The (Test – Operate- Test – Execute), which essentially

The main idea behind the chapter is that it suggests that the closed loop systems are fundamental to most cognitive systems and the modern teaching and experimentation theories, or the scientific pedagogy doesn’t give them the commensurate importance. The methods of conducting the experiments and the scientific thinking now are very open loop centric. This is easily highlighted by the fact that most experiments are performed with the foundation that the cognitive system in question would closely associate to an open loop system. This might be true in some instances but mostly cognition is a closed loop phenomenon, this can be showcased by several examples that rely on the basic principle of TOTE (Test – Operate- Test – Execute), which essentially means that a discrepancy from a benchmark value is identified , an action is taken to rectify the discrepancy , we gauge the system to see if the metric is within an acceptable tolerance, if the system is found to be in the tolerance we go ahead stop else we keep iterating through this process till we reach the tolerances decided upon .  The forced explication of primarily closed loop phenomena with open loop experimentation methodology can lead to erroneous results in cognitive sciences and confusions created in the same area , because both of these phenomena are fundamentally different, while the open loop system is visualized as a chain where each event triggers the next until the end of the chain , the closed loop system has each stage essentially effecting every other stage  forming an intricated web of feedback signals . Some of the concepts of the open loop systems when applied to the closed loop systems may not produce satisfactory results like the amount of gain is an indicator of the magnitude of the response for an open loop system, it is indicative of how fast a system would converge to the desired value for closed loop system.The chapter also talks about the challenges of prediction with noise in the system, the fundamental challenge centers at trying to isolate the signal based on which future predictions can be made, a finely tuned gain function is used to perform this task. The chapter also visits the regulators paradox which exists because the goal of the regulator is to eliminate variations in the system, and these variations in form of feedback provide information for the regulator to do its job thus a delicate balance is to be sought so that both these essential functions based on the variance of the system can be performed in synchronization.Natural cognitive systems take in a plethora of inputs, not all of the inputs have the same sensitivity towards achieving the target value and this is used by using a variety of gain values related to each input value to achieve the goal value. The sensitivity of the goal with respect to each input may vary in context to conditions represented by each input, this is somewhat similar to the phenomena of interaction from statistics where the simultaneous effect of two or more independent variables on the dependent variable is not always additive in nature , this reflects the complexity of interaction of systemic inputs that have to be considered before designing cognitive systems.The chapter also talks about the pros and cons of the number of degrees of freedom that a system has, on one hand, more degrees of freedoms provide for multi-pronged approaches towards moving the system to the desired  goal value it also provides you with the problem of executing extra computation for all those degrees of freedoms that can be altered , this is specially cumbersome when a system has a limit on how many variations can it attend to at a go .  Q1. The chapter talks about “circular systems being self-organizing, meaning that understanding will depend on the ability to discover the intrinsic co-ordinates ….. ” (Pg 32.), the ultimate goal of the system is to correspond to a goal value within said tolerance limits ( the tolerance limits are usually specified using extrinsic co-ordinates like m, seconds, grams ) so are the intrinsic co-ordinates essentially just a combination of these extrinsic co-ordinates? Q2.  When a cognitive system talks about limited bandwidth or limited ability to analyze signals and prioritizes those that essentially have the greatest sensitivity amongst plethora of input signals obtained, would it be possible to pre-process the inputs to extract the maximum information from the signals?(Like how we use Principal component analysis when we have to convert high dimensional data to low dimensional data capturing most of the variance in fewer dimensions )