1 three partitioned-model_unifi_cnr
TRANSCRIPT
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
AWASS 2012Edinburgh 10th-16th June
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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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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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Cognitive sciences fields
Neural-level (neural networks)
Functional areas and connections
Experimental framework (factorial analysis)
Dynamic behavior
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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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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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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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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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The minimal structure of a Self Awareness cognitive agent
AWASS 2012Edinburgh 10th-16th June
The minimal structure of a Self Awareness cognitive agent
Module IUnconscious knowledge
perceptive and attentive processes
Relevance Heuristic
Module IIReasoning
Goal HeuristicRecognition Heuristic
Solve Heuristic
Module IIILearning
Evaluation Heuristic
Reaction time
Flexibility
Cognitive costs
External Data
Behavior
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Some Unification ConceptsA first step for a Mathematical translation
Functions of distance estimation, correlation, minimization/maximization and combination among schemes.
Mental Schemes = knowledgeCognitive Heuristics = rules/functions
Input pattern
The input pattern weights the external information (Activation Score) and its relevance is given by the
factorial score obtained weighting the internal knowledge (context). If activated the A-Scheme modifies
the Knowledge (K) with the Extracted Factors (F)
A-Scheme model:
The goal scheme (GS) is activated according to its Factorial Activation Score (based on K). GS
modifies K which can cause it to deactivate. The Goal “Emotional” Factors are used to choose the
appropriate B-Scheme.
Goal-Schemes:
Cognitive Heuristics
Activation Factors (A)
Extracted Factors (F)
Activation Factors (G)
Extracted Factors (F)
Goal “Emotional”Factors (E)
Activation Factors (B)
The B-Scheme is activated depending both to its Factorial Activation Score and to the overlapping between the B-Extracted Factors and the Goal Emotional Factors. Also the cost of the scheme is considered as scheme selecting
criterion.
B-Scheme model:
Answer & Cost
Extracted Factors (F)
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Module I: The unconscious knowledgeFrom 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. PerceptionTop Down processes which filter the information - e.g. Attention
Fundamental featuresContinuous detection and encoding of the incoming informationNoise and dimensionality reduction of the information Updating of an associative representation of the context/environment (K)
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Module I: The unconscious knowledgeFrom 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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Module I - Some Unification ConceptsA-Scheme: The Knowledge Vector
A-Schemes: knowledge building blocks
Input Vector (I) Example: The KANITZA triangle
Example: The WORD recognition
ROSE
Scheme Sk
A Flower The past of Rise
Extracted Factors (Sk)
Scheme activation score
⌦(k)
W (k)1 , W (k)
2 , ...,W (k)n
I1, I2, ..., In
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Module 1 deals with external information, which is multiform, huge and has to be filtered in order to focus on important components.
The A-schemes do this, and extract information. They are "activated" by the score match of their input patterns with 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.
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Module I - Some Unification ConceptsThe Immanent Knowledge Vector, i.e. The context
The activated A-Schemes are continuously accumulated in a multidimensional and sparse representation of the reality (Immanent Knowledge - K).
IKV: Immanent representation of the environment
Silver Dish (S1)
A-SchemesExample:
T1
Green Pocket (S2)T2
Fork & Glasses (S3,4)T3
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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.
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Module I - Some Unification ConceptsThe dimensionality reduction i.e. The pre-attentive processing
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.
RF: The relevant features used to activate the reference Context Frame
Example:
Detected Context Frame
Silver Dish (S1)
A-Schemes
T1Green Pocket (S2)T2
Fork & Glasses (S3,4)T3
A Set Table
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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.
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Module I - Some Unification ConceptsA-Scheme: The Knowledge Vector
Pre-attentive activations determine the factorial scores
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)
Relevance Heuristic (R)
Example:
LUCKY STRIKE
Input Vector (I) Scheme S1
Scheme activation scores
I1I2I3...In
Scheme S2
K1K2K3...KN
Activation Factors
Factorial activation scores
A(2)1 , . . . , A(2)
N
A(1)1 , . . . , A(1)
NW (1)1 , W (1)
2 . . . , W (1)n
W (2)1 , W (2)
2 . . . , W (2)n
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Among the activation factors there is also the available time, which contributes (with cognitive cost and conflicts among schemes) to the stress or anxiety: this factor is at the basis of the choice between fast&frugal vs "rational" processing of information
The conflicts, failures, required times are also used in the evaluation/learning phase to promote/devaluate schemes
Context Knowledge (K)
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Module I: Overview
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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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Module II: The Conscious knowledgeFrom 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 processesBottom Up processes - e.g. Analogical Mapping of the informationTop Down processes - e.g. Reasoning (Decision Making, Problem Solving)
Fundamental featuresData oriented processesAnalogical representation of the Goal/TargetSelection/Evaluation and management of the B-SchemeB-Scheme mental simulation and activation
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Module II: The Conscious knowledgeFrom 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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Module II - Some Unification ConceptsThe goal Scheme
Goal-Schemes (Gk):
Context Knowledge(K)
K1, K2, K3, ..., KN
Extracted Factors
GoalFactors Factorial
activation scoresG(k)1 , G(k)
2 . . . , G(k)N
F (k)1 , . . . , F (k)
N E(k)1 , . . . , E(k)
N
G(k)
Goal Factors: Indicates the expected emotional/physical efforts provided by
the goal
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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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Module II - Some Unification ConceptsTHE recognition process
Goal-Schemes (Gk):Knowledge (K)
K1, K2, K3, ..., KN
GoalFactors
G(k)1 , G(k)
2 . . . , G(k)N F (k)
1 , . . . , F (k)N E(k)
1 , . . . , E(k)N
B-Scheme (Bk):Answer & Cost
Factorial activation scores
B(h)1 , B(h)
2 . . . , B(h)N
F (h)1 , . . . , F (h)
N B(h)
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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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Module II - Some Unification ConceptsTHE solve process
Goal-Schemes (Gk):K1, K2, K3, ..., KN
GoalFactors
G(k)1 , G(k)
2 . . . , G(k)N F (k)
1 , . . . , F (k)N E(k)
1 , . . . , E(k)N
B-Scheme (Bh):Answer & Cost
Factorial Goal scores
B(h)1 , B(h)
2 . . . , B(h)N
F (h)1 , . . . , F (h)
N
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Context Knowledge(K)
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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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 processesBottom Up processes - e.g. Hebbian learning (unconscious learning)Top Down processes - e.g. Social Learning and Mental Simulation
Fundamental featuresUpdating 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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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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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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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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