We can represent the fixed transfer algorithm using a State Transition Diagram. This transfer algorithm is hard-wired into the brain to maximize the speed of transfer for the Loci of Decision Making.
State 0: Zero State. For simplicity in this example, let's start with the scenario where the Loci of Decision is retained by System 2. The human is in a safe space and thinking about an event that may take place several months from now, perhaps planning a vacation.
Transition 1: The senses deliver new stimuli to the brain. Let's say the image of an adult male African lion appears suddenly on a webpage while the person is planning their vacation to Africa. System 1 is alerted because there is no sensory information about lion threats currently in short-term memory, so therefore this is a potential new threat. In System 1's view of the infinite, all possible reality, a moment before there was no lion (0% probability, or no chance of threat).
Now that there is a non-zero threat posed by a lion, System 1 must take the Loci of decision-making immediately. There may be a need for both fast thinking and fast decision-making, a fight or flight scenario. This is transition 1, from the slow thinking and decision-making of System 2 to our survival specialist, System 1.
As time progresses, as defined by the limits of System 1's short-term memory capacity, and incoming stimuli indicate a diminished threat, System 1 will 'relax control' and allow the Loci of decision-making to pass to System 2, if it is needed. Since System 2 is 'lazy', it will defer the Loci of control to System 1 for as long as System 1 thinks it can manage operations.
When the center of decision-making passes to System 2 for cognitive effort, that is transition #3 on the chart below. This transition is physically manifested by pupil dilation.
Note: I am in search of an observable and measurable behavioral manifestation of this transition.
While System 2 is thinking and making decisions, it's also dealing with the probability of future events that are not yet close to certain or impossible. For this explanation, let's call it things in the range of 5%-95% probability. System 1 cannot afford to expend its high-speed resources on scenarios that are less than near-certain or beyond the realm of possibility.
However, System 1 is very sensitive to probabilities as they approach certainty, because certainty is System 1's stock in trade. Transition #3 and the observable Certainty Effect is the observable and measurable behavioral manifestation of this transition.
System 2 -> (probability of threat or reward transitions from 0% to >0%) -> System 1, explained by the Probability Effect.
System 1 -> (Initial threat or reward diminishes to neutral, or probability of threat or reward is beyond the capacity of System 1's 'infinity') -> System 2 can have the Loci of decision-making, if needed, measured by pupil dilation.
System 2 -> (probability of threat or reward transitions to near certainty <100% - 100%) -> System 1, explained by the Certainty Effect.
In these transitions, it does not matter if the event that triggered System 1's attention is a threat or a reward; the transition is the same. This makes sense as deciding on threat or reward BEFORE the transition is inefficient. It's faster to make the switch on every alert and let System 1 decide if it is pleasure or pain.
This would also explain why children, and some adults, consciously feel fear from things we know can't be possible because System 1 is so effective at survival. It may also explain why humans can have conflicting definitions of what events are painful and which are pleasurable. This is because some of that discernment is being processed and decided unconsciously by System 1.
These three simple transition states and their rules of transfer regarding the Loci of decision-making could explain the context-switching process used by our brains to
optimize the capabilities of both System 1 and System 2. Not only do these systems keep our species surviving and evolving over millions of years, but they also enable us to perform complex thinking, forecasting, and decision-making required to thrive
In conclusion, the delineation between System 1 and System 2 thinking, as represented in these three transition states, offers a framework to understand how our brains navigate between rapid, instinctive responses and slower, more deliberative processes. This dynamic interplay is not only fundamental to our survival but also underpins our ability to engage in complex, forward-thinking activities.
However, much remains to be explored about the nuances of these transitions, especially the shift from System 1 to System 2 thinking. Observations of real-world behaviors that align with these transitions could provide valuable insights into this cognitive process. For instance, instances where rapid, instinctive reactions give way to more considered, analytical thought processes might be indicative of this shift. This could manifest in various contexts, from everyday decision-making to more complex problem-solving scenarios.
Therefore, I invite readers, to share any behaviors or instances they believe might exemplify this System 1 to System 2 transition. Such contributions could further our understanding of this fascinating aspect of human cognition, shedding light on how we process information and make decisions in an ever-changing world. Your observations and insights could be instrumental in unraveling the complexities of these cognitive processes and how they influence our interactions, decisions, and perception
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