Challenging the Intuition about Memory and Computation in Theories of

Cognition

Jochen Kerdels and Gabriele Peters

FernUniversit

at in Hagen, University of Hagen, Human-Computer Interaction,

Faculty of Mathematics and Computer Science, Universit

atsstrasse 1, D-58097 Hagen, Germany

Keywords:

Memory, Computation, Theories of Cognition, Cognitive Architectures, Neurobiology.

Abstract:

In this position paper we argue that the concepts of memory and computation as they are commonly used

in theories of cognition are strongly inﬂuenced by our intuitive understanding of the corresponding concepts

in contemporary computer systems, leading to an implicit loss of biological plausibility. To support our ar-

gument we provide an alternative perspective on memory and computation that allows a closer comparison

of the capabilities of computer programs running on computer systems and neurobiological systems showing

that computer programs exhibit a computational ﬂexibility that is difﬁcult to reconcile with neurobiological

constraints. We end this paper by offering a neurobiologically plausible perspective on memory that views

memory as a dynamic, distributed process that is an intrinsic part of a neurobiological network that integrates

information, e.g., sensory information.

1 INTRODUCTION

Theories of cognition seek to present models of the

human mind that enlighten our understanding of a

wide spectrum of cognitive processes including inte-

gration of sensory information, generation of behav-

ior, and processes of high-level thought. Cognitive

architectures in particular aim at providing compu-

tational instantiations of such models that facilitate

the exploration of dynamic properties and behaviors.

Prominent examples of such architectures are ACT-

R (Anderson, 1996), SOAR (Laird et al., 1987; Laird,

2008), CLARION (Sun, 2007), LIDA (Franklin et al.,

2013), or the highly inﬂuential Global Workspace

Theory (GWT) by Bernard Baars (Baars, 2017).

In this paper we would like to discuss two basic

assumptions that commonly underlie theories of cog-

nition that seem to be true almost trivially but may

impact the neurobiological plausibility of these theo-

ries severely. The ﬁrst assumption concerns the con-

cept of memory, the second assumption concerns the

related concept of computation. We will argue that

the concept of memory as it is commonly used in the-

ories of cognition is strongly inﬂuenced by our un-

derstanding of memory as it is used in typical com-

puter systems with von Neumann architecture. It is

assumed that memory is a kind of container that al-

lows to store pieces of information and to retrieve

these pieces again later on. Similarly, the second as-

sumption that we would like to challenge concerns the

concept of computation. It is the idea that pieces of

information present in such a memory can be used

by different parts of a cognitive system implying an

encoding of information whose meaning is shared by

these different parts. We hope to show that both as-

sumptions, when scrutinized, are difﬁcult to reconcile

with the properties and constraints inherent to a plau-

sible neurobiological substrate. As an alternative, we

present a different perspective that views memory as

a process that is an intrinsic, distributed part of a neu-

robiological system.

The following section highlights typical ideas of

memory and its usage in a number of cognitive ar-

chitectures, and relates these ideas to a common and

widespread intuition about computation. Section 3

elaborates on this intuition and proposes an alterna-

tive perspective on computation that views computer

programs as transformation networks, which enable

a closer comparison of the computational capabili-

ties of general purpose computer systems and neu-

robiological systems. Section 4 then proposes an

alternative perspective on the structure and function

of memory that emphasizes its integrative and dis-

tributed characteristics. Finally, section 5 concludes

this position paper.

522

Kerdels, J. and Peters, G.

Challenging the Intuition about Memory and Computation in Theories of Cognition.

DOI: 10.5220/0008493605220527

In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019), pages 522-527

ISBN: 978-989-758-384-1

2 MEMORY IN THEORIES OF

COGNITION

Memory is a central part of many theories of cogni-

tion where it is used for multiple purposes. Amongst

other uses, it can serve as a communication hub that

allows to dynamically route information between dif-

ferent parts of the cognitive system and establish a

kind of global context (seen as analog to “short term

memory”), it can serve as a storage of past expe-

riences (“episodic memory”) and knowledge about

the world (“declarative memory”), or it can serve as

a mechanism that facilitates the acquisition and re-

production of behavioral patterns (“procedural mem-

ory”). In all these cases, either explicitly or implicitly,

memory is typically seen as a container that can store

and reproduce signal patterns much like the memory

in a computer system that stores and reproduces bit

patterns. In particular, pieces of information are seen

as self-contained entities that can “move around” and

exist at different locations within the computational

system. This notion is reﬂected by, e.g., John Laird

(SOAR) as he outlines what a cognitive architecture

consists of in his view (Laird, 2008):

A cognitive architecture consists of:

• memories for storing knowledge

• processing units that extract, select, com-

bine, and store knowledge

• languages for representing the knowledge

that is stored and processed

[..] Cognitive architectures must embody

strong hypotheses about the building blocks

of cognition that are shared by all tasks, and

how different types of knowledge are learned,

encoded, and used, making a cognitive archi-

tecture a software implementation of a general

theory of intelligence.

Another example that highlights this perspective is

given by John Anderson (ACT-R) in the context of

describing how knowledge about the environment is

processed in their system (Anderson, 1996):

We have only developed our ideas about en-

vironmental encodings of knowledge with re-

spect to the visual modality. In this area,

it is assumed that the perceptual system has

parsed the visual array into objects and has

associated a set of features with each object.

ACT-R can move its attention over the visual

array and recognize objects. [..] Features

within the spotlight can be synthesized into

recognized objects. Once synthesized, the ob-

jects are then available as chunks in ACT’s

working memory for further processing.

Finally, a good example of how memory is utilized

as a central mechanism to coordinate and control the

ﬂow of information in a cognitive system is given by

Bernard Baars as he describes the core idea of his

Global Workspace Theory (Baars, 2017):

Global Workspace Theory (GWT) suggests

that the brain has a ﬂeeting integrative ca-

pacity that enables access between functions

that are otherwise separate. This makes sense

in a brain that is viewed as a massive paral-

lel set of highly specialized neuronal proces-

sors. In such a system coordination and con-

trol may take place by way of such a central

information exchange, allowing some special-

ized processors – such as sensory regions in

cortex – to distribute information to the system

as a whole. This solution also works in large-

scale computer architectures, which show typ-

ical “limited capacity” behavior when infor-

mation ﬂows by way of a global workspace.

These quotes illustrate how the concept of memory in

cognitive architectures is closely aligned with the cor-

responding concept in contemporary computer sys-

tems. Linked to this understanding of memory is a

matching intuition about computation. In its most ba-

sic form this intuition about computation consists in

the idea of executing a list of basic operations that

each take some data as input and produce some data

as output that affects the subsequent steps of the com-

putation in a deterministic way, e.g., by generating

new input data, controlling the program ﬂow, or caus-

ing desired side effects. For many, this intuition about

computation matches their introspective understand-

ing of the apparent sequential nature of high-level,

linguistic thought or the planning and control of de-

liberate actions necessary to perform a complex task.

One important aspect of this analogy between com-

putation and high-level mental processes is the sense

of agency that the latter imply. We think thoughts.

We perform actions. The computer

processes data.

This implicitly assumed existence of agency makes

thoughts and data alike into objects that are acted

upon. This object quality in turn implies the existence

of such objects as coherent entities in some medium.

In case of data this medium appears to be memory.

This basic intuition about computation is a pow-

erful tool, an abstraction, to reason about and write

complex computer programs, and it is tempting to use

it to interpret and model the function of neurobiolog-

ical information processing systems as well. How-

ever, we argue that the latter use of this abstraction is

counterproductive and leads to misleading analogies

or its central processing unit (CPU)

Challenging the Intuition about Memory and Computation in Theories of Cognition

523

between computer and brain, especially regarding the

purpose and function of memory in both systems. To

elucidate this aspect the next section provides an alter-

native intuition about memory and computation that

allows for a more direct comparison of computer and

neurobiological systems. It will highlight fundamen-

tal differences of both systems regarding their respec-

tive capabilities to process information.

3 COMPUTER PROGRAMS AS

TRANSFORMATION

NETWORKS

The intuition about computation outlined above is

supported and reinforced by modern programming

languages as they facilitate the deﬁnition of com-

plex data types and their instantiation as objects that

seem to exist as such entities in memory. In addi-

tion, these objects are commonly described in a way

that make it seem as if these objects exhibit forms

of agency by possessing a local, encapsulated state,

and being responsible for a speciﬁc set of subprob-

lems. This useful abstraction allows a programmer to

describe the solution to a computational problem in

a way that is both meaningful to other programmers

and herself as well as “understandable” by the com-

puter system. This “understanding”, however, is for

the most part just an illusion. When a program is ac-

tually loaded into memory and executed, the resulting

bit patterns that are stored in memory are by them-

selves meaningless. Their meaning is only encoded

in the program that reads and interprets these patterns,

and much of this encoded meaning is already stripped

away when the high-level program is translated into

machine code. More precisely, most of the meaning

attributed to certain high-level program structures by

a programmer are not part of the program that will ac-

tually run on the computer. In that sense, the “actual

meaning” of a bit pattern in memory is as elusive to

the computer system as an evolved, instinctive behav-

ior might be to an insect. Yet, the implicit sense of

agency mentioned above is suggestive of locating the

meaning of a bit pattern intuitively somewhere within

the machine rather than within the programmer who

is describing and interpreting the machine’s behavior.

To gain an alternative perspective that avoids this

intuitive, but – in our view – misleading interpreta-

tion of a program we take a closer look at the idea of

computation as executing a list of basic operations. In

essence, such a basic operation can be understood as a

prescription of how a particular input pattern is trans-

formed into a new output pattern given a limited set

of hardware resources. Using such basic operations

over and over again to build a complex web of input

output transformations is what a computer program

does. The “glue” that enables the computer program

to form this network of transformations is memory.

When a computer program executes it orchestrates

a constant circulation of bit patterns between memory

and CPU. By doing so the limited hardware resources

of the CPU are reused over and over again to suc-

cessively perform all the input output transformations

in the network of transformations that is deﬁned by

the program. Hence, every computer program can be

seen as the emulation of a vastly more complex, spe-

cial purpose CPU that would implement every input

output transformation deﬁned in the program in hard-

ware. In that sense, every memory retrieval within a

computer program - even retrieval from distant mem-

ories like a hard disk or some network resource - cor-

responds to a dynamic reconﬁguration and/or expan-

sion of this virtual, special purpose CPU.

In this alternative view on computation memory

serves as a pattern buffer between transformations

that endows the resulting, virtual transformation net-

work with a number of capabilities that would be

challenging or impossible to implement in a real,

physical network in which the hardware performing

each transformation is connected directly. In particu-

lar, the use of memory enables the network to oper-

ate independent of timing constraints, i.e., the output

of a transformation can be buffered for an arbitrary

amount of time before it is processed by further trans-

formations. Typical uses of this capability include

waiting for the results of multiple transformations

before proceeding, combining the output of a trans-

formation with its past outputs, or even distributing

a computation over multiple devices. Furthermore,

memory enables the virtual transformation network to

have arbitrary connectivity patterns that are not bound

to physical constraints like limited signal speed, lim-

ited fan-out and fan-in of the transformation hard-

ware, or limited physical space. Together, these ca-

pabilities of the virtual transformation network reﬂect

the ﬂexibility and power of computer programs run-

ning on contemporary computer systems. The key

to this ﬂexibility and power is the use of memory as

the central interface mechanism for this transforma-

tion network. We argue that this view on computa-

tion provides a more accurate and simpler picture of

the function and purpose of memory than the high-

level perspective provided by modern programming

languages where the apparent program structure dif-

fuses into the structure of the virtual transformation

network when the program is translated into machine

code.

NCTA 2019 - 11th International Conference on Neural Computation Theory and Applications

524

Using this alternative view on computation allows

a more direct comparison to the processing of infor-

mation in neurobiological systems. From an evo-

lutionary perspective neurobiological systems devel-

oped to solve speciﬁc computational problems, e.g.,

to control muscles or to process the information com-

ing from a sensory system. As such, the respec-

tive networks of neurons or groups of neurons could

be seen as specialised transformation networks sim-

ilar to the transformation networks described above.

However, instead of being emulated and virtual, these

natural transformation networks are actually imple-

mented within the neurobiological substrate removing

the necessity for memory as a “transformation glue”

while at the same time adding a number of constraints

regarding possible connection patterns and types of

transformations. In particular, an on-the-ﬂy reconﬁg-

uration and expansion of the neurobiological transfor-

mation network is not possible in general. Instead, the

basic structure of the network emerges during the de-

velopment of the organism and is controlled by, e.g.,

periods of cell proliferation, cell migration along cel-

lular support structures, or guidance by short and long

range chemical gradients (Squire et al., 2008). The

variability inherent in this self-organized formation

limits the extent with which the solution to a speciﬁc

computational problem, e.g., a behavioral pattern, can

be hardwired into the network structure and adapted

over time. To mitigate this constraint neurobiologi-

cal networks show forms of plasticity that facilitate

fast changes to the network by adapting the response

of individual neurons or local groups of neurons to a

given input signal. As a consequence, the correspond-

ing input output transformation implemented by those

neurons changes accordingly. However, subsequent

transformations are not informed about this change

but have to adapt themselves as well if necessary and

propagate the change further. This means that from a

global perspective the way by which signals are en-

coded and processed within the transformation net-

work is only known locally and remains in a constant

ﬂux.

This dynamic change of how information is en-

coded and processed by different parts of the trans-

formation network conﬂicts with an idea of memory

that sees memory as a container that stores patterns

of information for later use since the encoding of that

information might have changed while it was stored.

Such an outdated encoding would then become unin-

telligible for the network. Similarly, the idea of mem-

ory as a means to coordinate and control the ﬂow of

information relies as well on a consistent encoding,

which is not guaranteed when local neuroplasticity is

taken into account. Despite these doubts regarding

the biological plausibility of a container-like memory

one might argue that neurobiological systems clearly

do have the ability to remember, e.g., past experiences

and therefore must have some form of memory. To

address this point we will outline our view on a neu-

robiologically plausible memory in the next section.

4 MEMORY AS A PROCESS

In the previous section we described how local neuro-

plasticity continually changes the way how neurons

or local groups of neurons respond to their inputs

and thus encode these inputs differently for neurons

downstream in the network. A memory system that is

based on storing and recreating patterns of informa-

tion is not well suited to cope with this drift of encod-

ing. We therefore suggest that memory in a neurobio-

logical network does not store and recreate the signals

that pass through the network but is rather a mecha-

nism that allows to store and reestablish the activation

state of the network or parts thereof, i.e., to not recre-

ate the result of some computation but to reestablish

the conditions that led to the result.

We argue that such a type of memory has to be a

distributed, intrinsic part of the network instead of be-

ing a dedicated, localized memory system. Moreover,

we see local neuroplasticity, the characteristic respon-

sible for the drift of encoding, as a key mechanism for

this memory. It enables individual neurons to learn

typical input patterns that capture some information

about the statistical nature of their inputs (Kerdels and

Peters, 2018). If such a neuron n

receives its inputs

from sensory cells, then the neuron learns something

about the statistical nature of that part of the world

that is transduced by these cells. If the neuron n

receives its inputs from multiple other neurons n

, it

learns something about the statistical nature of the co-

activation of these input neurons. It learns or forms

an association between these cells, i.e., it becomes a

small associative memory. However, at this stage it

is more of an association detector or something akin

to a hash function. The cell could answer the ques-

tion “Have I seen this activation pattern before?”, but

it can not be “read out” to reestablish that activation

pattern. A solution to this problem arises when one

assumes that the neurons n

are capable of forming as-

sociative memories themselves. In that case, the orig-

inal associative memory neuron n

would just have

to form reciprocal feedback connections to all its in-

puts. Reading out this cell, i.e., activating it, would

then result in reestablishing the activation state of the

input neurons n

. Although this reciprocal connec-

tion appears rather simple it can exhibit a range of

Challenging the Intuition about Memory and Computation in Theories of Cognition

525

interesting behaviors in relation to memory function

depending on the balance of activation between the

cell n

and its inputs n

. If the feedback connection

between n

and n

is inactive then signals can ﬂow

in a bottom up direction allowing the neuron n

learn typical activation patterns of the neurons n

. If

the feedback connection becomes active the neuron n

can keep the neurons n

in a speciﬁc pattern of activa-

tion even if the original input signals to the neurons n

are no longer present, i.e., the cells n

and n

behave

now like a small short term memory of the original

inputs to the cells n

. Lastly, if the feedback of neu-

ron n

is dominant it can force the cells n

to take on a

pattern of activation that might disagree with the cur-

rent bottom up input to the neurons n

. In this case the

behavior resembles the recall of a long term memory

that overrides the current, bottom up driven state.

Viewed in isolation it might appear as a bold claim

that the small neuronal circuit described above would

constitute a proper memory mechanism. In particular,

the capacity of the single cell n

to learn and represent

the activity patterns of its inputs n

seems too limited.

This issue can be addressed by expanding this basic

mechanism to a local group of cells g

that exhibit a

form of competitive Hebbian learning via local inhi-

bition. It can be shown that the resulting ensemble

activity of such a group has a signiﬁcantly higher ca-

pacity to learn and represent the activity patterns of

their inputs (Kerdels and Peters, 2017). Expanding

this model further one might imagine a network of

such reciprocally connected groups of neurons that

interact with each other according to the dynamics

described above. Depending on the respective inﬂu-

ences between the groups such a network would be

continually driven towards states of temporarily sta-

ble, local equilibria that integrate current bottom up

signals from, e.g., sensory cells, hold onto parts of

this bottom up stream at appropriate stages of integra-

tion using local short term memory, and augment this

integrated information by forcing activity patterns top

down that correspond to a form of long term knowl-

edge. Reaching a temporary equilibrium in this net-

work could signal an episodic memory mechanism

like the hippocampus in mammals to capture the ac-

tivity of a small subset s of neuron groups at key lo-

cations within the network. When the activity pat-

tern of this subset s is reestablished again, it would

then force large parts of the network into a conﬁgura-

tion that would approximate the state of the network

when the episodic memory was formed. In principle,

the episodic memory itself could rely on similar basic

mechanisms of associative pattern recognition as de-

scribed above.

In summary, we argue that memory in a neurobio-

logical network is a distributed, dynamic process that

is an intrinsic part of the network both structurally and

functionally. This perspective of memory is in strong

contrast to the concept of memory prevalent in well-

known cognitive architectures that see memory as a

distinct module that stores and retrieves data like the

memory in a contemporary computer system. Inte-

grating neurobiologically plausible forms of memory

and computation in theories of cognition is a chal-

lenging task as it requires a signiﬁcant shift in the

perspective on typical computational paradigms. A

recent example of a ﬁrst step at such an integration is

the work of Hamid and Braun who suggest to infuse

the theory of reinforcement learning with the frame-

work of attractor neural networks in order to beneﬁt

from the latter’s capacity of pattern-completion and

noise-insensitivity (Hamid and Braun, 2019).

5 CONCLUSION

In this position paper we argued that common intu-

itions about memory and computation adversely af-

fect how we think about the processing of information

in neurobiological systems, and how this perspective

is reﬂected by prominent cognitive architectures. We

provided an alternative view on memory and compu-

tation to show more clearly how these intuitions imply

computational capabilities that are difﬁcult to recon-

cile with the constraints inherent in neurobiological

systems. In order to present a perspective on memory

that is more neurobiologically plausible we outlined

the idea of memory as an intrinsic, distributed part of

a neurobiological network whose dynamic processes

combine aspects of memory and computation in a

way that questions the otherwise sharp delineations of

these two concepts present in contemporary theories

of cognition.

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