bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Bayesian approach to the evolution of perceptual and cognitive systems
Authors: Geisler Wilson S. , Diehl Randy L. 
Published: August 2005

AbstractWe describe a formal framework for analyzing how statistical properties of natural environments and the process of natural selection interact to determine the design of perceptual and cognitive systems. The framework consists of two parts: a Bayesian ideal observer with a utility function appropriate for natural selection, and a Bayesian formulation of Darwin's theory of natural selection. Simulations of Bayesian natural selection were found to yield new insights, for example, into the co‐evolution of camouflage, color vision, and decision criteria. The Bayesian framework captures and generalizes, in a formal way, many of the important ideas of other approaches to perception and cognition.
Persistence and accommodation in short‐term priming and other perceptual paradigms: temporal segregation through synaptic depression
Authors: Huber David E. , O'Reilly Randall C. 
Published: August 2005

AbstractPerceptual input changes constantly in an unpredictable fashion, often changing before our somewhat sluggish perceptual systems have adequately processed this input. This can give rise to source confusion—how do we know whether a given perceptual activation is due to the current input, or a previous input that had yet to be completely processed? We propose that activity‐dependent neural accommodation naturally limits this source confusion by suppressing items once they have been identified. We review behavioral paradigms from different literatures that measure the correlates of persistence and accommodation. Of the various accommodative mechanisms, we focus on synaptic depression, deriving a rate‐coded expression that can be used to produce accommodating dynamics in any neural network with real valued activation. We implement this expression in a hierarchical model of perception termed, “a neural mechanism for responding optimally with unknown sources of evidence” (nROUSE). This model can be viewed as a more detailed version of the more abstract ROUSE model of Huber, Shiffrin, Lyle, and Ruys (2001), which produces accommodated levels of feature evidence through an optimal calculation. We apply nROUSE to three short‐term priming experiments that manipulated prime duration.
Spacing and repetition effects in human memory: application of the SAM model
Authors: Raaijmakers Jeroen G.W. 
Published: August 2005

AbstractSpacing between study trials of an item increases the probability that item will be recalled. This article presents a new model for spacing based on the SAM theory of memory developed by Raaijmakers and Shiffrin (1980, 1981). The model is a generalization of the SAM model as applied to interference paradigms (Mensink & Raaijmakers, 1988, 1989) and may be viewed as a mathematical version of the Component‐Levels theory proposed by Glenberg (1979). It is assumed that on a second presentation of an item, information is added to an existing trace if the episodic memory image corresponding to that item is retrieved. If it is not retrieved, a new image is stored. It is shown that the model predicts many standard findings including the lack of a spacing effect for the recall of at least one of two items each presented once (Ross & Landauer, 1978).
Inferring causal networks from observations and interventions
Authors: Steyvers Mark , Tenenbaum Joshua B. , Wagenmakers Eric‐Jan , Blum Ben 
Published: August 2005

AbstractInformation about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.
Data‐driven approaches to information access
Authors: Dumais Susan 
Published: August 2005

AbstractThis paper summarizes three lines of research that are motivated by the practical problem of helping users find information from external data sources, most notably computers. The application areas include information retrieval, text categorization, and question answering. A common theme in these applications is that practical information access problems can be solved by analyzing the statistical properties of words in large volumes of real world texts. The same statistical properties constrain human performance, thus we believe that solutions to practical information access problems can shed light on human knowledge representation and reasoning.
Controlled & automatic processing: behavior, theory, and biological mechanisms
Authors: Schneider Walter , Chein Jason M. 
Published: August 2005

AbstractThis paper provides an overview of developments in a dual processing theory of automatic and controlled processing that began with the empirical and theoretical work described by Schneider and Shiffrin (1977) and Shiffrin and Schneider (1977) over a quarter century ago. A review of relevant empirical findings suggests that there is a set of core behavioral phenomena reflecting differences between controlled and automatic processing that must be addressed by a successful theory. These phenomena relate to: consistency in training, serial versus parallel processing, level of effort, robustness to stressors, degree of control, effects on long‐term memory, and priority encoding. We detail a computational model of controlled processing, CAP2, that accounts for these phenomena as emergent properties of an underlying hybrid computational architecture. The model employs a large network of distributed data modules that can categorize, buffer, associate, and prioritize information. Each module is a connectionist network with input and output layers, and each module communicates with a central Control System by outputting priority and activity report signals, and by receiving control signals. The Control System is composed of five processors including a Goal Processor, an Attention Controller, an Activity Monitor, an Episodic Store, and a Gating & Report Relay. The transition from controlled to automatic processing occurs in this model as the data modules become capable of transmitting their output without mediation by the Control System. We describe recent progress in mapping the components of this model onto specific neuroanatomical substrates, briefly discuss the potential for applying functional neuroimaging techniques to test the model's predictions, and its relation to other models.
Two ways of learning associations
Authors: Boucher Luke , Dienes Zoltán 
Published: August 2005

AbstractHow people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two types of interference was determined: prediction conflicts, in which a given letter can predict two other letters that appear next with an unequal frequency; and retroactive interference, in which the prediction made by a letter changes in the second half of training. The predictions of the models were determined by exploring parameter space and seeing howdensely different regions of the space of possible experimental outcomes were populated by model outcomes. For both types of interference, human data fell squarely in regions characteristic of CCN performance but not characteristic of SRN performance.
A construction based analysis of child directed speech
Authors: Cameron‐Faulkner Thea , Lieven Elena , Tomasello Michael 
Published: August 2005

AbstractThe child directed speech of twelve English‐speaking motherswas analyzed in terms of utterance‐level constructions. First, the mothers' utterances were categorized in terms of general constructional categories such as Wh‐questions, copulas and transitives. Second, mothers' utterances within these categories were further specified in terms of the initial words that framed the utterance, item‐based phrases such as Are you …, I'll …, It's …, Let's …, What did … The findings were: (i) overall, only about 15% of all maternal utterances had SVO form (most were questions, imperatives, copulas, and fragments); (ii) 51% of all maternal utterances began with one of 52 item‐based phrases, mostly consisting of two words or morphemes (45% began with one of just 17 words); and (iii) children used many of these same item‐based phrases, in some cases at a rate that correlated highly with their own mother's frequency of use. We suggest that analyses of adult–child linguistic interaction should take into account not just general constructional categories, but also the item‐based constructions that adults and children use and the frequency with which they use them.
Modeling knowledge‐based inferences in story comprehension
Authors: Frank Stefan L. , Koppen Mathieu , Noordman Leo G.M. , Vonk Wietske 
Published: August 2005

AbstractA 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 [Discourse Proc 16 (1993) 203] 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.
A tale of two similarities: comparison and integration in conceptual combination
Authors: Estes Zachary 
Published: August 2005

AbstractThe perception of semantic similarity derives from distinct processes of comparison and integration. A dual process model of conceptual combination claims that attributive combination (e.g., umbrella tree) entails comparison, while relational combination (e.g., pancake spatula) requires integration. The present research uses similarity as a test of this dual process model. Participants (N = 168) were presented attributive and relational conceptual combinations. Half of the participants interpreted the combinations before rating the similarity of their constituent concepts, while the other half provided similarity ratings without interpreting the concepts together. The experiment revealed that attributive combination decreased the perceived similarity of the constituent concepts, whereas relational combination increased the similarity of the constituents. This result indicates that attributive and relational combination occur via distinct processes. Results of a post‐test (N = 60) suggested that these effects were specific to the particular concepts compared or integrated, and do not generalize to other concepts not compared or integrated. The present research thus supported a dual process model of conceptual combination by demonstrating differential effects of comparison and integration on the perception of semantic similarity.
What goes up may come down: perceptual process and knowledge access in the organization of complex visual patterns by young infants
Authors: Quinn Paul C. , Schyns Philippe G. 
Published: August 2005

AbstractThe relationship between perceptual categorization and organization processes in 3‐ to 4‐month‐old infants was explored. The question was whether an invariant part abstracted during category learning could interfere with Gestalt organizational processes. Experiment 1 showed that the infants could parse a circle in accord with good continuation from visual patterns consisting of a circle and a complex polygon. In Experiments 2 and 3, however, this parsing was interfered with by a prior category familiarization experience in which infants were presented with visual patterns consisting of a pacman shape and a complex polygon. Part 1 of Experiments 2 and 3 showed that the infants recognized the pacman as familiar, and Part 2 demonstrated that the representation of the pacman blocked the subsequent parsing of the circle. The results suggest that a cognitive system of flexible feature creation can override organizational principles with which a perceptual system may come pre‐equipped.
Interpretation‐based processing: a unified theory of semantic sentence comprehension
Authors: Budiu Raluca , Anderson John R. 
Published: August 2005

AbstractWe present interpretation‐based processing—a theory of sentence processing that builds a syntactic and a semantic representation for a sentence and assigns an interpretation to the sentence as soon as possible. That interpretation can further participate in comprehension and in lexical processing and is vital for relating the sentence to the prior discourse. Our theory offers a unified account of the processing of literal sentences, metaphoric sentences, and sentences containing semantic illusions. It also explains how text can prime lexical access. We show that word literality is a matter of degree and that the speed and quality of comprehension depend both on how similar words are to their antecedents in the preceding text and how salient the sentence is with respect to the preceding text. Interpretation‐based processing also reconciles superficially contradictory findings about the difference in processing times for metaphors and literals. The theory has been implemented in ACT‐R [Anderson and Lebiere, The Atomic Components of Thought, Lawrence Erlbaum Associates Publishers, Mahwah, NJ, 1998].
Feature centrality and property induction
Authors: Hadjichristidis Constantinos , Sloman Steven , Stevenson Rosemary , Over David 
Published: August 2005

AbstractA feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature's dependencies was manipulated by varying the similarity of category pairs. Participants' ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well‐specified and vague dependency structures.
A rhythm recognition computer program to advocate interactivist perception
Authors: Buisson Jean‐Christophe 
Published: August 2005

AbstractThis paper advocates the main ideas of the interactive model of representation of Mark Bickhard and the assimilation/accommodation framework of Jean Piaget, through a rhythm recognition demonstration program. Although completely unsupervised, the program progressively learns to recognize more and more complex rhythms struck on the user's keyboard. It does so without any recording of the input flow, and without any pattern matching in the usual sense. On the contrary, internal processes are dynamically constructed to follow and anticipate the user's actions. We show that these processes are representations of the rhythms in the interactivist sense, and that they emerge from non representational grounds, avoiding the symbol‐grounding problem. They are not copies or transductions of reality, but ideal internal constructions of the agent, avoiding the circularity pointed out by Piaget. In practice, the active nature of this recognition process allows it to work even with noisy and complex input flows.
Modelling the effects of semantic ambiguity in word recognition
Authors: Rodd Jennifer M. , Gaskell M. Gareth , Marslen‐Wilson William D. 
Published: August 2005

AbstractMost words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of word recognition. We present a model in which there is competition to activate distributed semantic representations. The model performs well on the task of retrieving the different meanings of ambiguous words, and is able to simulate data reported by Rodd, Gaskell, and Marslen‐Wilson [J. Mem. Lang. 46 (2002) 245] on how semantic ambiguity affects lexical decision performance. In particular, the network shows a disadvantage forwords with multiple unrelated meanings (e.g., bark) that coexists with a benefit for words with multiple related word senses (e.g., twist). The ambiguity disadvantage arises because of interference between the different meanings, while the sense benefit arises because of differences in the structure of the attractor basins formed during learning. Words with few senses develop deep, narrow attractor basins, while words with many senses develop shallow, broad basins. We conclude that the mental representations of word meanings can be modelled as stable states within a high‐dimensional semantic space, and that variations in the meanings of words shape the landscape of this space.
Pragmatic effects on reference resolution in a collaborative task: evidence from eye movements
Authors: Hanna Joy E. , Tanenhaus Michael K. 
Published: August 2005

AbstractIn order to investigate whether addressees can make immediate use of speaker‐based constraints during reference resolution, participant addressees' eye movements were monitored as they helped a confederate cook follow a recipe. Objects were located in the helper's area, which the cook could not reach, and the cook's area, which both could reach. Critical referring expressions matched one object (helper's area) or two objects (helper's and cook's areas), and were produced when the cook's hands were empty or full, which defined the cook's reaching ability constraints. Helper's first and total fixations showed that they restricted their domain of interpretation to their own objects when the cook's hands were empty, and widened it to include the cook's objects only when the cook's hands were full. These results demonstrate that addressees can quickly take into account task‐relevant constraints to restrict their referential domain to referents that are plausible given the speaker's goals and constraints.
Children's attributions of beliefs to humans and God: cross‐cultural evidence
Authors: Knight Nicola , Sousa Paulo , Barrett Justin L. , Atran Scott 
Published: August 2005

AbstractThe capacity to attribute beliefs to others in order to understand action is one of the mainstays of human cognition. Yet it is debatable whether children attribute beliefs in the same way to all agents. In this paper, we present the results of a false‐belief task concerning humans and God run with a sample of Maya children aged 4–7, and place them in the context of several psychological theories of cognitive development. Children were found to attribute beliefs in different ways to humans and God. The evidence also speaks to the debate concerning the universality and uniformity of the development of folk‐psychological reasoning.
Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions
Authors: Hmelo‐Silver Cindy E. , Pfeffer Merav Green 
Published: August 2005

AbstractComplex systems are pervasive in the world around us. Making sense of a complex system should require that a person construct a network of concepts and principles about some domain that represents key (often dynamic) phenomena and their interrelationships. This raises the question of how expert understanding of complex systems differs from novice understanding. In this study we examined individuals' representations of an aquatic system from the perspective of structural (elements of a system), behavioral (mechanisms), and functional aspects of a system. Structure–Behavior–Function (SBF) theory was used as a framework for analysis. The study included participants from middle school children to preservice teachers to aquarium experts. Individual interviews were conducted to elicit participants' mental models of aquaria. Their verbal responses and pictorial representations were analyzed using an SBF‐based coding scheme. The results indicated that representations ranged from focusing on structures with minimal understanding of behaviors and functions to representations that included behaviors and functions. Novices' representations focused on perceptually available, static components of the system, whereas experts integrated structural, functional, and behavioral elements. This study suggests that the SBF framework can be one useful formalism for understanding complex systems.
Reverse correlation in neurophysiology
Authors: Ringach Dario , Shapley Robert 
Published: August 2005

AbstractThis article presents a review of reverse correlation in neurophysiology. We discuss the basis of reverse correlation in linear transducers and in spiking neurons. The application of reverse correlation to measure the receptive fields of visual neurons using white noise and m‐sequences, and classical findings about spatial and color processing in the cortex resulting from such measurements, are emphasized. Finally, we describe new developments in reverse correlation, including “sub‐space” and categorical reverse‐correlation. Recent results obtained by applying such methods in the orientation, spatial‐frequency and Fourier domains have revealed the importance of cortical inhibition in the establishment of sharp tuning selectivity in single neurons.