;819. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4067/S0717\u0026ndash;95022017000300004\u003c/span\u003e\u003cspan address=\"10.4067/S0717\u0026ndash;95022017000300004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllrich J (2000) R3 component of the blink reflex is not a suitable model to investigate trigeminal nociception. Comment on J\u0026auml;\u0026auml;skel\u0026auml;inen PAIN 80 (1999) 191\u0026ndash;200: PAIN 4051. PAIN, 84(2), 440. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0304\u0026ndash;3959(99)00188\u0026ndash;8\u003c/span\u003e\u003cspan address=\"10.1016/S0304\u0026ndash;3959(99)00188\u0026ndash;8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllrich J, Hopf HC (1996a) The R3 component of the blink reflex: Normative data and application in spinal lesions. Electroencephalogr Clin Neurophysiology/Electromyography Motor Control 101(4):349\u0026ndash;354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0924\u0026ndash;980x(96)95684\u0026ndash;2\u003c/span\u003e\u003cspan address=\"10.1016/0924\u0026ndash;980x(96)95684\u0026ndash;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllrich J, Hopf HC (1996b) The R3 component of the blink reflex: Normative data and application in spinal lesions. Electroencephalogr Clin Neurophysiology/Electromyography Motor Control 101(4):349\u0026ndash;354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0924\u0026ndash;980X(96)95684\u0026ndash;2\u003c/span\u003e\u003cspan address=\"10.1016/0924\u0026ndash;980X(96)95684\u0026ndash;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllrich J, Katsarava Z, Przywara S, Kaube H (2001) Is the R3 component of the human blink reflex nociceptive in origin? Pain 91(3):389\u0026ndash;395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0304\u0026ndash;3959(00)00465\u0026ndash;6\u003c/span\u003e\u003cspan address=\"10.1016/S0304\u0026ndash;3959(00)00465\u0026ndash;6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErno Rubik | Cube Creator, Master P (2025) Architect | Britannica. (n.d.). Retrieved August 3, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.britannica.com/biography/Erno-Rubik\u003c/span\u003e\u003cspan address=\"https://www.britannica.com/biography/Erno-Rubik\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang C, Kim J (2018) (Jason). The power and limits of modularity: A replication and reconciliation. Strategic Management Journal, 39(9), 2547\u0026ndash;2565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/smj.2918\u003c/span\u003e\u003cspan address=\"10.1002/smj.2918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFineschi S, Fahlstr\u0026ouml;m M, F\u0026auml;llmar D, Haller S, Wikstr\u0026ouml;m J (2024) Comprehensive MRI assessment reveals subtle brain findings in non-hospitalized post-COVID patients with cognitive impairment. Front NeuroSci 18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2024.1435218\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2024.1435218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForney D Jr. (2005) Discrete-time and continuous-time AWGN channels. In Principles of Digital Communication II (p. Chapter 2)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriis EA (ed) (2017) Mechanical testing of orthopaedic implants. Woodhead Publishing, an imprint of Elsevier\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrijns CJM, Laman DM, van Duijn MAJ, van Duijn H (1997) Normal values of patellar and ankle tendon reflex latencies. Clin Neurol Neurosurg 99(1):31\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0303\u0026ndash;8467(96)00593\u0026ndash;8\u003c/span\u003e\u003cspan address=\"10.1016/S0303\u0026ndash;8467(96)00593\u0026ndash;8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFujimoto G, Ubukata S, Sugihara G, Oishi N, Aso T, Murai T, Ueda K (2023) A model for estimating the brainstem volume in normal healthy individuals and its application to diffuse axonal injury patients. Sci Rep 13:33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-27202-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-27202-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGabrielli L, Nocita P, Pasetti C, Bonuccelli U, Dell\u0026rsquo;Agnello G, Carboncini C, M., Rossi B (2002) Impaired attention modulation of the blink reflex R3 component in Parkinson\u0026rsquo;s disease: A non-task warning paradigm study. Int J Psychophysiol 44(1):83\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0167\u0026ndash;8760(01)00195\u0026ndash;7\u003c/span\u003e\u003cspan address=\"10.1016/S0167\u0026ndash;8760(01)00195\u0026ndash;7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGennatas ED, Avants BB, Wolf DH, Satterthwaite TD, Ruparel K, Ciric R, Hakonarson H, Gur RE, Gur RC (2017) Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood. J Neurosci 37(20):5065\u0026ndash;5073. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.3550\u0026ndash;16.2017\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.3550\u0026ndash;16.2017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiffin NJ, Katsarava Z, Pfundstein A, Ellrich J, Kaube H (2004) The effect of multiple stimuli on the modulation of the nociceptive blink reflex. Pain 108(1\u0026ndash;2):124\u0026ndash;128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pain.2003.12.014\u003c/span\u003e\u003cspan address=\"10.1016/j.pain.2003.12.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGold JI, Shadlen MN (2000) Representation of a perceptual decision in developing oculomotor commands. Nature 404(6776):390\u0026ndash;394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/35006062\u003c/span\u003e\u003cspan address=\"10.1038/35006062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGray RM (2011) Entropy and Information Theory, 2nd edn. Springer Science\u0026thinsp;+\u0026thinsp;Business Media, LLC\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreco A, Moser J, Preissl H, Siegel M (2024) Predictive learning shapes the representational geometry of the human brain. Nat Commun 15(1):9670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-024-54032\u0026ndash;4\u003c/span\u003e\u003cspan address=\"10.1038/s41467-024-54032\u0026ndash;4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGunduz A, Valls-Sol\u0026eacute; J, Serranov\u0026aacute; T, Coppola G, Kofler M, J\u0026auml;\u0026auml;skel\u0026auml;inen SK (2024) The blink reflex and its modulation \u0026ndash; Part 2: Pathophysiology and clinical utility. Clin Neurophysiol 160:75\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clinph.2024.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.clinph.2024.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHabibollahi F, Kagan BJ, Burkitt AN, French C (2023) Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks. Nat Commun 14(1):5287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-023-41020\u0026ndash;3\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-41020\u0026ndash;3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHegel GWF (2010) In: Brinkmann K, Dahlstrom DO (eds) Georg Wilhelm Friedrich Hegel: Encyclopedia of the Philosophical Sciences in Basic Outline. Cambridge University Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/9780511780226\u003c/span\u003e\u003cspan address=\"10.1017/9780511780226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenmar S, Simonsen EB, Berg RW (2020) What are the gray and white matter volumes of the human spinal cord? J Neurophysiol 124(6):1792\u0026ndash;1797. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/jn.00413.2020\u003c/span\u003e\u003cspan address=\"10.1152/jn.00413.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHisted MH (2025) Active filtering: A predictive function of recurrent circuits of sensory cortex (No. arXiv:2501.10521). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.2501.10521\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2501.10521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. arXiv\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHusemann U, Behse F (1993) Reflex time of the knee jerk in controls, spasticity, radiculopathy, and neuropathy. EEG-EMG Zeitschrift F\u0026uuml;r Elektroenzephalographie, Elektromyographie Und Verwandte Gebiete, 24(4), 280\u0026ndash;285\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuys R, Studenka BE, Rheaume NL, Zelaznik HN, Jirsa VK (2008) Distinct Timing Mechanisms Produce Discrete and Continuous Movements. PLoS Comput Biol 4(4):e1000061. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pcbi.1000061\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1000061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJankowska E, Johannisson T, Lipski J (1981) Common interneurones in reflex pathways from group 1a and 1b afferents of ankle extensors in the cat. J Physiol 310(1):381\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1113/jphysiol.1981.sp013556\u003c/span\u003e\u003cspan address=\"10.1113/jphysiol.1981.sp013556\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohansen A, Beliveau V, Colliander E, Raval NR, Dam VH, Gillings N, Aznar S, Svarer C, Plav\u0026eacute;n-Sigray P, Knudsen GM (2024) An In Vivo High-Resolution Human Brain Atlas of Synaptic Density. J Neurosci 44(33). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.1750\u0026ndash;23.2024\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1750\u0026ndash;23.2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohns P (2014) Chapter 4\u0026mdash;Sensory and motor pathways. In P. Johns (Ed.), Clinical Neuroscience (pp. 49\u0026ndash;59). Churchill Livingstone. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-443\u0026ndash;\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-443\u0026ndash;\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e10321\u0026ndash;6.00004\u0026ndash;7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaram DB (1977a) Tendon reflex latencies in the upper extremity. Arch Phys Med Rehabil 58(7):306\u0026ndash;308\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaram DB (1977b) Tendon reflex latencies in the upper extremity. Arch Phys Med Rehabil 58(7):306\u0026ndash;308\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarl V, Rohe T (2023) Structural brain changes in emotion recognition across the adult lifespan. Soc Cognit Affect Neurosci 18(1):nsad052. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/scan/nsad052\u003c/span\u003e\u003cspan address=\"10.1093/scan/nsad052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKegoye ES, Ojewale AO, Ezekiel W, Usman IM, Aigbogun EO, Adewale AO, Fernandez EM, Kasozi KI, Nalugo H, Echoru I, Afodun AM, Kenganzi R, Segun OO, Ssempijja F (2023) Morphologic and morphometric bilateral analysis and sexual dimorphism in sciatic nerves of adults: An anatomic investigation of cadaveric specimens in Uganda with implications on sciatic nerve neuropathologies. Rev. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs\u0026ndash;3245048/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs\u0026ndash;3245048/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan R, Zhong H, Das S, Cai J, Niemeier M (2025) Predictive Coding Explains Asymmetric Connectivity in the Brain: A Neural Network Study (p. 2025.02.27.640572). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2025.02.27.640572\u003c/span\u003e\u003cspan address=\"10.1101/2025.02.27.640572\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. bioRxiv\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKofler M, Hallett M, Iannetti GD, Versace V, Ellrich J, T\u0026eacute;llez MJ, Valls-Sol\u0026eacute; J (2024) The blink reflex and its modulation \u0026ndash; Part 1: Physiological mechanisms. Clin Neurophysiol 160:130\u0026ndash;152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clinph.2023.11.015\u003c/span\u003e\u003cspan address=\"10.1016/j.clinph.2023.11.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuban KC, Skouteli HN, Urion DK, Lawhon GA (1986) Deep tendon reflexes in premature infants. Pediatr Neurol 2(5):266\u0026ndash;271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0887\u0026ndash;8994(86)90017\u0026ndash;2\u003c/span\u003e\u003cspan address=\"10.1016/0887\u0026ndash;8994(86)90017\u0026ndash;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuznetsova KA, Maniega SM, Ritchie SJ, Cox SR, Storkey AJ, Starr JM, Wardlaw JM, Deary IJ, Bastin ME (2016) Brain white matter structure and information processing speed in healthy older age. Brain Struct Function 221:3223\u0026ndash;3235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00429-015-1097\u0026ndash;5\u003c/span\u003e\u003cspan address=\"10.1007/s00429-015-1097\u0026ndash;5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi W-X, Yuan J, Han F, Zhou L-X, Ni J, Yao M, Zhang S-Y, Jin Z-Y, Cui L-Y, Zhai F-F, Zhu Y-C (2023) White matter and gray matter changes related to cognition in community populations. Front Aging Neurosci 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnagi.2023.1065245\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2023.1065245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLorca-Puls DL, Gajardo-Vidal A, Team PLORAS, Oberhuber M, Prejawa S, Hope TMH, Leff AP, Green DW, Price CJ (2021) Brain regions that support accurate speech production after damage to Broca\u0026rsquo;s area. Brain Commun 3(4):fcab230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/braincomms/fcab230\u003c/span\u003e\u003cspan address=\"10.1093/braincomms/fcab230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLudlow CL (2015) Central Nervous System Control of Voice and Swallowing. J Clin Neurophysiol 32(4):294\u0026ndash;303. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/WNP.0000000000000186\u003c/span\u003e\u003cspan address=\"10.1097/WNP.0000000000000186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMagistro D, Takeuchi H, Nejad KK, Taki Y, Sekiguchi A, Nouchi R, Kotozaki Y, Nakagawa S, Miyauchi CM, Iizuka K, Yokoyama R, Shinada T, Yamamoto Y, Hanawa S, Araki T, Hashizume H, Sassa Y, Kawashima R (2015) The Relationship between Processing Speed and Regional White Matter Volume in Healthy Young People. PLoS ONE 10(9):e0136386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0136386\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0136386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarin J, Gantenbein A, Paemeleire K, Kaube H, Goadsby P (2015) Nociception-specific blink reflex: Pharmacology in healthy volunteers. J Headache Pain 16:87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s10194-015-0568\u0026ndash;7\u003c/span\u003e\u003cspan address=\"10.1186/s10194-015-0568\u0026ndash;7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMay T, Kowalewski B, Dau T (2018) Signal-to-Noise-Ratio-Aware Dynamic Range Compression in Hearing Aids. Trends Hear 22:2331216518790903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2331216518790903\u003c/span\u003e\u003cspan address=\"10.1177/2331216518790903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeunier D, Lambiotte R, Bullmore ET (2010) Modular and Hierarchically Modular Organization of Brain Networks. Front NeuroSci 4:200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2010.00200\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2010.00200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoini J, Avgeropoulos NG, Samsam M (2021) Chapter 1\u0026mdash;Anatomy and physiology. In J. Moini, N. G. Avgeropoulos, \u0026amp; M. Samsam (Eds.), Epidemiology of Brain and Spinal Tumors (pp. 3\u0026ndash;40). Academic Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12\u0026ndash;821736\u0026ndash;8.00002\u0026ndash;9\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12\u0026ndash;821736\u0026ndash;8.00002\u0026ndash;9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoini J, Piran P (2020) Chapter 19\u0026mdash;Spinal cord. In J. Moini \u0026amp; P. Piran (Eds.), Functional and Clinical Neuroanatomy (pp. 585\u0026ndash;616). Academic Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12\u0026ndash;817424\u0026ndash;1.00019\u0026ndash;7\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12\u0026ndash;817424\u0026ndash;1.00019\u0026ndash;7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorales-Gregorio A, van Meegen A, van Albada SJ (2023) Ubiquitous lognormal distribution of neuron densities in mammalian cerebral cortex (p. 2022.03.17.480842). bioRxiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2022.03.17.480842\u003c/span\u003e\u003cspan address=\"10.1101/2022.03.17.480842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNair S, Ankolekar VH, Hosapatna M, DSouza A (2020) A morphologic and histologic study of the radial nerve and its branches at potential compression sites. J Taibah Univ Med Sci 15(5):358\u0026ndash;362. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtumed.2020.07.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jtumed.2020.07.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNicolini C, Bifone A (2016) Modular structure of brain functional networks: Breaking the resolution limit by Surprise. Sci Rep 6(1):19250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep19250\u003c/span\u003e\u003cspan address=\"10.1038/srep19250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNot! R (2025) B. I. or. (n.d.). Spinning With Rubik\u0026rsquo;s Cube Speedcubers. EverythingGP. Retrieved August 3, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://everythinggp.com/2020/07/29/spinning-with-rubiks-cube-speedcubers/\u003c/span\u003e\u003cspan address=\"https://everythinggp.com/2020/07/29/spinning-with-rubiks-cube-speedcubers/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOizumi M, Albantakis L, Tononi G (2014) From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0. PLoS Comput Biol 10(5):e1003588. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pcbi.1003588\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1003588\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOngerboer de Visser DW, Cruccu G (1993) Neurophysiologic examination of the trigeminal, facial, hypoglossal, and spinal accessory nerves in cranial neuropathies and brain stem disorders. Clinical Electromyography, 2nd edn. Butterworth-Heinemann}, pp 61\u0026ndash;92\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOschwald J, Guye S, Liem F, Rast P, Willis S, R\u0026ouml;cke C, J\u0026auml;ncke L, Martin M, M\u0026eacute;rillat S (2019) Brain structure and cognitive ability in healthy aging: A review on longitudinal correlated change. Rev Neurosci 31(1):1\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/revneuro\u0026ndash;2018\u0026ndash;0096\u003c/span\u003e\u003cspan address=\"10.1515/revneuro\u0026ndash;2018\u0026ndash;0096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePadamsey Z, Rochefort NL (2023) Paying the brain\u0026rsquo;s energy bill. Curr Opin Neurobiol 78:102668. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.conb.2022.102668\u003c/span\u003e\u003cspan address=\"10.1016/j.conb.2022.102668\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapp KV, Kaplan RF, Springate B, Moscufo N, Wakefield DB, Guttmann CRG, Wolfson L (2014) Processing Speed in Normal Aging: Effects of White Matter Hyperintensities and Hippocampal Volume Loss. Neuropsychology, Development, and Cognition. Section B. Aging Neuropsychol Cognition 21(2):197\u0026ndash;213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13825585.2013.795513\u003c/span\u003e\u003cspan address=\"10.1080/13825585.2013.795513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePessoa L (2017) Chapter 29\u0026mdash;The Emotional Brain. In P. M. Conn (Ed.), Conn\u0026rsquo;s Translational Neuroscience (pp. 635\u0026ndash;656). Academic Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12\u0026ndash;802381\u0026ndash;5.00046\u0026ndash;4\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12\u0026ndash;802381\u0026ndash;5.00046\u0026ndash;4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeterson DC, Hamel RN (2025) Corneal Reflex. In StatPearls. StatPearls Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/books/NBK534247/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/books/NBK534247/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen S, Shao H, He S (2021) Interaction Between Conscious and Unconscious Information-Processing of Faces and Words. Neurosci Bull 37(11):1583\u0026ndash;1594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12264-021-00738\u0026ndash;0\u003c/span\u003e\u003cspan address=\"10.1007/s12264-021-00738\u0026ndash;0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoland PE (2023) How far neuroscience is from understanding brains. Front Syst Neurosci 17:1147896. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnsys.2023.1147896\u003c/span\u003e\u003cspan address=\"10.3389/fnsys.2023.1147896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRomaniello A, Valls-Sol\u0026eacute; J, Iannetti GD, Truini A, Manfredi M, Cruccu G (2002) Nociceptive Quality of the Laser-Evoked Blink Reflex in Humans. J Neurophysiol 87(3):1386\u0026ndash;1394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/jn.00041.2001\u003c/span\u003e\u003cspan address=\"10.1152/jn.00041.2001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenberg PH, Heinonen E (1983) Differential sensitivity of A and C nerve fibres to long-acting amide local anaesthetics. Br J Anaesth 55(2):163\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bja/55.2.163\u003c/span\u003e\u003cspan address=\"10.1093/bja/55.2.163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRossano S, Toyonaga T, Bini J, Nabulsi N, Ropchan J, Cai Z, Huang Y, Carson RE (2022) Feasibility of imaging synaptic density in the human spinal cord using [11C]UCB-J PET. EJNMMI Phys 9:32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40658-022-00464\u0026ndash;0\u003c/span\u003e\u003cspan address=\"10.1186/s40658-022-00464\u0026ndash;0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRossi B, Risaliti R, Rossi A (1989) The R3 component of the blink reflex in man: A reflex response induced by activation of high threshold cutaneous afferents. Electroencephalogr Clin Neurophysiol 73(4):334\u0026ndash;340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0013\u0026ndash;4694(89)90111\u0026ndash;9\u003c/span\u003e\u003cspan address=\"10.1016/0013\u0026ndash;4694(89)90111\u0026ndash;9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRossi R, Arjmand S, B\u0026aelig;rentzen SL, Gjedde A, Landau AM (2022) Synaptic Vesicle Glycoprotein 2A: Features and Functions. Front NeuroSci 16:864514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnins.2022.864514\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2022.864514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaberi M, Rieck JR, Golafshan S, Grady CL, Misic B, Dunkley BT, Khatibi A (2024) The brain selectively allocates energy to functional brain networks under cognitive control. Sci Rep 14(1):32032. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-83696\u0026ndash;7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-83696\u0026ndash;7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSattin D, Magnani FG, Bartesaghi L, Caputo M, Fittipaldo AV, Cacciatore M, Picozzi M, Leonardi M (2021) Theoretical Models of Consciousness: A Scoping Review. Brain Sci 11(5):535. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/brainsci11050535\u003c/span\u003e\u003cspan address=\"10.3390/brainsci11050535\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchneider TD (2013) Information Theory Primer With an Appendix on Logarithms PDF version. Unpublished. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13140/2.1.2607.2000\u003c/span\u003e\u003cspan address=\"10.13140/2.1.2607.2000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeth AK, Bayne T (2022) Theories of consciousness. Nat Rev Neurosci 23(7):439\u0026ndash;452. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41583-022-00587\u0026ndash;4\u003c/span\u003e\u003cspan address=\"10.1038/s41583-022-00587\u0026ndash;4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShannon CE (1948) A Mathematical Theory of Communication. Bell Syst Tech J 27(3):379\u0026ndash;423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/j.1538\u0026ndash;7305.1948.tb01338.x\u003c/span\u003e\u003cspan address=\"10.1002/j.1538\u0026ndash;7305.1948.tb01338.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStam J, van Crevel H (1989) Measurement of tendon reflexes by surface electromyography in normal subjects. J Neurol 236(4):231\u0026ndash;237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00314505\u003c/span\u003e\u003cspan address=\"10.1007/BF00314505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSugimoto YA, Yadohisa H, Abe MS (2024) How neural network structure alters the brain\u0026rsquo;s self-organized criticality (p. 2024.09.24.614702). bioRxiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2024.09.24.614702\u003c/span\u003e\u003cspan address=\"10.1101/2024.09.24.614702\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTham LK, Osman NAA, Abas WABW, Lim KS (2013) Motion Analysis of Normal Patellar Tendon Reflex. Can J Neurol Sci / J Canadien Des Sci Neurologiques 40(6):836\u0026ndash;841. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0317167100015985\u003c/span\u003e\u003cspan address=\"10.1017/S0317167100015985\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThoma P, Dr\u0026auml;mel N, Grothe M, Lotze M, Fleischmann R, Strauss S (2022) Impaired Pain Processing at a Brainstem Level Is Involved in Maladaptive Neuroplasticity in Patients with Chronic Complex Regional Pain Syndrome. Int J Mol Sci 23(23):15368. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms232315368\u003c/span\u003e\u003cspan address=\"10.3390/ijms232315368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTononi G, Boly M, Massimini M, Koch C (2016) Integrated information theory: From consciousness to its physical substrate. Nat Rev Neurosci 17(7):450\u0026ndash;461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrn.2016.44\u003c/span\u003e\u003cspan address=\"10.1038/nrn.2016.44\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVasanthakumar P, Kumar P, Rao M (2013) Anthropometric Analysis of Palpebral Fissure Dimensions and its Position in South Indian Ethnic Adults. Oman Med J 28(1):26\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5001/omj.2013.06\u003c/span\u003e\u003cspan address=\"10.5001/omj.2013.06\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVergara RC, Jaramillo-Riveri S, Luarte A, Mo\u0026euml;nne-Loccoz C, Fuentes R, Couve A, Maldonado PE (2019) The Energy Homeostasis Principle: Neuronal Energy Regulation Drives Local Network Dynamics Generating Behavior. Front Comput Neurosci 13:49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fncom.2019.00049\u003c/span\u003e\u003cspan address=\"10.3389/fncom.2019.00049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVickery HSF, Smith PA (2012) Surface electromyography reveals males have a slower patellar reflex than females. J Electromyogr Kinesiol 22(6):990\u0026ndash;996. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jelekin.2012.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jelekin.2012.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Friedman JM, Suppa P, Buchert R, Mautner V-F (2022) White matter is increased in the brains of adults with neurofibromatosis 1. Orphanet J Rare Dis 17(1):115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13023-022-02273\u0026ndash;1\u003c/span\u003e\u003cspan address=\"10.1186/s13023-022-02273\u0026ndash;1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatanabe N, Haruno M (2015) Effects of subconscious and conscious emotions on human cue\u0026ndash;reward association learning. Sci Rep 5(1):8478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep08478\u003c/span\u003e\u003cspan address=\"10.1038/srep08478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatts ME, Pocock R, Claudianos C (2018) Brain Energy and Oxygen Metabolism: Emerging Role in Normal Function and Disease. Front Mol Neurosci 11:216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnmol.2018.00216\u003c/span\u003e\u003cspan address=\"10.3389/fnmol.2018.00216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWise R, Greene J, B\u0026uuml;chel C, Scott S (1999) Brain regions involved in articulation. Lancet 353(9158):1057\u0026ndash;1061. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140\u0026ndash;6736(98)07491\u0026ndash;1\u003c/span\u003e\u003cspan address=\"10.1016/S0140\u0026ndash;6736(98)07491\u0026ndash;1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWyss LS, Bray SR, Wang B (2024) Neuropeptide-mediated temporal sensory filtering in a primordial nervous system (p. 2024.12.17.628859). bioRxiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2024.12.17.628859\u003c/span\u003e\u003cspan address=\"10.1101/2024.12.17.628859\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXing B, Li Y-C, Gao W-J (2016) Norepinephrine versus dopamine and their interaction in modulating synaptic function in the prefrontal cortex. Brain Res 1641:217\u0026ndash;233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.brainres.2016.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.brainres.2016.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYiHeng (2025) Wang breaks world record 3x3 single with 3.08. (n.d.). Speedcubing.Org. Retrieved August 3, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://speedcubing.org/blogs/news/yiheng-wang-breaks-world-record\u0026ndash;3x3-single-with\u0026ndash;3\u0026ndash;08\u003c/span\u003e\u003cspan address=\"https://speedcubing.org/blogs/news/yiheng-wang-breaks-world-record\u0026ndash;3x3-single-with\u0026ndash;3\u0026ndash;08\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoo H, Mihaila DM (2025) Neuroanatomy, Pupillary Light Reflexes and Pathway. In StatPearls. StatPearls Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/books/NBK553169/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/books/NBK553169/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZaborszky L, Csordas A, Mosca K, Kim J, Gielow MR, Vadasz C, Nadasdy Z (2015) Neurons in the Basal Forebrain Project to the Cortex in a Complex Topographic Organization that Reflects Corticocortical Connectivity Patterns: An Experimental Study Based on Retrograde Tracing and 3D Reconstruction. Cerebral Cortex (New York, NY), 25(1), 118\u0026ndash;137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cercor/bht210\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bht210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeraati R, Priesemann V, Levina A (2021) Self-organization toward criticality by synaptic plasticity. Front Phys 9:619661. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphy.2021.619661\u003c/span\u003e\u003cspan address=\"10.3389/fphy.2021.619661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng J, Meister M (2025) The unbearable slowness of being: Why do we live at 10 bits/s? Neuron 113(2):192\u0026ndash;204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuron.2024.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2024.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidade Estadual do Centro-Oeste","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Entropy, Gray Matter, Information theory, Latent computation, Neuron Computation, Predictive coding, Unconsciousness, White Matter","lastPublishedDoi":"10.21203/rs.3.rs-7402915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7402915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA theoretical estimation was conducted to explore the potential speed of the unconscious mind. This estimation was based on five reflexes. A series of reflexes were analyzed, including the Achilles, pupillary contraction, triceps, blink, and knee patellar reflex. The analysis entailed the calculation of their bits through differential entropy and standardized discretization. Subsequently, the value obtained from this calculation was divided by their onset latency. This calculation took into account the time of stimulus nerve travel under equiprobable probabilities. In an effort to incorporate biological factors into the analysis, the pupillary contraction reflex was examined through the lens of an adaptation of the Shanon-Hartley theorem. Subsequently, the processing speeds were analyzed in relation to the action's primary roots' gray matter volume and synaptic protein SV2A concentration. This analysis was conducted through the utilization of two-dimensional graphs and a three-dimensional combined graph. Monosynaptic reflexes are theorized to project a line higher in graph space when compared to polysynaptic reflexes and cortical actions. This phenomenon is attributed to the brain's greater optimization of the former compared to the latter. Consequently, the theoretical speed limit of the central nervous system would be contingent upon parameters such as neuron count, connectivity, and gray matter volume fraction. Furthermore, we explore different processing speeds across different gray matter volume fractions, modularization as a computational consequence and central nervous system filtering.\u003c/p\u003e","manuscriptTitle":"On the Speed of Unconscious Processing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 09:45:17","doi":"10.21203/rs.3.rs-7402915/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a4be788-56a6-4dab-80f7-4848678f45b9","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53340841,"name":"Computational Neuroscience"}],"tags":[],"updatedAt":"2025-08-20T09:45:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 09:45:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7402915","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7402915","identity":"rs-7402915","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}