1. A tri-partite model of computational knowledge
Andrea Guazzini°
* University of Florence
° IIT - National Research Council of Italy (CNR)
Funded by the EC FP7 Future Emerging Technologies Programme
(Awareness), grant 257756
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2. From humans to computer
Humans have developed (through natural selection) “fast and frugal”
methods for understanding the context, taking decisions and solving social
problems in limited time and using bounded cognitive resources.
These methods can have fruitful applications in ubiquitous computer
appliances.
Moreover, electronic devices are asked to interact with humans
... and to act in their delegation.
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3. Self-awareness
Human information processing is context-based.
Human-computer interfaces are expected to behave in a personalized way,
possibly extracting “sideways” information from geographic location, user
profiles, past interactions.
But more information could be gathered by psychological analysis and
characterization.
Human-based heuristics can also result in more effective and optimized
solutions for the typical case.
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4. Cognitive sciences fields
Neural-level (neural networks)
Functional areas and connections
Experimental framework (factorial analysis)
Dynamic behavior
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5. Digging into cognition
There is a lot of knowledge accumulated by cognitive sciences, psychology
and sociology, but little is modeled in quantitative (and procedural) form.
Most of modeling concerns basic functionalities (like the perceptive
system), for instance using the ACTr scheme.
There is a general agreement on different levels of information processing,
related with response time and possibly with evolutive brain structures as
revealed by fMRI.
We aim at implementing algorithmically the levels “below” rational
reasoning.
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6. The three levels of our modeling
Let us suppose that the context is known. The modeling is performed according
to this scheme:
Perception encoding. Most of information in input behaves as noise (it is
uncorrelated with the task – given the context). Reduction of
information by projection on a subspace with limited number of
dimensions.
Representation and activation of knowledge. Implementation of action
and redefinition of the context.
Evaluation processes
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7. The minimal structure of a Self Awareness cognitive
agent
Self awareness could be considered as an epiphenomenon of the cognitive processes of information
analysis. Such processes can be classified on the basis of three criteria: Timescales, Cognitive Costs
and Evolutionary features.
Timescales -(Reaction times)
Unconscious Knowledge (Perception and Pre-attentive activations)-> Fast (<.500 ms)
Conscious Knowledge (Reasoning) -> Medium (From seconds to hours)
Learning/Development -> Slow (From minutes to month)
Cost (Cognitive Economy Principle - Amount of neural activation)
Unconscious Knowledge -> Light (small and local activations)
Conscious Knowledge -> Heavy (large and diffused activations)
Learning/Development -> Very Heavy (diffused activations)
Evolutionary features (Cognitive development)
Unconscious Knowledge -> Critical period and “classical-hebbian” learning only (ACTr)
Conscious Knowledge -> Trial and Error, Observation/Imitation and Induction learnings.
Learning/Development -> Fixed hard wired rules.
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8. The minimal structure of a Self Awareness cognitive
agent
We denotes as schemes the procedure that manage information and
perform actions, and by heuristics the management of scheme
(activation, modification, learning).
We divide schemes and heuristics in three modules: in the first one we
put the structures that deal with input, in the second the actual
management of information and actions and in the third the learning.
This division is consistent with the the response time, but we think
that there is a common structure of heuristics and schemes
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9. The minimal structure of a Self Awareness cognitive
agent
External
Data
Reaction time
Module I Flexibility
Unconscious knowledge
perceptive and attentive processes
Cognitive costs
Relevance Heuristic
Module II
Reasoning
Goal Heuristic
Recognition Heuristic
Solve Heuristic
Module III
Learning
Behavior
Evaluation Heuristic
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10. Some Unification Concepts
A first step for a Mathematical translation
Mental Schemes = knowledge
Cognitive Heuristics = rules/functions
A-Scheme model: The input pattern weights the external information
Extracted Activation (Activation Score) and its relevance is given by the
Input pattern Factors (F) Factors (A) factorial score obtained weighting the internal
knowledge (context). If activated the A-Scheme modifies
the Knowledge (K) with the Extracted Factors (F)
Goal-Schemes: The goal scheme (GS) is activated according to its
Goal Factorial Activation Score (based on K). GS
Activation Factors Extracted “Emotional” modifies K which can cause it to deactivate. The
(G) Factors (F) Factors (E) Goal “Emotional” Factors are used to choose the
appropriate B-Scheme.
B-Scheme model:
The B-Scheme is activated depending both to its Factorial
Activation Factors Extracted Answer Activation Score and to the overlapping between the B-
Factors (F) Extracted Factors and the Goal Emotional Factors. Also
(B) & Cost the cost of the scheme is considered as scheme selecting
criterion.
Cognitive Heuristics
Functions of distance estimation, correlation, minimization/maximization and combination among
schemes.
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11. Module I: The unconscious knowledge
From Gestalt to Relevance Theory
Cognition is able to extract the relevant features from a given context
“unconsciously”, integrating them continuously within the higher decisional
processes. e.g. the active process of perception (Data encoding) is the results of the
combination of the external information with the pre attentive activations.
Involved cognitive processes
Bottom Up processes which encode the information - e.g. Perception
Top Down processes which filter the information - e.g. Attention
Fundamental features
Continuous detection and encoding of the incoming information
Noise and dimensionality reduction of the information
Updating of an associative representation of the context/environment (K)
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12. Module I: The unconscious knowledge
From Gestalt to Relevance Theory
Dynamics of MODULE I:
Relevance Heuristic integrates the external information (EI) with the “pre attentive
activations” (PA) in order to “choose” if activate a certain A-scheme. An A-scheme so
can be characterized in terms of cognitive salience based on its overlapping with the
vector (EI*PA)
The activated A-Schemes are continuously accumulated in a multidimensional and
sparse representation of the reality (Immanent Knowledge Vector - K). K integrates
also projection from the module II.
K is continuously analyzed by a factorial analysis, which drives the new steps of
encoding/perception affecting the PA (weighting/selecting the new information - aka
searching heuristics). Finally the Relevant Features (RF) for the next stages of the
decisional process are extracted.
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13. Module I - Some Unification Concepts
A-Scheme: The Knowledge Vector
A-Schemes: knowledge building blocks
Input Vector (I) I1 , I2 , ..., In Example: The KANITZA triangle
(k) (k) (k)
Scheme Sk W1 , W2 , ..., Wn Extracted
Factors (Sk)
Scheme activation score
⌦ (k)
Module 1 deals with external information, which is
multiform, huge and has to be filtered in order to focus on
Example: The WORD recognition
important components.
The A-schemes do this, and extract information. They are
"activated" by the score match of their input patterns with ROSE
the context vector, they are validated by means of their
relevance with the input, and, if accepted, they contribute to
the context and pass information to schemes in module 2.
A Flower The past of Rise
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14. Module I - Some Unification Concepts
The Immanent Knowledge Vector, i.e. The context
IKV: Immanent representation of the
environment
A-Schemes
Example:
T1 Silver Dish (S1)
T2 Green Pocket (S2)
T3 Fork & Glasses (S3,4)
We assume that there is a structure that denotes the
context frame, and we denote it as the Knowledge/
Context Vector.
It is called vector since we assume that it represents
the knowledge projected on a limited number of
internal dimensions.
The activated A-Schemes are continuously accumulated in a multidimensional and
sparse representation of the reality (Immanent Knowledge - K).
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15. Module I - Some Unification Concepts
The dimensionality reduction
i.e. The pre-attentive processing RF: The relevant features used to
activate the reference Context Frame
A-Schemes Detected
Context Frame
T1 Silver Dish (S1)
Example:
A Set Table
T2 Green Pocket (S2)
T3 Fork & Glasses (S3,4)
Schemes have an activation pattern, that can be
modified at the learning level to "enhance" their
range of usability (typical of the recognition
heuristics).
The extracted factors may be divided into the input
factors, and goals.
The dimensionality of the input is continuously reduced by a “projection” which drives the new steps
of encoding/perception affecting the PA (weighting/selecting the new information), and extracts the
Relevant Features (RF) for the next stages of the decisional processes.
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16. Module I - Some Unification Concepts
A-Scheme: The Knowledge Vector
Pre-attentive activations determine the
Input Context Knowledge factorial scores
Activation
Vector (I) (K)
Scheme S1 Factors Example:
(1) (1) (1) (1) (1)
I1 W1 , W2 . . . , Wn A1 , . . . , AN K1
I2 K2
I3 Scheme S2 K3 LUCKY STRIKE
... (2) (2) (2) (2) (2) ...
W1 , W2 . . . , Wn A1 , . . . , AN
In KN
Scheme activation Factorial
scores activation scores
Among the activation factors there is also the
available time, which contributes (with cognitive cost
Relevance Heuristic (R)
and conflicts among schemes) to the stress or
anxiety: this factor is at the basis of the choice Integrates the external information (EI)
between fast&frugal vs "rational" processing of with the “pre attentive activations” (PA)
information in order to “choose” if activate a certain
A-scheme. An A-scheme so can be
characterized in terms of cognitive
The conflicts, failures, required times are also used in salience based on its overlapping with the
the evaluation/learning phase to promote/devaluate vector (EI*PA)
schemes
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17. Module I: Overview
The schemes in module 1 deal with the input factors, while those in module 2
propose the goal factor (emotionally related) and when accepted by the goal
heuristics these factors may conclude the processing of a given piece of
information
The relevance heuristic deals with conflicts among schemes: for instance
more than one scheme may be activated, and the proposed modifications to
the context are in conflict (perceptive dissonance).
As schemes in module 1 one may thing that these schemes have an
activation pattern that has to match the context, and a general score that
depends on past activity (learning), and that they actively modify the
context, both the input part and the goal.
A possible mechanism of the pattern matching is that the highest the match
with the context, the faster is the activation of a scheme.
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18. Module II: The Conscious knowledge
From Cognitive psychology to Probabilistic Reasoning
The theoretical structure of the module II has been developed on the basis of the
most relevant models of probabilistic reasoning and social cognition theories, and
tries to integrate in a general and psychologically coherent framework their crucial
features. Moreover very recent neurophysiological evidences suggest the existence
of different kind of Heuristics (processes) at this stage.
Involved cognitive processes
Bottom Up processes - e.g. Analogical Mapping of the information
Top Down processes - e.g. Reasoning (Decision Making, Problem Solving)
Fundamental features
Data oriented processes
Analogical representation of the Goal/Target
Selection/Evaluation and management of the B-Scheme
B-Scheme mental simulation and activation
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19. Module II: The Conscious knowledge
From Cognitive psychology to Probabilistic Reasoning
Dynamics of MODULE II:
Goal Heuristic uses some “components” of K to create the most probable Goal Scheme (GS)
(i.e. representation of the goal). This low dimensional scheme has the form of a B-Scheme
and is updated with (and updates too) K.
Recognition Heuristic integrates the RF coming from module I with GS in order to activate
the most relevant B-Scheme. This could be considered as a continuous and incremental
process which is interrupted only by the Solve Heuristic and where a temporary new B-
Scheme can be built if required as a linear combination of the previously activated ones
(Representativeness, anchoring, availability).
Solve Heuristic explicitly explores (frontal activity) the probability of success (distance
between GS and activated B-Scheme) and the cognitive costs of the activated/created B-
Scheme. With a simple function of the previous two arguments the recognition heuristic is
stopped (Fast and Frugal, Less is More) when the ratio among goal closeness and cognitive
costs find a local maximum. Alternatively it drives the gathering of new information by the
modification (enlargement) of the RF and K.
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20. Module II - Some Unification Concepts
The goal Scheme
Goal Factors: Indicates the expected
emotional/physical efforts provided by
Context Knowledge(K) the goal
K1, K2, K3, ..., KN
Goal
Goal-Schemes (Gk): Factors
Factorial
(k) (k) (k) (k) (k) (k) (k) activation scores
G1 , G2 . . . , GN F1 , . . . , F N E1 , . . . , E N
Extracted G(k)
Factors
Schemes in module 2 perform actions, and to be accepted they propose emotional
goals (solution of the problem) that originate from internal, qualitative goals
(bring food to the mouth).
In general schemes tends to activate other schemes (mainly by modification of
the context), but the actual activation is governed by heuristics, given the
available time, cognitive cost, etc.
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21. Module II - Some Unification Concepts
THE recognition process
Knowledge (K) Goal
Goal-Schemes (Gk): Factors
K1, K2, K3, ..., KN (k) (k) (k)
G1 , G2 . . . , GN
(k) (k)
F1 , . . . , F N (k) (k)
E1 , . . . , E N
B-Scheme (Bk): Factorial
Answer activation scores
(h) (h) (h) (h) (h)
B1 , B2 . . . , BN F1 , . . . , F N
& Cost (h)
B
Recognition heuristic (RH): the activation of pattern/modification of context in principle
is a sort of dynamical process that may end in fixed point or be trapped into a cycle
(indecision), but has a structure of an attractor, ... and it takes time to emerge (due to
the action of the recognition heuristics).
The first activated schemes are those that have a strong match with the context, and if
time or cognitive resources are limited the goal heuristic may decide that the goal level
is enough to stop the process.
Therefore, for short times, the decision process is essentially a tree, with quite skewed
branches: it is essentially the principle "take the best" (match) of the fast and frugal
process.
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22. Module II - Some Unification Concepts
THE solve process
Context Knowledge(K)
Goal
Goal-Schemes (Gk): Factors
K1, K2, K3, ..., KN (k) (k) (k)
G1 , G2 . . . , GN
(k) (k)
F1 , . . . , F N (k) (k)
E1 , . . . , E N
Factorial
B-Scheme (Bh):
Goal scores
(h) (h) (h) (h) (h) Answer
B1 , B2 . . . , BN F1 , . . . , FN & Cost
Solve Heuristic (SH) explores the probability of success and the cognitive costs of the
activated/created B-Scheme.
SH stops the Recognition Heuristic (Fast and Frugal, Less is More) when the ratio among
goal closeness and cognitive costs find a local maximum.
Alternatively it drives the gathering of new information by the modification
(enlargement) of the Relevant Factors and Knowledge/Context vector.
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23. Module III: Learning
Inside this framework the Learning can be seen as a reinforcement of schemes by means of
comparisons between expected goals and obtained results. In this sense it can be considered
analogous to the Hebbian reinforcement assumptions. Nevertheless a fundamental ingredient
of learning is the forgetting process, which for instance enables the recognition heuristic and
the fluency heuristic to make better inferences.
Involved cognitive processes
Bottom Up processes - e.g. Hebbian learning (unconscious learning)
Top Down processes - e.g. Social Learning and Mental Simulation
Fundamental features
Updating and management of the associative and analogical maps (A,B-Schemes)
Evaluation of the behaviour related outputs
Imitation and Mental Simulation (e.g. internal use of the M-II heuristics)
Oblivion processes
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24. Module III: Learning
Dynamics of MODULE III:
Evaluation Heuristic compares the External Input with the expected Goal Scheme, and
assesses the goodness of the answer (emotional activations).
Automatic Learning: Active on A and B-Schemes - Hebbian like reinforcement based on
frequency of occurrences.
Observation/Imitation - (Social Learning) Active on B-Scheme - Activation of the same
observed B-schemes and a consequent Hebbian evolution on the bases of the Evaluation
Heuristic result (Symbolic Interactionism theory and Attribution theory).
Trial and Error- Active on Scheme B - Evaluation heuristic and Hebbian managing of the B-
scheme.
Mental Simulation - Induction - Active on Scheme B - New associations or acquaintances can
be represented as new B-Schemes, which are compared with the existing ones by the
module II and then possibly reinforced by the module III (Cognitive dissonance theory).
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25. Conclusion
The human cognitive dynamics is based on relatively simple "fast and frugal"
procedures, that cooperate in a complex environment.
We denote as "schemes" the active procedure that manage information and
perform actions, and by "heuristics" the management of schemes: activation,
conflict resolution, tuning, learning.
Based on time response and imaging techniques it is possible to suggest a
hierarchical structure.
We propose a unified, tri-partitioned model: a perceptive module I, an action
module II and a learning module III.
The main connection among schemes is by means of the context frame: a series of
factors and of emotional goals (the latter only affecting schemes in module II).
Schemes have an associated score, that measures the efficacy of the procedure,
the conflicts with other schemes, the cognitive costs.
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26. Conclusion
Schemes in module I are responsible for input processing, extraction of relevant
factors (and of focussing on important pieces of information), and activation of module
II schemes. The factors contribute to the context frame, which is also the mechanism
for activating other schemes through pattern matching. The only heuristic in module I
is the Relevance Heuristic, responsible of resolving conflicts among schemes.
Schemes in module II perform actions and activate other schemes, through the
context frame. These modules have goals (internal, specific ones and emotional,
common ones).
There are three heuristics in module 2: the Goal Heuristic that manages the goals, the
Solve Heuristic that manages the computational cost of schemes, and the Recognition
Heuristic that eventually activates schemes based on partial matching.
Module III is devoted to learning, either by a simple unconscious Hebbian
reinforcement based on the score of modules, or on social learning (imitation) and
mental simulation (Evaluation Heuristic).
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27. AWASS 2012
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... and thanks for the attention!