The Role of Iteration in Working Memory Updating
The following describes a new theoretical article that I am developing on the brain basis of thought and consciousness. It is an outgrowth of a previously published article that can be found on at aithought.com
How The Concept of Iteration Ties Consciousness to Working Memory
As the stream of thought progresses through time some
elements stay the same. Of course, elements are always being added to and
subtracted from the stream of thought but working memory ensures that some of
these concepts are held online temporarily. This maintenance function keeps
your train of thought from being derailed and makes it so that what you are
thinking at this moment is highly related to what you will be thinking one
second from now. This interrelatedness of consecutive thoughts provides the
fabric of consciousness and allows thought to progress.
The maintenance of working memory is made possible by a cellular phenomenon called sustained firing. Sustained firing happens when a neuron is signaled by dopamine to fire repeatedly over the course of several seconds. This type of neural activity happens in neurons in the higher brain areas (i.e. the prefrontal and parietal cortices). Sustained firing is what allows us to keep important concepts in mind so that we can manipulate them, reason with them, and think about them. Without sustained firing we would continuously forget what we were just thinking. We wouldn’t be able to think or reason. This continuity of neural activity allows us to build chains of logical thoughts that progress toward a solution or plan.
Now let’s consider the concept of iteration.
Iteration
I organized this article around the term “iteration.” I think that iteration is easy for people to understand because everyone is aware of the concept of product development. Personally, I think of the iPhone iterations. The customer feedback and R&D on the iphone 7 is combined with some of the original features of the iphone 7 to create the newest iteration: the iphone 8. In a similar way the computational products of the previous thought are combined with some of the original elements of that previous thought to create the next thought. I think the word "iterative" captures the meaning of “incremental change” and is a good word to describe the model.
The article addresses the role of iteration in the processing stream of working memory. This is a concept that has not been addressed by contemporary cognitive science. As the article documents, it was considered by William James over 100 years ago. The article describes how, when working memory is updated, elements from the previous state remain active due to sustained firing. This ensures that each state of working memory is a revised iteration of the state before it. This pattern of iterative updating is discussed in detail and it is related to a large number of cognitive and neuroscientific phenomena.
The model views working memory as activated long-term memory. It features a brief sensory store, and an FOA (focus of attention) embedded within a STM store, which in turn is embedded within LTM. Instead of focusing on stimulus reception and the controlled and automatic responses to incoming stimuli, it focuses on ongoing activity within working memory, the repeating iterative pattern inherent in it, and the search and selection of new items to be added to working memory.
The model views each instantaneous state of working memory
as two things: 1) a set of solutions to the last state’s search, and 2) a set
of parameters for the next search. The active items in working memory spread
their activation energy throughout the brain to select the next ensembles to be
added to working memory. This is how “search” is performed.
Fig. 1. Flowchart of Iterative Updating
In an iterative process, a thing is
modified repetitively to generate a series of updated states. Each state is an
iteration as well as the starting point for the next iteration. One way to
accomplish iterative modification is to alter a given state by retaining
pertinent elements and then subtracting and adding others. In the brain, the items to be added
and subtracted may be determined by spreading activation.
I suggest that iteration is fundamental to working memory in
that it allows context to be carried from one brain state to the next. This
recursive property is instrumental in implementing learned algorithms, in
allowing mental continuity (interrelatedness of consecutive mental states), and
creating progressive changes to the contents of working memory.
Please take a look at the figures below. These original figures are helpful in conveying the model.
Fig. 5. Venn Diagrams of Information Shared Between
Successive States of Working Memory
These Venn diagrams depict informational
overlap between successive states of working memory. The horizontal axis represents
time. The small circles represent information within the FoA, and the large
circles represent information within the short-term store. Diagrams 1 and 2
show no Venn overlap between states from different periods; 3 and 4 show
overlap in the short-term store only; 5 and 6 show the short-term store of one
state overlapping with the FoA of the neighboring state; and 7 and 8 show the
FoA of separate states overlapping, suggesting attentive continuity. It may be
plausible that Diagrams 1 and 2 roughly depict sampling of cortical activity hours
apart, 3 and 4 depict sampling several minutes apart, 5 and 6 depict sampling
every minute, and 7 and 8 depict sampling every second.
|
Sensory
Memory |
Focus
of Attention |
Short-Term
Store |
Long-Term
Memory |
References |
Description
|
Early sensory processing |
Attended content |
Temporary store of content |
Inactive, but retrievable content |
Baddeley et al., 2018 |
Brain
Correlate
|
Firing neuron |
Sustained firing |
Synaptic potentiation |
Long-term potentiation |
Christophel et al., 2017 |
Capacity
|
Numerous features |
1 to 9 items |
Very large |
Extremely large |
Shipstead et al., 2015 |
Duration |
Visual: .25 s. Auditory: 2.5 s. |
Up to a minute |
Minutes |
Days to lifetime
|
Brydges et al., 2018 |
Origin
|
Pre-attentive |
Attentive |
Recent attention |
Memorization |
Cowan, 2017 |
Location |
Sensory cortex |
Association cortex |
Cerebral cortex |
Cerebral cortex |
Eriksson et al., 2015 |
Maintenance
|
Not possible |
Continued attention |
Rehearsal |
Repetition, mnemonics |
Chia et al., 2018 |
Departure
|
Decay, highly volatile |
Replacement |
Forgetting |
Irretrievability |
Constantinidis et al., 2018 |
Iteration
|
Repetition of a computational procedure applied to the product of a previous state, used to obtain successively closer approximations to the solution of a problem.
|
Working Memory
|
A mechanism dedicated to maintaining selected representations available for use in further cognitive processing.
|
Working Memory Updating
|
Changes in the contents of working memory occurring as processing proceeds through time.
|
Iterative Updating
|
A partial shift in the contents of working memory that occurs during updating as some contents are added, others are removed, and others are repeated.
|
Table 2. Definition of key terms
Fig. 9. Two Rates of Updating Carried Out
Over 4 Time Periods
In the first scenario, 71% (5÷7) updating
is carried out over four different time periods. In the second scenario, 29% (2÷7)
updating is carried out. This comparison delineates the difference between
unfocused, minimally overlapping thought (loose iterative coupling) and highly
focused, closely overlapping thought (tight iterative coupling). To better
illustrate this point, the capacity of the FoA is depicted here as seven items
after Miller (1956). The Venn diagrams to the right of each diagram illustrate
the percentage of iterative updating in the FoA using the style of Figure 6.
Fig. 17. Iterative Inhibition
An original problem
is activated in time 1 (B, C, D), and their spreading activity triggers a new
item at time 2 (E). Executive processes determine that E is not a suitable
behavioral parameter and E is inhibited. With E unavailable, B, C, and D
continue to spread activation energy that converges on F (at time 2). The same
iterative inhibition occurs with F (at time 4). G is then activated, and
iterative updating continues.
Fig. 18. Artificial Neural Network
Implementation of Iterative Updating
Each enclosed set of circular nodes
represents a specialized neural network wired to receive a different modality of
input. Networks at the bottom (left) of the hierarchy take input of a single
modality from the environment. Others take input from multiple neural networks
below them in the hierarchy. Spreading network activity would oscillate between
the top and bottom of the hierarchy while also allowing recurrent feedback
between and within networks, creating a serial cognitive cycle. This figure
features 24 networks, each with 19 nodes. An actual build would necessitate
dozens of networks, each with millions of nodes.
Fig. 19. Venn Diagrams of Working Memory in Different
Systems
These diagrams depict informational overlap between states of working memory. The diagrams on the left use the format from Figure 6 and those on the right use the format from Figure 7. Diagram 1 shows zero overlap between working memory at time 1 and that at time 2. This would make it more difficult for system 1, a hypothetical mouse, to make associations between events that are separated by the delay. For example, calling this mouse’s name and feeding it 10 seconds later may not condition it to come when called, whereas feeding it one second later might. Training an AI should involve a maturational process where the system begins learning with a very limited working memory span (e.g., Diagram 1), and gradually develops a superhuman capacity for working memory span (Diagram 4) as formative experiences accumulate.
Figure 20. A Hypothetical Example of How Iterative Updating Could Be Found Using Electrodes
Term |
Definition |
Iteration |
Repetition of a computational procedure applied to the product
of a previous state, used to obtain successively closer approximations to the
solution of a problem. |
Working Memory |
A
mechanism dedicated to maintaining selected representations available for use
in further cognitive processing. |
Working Memory Updating |
Changes
in the items held in working memory occurring as processing proceeds through
time. |
Iterative Updating |
A shift in the contents of working memory
that occurs during updating as some items are added, others are removed, and
others are maintained |
Coactive |
A group of items that are active in the same instantaneous
state |
Cospreading |
A group of coactive items that combine their spreading
activation energy to search the same global network |
Multiassociative Search |
A type of search where all of the coactive, cospreading
contents converge in parallel on the update for the next state |
State-spanning
Coactivity (SSC) |
Sustained coactivity exhibited by a set of
two or more items that span two or more consecutive brain states |
Incremental
Change in State-spanning Coactivity (icSSC) |
The process in which a set of items
exhibiting SSC undergoes a change in group membership, where some items
remain in SSC and others are deactivated and replaced |
Rate of Updating |
The proportion of items that are updated as a function of time |
Mental Continuity |
The recursive interrelatedness of consecutive mental states
made possible by iterative updating |
Iterative Compounding |
Search results from a previous state are reused to update the
system, incorporating them into the present search, prolonging their
influence |
Progressive Modification |
The logical or algorithmic progress in information processing
made possible by iteration, continuity, or compounding |
Iterative Thread |
A chain of iteratively updated states that underlies a line of
thought |
Merging of Subsolutions |
When select contents from two separate instances of iteration,
or separate lines of thought, are coactivated in a new state and used
together for multiassociative search |
Table 3
This article applied iterative updating to
the traditional model of working memory items. However, it can similarly be
applied to a large number of compatible frameworks that model item-like
constructs including: ACT-R’s “symbols” (Anderson, 1996), adaptive resonance theory’s
“templates” (Grossberg, 2013), global workspace theory’s “processes” (Baars,
2005), the pattern recognition theory of mind’s “pattern recognizers”
(Kurzweil, 2013), hierarchical temporal memory’s “time-based patterns” (Hawkins
et al., 2007), SPAUN’s “semantic pointers” (Eliasmith, 2013), SOAR’s
“operators” (Laird, 2012), LIDA’s “codelets” (Baars & Franklin, 2007),
OpenCog’s “atoms” (Goertzel, 2014), Fuster’s “cognits” (2005), Hofstadter’s
“simmballs” (2007), Edelman’s “neuronal groups” (2004), Minsky’s “agents” (1986),
and Damasio’s “convergent-divergent zones” (1989). These models, and many
others, provide detailed mechanistic explanations for critical cognitive
components underspecified by the present model (Table 4).
Fig. 21. Imagery and Behavior in the
Iterative Updating Model
Iteratively updated items in working
memory interact with sensory cortices to progressively construct mental
imagery, and they interact with motor cortices to progressively construct
behavior. In the next state, the items in working memory will undergo partial
replacement. The parameters used in the sensory and motor cortices will reflect
this change, making their output an advancement on their previous output, resulting
in a capacity to support purposeful action. Related cognitive processes are
included as arrows.
Thought Is Structured by the Iterative Updating of Working Memory
Fig. 14. Conditional Dependencies Between Consecutive Events
Each
arc represents the span of time since an event occurred. S’s represent stimuli,
R’s represent responses, and other capital letters represent items. To provide
an illustrative example, the variables named above could correspond to the
following events: S1 = friend, S2 = enemy, S3 = approach, S4 = depart, R1 = act
friendly, R2 = act aggressive, R3 = wait, R4 = follow, A = foraging alone, B=
feel hungry, C= find berries, D = not poisonous, Z = poisonous, Y = friend
approaching, R5 = eat, R6 = don’t eat, R7 = share berries, R8 = eat berries
before friend arrives.
Fig. XX Reiterating Through an Earlier Sequence
A set of six items is held in working memory, then
iteratively updated over the next three time steps, creating a series of four
related states. This activity, occurring from t1 through t4, might be
considered a self-contained thought. Starting at t5, attention shifts
completely as an unrelated thought takes place using an entirely different set
of items. From t9, the first sequence is reiterated as before. This might
happen when someone revisits an earlier thought, such as when rehashing a plan
of action, retracing a set of previous steps, or retelling a story.
Fig. XX Revisiting the Endpoint of an Earlier Iterative
Sequence and Continuing It
Six items are modified over the first three time steps,
creating a thought composed of four related states. Attention shifts completely
at t5 and an unrelated thought occurs. Starting at t9, attention shifts back to
the items from t4, and they are iterated without using any of the items from t5
through t8. This might happen when someone picks up a thought where it left off
and continues to think about the issues from the last point at which they were
considered.
An original problem is activated at t1. Iterative updating is used to reach a subsolution at t4. This subsolution is saved in the short-term store, and a related subproblem is introduced at t5. This subproblem iterates until a second subsolution is generated at t8. At t9, relevant items from the first subsolution are combined with those from the second subsolution and iterated to generate a final solution at t12. This might happen when two nearby thoughts are mutually informative and elements of each can be used to draw a higher-order inference.
Six items are modified over seven time steps, creating a line of thought composed of eight related states. At t9, attention shifts back to a point in the middle of this sequence. This set or subproblem from t4 is then iterated without including any of the items that were introduced from t5 through t8. This creates an alternate branch and a “forking” of the iterative sequence. This might happen when someone decides to assume a previous intermediate step in a problem-solving sequence and solve the problem in a different way.
A set of six items is held in working memory, then 67% (4÷6) updating is carried out over four time periods. At t5, the rate of updating is reduced to 17% (1÷6). This might happen when a person encounters a novel set of stimuli that causes the brain to release dopamine (at t4) and switch from default mode to attentive processing. The activity of the items from t5 is sustained, and the concepts are anchored upon giving them more processing priority so that greater focus can be brought to bear on them.