Cognitive Modeling

Misyurov D.A. Dialectical formulas based on the binary notation as the development formulas // Credo New. 2012. №2

by Dmitry Misyurov

The article suggests dialectical formulas based on the binary notation as the development formulas: formula with... more The article suggests dialectical formulas based on the binary notation as the development formulas: formula with dominant and the non-dominant elements; universal formula; formula with symbolic weight of elements; tautological formula. For example, it suggests an opportunity to use the dialectical formulas for modeling and artificial intelligence creation, etc.

ICCM Symposium on Cognitive Modeling of Processes Beyond Rational

by Bill Kennedy

One mode of human decision-making is considered intuitive,
i.e., unconscious situational pattern recognition.... more One mode of human decision-making is considered intuitive,
i.e., unconscious situational pattern recognition. Implicit
statistical learning, which involves the sampling of
invariances from the environment and is known to involve
procedural (i.e., non-declarative) memory, has been shown to
be a foundation of this mode of decision making. We present
an ACT-R model of implicit learning whose implementation
entailed a declarative memory-based learner of the
classification of example strings of an artificial grammar. The
model performed very well when compared to humans. The
fact that the simulation of implicit learning could not be
implemented in a straightforward way via a non-declarative
memory approach, but rather required a declarative memory based implementation, suggests that the conceptualization of
procedural memory in the ACT-R framework may need to be
expanded to include abstract representations of statistical
regularities. Our approach to the development and testing of
models in ACT-R can be used to predict the development of
intuitive decision-making in humans.

Modeling Intuitive Decision Making in ACT-R

by Bill Kennedy

One mode of human decision-making is considered intuitive,
i.e., unconscious situational pattern recognition.... more One mode of human decision-making is considered intuitive,
i.e., unconscious situational pattern recognition. Implicit
statistical learning, which involves the sampling of
invariances from the environment and is known to involve
procedural (i.e., non-declarative) memory, has been shown to
be a foundation of this mode of decision making. We present
an ACT-R model of implicit learning whose implementation
entailed a declarative memory-based learner of the
classification of example strings of an artificial grammar. The
model performed very well when compared to humans. The
fact that the simulation of implicit learning could not be
implemented in a straightforward way via a non-declarative
memory approach, but rather required a declarative memory based implementation, suggests that the conceptualization of
procedural memory in the ACT-R framework may need to be
expanded to include abstract representations of statistical
regularities. Our approach to the development and testing of
models in ACT-R can be used to predict the development of
intuitive decision-making in humans.

Kleinschmidt, D. F., Fine, A. B., & Jaeger, T. F. (2012). A belief-updating model of adaptation and cue combination in syntactic comprehension. In CogSci12

by Dave Kleinschmidt

Talk to be presented at CogSci12 in Sapporo, Japan.

We develop and evaluate a preliminary belief-updating model which links intermediate-term (i.e., over several days)... more We develop and evaluate a preliminary belief-updating model which links intermediate-term (i.e., over several days) syntactic adaptation to the joint statistics of syntactic structures and lexical cues to those structures. This model shows how subjects differentially depend on different cues to syntactic structure following changes in the reliability of those cues, as shown by Fine and Jaeger (2011). By relating syntactic adaptation and cue combination to rational inference under uncertainty, this work links learning and adaptation in sentence processing with adaptation in speech perception and non-linguistic domains.

The Turing Machine as a cognitive model of human computation

by Simone Pinna

Published in Franco Rubinacci, Angelo Rega, Nicola Lettieri
(editors), "Le scienze Cognitive in Italia 2011. AISC’11", Napoli: Università degli Studi Federico II, 2011, 147-150

Classical computationalism considers the Turing Machine to be a psychologically implausible model of humanmore Classical computationalism considers the Turing Machine to be a psychologically implausible model of human
computation. In this paper, I will first elaborate on Andrew Wells' thesis that the claim of psychological implausibility
derives from a wrong interpretation of the TM as originally conceived by Turing. Then, I will show how Turing's original
interpretation of the TM could be useful to construct cognitive models of simple phenomena of human computation, such as
counting using our fingers or performing arithmetical operations using paper and pencil.

Categorization and Reinforcement Learning: State Identification in Reinforcement Learning and Network Reinforcement Learning

by Vladislav "Dan" Veksler

Second Life as a Simulation Environment: Rich, high-fidelity world, minus the hassles.

by Vladislav "Dan" Veksler

Goal-Proximity Decision Making: Who needs reward anyway?

by Vladislav "Dan" Veksler

An Integrated Model of Associative and Reinforcement Learning

by Vladislav "Dan" Veksler

Any successful attempt at explaining and replicating the complexity and generality of human and animal learning will... more Any successful attempt at explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here we introduce a computational model which integrates associative learning and reinforcement learning. We contrast the integrated model with associative learning and reinforcement learning models in two simulation studies. The first simulation demonstrates performance advantages for the integrated model in an environment with a dynamic and complex reward structure. The second simulation contrasts the performances of the three models in a classic latent learning experiment (Blodgett, 1929), demonstrating advantages for the integrated model in predicting and explaining the behavioral data.

Supporting cognitive models as users

by Gary Jones

Ritter, F. E., Baxter, G. D., Jones, G., & Young, R. M. (2000). Supporting cognitive models as users. ACM Transactions on Computer-Human Interaction, 7, 1-33.

Did Social Networks Shape Language Evolution? A Multi-Agent Cognitive Simulation

by David Reitter

Modeling knowledge-based inference in story comprehension

by Stefan Frank

Frank, S.L., Koppen, M., Noordman, L.G.M., & Vonk, W. (2003). Modeling knowledge-based inference in story comprehension. Cognitive Science, 27, 875-910

A computational model of inference during story comprehension is presented, in which story situations are represented... more A computational model of inference during story comprehension is presented, in which story situations are represented distributively as points in a high-dimensional “situation-state space.” This state space organizes itself on the basis of a constructed microworld description. From the same description, causal/temporal world knowledge is extracted. The distributed representation of story situations is more flexible than Golden and Rumelhart’s (1993) localist representation.
A story taking place in the microworld corresponds to a trajectory through situation-state space. During the inference process, world knowledge is applied to the story trajectory. This results in an adjusted trajectory, reflecting the inference of propositions that are likely to be the case. Although inferences do not result from a search for coherence, they do cause story coherence to increase. The results of simulations correspond to empirical data concerning inference, reading time, and depth of
processing.
An extension of the model for simulating story retention shows how coherence is preserved during retention without controlling the retention process. Simulation results correspond to empirical data concerning story recall and intrusion.

Coherence-driven resolution of referential ambiguity: a computational model

by Stefan Frank

Frank, S.L., Koppen, M., Noordman, L.G.M., & Vonk, W. (2007). Coherence-driven resolution of referential ambiguity: a computational model. Memory & Cognition, 35, 1307-1322

We present a computational model that provides a unified account of inference, coherence, and disambiguation. It... more We present a computational model that provides a unified account of inference, coherence, and disambiguation. It simulates how the build-up of coherence in text leads to the knowledge-based resolution of referential ambiguity. Possible interpretations of an ambiguity are represented by centers of gravity in a high-dimensional space. The unresolved ambiguity forms a vector in the same space. This vector is attracted by the centers of gravity, while also being affected by context information and world knowledge. When the vector reaches one
of the centers of gravity, the ambiguity is resolved to the corresponding interpretation. The model accounts for reading time and error rate data from experiments on ambiguous pronoun resolution and explains the effects of context informativeness, anaphor type, and processing depth. It shows how implicit causality can have an early effect during reading. A novel prediction is that ambiguities can remain unresolved if there is insufficient disambiguating information.

World knowledge in computational models of discourse comprehension

by Stefan Frank

Frank, S.L., Koppen, M., Noordman, L.G.M., & Vonk, W. (2008). World knowledge in computational models of discourse comprehension. Discourse Processes, 45, 429-463

Since higher-level cognitive processes generally involve the use of world knowledge, computational models of these... more Since higher-level cognitive processes generally involve the use of world knowledge, computational models of these processes require the implementation of a knowledge base. We identify and discuss four strategies for dealing with world knowledge in computational models: disregarding world knowledge, ad hoc selection, extraction from text corpora, and implementation of all knowledge about a simplified ‘microworld’. Each of these strategies is illustrated by a detailed discussion of a model of discourse comprehension. It is argued that seemingly successful modeling results are uninformative if knowledge is implemented ad hoc or not at all; that knowledge extracted from large text corpora is not appropriate for discourse comprehension; and that a suitable implementation can be obtained by applying the microworld strategy.

Sentence comprehension as mental simulation: an information-theoretic perspective

by Stefan Frank

Frank, S.L. & Vigliocco, G. (2011). Information Sentence comprehension as mental simulation: an information-theoretic perspective. Information, 2, 672-696

It has been argued that the mental representation resulting from sentence comprehension is not (just) an abstract... more It has been argued that the mental representation resulting from sentence comprehension is not (just) an abstract symbolic structure but a `mental simulation' of the state-of-affairs described by the sentence. We present a particular formalization of this theory and show how it gives rise to quantifications of the amount of syntactic and semantic information conveyed by each word in a sentence. These information measures predict simulated word-processing times in a dynamic connectionist model of sentence comprehension as mental simulation. A quantitatively similar relation between information content and reading time is known to be present in human reading-time data.

Conventional Models of Time and their Extensions in Science Fiction

by Krystian Aparta

Master's Thesis, Jagiellonian University, Decomber 2006

This thesis overviews conventional conceptual models of  TIME, as described by cognitive linguistics, as well as... more This thesis overviews conventional conceptual models of  TIME, as described by cognitive linguistics, as well as the novel extensions of conventional models of TIME found in science fiction. The first chapter presents an overview of conceptual metaphor theory and a discussion of conventional metaphorical models of  TIME,  such as the MOVING TIME  and MOVING OBSERVER metaphors. Chapter two provides an outline of conceptual blending theory, and presents a conceptual blending account of the structure of conventional models of TIME. The third chapter contains a discussion of the role of episodic memory, mental time travel, and conventional models of LOCATION and CHANGE OF LOCATION in the conceptualization of the MOVEMENT IN TIME,  the analysis of novel scenarios of MOVEMENT IN TIME in science fiction, in relation to the model of  NATURAL LOCATIONS of the  SELF that the particular scenario activates, the discussion of the possible clashes between the frames of MOVEMENT IN TIME and MOVEMENT IN SPACE in time-travel science fiction, and  a survey of several extensions of conventional models of CAUSATION, as related to the novel extensions of conventional models of  TIME  in science fiction. The analyses employ theoretical models provided by cognitive linguistics, notably conceptual blending theory and conceptual metaphor theory. The source texts discussed in the third chapter include science fiction stories by Terry Carr, L. Sprague de Camp, Henry Kuttner and James Tiptree Jr., as well as the novel The Time Machine by H.G. Wells.

A power-law model of psychological memory strength in short-term and long-term recognition

by Chris Donkin

Psychological Science 2012

Proactive Intention Recognition for Home Ambient Intelligence

by The Anh Han

We explore a coherent combination of two jointly implemented logic programming based systems, namely those of... more We explore a coherent combination of two jointly implemented logic programming based systems, namely those of Evolution Prospection and Intention Recognition, to address a number of issues pertinent for Ambient Intelligence (AmI), namely in the home environment context. The Evolution Prospection system designs and implements several kinds of well-studied preferences and useful environment-triggering constructs for decision making. These enable a convenient declarative encoding of users' preferences and needs, as well as reactive constructs like goal triggering rules. The other system performs intention recognition by means of Causal Bayes Nets and a planner. This approach to intention recognition is appropriate to tackle several AmI issues, such as security and emergency. We also present a novel method for collective intention recognition to allow tackling the case where multiple users are of concern. We exemplify our methods with examples in the elder care domain as it is one typical concern in the home environment context.

Evolution prospection in decision making

by The Anh Han

co-authored with LM Pereira, Journal of Intelligent Decision Making, 2009

This work concerns the problem of modelling evolving prospective agent systems. Inasmuch a prospective agent [1] looks... more This work concerns the problem of modelling evolving prospective agent systems. Inasmuch a prospective agent [1] looks ahead a number of steps into the future, it is confronted with the problem of having several different possible courses of evolution, and therefore needs to be able to prefer amongst them to decide the best to follow as seen from its present state. First it needs a priori preferences for the generation of likely courses of evolution. Subsequently, this being one main contribution of this paper, based on the historical information as well as on a mixture of quantitative and qualitative a posteriori evaluation of its possible evolutions, we equip our agent with so-called evolution-level preferences mechanism, involving three distinct types of commitment. In addition, one other main contribution, to enable such a prospective agent to evolve, we provide a way for modelling its evolving knowledge base, including environment and course of evolution triggering of all active goals (desires), context-sensitive preferences and integrity constraints. We exhibit several examples to illustrate the proposed concepts.

The Role of Intention Recognition in the Evolution of Cooperative Behavior

by The Anh Han

co-authored with LM Pereira and FC Santos, IJCAI 2011.

Given its ubiquity, scale and complexity, few problems have created the combined interest of so many unrelated areas... more Given its ubiquity, scale and complexity, few problems have created the combined interest of so many unrelated areas as the evolution of cooperation. Using the tools of evolutionary game theory, here we address, for the first time, the role played by intention recognition in the final outcome of cooperation in large populations of self-regarding individuals. By equipping individuals with the capacity of assessing intentions of others in the course of repeated Prisoner's Dilemma interactions, we show how intention recognition opens a window of opportunity for cooperation to thrive, as it precludes the invasion of pure cooperators by random drift while remaining robust against defective strategies. Intention recognizers are able to assign an intention to the action of their opponents based on an acquired corpus of possible intentions. We show how intention recognizers can prevail against most famous strategies of repeated dilemmas of cooperation, even in the presence of errors. Our approach invites the adoption of other classification and pattern recognition mechanisms common among Humans, to unveil the evolution of complex cognitive processes in the context of social dilemmas.