Rational analyses of information foraging on the web

This article describes rational analyses and cognitive models of Web users developed within information foraging theory. This is done by following the rational analysis methodology of (a) characterizing the problems posed by the environment, (b) developing rational analyses of behavioral solutions to those problems, and (c) developing cognitive models that approach the realization of those solutions. Navigation choice is modeled as a random utility model that uses spreading activation mechanisms that link proximal cues (information scent) that occur in Web browsers to internal user goals. Web-site leaving is modeled as an ongoing assessment by the Web user of the expected benefits of continuing at a Web site as opposed to going elsewhere. These cost–benefit assessments are also based on spreading activation models of information scent. Evaluations include a computational model of Web user behavior called Scent-Based Navigation and Information Foraging in the ACT Architecture, and the Law of Surfing, which characterizes the empirical distribution of the length of paths of visitors at a Web site.     

The Explicit and Implicit Domains in Psychoanalytic Change

New research findings of the development and organization of the mind, brain, and behavior bolster the ongoing relational- or intersubjective-field paradigmatic revision of psychoanalytic theory. A multisystems view of learning, memory, and knowledge provide us with a more complex picture of information processing that has fundamental implications for a psychoanalytic theory of therapeutic action.

If the implicit and explicit learning/memory systems are viewed as parallel processes, not easily translatable from one to the other, then new implicit relational experience carries considerably more power as compared to explicit/declarative processing in changing and establishing new implicit mental models. When these cognitive processing systems are viewed as more closely interconnected with a developmental emphasis on connecting them through language, then exploratory/interpretive work becomes more central. REM, dream, infant, and cognitive research evidence suggests that imagistic symbolic capacity exists at birth. Learning and remembering using imagistic symbolic processing could suggest a more easily translatable connection with later developed verbal symbolic processing and a closer, although varied, interconnection between implicit and explicit (symbolic) memory systems.

Explicit attitudes are more directly modifiable through an explicit/declarative focus. Implicit mental models, it is proposed, are variably modifiable through two different change processes: (1) accommodation or transformation of expectancies through new implicit procedural experience (not requiring explicit focus) and (2) diminished activation and increased capacity to deactivate implicit mental models through explicit/declarative processing and the establishment of contrasting implicit models through new implicit procedural experience.     

Models of word production

Research on spoken word production has been approached from two angles. In one research tradition, the analysis of spontaneous or induced speech errors led to models that can account for speech error distributions. In another tradition, the measurement of picture naming latencies led to chronometric models accounting for distributions of reaction times in word production. Both kinds of models are, however, dealing with the same underlying processes: (1) the speaker’s selection of a word that is semantically and syntactically appropriate; (2) the retrieval of the word’s phonological properties; (3) the rapid syllabification of the word in context; and (4) the preparation of the corresponding articulatory gestures. Models of both traditions explain these processes in terms of activation spreading through a localist, symbolic network. By and large, they share the main levels of representation: conceptual/semantic, syntactic, phonological and phonetic. They differ in various details, such as the amount of cascading and feedback in the network. These research traditions have begun to merge in recent years, leading to highly constructive experimentation. Currently, they are like two similar knives honing each other. A single pair of scissors is in the making.     

Initiating the formalization of theories of cognitive aging

Although the relevant knowledge base is still impoverished, the time may be appropriate to attempt to develop and investigate formal models of cognitive aging that incorporate explicit mechanisms to account for age differences frequently observed in measures of cognitive functioning. An initial model based on an associative network metaphor is described, in which age declines in fluid or process aspects of cognition are attributed to a reduction in either the number of simultaneously active nodes, the maximum allowable activation, or the rate of propagating activation between nodes. Results from a computer simulation are consistent at a first level of approximation with many cognitive aging phenomena, and the model is shown to provide a plausible account of age differences in an area that has previously resisted explanation. Although this particular model is only one of many that could be formulated at the present time, it is argued that more rapid progress in theoretical understanding of cognitive aging phenomena will be achieved by attempting to specify one's assumptions and hypotheses in a formal and explicit manner.     

Competitive Learning: From Interactive Activation to Adaptive Resonance

Functional and mechanistic comparisons are made between several network models of cognitive processing: competitive learning, interactive activation, adaptive resonance, and back propagation. The starting point of this comparison is the article of Rumelhart and Zipser (1985) on feature discovery through competitive learning. All the models which Rumelhart and Zipser (1985) have described were shown in Grossberg (1976b) to exhibit a type of learning which is temporally unstable. Competitive learning mechanisms can be stabilized in response to an arbitrary input environment by being supplemented with mechanisms for learning top-down expectancies, or templates; for matching bottom-up input patterns with the top-down expectancies; and for releasing orienting reactions in a mismatch situation, thereby updating short-term memory and searching for another internal representation. Network architectures which embody all of these mechanisms were called adaptive resonance models by Grossberg (1976c). Self-stabilizing learning models are candidates for use in real-world applications where unpredictable changes can occur in complex input environments. Competitive learning postulates are inconsistent with the postulates of the interactive activation model of McClelland and Rumelhart (1981), and suggest different levels of processing and interaction rules for the analysis of word recognition. Adaptive resonance models use these alternative levels and interaction rules. The self-organizing learning of an adaptive resonance model is compared and contrasted with the teacher-directed learning of a back propagation model. A number of criteria for evaluating real-time network models of cognitive processing are described and applied.     

A graph theory model of the semantic structure of attitudes

The semantic structure underlying the attitudes of pretreatment and posttreatment drug addicts was modeled using a network analysis of free word associations. Measures of graph theoretic properties were used to assess structural differences in the associative networks of the two populations. These measures modeled the information processes of associative networks proposed in the spreading activation theory of semantic processing. As expected based on graph theory, the structure of the associative networks of post-treatment subjects was more dense, less constrained, and more hierarchically organized by the self concept. In a test of the network model, the subjects' evaluations of concepts in the associative network were found to be a function of their evaluations of semantically similar concepts. Although preliminary and limited, the results suggest that graph theory may provide a broad mathematical foundation for diverse models of cognitive systems.The research was funded by a grant from the National Institute for Drug Abuse to the Institute of Cultural Studies, Inc.Daytop Village.     

Hybrid preference machines based on inspiration from neuroscience

In the past, a variety of computational problems have been tackled with different connectionist network approaches. However, very little research has been done on a framework which connects neuroscience-inspired models with connectionist models and higher level symbolic processing. In this paper, we outline a preference machine framework which focuses on a hybrid integration of various neural and symbolic techniques in order to address how we may process higher level concepts based on concepts from neuroscience. It is a first hybrid framework which allows a link between spiking neural networks, connectionist preference machines and symbolic finite state machines. Furthermore, we present an example experiment on interpreting a neuroscience-inspired network by using preferences which may be connected to connectionist or symbolic interpretations.     

Emotional influences on evaluative judgments: Roles of arousal,attention, and spreading activation

A conceptual framework and experimental technique for investigating the influence of phasic emotional states on information processing are described. The theoretical framework utilizes the constructs of arousal, attention, and spreading activation to characterize the potential nature of emotional effects. The experimental technique employs a game format within which simple emotional states are elicited by incentive warning signals and feedback signals occurring within each trial. Subjects judged the emotional tone of a target word presented following the warning signal, and reaction times and error rates were measured. Two experiments demonstrated an effect related to general arousal; responses were faster and less accurate following valuable as compared to neutral warnings. In addition, both studies found several more selective effects. A congruent incentive influence was evident in the more efficient processing of positive words following positive incentives and negative words following negative incentives. An incongruent outcome influence was evident in the more efficient processing of positive words following trials involving negative feedback. Analyses of processing benefits and costs indicated that these selective influences arose from attentional rather than spreading activation mechanisms. Control conditions demonstrated that neither effect was influenced by cognitive variables involving the symbolic form of the warning, the location of the warning, or the location of positive responses. Instead, the two effects depended primarily on the presence of a valuable as compared to neutral incentive signal. These results are discussed in light of contemporary model of interactions between emotion and cognition.These studies were conducted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the University of Oregon. I would like to thank my committee members S. W. Keele, M. I. Posner, and M. K. Rothbart, and also Marjorie A. Reed, for their many helpful suggestions.     

Reaction time and musical expectancy: priming of chords

The cognitive processes underlying musical expectation were explored by measuring reaction time in a priming paradigm. Subjects made a speeded true/false decision about a target chord following a prime chord to which it was either closely or distantly related harmonically. Using a major/minor decision task in Experiment 1, we found that major targets were identified faster, and with fewer errors, when they were related than when unrelated. An apparent absence (and possible reversal) of this effect for minor targets can be attributed to the prime's biasing effect on the target's stability. In Experiments 2 and 3 we tested this hypothesis by employing an in-tune/out-of-tune decision for major and minor targets separately. Both major and minor in-tune targets were identified faster when related than when unrelated. We outline a spreading activation model which consists of a network of harmonic relations. Priming results from the indirect activation of chord nodes linked through the network.     

An adaptive network model covering metacognition to control adaptation for multiple mental models

Learning processes can be described by adaptive mental (or neural) network models. If metacognition is used to regulate learning, the adaptation of the mental network becomes itself adaptive as well: second-order adaptation. In this paper, a second-order adaptive mental network model is introduced for metacognitive regulation of learning processes. The focus is on the role of multiple internal mental models, in particular, the case of visualisation to support learning of numerical or symbolic skills. The second-order adaptive network model is illustrated by a case scenario for the role of visualisation to support learning multiplication at the primary school.     

The structure of semantic person memory: evidence from semantic priming in person recognition

This paper reviews research on the structure of semantic person memory as examined with semantic priming. In this experimental paradigm, a familiarity decision on a target face or written name is usually faster when it is preceded by a related as compared to an unrelated prime. This effect has been shown to be relatively short lived and susceptible to interfering items. Moreover, semantic priming can cross stimulus domains, such that a written name can prime a target face and vice versa. However, it remains controversial whether representations of people are stored in associative networks based on co-occurrence, or in more abstract semantic categories. In line with prominent cognitive models of face recognition, which explain semantic priming by shared semantic information between prime and target, recent research demonstrated that priming could be obtained from purely categorically related, non-associated prime/target pairs. Although strategic processes, such as expectancy and retrospective matching likely contribute, there is also evidence for a non-strategic contribution to priming, presumably related to spreading activation. Finally, a semantic priming effect has been demonstrated in the N400 event-related potential (ERP) component, which may reflect facilitated access to semantic information. It is concluded that categorical relatedness is one organizing principle of semantic person memory.     

From Pixels to People: A Model of Familiar Face Recognition

Research in face recognition has largely been divided between those projects concerned with front-end image processing and those projects concerned with memory for familiar people. These perceptual and cognitive programmes of research have proceeded in parallel, with only limited mutual influence. In this paper we present a model of human face recognition which combines both a perceptual and a cognitive component. The perceptual front-end is based on principal components analysis of face images, and the cognitive back-end is based on a simple interactive activation and competition architecture. We demonstrate that this model has a much wider predictive range than either perceptual or cognitive models alone, and we show that this type of combination is necessary in order to analyse some important effects in human face recognition. In sum, the model takes varying images of “known” faces and delivers information about these people.     

Symbolic representation of probabilistic worlds

Symbolic representation of environmental variables is a ubiquitous and often debated component of cognitive science. Yet notwithstanding centuries of philosophical discussion, the efficacy, scope, and validity of such representation has rarely been given direct consideration from a mathematical point of view. This paper introduces a quantitative measure of the effectiveness of symbolic representation, and develops formal constraints under which such representation is in fact warranted. The effectiveness of symbolic representation hinges on the probabilistic structure of the environment that is to be represented. For arbitrary probability distributions (i.e., environments), symbolic representation is generally not warranted. But in modal environments, defined here as those that consist of mixtures of component distributions that are narrow ("spiky") relative to their spreads, symbolic representation can be shown to represent the environment with a relatively negligible loss of information. Modal environments support propositional forms, logical relations, and other familiar features of symbolic representation. Hence the assumption that our environment is, in fact, modal is a key tacit assumption underlying the use of symbols in cognitive science.     

Modeling individual differences in working memory performance: a source activation account

Working memory resources are needed for processing and maintenance of information during cognitive tasks. Many models have been developed to capture the effects of limited working memory resources on performance. However, most of these models do not account for the finding that different individuals show different sensitivities to working memory demands, and none of the models predicts individual subjects' patterns of performance. We propose a computational model that accounts for differences in working memory capacity in terms of a quantity called source activation , which is used to maintain goal-relevant information in an available state. We apply this model to capture the working memory effects of individual subjects at a fine level of detail across two experiments. This, we argue, strengthens the interpretation of source activation as working memory capacity.     

Animal Cognition: The Bridge BetweenAnimal Learning and Human Cognition

Carefully controlled research with animals has demonstrated cognitive capacities for which traditional theories of associative learning cannot account. One example is the symbolic representation of stimuli. If two arbitrary stimuli are associated with a third event, an emergent relation can be shown to develop. A second example is the ability of animals to plan ahead, and to choose whether to plan ahead or not. Although animals have often been used to model drug effects in humans, these models have rarely been used to examine the effects of drugs on cognitive functioning. Furthermore, examination of the effects of drugs on the cognitive behavior of animals may help to identify the nature of the representations underlying the cognition.     

A critique of top-down independent levels models of speech production: Evidence from non-plan-internal speech errors

A number of speech errors are examined which are difficult to account for by top-down serial processing models of speech production which hove independent levels of processing. In particular, most of these errors are characterized by the presence of an interfering element which is external to the utterance under current construction. This paper has two main aims: to classify these errors, and to examine the constraints upon them. It is found that phonological similarity between the target and intrusion is a major determinant of error occurrence. The consequences for models of speech production are discussed in a framework consisting of the architecture and control structure of those models. A particular model to account for these data is proposed, consisting of a spreading activation lexical network.     

The Rediscovery of the Human Mind: The Discursive Approach

The demise of behaviorism was followed by a period of cognitive model building, on Cartesian lines, invoking unobservable cognitive processes, then "mind behind the mind". This has been followed by a second cognitive revolution which emphasizes the idea that mind is a flow of private and public symbolic patterns, created according to local norms. Basing psychology on this insight requires the preservation of the psychological concepts of ordinary languages as part of the basis for scientific psychology. The concept of 'skill' can be used to link individuals with the matrix of interpersonal symbolic and practical interactions. The second revolution can be illustrated by recent work on emotion displays as discursive acts, and by studies on the role of pronouns in the expression of a sense of personal identity.     

Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates

Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-difference learning mechanisms. Less progress has been made toward formalizing the processes involved in goal-directed decision making. We draw on recent work in cognitive neuroscience, animal conditioning, cognitive and developmental psychology, and machine learning to outline a new theory of goal-directed decision making. Our basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model. We present a set of simulations implementing the account, which address benchmark behavioral and neuroscientific findings, and give rise to a set of testable predictions. We also discuss the relationship between the proposed framework and other models of decision making, including recent models of perceptual choice, to which our theory bears a direct connection.     

The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition

Recent findings in brain imaging, particularly in fMRI, are beginning to reveal some of the fundamental properties of the organization of the cortical systems that underpin complex cognition. We propose an emerging set of operating principles that govern this organization, characterizing the system as a set of collaborating cortical centers that operate as a large-scale cortical network. Two of the network’s critical features are that it is resource constrained and dynamically configured, with resource constraints and demands dynamically shaping the network topology. The operating principles are embodied in a cognitive neuroarchitecture, 4CAPS, consisting of a number of interacting computational centers that correspond to activating cortical areas. Each 4CAPS center is a hybrid production system, possessing both symbolic and connectionist attributes. We describe 4CAPS models of sentence comprehension, spatial problem solving, and complex multitasking and compare the accounts of these models with brain activation and behavioral results. Finally, we compare 4CAPS with other proposed neuroarchitectures.