Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
Behaviorism Revisited: Linking Perception and Action
Through Symbolic Models of Cognition
Pierre Bonzon1
1
Dept of Information Systems, Faculty of HEC, University of Lausanne, Switzerland
pierre.bonzon@unil.ch
Abstract. Brain simulations as performed today in computational neuroscience
rely on analytical methods i.e., mainly differential equations, to model physical
processes. The question then arises: will cognitive abilities of real brains
spontaneously emerge from these simulations, or should they be encoded and
executed on top of them, similarly to the way computer software runs on
hardware? Towards this later goal, a new framework linking neural dynamics to
behaviors through a virtual machine has been reported and is used here to model
brain functionalities in two domains, namely evolutive cases of analogical
reasoning and a simple case of meta-cognition. It is argued that this approach to
brain modeling could lead to an actualization of the concept of behaviorism as a
model for the development of cognition.
Keywords: brain simulation, virtual machine, neural dynamics, cognitive
process, analogical reasoning, meta-cognition
1 Introduction
Since the advent of the cognitive revolution [1], the psychological study of behavior
has moved away from behaviorist theories based on associative explanations [2]
towards the description of mental states and cognitive processes [3]. Early models of
cognitive processes (see e.g., Anderson et al 2004 [4]) were symbolic information
processors originating from the research in Artificial Intelligence. These top down
approaches allow for performing behavior simulations regardless of any grounding
issues i.e., they do not address the problem of binding cognitive processes to
underlying brain processes. In parallel, another field of research focuses on the actual
support of cognition i.e., on brain structures. Computational platforms for simulating
networks of neurons range from artificial neural networks (McCulloch & Pitts 1943
[5]) to enhanced neural networks simulators based on differential equations (Hodgkin
& Huxley 1952 [6]). These bottom up approaches detailing the ground level
constituents of the brain (i.e., the neuronal cells and their connections) are not
explicitly related to the cognitive tasks they support, and as such are not concerned
with the definition of processes. Attempts have been made to turn cognitive models
into so-called “neurally plausible cognitive architectures” associated with some form
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
of brain modeling. As an example, symbolic connectionist models (see e.g., Shastri &
Ajjanagadde 1993 [7]; Hummel & Holyoak 2003 [8]) constitute an attempt to
conciliate the benefits of artificial neural networks and the versatility of the symbolic
models of Artificial Intelligence. According to a notable attempt in this direction (Jilk
et al. 2008 [9]), "the incommensurable categories at the various levels of description
will remain necessary to explain the full range of phenomena”.
Another recurrent theme is illustrated by the progressive shift from the traditional
“sense-think-act” paradigm of cognitivist systems [10] towards the simplified “sense-
act” paradigm of embodied cognition [11]. According to this new approach, cognitive
processes are executed in connection structures that link sensory circuits (i.e.,
perception) with motors (i.e., action) [12]. In this perspective, bottom-up approaches
based on artificial neural networks, as well as top-down approaches via analytical
and/or abstract mathematical tools such as Bayesian inference rules, are well suited for
describing “computations” according to Marr’s classical tri-level hypothesis [13] that
distinguishes computational, algorithmic and implementation levels. They fail
however to “identify algorithms and underlying circuits” [14]. In other words, the
mechanisms for structure learning remain to be fully understood. What is then needed
is a ‘‘middle-out’’ approach that can lead to plausible structures linking biology and
cognition.
As a possible solution, we recently proposed [15] to resort to computer science
methods that allow for a delineation and implementation of successive levels of
complexity. Among the concepts that could be applied towards this goal, two are of
particular relevance, namely concurrent communicating systems, on one hand, and
virtual machines, on the other. The notion of a concurrent communicating system,
which can be used to model the interaction of objects obeying various communication
protocols, reflects a high level view of a network of interactive neurons. The concept
of a virtual machine interpreting a compiled code that differs from a processor's native
code constitutes the key mechanism that allows for interfacing high level abstract
objects i.e., software, with their low level physical support i.e., hardware. In the
context of a multi-level model of brain structures and processes, this means that low
level physiological details can be put aside, and grounded models of cognition be
formulated by relating input and output (i.e., perception and behavior) at a symbolic
level. The corresponding formal framework distinguishes itself from the usual methods
by focusing on processes. It relies for this on a fundamental concept of mathematical
logic i.e., the formal notions of an object in context represented by symbolic
expressions in a logical language [16]. In other words, our virtual machine, which does
function as an interface between the neural and cognitive levels, actually performs
contextual deductions.
This formalism will be reviewed in the Material and methods section below. An
assessment of brain functionalities in two domains, namely evolutive analogical
reasoning, on one hand, and a simple case of meta-cognition, on the other, is proposed
in the Results section under the form of virtual circuits. It is argued in the Conclusion
that this overall approach to brain modeling could lead to a revival of the old concept
of behaviorism as a model for the development of cognition.
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2 Material and methods
Our overall methodology can be described as follows (see [15] for more details):
a) micro scale virtual circuits implementing synaptic plasticity through
asynchronous communicating processes are first defined
b) meso scale virtual circuits corresponding to basic cognitive processes are then
composed out of these micro scale circuits
c) both types of virtual circuits are finally compiled into virtual code to be
interpreted by a virtual machine.
The concept of a virtual machine that we use here basically allows for emulating the
execution of a program in language S on a system having its own language L. Such a
machine constitutes an interface which allows for defining mesoscale circuits
independently of the way the microcircuits are actually implemented. Mesoscale
circuits thus somehow correspond to cognitive software running on top of a biological
substrate. More precisely, these circuits define source code expressed in S which are
then compiled into virtual object code expressed in L to be interpreted by the virtual
machine. We follow a bidirectional approach and present in turn the bottom up design
of virtual circuits followed by the top down construction of a virtual machine.
2.1 Bottom up design of virtual circuits
The basic entities of the proposed formalism for representing virtual circuits are
constituted by threads. In computer science, a thread is a sequence of instructions that
executes concurrently with other threads, may coexist with other threads to form a
process and share resources such as memory. In the present context, a thread
corresponds to a single or a group of neurons and will be represented by a symbolic
expression enclosing an instruction tree (see below for the definition of the
corresponding formal language S). Threads are communicating entities. Each
communication does involve a pair of threads and entails on one side the signal
transmitted by a pre-synaptic (source) thread, and on the other side its reception, via a
given synapse, by a post-synaptic (recipient) thread. Similarly to a neuron, a thread
can be both a source and a recipient and functions as a gate receiving incoming signals
from different sources and sending an outgoing signal to possibly many recipients.
There are however two essential differences between threads and neurons that allow
for a single thread to represent a group of neurons i.e.,
contrary to a neuron that alternates roles, a thread can be simultaneously a
source and a recipient by maintaining parallel communications
contrary to traditional neuron models in which incoming signals are summed
into an integrated value, thread inputs can be processed individually.
Threads can be grouped into disjoint sets, or fibers, to model neural assemblies
(Palm 1982 [17]), and discrete weights can be attached to pairs of communicating
threads belonging to the same fiber. The interaction of threads within a given fiber
obeys various communication protocols. To illustrate this, we reproduce below from
[15] circuits modeling two cases of simple animal behaviors.
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
2.1.1 A case of classical conditioning
As a general evolution principle, organisms devise and use “tricks” for their survival.
The ability to evaluate a threat by learning predictive relationships e.g., by associating
a noise and the presence of a predator, is an example of such tricks realized by
classical conditioning. Let us consider the classical conditioning in the defensive
siphon and gill withdrawal reflex of aplysia californica [18]. In this experiment, a light
tactile conditioned stimulus cs elicits a weak defensive reflex, and a strong noxious
unconditioned stimulus us produces a massive withdrawal reflex. After a few pairings
of stimuli cs and us, where cs slightly precedes us, a stimulus cs alone triggers
a significantly enhanced withdrawal reflex i.e., the organism has learned a new
behavior. This can be represented by virtual circuit given in Fig. 1.
sense(cs)-*->=>-motor(cs)
/|\
ltp
|
sense(us)-+->=>-motor(us)
Fig. 1. A mesoscale virtual circuit implementing classical conditioning.
In Figure 1, the threads sense(us) and sense(cs) are coupled with sensors
capturing external stimuli us and cs and correspond to sensory neurons. The
threads motor(us) and motor(cs) are coupled with effectors and correspond to
motor neurons. Finally, the thread ltp (for long term potentiation) acts as a
facilitatory interneuron reinforcing the pathway (i.e. augmenting its weight) between
sense(cs) and motor(cs). The communication protocols depicted by the symbol
->=>- and /|\ represent a synaptic transmission (i.e., the symbol ->=>- stands
for a synapse) and the modulation of a synapse, respectively. The symbols * and +
stand for conjunctive and disjunctive operators i.e., they are used to represent the
convergence of incoming signals and the dissemination of an outgoing signal,
respectively. Classical conditioning then follows from the application of hebbian
learning [19] i.e., “neurons that fire together wire together”. Though it is admitted
today that classical conditioning in aplysia is mediated by multiple neuronal
mechanisms including a postsynaptic retroaction on a presynaptic site, the important
issue is that the learning of a new behavior requires a conjoint activity of multiple
neurons. This activity in turn depends on the temporal pairing of the conditioned and
unconditioned stimuli, which in conclusion leads to implement the thread ltp as a
detector of coincidence.
2.1.2 A simple case of operant conditioning
The ability to assess and to remember the consequences of one's own actions is
another example of associative learning providing survival advantages. In this case,
operant conditioning [2] associates an action and its result, which can be positive or
negative. Toward this goal, the organism will receive either an excite or an inhibit
internal stimulus (corresponding for instance to a reward or punishment) that will lead
in turn to a reinforcement or a rejection of the action.
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
Let us consider a simple thought experiment where a pigeon is learning to
discriminate between grains and pebbles corresponding to two possible vectors I of
external visual stimuli. The circuit given in Fig. 2, represents the interaction of four
threads sense(I), learn(accept(I)), accept(I) and reject(I),
together with two threads ltp and two opposite threads ltd (for long term
depression). In addition to the external stimuli, this circuit incorporates two internal
stimuli excite(accept(I)) and inhibit(accept(I)) that correspond to
feedbacks from probing the food according to a predefined set of accepted elements.
---*->=>-accept(I)
| /|\
| ltp
| |
| +-----------------------------------------
| | |
| ltd |
| \|/ |excite(accept(I))--
sense(I)-+---*->=>-learn(accept(I))|
| /|\ |inhibit(accept(I))-
| ltd |
| | |
| +-----------------------------------------
| |
| ltp
| \|/
---*->=>-reject(I)
Fig. 2. A meso scale virtual circuit implementing simple operant conditioning
At the beginning of the simulation, the pathways from sense(I) to
learn(accept(I)) is open, while the pathways to both accept(I) and
reject(I) are closed. After a few trials, the pigeon will have learned to close
learn(accept(I)) and to open either accept(I) or reject(I). This
process matches a fundamental principle in circuit neuroscience according to which
inhibition in neuronal networks during baseline conditions allows in turn for
disinhibition, which then stands as a key mechanism for circuit plasticity, learning, and
memory retrieval [20].
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
2.1.3 Representing virtual circuits by symbolic expressions
The circuit given in Fig. 1 gives rise to the following symbolic expression:
threads(aplysia(reflex)):
[thread(sense(us),
[fire(ltp(sense(cs),motor(cs))),
send(motor(us))]),
thread(sense(cs),
[merge(ltp(sense(cs),motor(cs))),
send(motor(cs))]),
thread(motor(us),
[receive(sense(us)),
effector(motor(us))]),
thread(motor(cs),
[receive(sense(cs)),
effector(motor(cs))]),
thread(ltp(Q,R),
[join(Q),
increment(weight(Q,R))])].
In this example, the instruction tree of each thread reduces to a sequence of virtual
instructions such as fire, send, merge, etc, but more generally an instruction tree
can contain alternatives commanded by guards. Formally, expressions for instruction
trees belong to a language S whose BNF syntax is:
::= [] || || []
::= [|]
::= || (;)
::= (|)
Whereas the non-terminal symbol represents conditions derived from
internal stimuli, stands for virtual machine instructions This language S
of instruction trees is not to be confused with thelogical language L that will be used
to define virtual code implications into which instruction trees will be then compiled.
2.1.4 Compiling instruction trees into virtual code implications
Virtual code implications are compiled from thread expressions and have the form
Guard => T:Instruction
where Instruction is a virtual machine instruction and T its clock time. As an example,
virtual code implications compiled from the above thread sense(us)are
true => 1:fire(ltp(sense(cs),motor(cs)))
true => 2:send(motor(us))
true => 3:end
In this simple example, successive clock time values (i.e., 1,2,3) correspond to a
linear list traversal with no guards. More generally, this will give rise to a descent into
trees containing guards.
2.1.5 Microcircuits implementing synaptic plasticity
Virtual circuits rely on communication protocols that are pictured in thread diagrams
by iconic symbols representing themselves microcircuits. These protocols can be
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
defined by means of procedures that operate in pairs:
- send/receive denoted by ->=>- or -<=<-
represents a synaptic transmission
- join/merge denoted by /|\ or \|/
implements long term potentiation/depression (ltp/ltd)
- push/pull denoted by --
models a short term cache memory
- store/retrieve denoted by -{P}-
models an associative memory based on long term storage and retrieval (lts/ltr).
These protocols are detailed in [15] together with their corresponding microcircuits.
2.2 Top down construction of a virtual machine
Let us consider a set of fibers together with sets of initial weights for pairs of
communicating threads within fibers and sets of elements accepted by fibers, and let
Model designate the state of the virtual machine associated with these sets. The
machine itself functions as non deterministic learning automaton that operates on the
constrained neural substrate represented by Model. Formally, it consists in repeating a
sense-react cycle of embodied cognition defined by the following run procedure:
run(Model)
loop sense(Model)
react(Model)
At the next level below, the sense procedure reflects the triggering of spike trains
directed to sensory neurons. After possibly capturing an interrupt from sensors
directed to a given active fiber, or stream, it updates Model using a transition function
input:
sense(Model)
if interrupt(Stream(Input))
then Model input(Model(Stream),Input)
The function input first terminates the interrupted stream by clearing all its registers
and queues and then resets the clocks of the sensory threads associated with sensors.
The react procedure consists of a loop calling on each active thread in any stream to
first deduce a virtual machine instruction and then update Model using a transition
function output:
react(Model)
for each Stream(Thread),T:Instruction,
such that ist(Model(Stream)(Thread),(clock(T), T:Instruction))
do Model output(Model(Stream)(Thread), T:Instruction)
The transition function output corresponds to the execution of virtual machine
instructions implementing communication protocols. The ist predicate (standing for “is
true”) implements contextual deduction [16]. Clock register values T are used to
deduce, for each active thread, the next instruction satisfying the guard. Whenever a
transition initiated by a thread succeeds, the thread clock is advanced and the next
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
instruction is deduced and executed, and whenever it fails, the current instruction is
executed again i.e., the transition is attempted until it eventually succeeds. Altogether,
this amounts to descending into an instruction tree, with its local clock time
corresponding to the currently reached depth. As postulated by Zeki 2015 [21], there is
no central clock, leading thus to the modeling of the brain as a massively
asynchronous, parallel organ . The complete specifications of this machine are given
in open access in [15].
3. Results
Previous application results [15] include a simulation of successive models of animal
awareness up to its third level according to Pepperberg & Lynn 2001 [22]. Briefly, the
first level of animal awareness corresponds to the ability to follow a simple rule
involving the perception of a specific item or event and then either its acceptation or
its rejection (i.e., a case of matching/oddity to sample). Whereas this first level does
not allow for an immediate transfer to a similar task, an organism with the second level
is aware enough of a rule to transfer it across situations and thus to adopt for example
a win/stay lose/shift rule. The third level of animal awareness provides an organism
with the additional capacity to integrate two different sets of stored information in
order to make a categorical judgment (e.g., to sort items). The results presented below
lie outside of this hierarchy and propose evolutive simulations of analogical reasoning,
on one hand, and of memory awareness, on the other.
3.1 Modeling evolutive cases of analogical reasoning
3.1.1 Learning a simple analogical inference schema
Let us first consider a simple analogical inference schema involving two predicates p
and q applied to objects x1 and x2, i.e.
{p(x1)} {big(dog)}
{p(x2)} e.g., {big(bear)}
q(x1) strong(dog)
q(x2) strong(bear)
where {F} represents a fact F, or proposition, that has been previously memorized.
This inference schema can be viewed as first inducing an implication i.e.,
p(X) -> q(X)
where X is a variable, and then applying modus ponens i.e,
p(X) -> q(X)
p(X)
q(X)
The corresponding circuits (where A, B are parameters defining a context e.g., left,
right, and I,J vectors of percepts representing properties p,q) are given in Fig.3
and 4. In Fig. 3, both circuits build an associative long term memory of perceived
facts. More precisely, facts p(x1) and p(x2), as perceived by the threads
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
sense(A(X1(I))) and sense(B(X2(I))) are first learned through a simple case of
operant conditioning (see section 2.1.2) and accepted by the threads see(A(X1(I)))
and see(B(X2(I))), which are then memorized via a long term storage (lts)
process into a memory trace {see(A(X1(I)))} and {see(B(X2(I)))}
p(x1) {p(x1)}
--*>=>-see(A(X1(I)))-+---*--{see(A(X1(I)))}
| /|\ | /|\
| ltp | lts
| | | |
| | ---
| +--------------------------------------------
| | |
| ltd |
p(x1) | \|/ |
sense(A(X1(I)))-+--*->=>-learn(see(A(X1(I))))|excite(see(A(I)))-
sense(B(X2(I)))-+--*->=>-learn(see(B(X2(I))))|excite(see(B(I)))-
p(x2) | /|\ |
| ltd |
| | |
| +--------------------------------------------
| | ---
| | | |
| ltp | lts
| \|/ | \|/
--*>=>-see(B(X2(I)))-+---*--{see(B(X2(I)))}
p(x2) {p(x2)}
Fig. 3. Virtual circuit for memorizing perceptions
In Fig. 4, a circuit pattern representing an implication is first build in the upper half
and then applied in the lower half to make an inference. More precisely, fact q(x1),
as perceived by the thread sense(A(X1(J))), is first learned through a simple case
of operant conditioning and accepted by thread see(A(X1(J))). In parallel, fact
p(x1) is recalled from its trace {see(A(X1(I)))} via an ltr process and
classically conditioned (see section 2.1.1) to infer q(x1) in thread
infer(A(X1(J))). Simultaneously (or rather, slightly after), fact q(x2) is perceived
by thread sense(B(X2(_)))carrying a non instantiated property and is not accepted.
In parallel, fact p(x2) is recalled from its trace {see(B(X2(I)))} into thread
recall(B(X2(I))), which is conditioned to be eventually matched with p(x1) and
then to infer q(x2) in thread infer(B(X2(J))). Finally, thread sense(B(X2(_)))
is conditioned to be accepted in thread see(B(X2(J))), thus representing a
contextual and/or constrained reactive processes relating a perception to an action.
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--*->=>-learn(see(A(X1(J))))|excite(see(A(J)))--
| /|\ |
| ltd |
| | |
| +---------------------------------------------
| |
| ltp
| \|/ q(x1)
+--*->=>-see(A(X1(J)))-------
| |
| ltp
q(x1) | {p(x1)} \|/ q(x1)
sense(A(X1(J)))-+--*->=>-recall(A(X1(I)))-+--*->=>-infer(A(X1(J)))-------
| /|\ | |
| ltr | |
{p(x1)} | | | |
{see(A(X1(I)))}--*-- | |
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| |
{see(B(X2(I)))}--*-- | |
{p(x2)} | | | |
| ltr ltp ltp
| \|/ \|/ \|/
sense(B(X2(_)))-+--*->=>-recall(B(X2(I)))-*-->=>-match(A(X1(I),B(X2(I)))-*->=>-infer(B(X2(J)))-
q(x2) | {p(x2)} q(x2) |
| ltp
| \|/
+------------------------------------------------------------------------------*->=>-see(B(X2(J)))
| q(x2)
| ---------------------------------------------
| | |
| ltd |
| \|/ |
--*->=>-learn(see(B(X2(_))))|inhibit(see(B(_)))-
Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
Fig. 4. Virtual circuit for implementing a simple case of analogical inference
Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
3.1.2 Learning an analogical inference schema implementing transitive relations
Let us now consider a case of relational inference based on transitive relations i.e.,
{p(x1,y1),p(y1,z1)} {smaller(mouse,cat),smaller(cat,dog)}
{q(x2,y2),q(y2,z2)} e.g., {higher(tree,house),higher(house,car)}
p(x1,z1) smaller(mouse,dog)
q(x2,z2) higher(tree,car)
This inference consists in first inducing a second order implication representing a
generic transitive relation, where P,X,Y,Z are variables
P(X,Y), P(Y,Z) -> P(X,Z)
and then applying modus ponens
{P(X,Y), P(Y,Z) -> P(X,Z)}
P(X,Y)
P(Y,Z)
P(X,Z)
Let us extend the definition of the perceptual context by adding two parameters C, D
e.g., front, rear. The circuit implementing this inference schema is given in Fig. 5.
As a distinctive difference from the previous circuit, where properties in remembered
facts {p(x1)} and {p(x2)} need to match, the circuit in Fig. 5 relies on facts
{p(x1,y1),p(y1,z1)} and {q(x2,y2),q(x2,z2)} with properties that don’t need
to match. Whereas the analogical inference implemented in Fig. 4 allows for the
cognitive transfer of a property, the circuit of Fig. 5 allows for the transfer of an object.
Consequently, the conditioning step leading to infer(D(_(K),Z2(L))) in Fig. 5 still
carries a non instantiated object that can be instantiated only in the final step relating
sense(D(X2(_),Z2(_))) to see(D(X2(K),Z2(L).
3.1.3 Combining analogical inference schemas
Let us finally turn to an example of relational inference of the following type
{p(x1,y1),q(y1,z1)} {father(bill,mary),mother(mary,sam)}
{p(x2,y2),q(y2,z2)} e.g {father(tom,cathy),mother(cathy,jack)}
r(x1,z1) grandfather(bill,sam)
r(x2,z2) grandfather(tom,jack)
This extension involves the two types of cognitive transfers just considered. A
combination of the two corresponding circuits is however far from being
straightforward, the difficulty being here the parallel matching of multiple interleaved
properties. Whereas behaviors relying on simple analogical reasoning and transitive
inference, as modeled by the circuits of Fig. 4 and 5, have been observed in animals, this
more complex example is unarguably out of their reach. It is interesting to note that
previous modeling approaches relying on substitutions [8] have led to similar
conclusions [23].
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--*->=>-learn(see(C(X1(I),Z1(J))))|excite(see(C(I,J)))-
| /|\ |
| ltd |
| | |
| +----------------------------------------------------
| |
| ltp
| \|/ p(x1,z1)
+--*->=>-see(C(X1(I),Z1(J)))------------------------
| |
| ltp
p(x1,z1) | p(x1,y1) p(y1,z1) \|/ p(x1,z1)
sense(C(X1(I),Z1(J)))-+--*->=>-recall(C(A(…)))---+--*->=>-recall(C(B(…)))-*->=>-infer(C(X1(I),Z1(J)))-
| /|\ | /|\ |
| ltr | ltr |
{p(x1,y1)} | | {p(y1,z1)} | | |
{see(A(X1(I),Y1(J)))}--*-- {see(B(Y1(I),Z1(J)))}-*-- |
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|
|
{see(A(X2(K),Y2(L)))}--*-- {see(B(Y2(K),Z2(L)))}------------*-- |
{q(x2,y2)} | | {q(y2,z2)} | | |
| ltr | ltr ltp
| \|/ | \|/ \|/
sense(D(X2(_),Z2(_)))-+--*->=>-recall(D(A(X2(K),Y2(L)))))---+--*->=>-recall(D(B(Y2(K),Z2(L)))))-------*->=>-infer(D(_(K),Z2(L)))-
{q(x2,z2)} | q(x2,y2) q(y2,z2) q(x2,y2) |
| ltp
| \|/
+----------------------------------------------------------------------------------------------------------*->=>-see(D(X2(K),Z2(L)))
| q(x2,z2)
| -----------------------------------------------------
| | |
| ltd |
| \|/ |
--*->=>-learn(see(D(X2(_),Z2(_))))|inhibit(see(D(_,_)))-
Fig. 5. Virtual circuit implementing transitive inferences
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
3.2 Modeling a simple case of meta-cognition
The additional simulations presented below pertain to the domain of meta-cognition.
In broad terms, meta-cognition refers to "monitoring and control processes that allow
for maintaining a self-referential check and balance" (Fleming & al. 2012 [24]). A
simple case of meta-cognition is constituted by memory awareness. When applied to
humans, this allows them in particular to feel certain or uncertain, and ultimately, "to
know when they do not know" (Smith & al. 2012 [25]). Some non-human animals
(thereafter, animals), who selectively decline tests when their memory is weak, seem
to exhibit such capabilities. In order to validate this hypothesis, Templer and
Hampton 2012 [26] designed a two phase experiments. In the first phase, monkeys are
trained to take a "memory test". Each trial in this phase consists of an information
stage, in which a randomly chosen cup is openly baited in front of the subject and
then momentarily removed from its view, followed by a choice stage, in which the
subject is asked to retrieve the food. In the second phase of this experiment, a decline
option (also commonly referred to as the "uncertainty response") is introduced. This
option consists in a fixed cup offering a lesser, but guaranteed reward.
Actually, much on the debate around meta-cognition does focus on the following
question: can animals' uncertainty responses be interpreted within an associative
framework, or should they be considered as the expression of a higher-level cognitive
mechanism? An assessment of the research in this field has been proposed by
Dickinson 2012 [27], who argues that mechanisms based on constrained associative
processes can implement psychological rationality. By pointing out to an issue
originally raised by Holland 1990 [28], he raised the possibility of using retrospective
revaluation (Dickinson & Burke 1996 [29]): whereas simple associative theory
assumes that learning occurs when a stimulus is present, retrospective revaluation
allows for learning about associatively retrieved representations (see below in section
3.2.2).
3.2.1 Modeling a reliable memory.
Let us consider Templer and Hampton’s memory test [26] and let us first assume a
perfectly reliable memory without a decline option. Without any loss of generality,
two possible cups only i.e., A and B, are taken into account here. This memory test
can be implemented (see the circuit in Fig. 6) as an extended case of operant
conditioning with a choice between two alternatives A and B captured by the random
internal stimuli fetch(A) and fetch(B)and coupled with a recall from a short
term memory and . It is interesting to note that this circuit matches the one
used in [15] to implement the second level of animal awareness: in both cases,
subjects are trained to solve a task by learning a win/stay lose/shift rule.
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
sense(A(I),B([]))-------*-recall(A)->=>-pick(A)
|
---*->=>-
| /|\
| ltp
| |
| +--------------------------------------------------------
| | |
| ltd |
| \|/ |fetch(A)|excite(pick(A(I)))-
sense(A(I),B(J))-+---*->=>-learn(pick(A(I),B(J)))|
| /|\ |fetch(B)|excite(pick(B(J)))-
| ltd |
| | |
| +--------------------------------------------------------
| |
| ltp
| \|/
---*->=>-
|
sense(A([]),B(J))-------*-recall(B)->=>-pick(B)
Fig. 6. Circuit implementing a memory test
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
3.2.2 Modeling an unreliable memory
According to Templer and Hampton 2012 [26], memory awareness provides subjects
with the capacity to make "judgments" about the quality of their memory e.g., to
discriminate between strong and weak memories. Let us further quote Templer and
Hampton literal description of how monkeys possibly use their memory awareness
when confronted with a decline option: "When memory is weak, monkeys with
memory awareness should opt for the decline response rather than running the risk of
receiving no raisins by guessing where the two raisins were located. When memory is
strong, and they have a high probability of choosing the correct choice cup, they
should select the remembered cup". This description, which distinguishes the correct
cup (i.e., the one that actually holds the preferred reward) from the remembered cup
(i.e., the one the memory has, correctly or incorrectly, associated with the preferred
reward), can be rewritten under the form of two rules:
if memory is weak
-> there is a low chance of choosing the correct cup
-> opt for the decline response
if memory is strong
-> there is a good chance of choosing the correct cup
-> select the remembered cup.
These two rules rely on the definition and access to a mental state, representing in
this case a memory that besides holding an objective content (i.e., the remembered
cup) is subjectively perceived as either weak or strong. Obviously, this perception
involves a wide range of cumulative phenomena (such as distraction, fatigue, or other
decay mechanisms), and thus cannot be reduced to a single deterministic value e.g.,
"weak" or "strong". A possible way to represent and apply these rules is to use a
probability distribution as follows:
in order to represent a weak memory
-> assign a low probability to the correct location
and complementary higher probabilities to the other options
-> perform a weighted random choice.
A strong memory can be similarly represented by assigning a high probability to
choosing the correct location. Retrieving from the memory then reduces to weighted
random choices. For this scheme to be consistent, the “remembered location” is an
elusive concept that needs be replaced by the association of the correct location with
its probability. In other words, the physiological mechanism that creates the short-
term memory (i.e., -- in our wiring model) is supposed to be reliable, but the
process of retrieving from this memory is degraded by cumulative factors resulting in
an unreliable noisy transmission.
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
Model of an unreliable memory without a decline option
Let us now consider a model of unreliable memory, but without yet a decline option,
as given in Fig. 7, where additional wiring is in italics. In neural terms, two inter-
neurons noise(A|[A,B]) and noise(B|[A,B]) open a new pathway between
the memory neurons recall(A|[A,B]) and recall(B|[A,B]), on one hand,
and two new transmitter neurons filter(A|[A,B]) and filter(B|[A,B]),
on the other. Furthermore, whereas the internal stimuli fetch(A) and fetch(B)
attached to learn are purely random, the stimuli catch(A) and catch(B)
attached to filter to reflect a noisy transmission integrate multiple physiological
sources and, as argued above, follow from weighted random choices.
Model of an unreliable memory with a decline option
Memorizing a decline option at location C constitutes another example of an
associative long term memory given by a memory trace (see Fig. 3 of section 3.1.1),
in this case {accept(C)}. Introducing this option as an additional choice gives rise
to a new layer of noisy transmission. This scheme (see Fig. 8), which allows for
animals to learn about directly perceived stimuli as well as retrieved representations
such as the decline option, constitutes a particular case of retrospective revaluation
[29] alluded to above. Turning out to look like an extension of the second and third
levels of animal awareness combined together, it shows how a simple case of meta-
cognition can be reduced to successive layers of associative memories.
|catch(B)
sense(A(I),B([]))-------*-recall(A|[A,B])-*->=>-filter(A|[A,B])|
| /|\ |catch(A)-pick(A)
| ltp
| |
---*->=>-+-noise(A|[A,B])--
| /|\
| ltp
| |
| +--------------------------------------------------------
| | |
| ltd |
| \|/ |fetch(A)|excite(pick(A(I)))-
sense(A(I),B(J))-+---*->=>-learn(pick(A(I),B(J)))|
| /|\ |fetch(B)|excite(pick(B(J)))-
| ltd |
| | |
| +--------------------------------------------------------
| |
| ltp
| \|/
---*->=>-+-noise(B|[A,B])--
| |
| ltp
| \|/ |catch(B)-pick(B)
sense(A([]),B(J))-------*-recall(B|[A,B])-*->=>-filter(B|[A,B])|
|catch(A)
Fig. 7. Circuit implementing an unreliable memory without a decline option
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
{accept(C)}--*--
| |
| ltr
| \|/ |catch(B)-pick(B)
sense(C(K))-+--*->=>-------*-recall(C|[B,C])-*->=>-filter(C|[B,C])|
| /|\ |catch(C)-pick(C)
| ltp
| |
---*->=>-+-noise(C|[B,C])--
| /|\
| ltp
| |
---+------------------------------------------------------
|
|catch(B)-
sense(A(I),B([]))-------*-recall(A|[A,B])-*->=>-filter(A|[A,B])|
| /|\ |catch(A)-pick(A)
| ltp
| |
---*->=>-+-noise(A|[A,B])--
| /|\
| ltp
| |
| +--------------------------------------------------------
| | |
| ltd |
| \|/ |fetch(A)|excite(pick(A(I)))-
sense(A(I),B(J))-+---*->=>-learn(pick(A(I),B(J)))|
| /|\ |fetch(B)|excite(pick(B(J)))-
| ltd |
| | |
| +--------------------------------------------------------
| |
| ltp
| \|/
---*->=>-+-noise(B|[A,B])--
| |
| ltp
| \|/ |catch(B)-pick(B)
sense(A([]),B(J))-------*-recall(B|[A,B])-*->=>-filter(B|[A,B])|
|catch(A)-
|
---+------------------------------------------------------
| |
| ltp
| \|/
---*->=>-+-noise(C|[A,C])--
| |
| ltp
| \|/ |catch(C)-pick(C)
sense(C(K))-+--*->=>-------*-recall(C|[A,C])-*->=>-filter(C|[A,C])|
| /|\ |catch(A)-pick(B)
| ltr
| |
{accept(C)}--*--
Fig. 8. Circuit implementing an unreliable memory with a decline option
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
4 Discussion and conclusion
Various proposals have been made to close the gap between the level of individual
neurons and higher levels supporting behavior. A possible solution is to consider
group of neurons, or neural assemblies. It is proposed here to model neural
assemblies in a simulation framework driven by a virtual machine linking neural
dynamics to behaviors. While the usual approach to simulating neural dynamics starts
with current flows represented by differential equations, we opted for a conceptual
abstraction of synaptic plasticity represented by communicating processes. As a
consequence, there is no reference to any specific neural network model. While these
simulated neural structures are general-purpose entities, symbolic models of cognition
constrain them into specific-purpose processes driven by a virtual machine. As
reflected by the terms in the title of [15] (“i.e., linking neural dynamics to behaviors
through asynchronous communications”) and of this contribution (linking perception
and action through symbolic models of cognition), this virtual machine clearly stands
as an interface between the neural and cognitive levels.
Following previous results on modeling animal cognition up to the third level of
animal awareness, this formalism has been applied in section 3.1 to simulate the
learning of evolutive analogical inference schemas involving two kinds of cognitive
transfer, namely that of an object and that of a property. Whereas schemas involving
one kind of transfer correspond to cognitive abilities observed in animals, schemas
involving a combination of these two kinds correspond to abilities presumably
enjoyed by humans only. As these circuits show, higher level cognitive facilities can
emerge through learning schemas involving in turns both classical and operant
conditioning. If indeed classical and operand conditioning may be combined into
hybrid models, then (as speculated by Carew2002 [30]) “an exciting principle might
emerge: evolution may have come up with a neural 'associative cassette' that can be
used in either type of conditioning, depending of the neural circuit in which it is
embedded". Altogether, this approach to brain modeling could thus lead to an
actualization of the old concept of behaviorism [2], which was founded through the
experimental exploration of these two types of conditioning.
The same formalism has been applied in section 3.2 to simulate memory awareness
as a case of retrospective revaluation. This result shows that simple cases of meta-
cognition can be implemented by successive layers of associative processes reflecting
noisy transmissions.
In conclusion, it is argued that these results could lead to a reconsideration of the
whole concept of a “neural code” relating perception and behavior. Such a neural
code may well reside in the spatial arrangement of mesoscale circuit patterns (i.e., a
kind of population or sparse coding, as opposed to the more traditional rate or
temporal coding associated with spike trains). One might then even consider that there
is actually no code at all (in the sense of a specific arrangement always associating the
same response to a given stimulus), and that “the code is the overall structure itself”.
More precisely, perception might be related to behaviors through the paths found by
evolution via iterative hebbian learning.
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Proceedings of the KI 2017 Workshop on Formal and Cognitive Reasoning
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