1 three partitioned-model_unifi_cnr

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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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)


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


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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.


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.


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.


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)


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.


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.


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.


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.


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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... and thanks for the attention!


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