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343
Rule-plusexception model of classification learning
- Psychological Review
, 1994
"... The authors propose a rule-plus-exception model (RULEX) of classification learning. According to RULEX, people learn to classify objects by forming simple logical rules and remembering occasional exceptions to those rules. Because the learning process in RULEX is stochastic, the model predicts that ..."
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Cited by 286 (19 self)
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The authors propose a rule-plus-exception model (RULEX) of classification learning. According to RULEX, people learn to classify objects by forming simple logical rules and remembering occasional exceptions to those rules. Because the learning process in RULEX is stochastic, the model predicts that individual Ss will vary greatly in the particular rules that are formed and the exceptions that are stored. Averaged classification data are presumed to represent mixtures of these highly idiosyncratic rules and exceptions. RULEX accounts for numerous fundamental classification phenomena, including prototype and specific exemplar effects, sensitivity to correlational information, difficulty of learning linearly separable versus nonlinearly separable categories, selective attention effects, and difficulty of learning concepts with rules of differing complexity. RULEX also predicts distributions of generalization patterns observed at the individual subject level. Psychologists have witnessed a major shift in the study of category learning during the past few decades. Early research was dominated by the concept-identification paradigm, in which subjects learned well-defined categories structured according to simple logical rules. Owing to the influence of researchers such as Rosch (1973) and Posner and Keele (1968), interest shifted to more ill-defined categories as might be found in the natural world. For ill-defined categories, no simple logical rules exist for classifying objects, and the boundaries demarcating alternative categories are fuzzy. With the shift in emphasis from well-defined to ill-defined categories, there has also been a major shift in the types of models used for explaining classification learning. Early research was dominated by hypothesis-testing and rule-formation
A rational analysis of the selection task as optimal data selection
- 67 – 215535 Deliverable 4.1
, 1994
"... Human reasoning in hypothesis-testing tasks like Wason's (1966, 1968) selection task has been depicted as prone to systematic biases. However, performance on this task has been assessed against a now outmoded falsificationist philosophy of science. Therefore, the experimental data is reassessed ..."
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Cited by 247 (16 self)
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Human reasoning in hypothesis-testing tasks like Wason's (1966, 1968) selection task has been depicted as prone to systematic biases. However, performance on this task has been assessed against a now outmoded falsificationist philosophy of science. Therefore, the experimental data is reassessed in the light of a Bayesian model of optimal data selection in inductive hypothesis testing. The model provides a rational analysis (Anderson, 1990) of the selection task that fits well with people's performance on both abstract and thematic versions of the task. The model suggests that reasoning in these tasks may be rational rather than subject to systematic bias. Over the past 30 years, results in the psychology of reasoning have raised doubts about human rationality. The assumption of human rationality has a long history. Aristotle took the capacity for rational thought to be the defining characteristic of human beings, the capacity that separated us from the animals. Descartes regarded the ability to use language and to reason as the hallmarks of the mental that separated it from the merely physical. Many contemporary philosophers of mind also appeal to a basic principle of rationality in accounting for everyday, folk psychological explanation whereby we explain each other's behavior in terms of our beliefs and desires (Cherniak, 1986; Cohen, 1981; Davidson, 1984; Dennett, 1987; but see Stich, 1990). These philosophers, both ancient and modern, share a common view of rationality: To be rational is to reason according to rules (Brown, 1989). Logic and mathematics provide the normative rules that tell us how we should reason. Rationality therefore seems to demand that the human cognitive system embodies the rules of logic and mathematics. However, results in the psychology of reasoning appear to show that people do not reason according to these rules. In both deductive (Evans, 1982, 1989;
An exemplar-based random walk model of speeded classification
- Psychological Review
, 1997
"... The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R.M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instanc ..."
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Cited by 241 (35 self)
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The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R.M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within- and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity. Models of multidimensional perceptual classification have grown increasingly powerful and sophisticated in recent years, providing detailed quantitative accounts of patterns of classifi-cation learning, transfer, and generalization (e.g., Anderson,
SUSTAIN: A network model of category learning
- Psychological Review
, 2004
"... SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that ..."
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Cited by 187 (15 self)
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SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into
Information Foraging
- Psychological Review
, 1999
"... Information foraging theory is an approach to understanding how strategies and technologies for information seeking, gathering, and consumption are adapted to the flux of information in the environment. The theory assumes that people, when possible, will modify their strategies or the structure of t ..."
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Cited by 170 (11 self)
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Information foraging theory is an approach to understanding how strategies and technologies for information seeking, gathering, and consumption are adapted to the flux of information in the environment. The theory assumes that people, when possible, will modify their strategies or the structure of the environment to maximize their rate of gaining valuable information. The theory is developed by (a) adaptation (rational) analysis of information foraging problems and (b) a detailed process model (adaptive control of thought in information foraging [ACT-IF]). The adaptation analysis develops (a) information patch models, which deal with time allocation and information filtering and enrichment activities in environments in which information is encountered in clusters; (b) information scent models, which address the identification of information value from proximal cues; and (c) information diet models, which address decisions about the selection and pursuit of information items. ACT-IF is instantiated as a production system model of people interacting with complex information technology. Humans actively seek, gather, share, and consume information to a degree unapproached by other organisms. Ours might properly be characterized as a species of informavores (Dennett, 1991). Our adaptive success depends to a large extent on a vast and complex
On the nature and scope of featural representations of word meaning
- Journal of Experimental Psychology: General
, 1997
"... Behavioral experiments and a connectionist model were used to explore the use of featural representations i the computation of word meaning. The research focused on the role of correlations among features, and differences between speeded and untimed tasks with respect to the use of featural informat ..."
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Cited by 157 (9 self)
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Behavioral experiments and a connectionist model were used to explore the use of featural representations i the computation of word meaning. The research focused on the role of correlations among features, and differences between speeded and untimed tasks with respect to the use of featural information. The results indicate that featural representations are used in the initial computation of word meaning (as in an attractor network), patterns of feature correlations differ between artifacts and living things, and the degree to which features are intercorrelated plays an important role in the organization of semantic memory. The studies also suggest that it may be possible to predict semantic priming effects from independently motivated featural theories of semantic relatedness. Implications for related behavioral phenomena such as the semantic impairments associated with Alzheimer's disease (AD) are discussed. Many theories have assumed that word meaning is rep-resented, at least in part, in terms of featural primitives (see,
Learning overhypotheses with hierarchical Bayesian models
"... Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. To illustrate this claim, we develop models th ..."
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Cited by 116 (38 self)
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Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses — overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.
Recent views on conceptual structure
- Psychological Bulletin
, 1992
"... This article reviews theories of concept structure proposed since the mid-1970s, when the discov-ery of typicality effects led to the rejection of the view that instances of a concept share necessary and sufficient attributes. To replace that classical view, psychologists proposed the family resem-b ..."
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Cited by 110 (0 self)
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This article reviews theories of concept structure proposed since the mid-1970s, when the discov-ery of typicality effects led to the rejection of the view that instances of a concept share necessary and sufficient attributes. To replace that classical view, psychologists proposed the family resem-blance and exemplar views (and hybrids of the 2), which argue that instances of a concept share a certain level of overall similarity, rather than necessary and sufficient attributes. These similarity-based views account for much of the typicality data but fail to provide an adequate explanation of the coherence of conceptual categories and of various context effects. Recently proposed explana-tion-based accounts address these issues but raise further questions about the distinction between concept-specific information and general knowledge and about the relationship between concep-tual knowledge and various forms of inference. Psychologists have traditionally equated knowing the mean-ing of a word with knowing (or perhaps more accurately, having) the concept labeled by a word (e.g., Ogden & Richards, 1956; but see Clark, 1983). In this approach, a concept is assumed to be the mental representation of a category or class (Gleitman, Armstrong, & Gleitman, 1983; Medin & Smith, 1984). The con-tents of such a mental representation (i.e., the intension of a word), in concert with certain assumptions about how those contents are processed, have been taken to explain a wide vari-ety of phenomena, including people's knowledge of linguistic relations (e.g., synonymy, antynomy, hy ponomy), how people rec-ognize the objects, events, and so on properly labeled by the word (i.e., the extension of the word), how people understand novel combinations of the word with other words, and the infer-ences people are able to make about an object, event, and so on, properly labeled by the word (Johnson-Laird, Herrmann, &
A bayesian framework for word segmentation: Exploring the effects of context
- In 46th Annual Meeting of the ACL
, 2009
"... Since the experiments of Saffran et al. (1996a), there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of differen ..."
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Cited by 110 (30 self)
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Since the experiments of Saffran et al. (1996a), there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words – in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what’s that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered. 1
