Although speech categories are defined by multiple acoustic dimensions, some are perceptually weighted more than others and there are residual effects of native-language weightings in non-native speech perception. Recent research on nonlinguistic sound category learning suggests that the distribution characteristics of experienced sounds influence perceptual cue weights: Increasing variability across a dimension leads listeners to rely upon it less in subsequent category learning (Holt & Lotto, 2006). The present experiment investigated the implications of this among native Japanese learning English /r/-/l/ (...) categories. Training was accomplished using a videogame paradigm that emphasizes associations among sound categories, visual information, and players’ responses to videogame characters rather than overt categorization or explicit feedback. Subjects who played the game for 2.5 h across 5 days exhibited improvements in /r/-/l/ perception on par with 2–4 weeks of explicit categorization training in previous research and exhibited a shift toward more native-like perceptual cue weights. (shrink)

It is a reasonable assumption that universal properties of natural languages are not accidental. They occur either because they are underwritten by genetic code, because they assist in language processing or language learning, or due to some combination of the two. In this paper we investigate one such language universal: the suffixing preference across the world’s languages, whereby inflections tend to be added to the end of words. A corpus analysis of child‐directed speech in English found that suffixes were more (...) accurate at cuing the grammatical category of the root word than were prefixes. An artificial language experiment found that there was a learning advantage for suffixes over prefixes in terms of grammatical categorization within an artificial language. The results are consistent with an account of language universals that originate in general purpose learning mechanisms. (shrink)

The article explores digital literacy practices in children’s everyday lives at Norwegian preschools and some of the ways in which young children appropriate basic digital literacy skills through guided participation in situated activities. Building on an ethnomethodological perspective, the analyses are based on 70 hours of video recordings documenting the activities in which 45 children, aged 5-6, and eight preschool teachers participated. Through the detailed analysis of two categorization activities – identifying geometrical shapes and identifying feelings/thoughts –the use of (...) digital tools in the social organization of the activities are examined. The article finds that children’s digital literacy activities encompass visual, verbal, audio and embodied competencies that are made relevant, and thus accessible for learning, in interaction between the children and between the adults and children in the form of norms and guidelines for what constitutes correct categorizations (geometrical shapes and green and red feelings) in the situated activities, and that are appropriated and actualized by the children in interaction with their peers. The findings also show how the categorization practices in preschools deal with symbols and labels in ways that create and sustain socially organized ways of knowing, seeing and acting upon the world. Digital media are embedded in routines, procedures and socialites that are part of these categorization practices; they are part of how children are instructed to experience, interpret, understand and act in the world. Moreover, the different technologies created different conditions for the children’s participation. It was found that peer interaction was part of the digital literacy activities that involved such mobile technologies as smartphones and tablets, while when using non-mobile technologies, e.g. smartboards, the activities were structured more as ‘classic’ classroom activities, primarily guided by the teacher and the didactic material presented through the smartboard. (shrink)

Being able to categorize objects as similar or different is an essential skill. An important aspect of learning to categorize is learning to attend to relevant features and ignore irrelevant features of the to-be-categorized objects. Feature variability across objects of different categories is informative, because it allows inferring the rules underlying category membership. In this study, participants learned to categorize fictitious creatures. We measured attention to the aliens during learning using eye-tracking and calculated the attentional focus as the ratio of (...) attention to relevant versus irrelevant features. As expected, participants’ categorization accuracy improved with practice; however, in contrast to our expectations, their attentional focus did not improve with practice. When computing the attentional focus, attention to the aliens’ eyes was disregarded, because while eyes attract a lot of attention, they did not vary across aliens. Yet, an explorative analysis of attention to eyes suggested that participants’ attentional focus did become somewhat more efficient in that over time they learned to ignore the eyes. Results are discussed in the context of the need for instructional methods to improve attentional focus in learning to categorize. (shrink)

. This paper illustrates the applications of various ensemble methods for enhanced classification accuracy. The case in point is the Pima Indian Diabetic Dataset. The computational model comprises of two stages. In the first stage, k-means clustering is employed to identify and eliminate wrongly classified instances. In the second stage, a fine tuning in the classification was effected. To do this, ensemble methods such as AdaBoost, bagging, dagging, stacking, decorate, rotation forest, random subspace, MultiBoost and grading were invoked along with (...) five chosen base classifiers, namely support vector machine, radial basis function network, decision tree J48, naïve Bayes and Bayesian network. The k-fold cross validation technique is adopted. Computational experiments with the proposed method showed an improvement of 16.14% to 22.49% in the classification accuracy compared to literature survey. Among the ensemble methods tried, MultiBoost ensemble with SVM classifier and grading ensemble with naïve Bayes showed the best performance followed by MultiBoost, stacking and grading ensemble with Bayesian classifier, rotation forest ensemble with RBF and grading and rotation forest ensemble with J48. This investigation conclusively proves the significance of cascading k-means clustering with ensemble methods in the enhanced accuracy in categorization of diabetic dataset. (shrink)

Flexible categorization clearly requires an adaptive component, but at what level of representation? We have investigated categorization in sequence learning that requires the extraction of abstract rules, but no modification of sensory primitives. This motivates the need to make explicit the distinction between sensory-level “atomic” features as opposed to concept-level “abstract” features, and the proposal that flexible categorization probably relies on learning at the abstract feature level.

Evidence from neurophysiological and psychological studies is coming together to shed light on how we represent and recognize objects. This review describes evidence supporting two major hypotheses: the first is that objects are represented in a mosaic-like form in which objects are encoded by combinations of complex, reusable features, rather than two-dimensional templates, or three-dimensional models. The second hypothesis is that transform-invariant representations of objects are learnt through experience, and that this learning is affected by the temporal sequence in which (...) different views of the objects are seen, as well as by their physical appearance. (shrink)

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...) objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. (shrink)

This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross‐modal structure. The model has been implemented in a system using novel speech processing, computer vision, and machine learning algorithms. In evaluations the model successfully performed speech segmentation, word discovery and visual categorization from spontaneous infant‐directed speech paired with video images of single objects. These (...) results demonstrate the possibility of using state‐of‐the‐art techniques from sensory pattern recognition and machine learning to implement cognitive models which can process raw sensor data without the need for human transcription or labeling. (shrink)

A substantial body of experimental evidence has demonstrated that labels have an impact on infant categorization processes. Yet little is known regarding the nature of the mechanisms by which this effect is achieved. We distinguish between two competing accounts: supervised name‐based categorization and unsupervised feature‐based categorization. We describe a neurocomputational model of infant visual categorization, based on self‐organizing maps, that implements the unsupervised feature‐based approach. The model successfully reproduces experiments demonstrating the impact of labeling on infant (...) visual categorization reported in Plunkett, Hu, and Cohen (2008). It mimics infant behavior in both the familiarization and testing phases of the procedure, using a training regime that involves only single presentations of each stimulus and using just 24 participant networks per experiment. The model predicts that the observed behavior in infants is due to a transient form of learning that might lead to the emergence of hierarchically organized categorical structure and that the impact of labels on categorization is influenced by the perceived similarity and the sequence in which the objects are presented. The results suggest that early in development, say before 12 months old, labels need not act as invitations to form categories nor highlight the commonalities between objects, but they may play a more mundane but nevertheless powerful role as additional features that are processed in the same fashion as other features that characterize objects and object categories. (shrink)

Subfocal Color Categorization and Naming: The Role of Exposure to Language and Professional Experience The current state of the debate on the linguistic factors in color perception and categorization is reviewed. Developmental and learning studies were hitherto almost ignored in this debate. A simple experiment is reported in which 20 Academy of Fine Arts, Faculty of Painting students' performance in color discrimination and naming tasks was compared to the performance of 20 Technical University students. Subfocal colors were used. (...) While there was no difference in overall discrimination ability, AFA students had a much richer and specialized color vocabulary. Both groups also applied different strategies of discrimination and naming. However, naming system in neither group was coherent. This suggests that naming played primarily the role of markers for control processes rather than names for categories. It is concluded that up-to-date debate is too simplified and a complex model of interrelations between perceptual categorization and naming framed in the developmental context is needed rather than the search for a simple answer "language", "environment", or "perceptual universals". (shrink)

There is an active debate within the field of phonology concerning the cognitive status of substantive phonetic factors such as ease of articulation and perceptual distinctiveness. A new framework is proposed in which substance acts as a bias, or prior, on phonological learning. Two experiments tested this framework with a method in which participants are first provided highly impoverished evidence of a new phonological pattern, and then tested on how they extend this pattern to novel contexts and novel sounds. Participants (...) were found to generalize velar palatalization (e.g., the change from [k] as in keep to [t⌢∫S] as in cheap) in a way that accords with linguistic typology, and that is predicted by a cognitive bias in favor of changes that relate perceptually similar sounds. Velar palatalization was extended from the mid front vowel context (i.e., before [e] as in cape) to the high front vowel context (i.e., before [i] as in keep), but not vice versa. The key explanatory notion of perceptual similarity is quantified with a psychological model of categorization, and the substantively biased framework is formalized as a conditional random field. Implications of these results for the debate on substance, theories of phonological generalization, and the formalization of similarity are discussed. (shrink)

This study investigates the acquisition of integrated object manipulation and categorization abilities through a series of experiments in which human adults and artificial agents were asked to learn to manipulate two-dimensional objects that varied in shape, color, weight, and color intensity. The analysis of the obtained results and the comparison of the behavior displayed by human and artificial agents allowed us to identify the key role played by features affecting the agent/environment interaction, the relation between category and action development, (...) and the role of cognitive biases originating from previous knowledge. (shrink)

Perception provides us with access to the external world, but that access is shaped by our own experiential histories. Through perceptual learning, we can enhance our capacities for perceptual discrimination, categorization, and attention to salient properties. We can also encode harmful biases and stereotypes. This article reviews interdisciplinary research on perceptual learning, with an emphasis on the implications for our rational and normative theorizing. Perceptual learning raises the possibility that our inquiries into topics such as epistemic justification, aesthetic criticism, (...) and moral knowledge should include not only an examination of cognition but also of perception. (shrink)

The study of concepts lies at the intersection of various disciplines, both analytic and empiric. The rising cognitive sciences, for instance, are interested in concepts insofar as they are used in an explanation of such diverse epistemic phenomena like categorization, inference, memory, learning, and decision-making. In philosophy, the challenge imposed by conceptualization consists, among other things, in accommodating reverse intuitions about concepts like shareability, mind-dependency, mediation between reference, knowledge and reality, etc. While researchers have collaborated more and more to (...) contribute to a unified understanding of concepts and categorization, the joint venture unfortunately suffers (so far) from the fact that it is generally left unclear how exactly the different approaches undertaken in the participating sciences relate to each other. What do psychologists and philosophers mean by the notion of a concept? Is there a core-theory of concepts and categorization underlying analytical and empirical studies? The present collection of essays addresses these and related questions and tries to answer them from both a systematic and a historical perspective. (shrink)

Learning to recognize visual objects from examples requires the ability to find meaningful patterns in spaces of very high dimensionality. We present a method for dimensionality reduction which effectively biases the learning system by combining multiple constraints via the use of class labels. The use of extensive class labels steers the resulting lowdimensional representation to become invariant to those directions of variation in the input space that are irrelevant to classification; this is done merely by making class labels independent of (...) these directions. We also show that prior knowledge of the proper dimensionality of the target representation can be imposed by training a multi-layer bottleneck network. Computational experiments involving non-trivial categorization of parameterized fractal images and of human faces indicate that the low-dimensional representation extracted by our method leads to improved generalization in the learned tasks and is likely to preserve the topology of the original space. (shrink)

We report on rapid perceptual learning of intonation contour categories in adults and 9- to 11-year-old children. Intonation contours are temporally extended patterns, whose perception requires temporal integration and therefore poses significant working memory challenges. Both children and adults form relatively abstract representations of intonation contours: Previously encountered and novel exemplars are categorized together equally often, as long as distance from the prototype is controlled. However, age-related differences in categorization performance also exist. Given the same experience, adults form narrower (...) categories than children. In addition, adults pay more attention to the end of the contour, while children appear to pay equal attention to the beginning and the end. The age range we examine appears to capture the tail-end of the developmental trajectory for learning intonation contour categories: There is a continuous effect of age on category breadth within the child group, but the oldest children are adult-like. (shrink)

We explore different ways in which the human visual system can adapt for perceiving and categorizing the environment. There are various accounts of supervised (categorical) and unsupervised perceptual learning, and different perspectives on the functional relationship between perception and categorization. We suggest that common experimental designs are insufficient to differentiate between hypothesized perceptual learning mechanisms and reveal their possible interplay. We propose a relatively underutilized way of studying potential categorical effects on perception, and we test the predictions of different (...) perceptual learning models using a two‐dimensional, interleaved categorization‐plus‐reconstruction task. We find evidence that the human visual system adapts its encodings to the feature structure of the environment, uses categorical expectations for robust reconstruction, allocates encoding resources with respect to categorization utility, and adapts to prevent miscategorizations. (shrink)

The detection and categorization of animate motions is a crucial task underlying social interaction and perceptual decision making. Neural representations of perceived animate objects are partially located in the primate cortical region STS, which is a region that receives convergent input from intermediate-level form and motion representations. Populations of STS cells exist which are selectively responsive to specific animated motion sequences, such as walkers. It is still unclear how and to what extent form and motion information contribute to the (...) generation of such representations and what kind of mechanisms are involved in the learning processes. The article develops a cortical model architecture for the unsupervised learning of animated motion sequence representations. We demonstrate how the model automatically selects significant motion patterns as well as meaningful static form prototypes characterized by a high degree of articulation. Such key poses are selectively reinforced during learning through a cross talk between the motion and form processing streams. Furthermore, we show how sequence-selective representations are learned in STS by fusing static form and motion input from the segregated bottom-up driving input streams. Cells in STS, in turn, feed their activities recurrently to their input sites along top-down signal pathways. We show how such learned feedback connections enable predictions about future input as anticipation generated by sequence-selective STS cells. Network simulations demonstrate the computational capacity of the proposed model by reproducing several experimental findings from neurosciences and by accounting for recent behavioral data. (shrink)

Human kinship systems play a central role in social organization, as anthropologists have long demonstrated. Much less is known about how cultural schemas of relatedness are transmitted across generations. How do children learn kinship concepts? To what extent is learning affected by known cross-cultural variation in how humans classify kin? This review draws on research in developmental psychology, linguistics, and anthropology to present our current understanding of the social and cognitive foundations of kinship categorization. Amid growing interest in kinship (...) in the cognitive sciences, the paper aims to stimulate new research on the ontogeny of kinship categorization, a rich domain for studying the nexus of language, culture, and cognition. We introduce an interdisciplinary research toolkit to help streamline future research in this area. (shrink)

Three experiments with 88 college-aged participants explored how unlabeled experiences—learning episodes in which people encounter objects without information about their category membership—influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then made a large number of unsupervised categorization decisions about new items. In all three experiments, the unsupervised experience altered participants’ implicit and explicit mental category boundaries, their explicit beliefs about the most representative members of each category, and even (...) their memory for the items encountered during the supervised learning phase. These changes were influenced by both the range and frequency distribution of the unlabeled stimuli: mental category boundaries shifted toward the middle of the range and toward the trough of the bimodal distribution of unlabeled items, whereas beliefs about the most representative category members shifted toward the modes of the unlabeled distribution. One consequence of this shift in representations is a false-consensus effect (Experiment 3) where participants, despite receiving very disparate training experiences, show strong agreement in judgments about representativeness and boundary location following unsupervised category judgments. (shrink)

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning (...) derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

Capacities for abstract thinking and problem‐solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in fact be contingent on human‐specific modes of collaboration, dialogue, and shared attention. In an experimental study, we test the hypothesis that social interaction enhances cognitive processes of rule‐induction, which in (...) turn improves problem‐solving performance. Through three sessions of increasing complexity, individuals and groups were presented with a problem‐solving task requiring them to categorize a set of visual stimuli. To assess the character of participants’ problem representations, after each training session, they were presented with a transfer task involving stimuli that differed in appearance, but shared relations among features with the training set. Besides, we compared participants’ categorization behaviors to simulated agents relying on exemplar learning. We found that groups performed superior to individuals and agents in the training sessions and were more likely to correctly generalize their observations in the transfer phase, especially in the high complexity session, suggesting that groups more effectively induced underlying categorization rules from the stimuli than individuals and agents. Crucially, variation in performance among groups was predicted by semantic diversity in members’ dialogical contributions, suggesting a link between social interaction, cognitive diversity, and abstraction. (shrink)

The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide (...) a mapping of words with similar meaning to have similar representation. In this study, we use different document representations with the benefit of word embeddings and an ensemble of base classifiers for text classification. The ensemble of base classifiers includes traditional machine learning algorithms such as naïve Bayes, support vector machine, and random forest and a deep learning-based conventional network classifier. We analysed the classification accuracy of different document representations by employing an ensemble of classifiers on eight different datasets. Experimental results demonstrate that the usage of heterogeneous ensembles together with deep learning methods and word embeddings enhances the classification performance of texts. (shrink)

Previous research suggests that early performance of amnesic individuals in a probabilistic category learning task is relatively unimpaired. When combined with impaired declarative knowledge, this is taken as evidence for the existence of separate implicit and explicit memory systems. The present study contains a more fine-grained analysis of learning than earlier studies. Using a dynamic lens model approach with plausible learning models, we found that the learning process is indeed indistinguishable between an amnesic and control group. However, in contrast to (...) earlier findings, we found that explicit knowledge of the task structure is also good in both the amnesic and the control group. This is inconsistent with a crucial prediction from the multiple-systems account. The results can be explained from a single system account and previously found differences in later categorization performance can be accounted for by a difference in learning rate. (shrink)

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...) memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)

Cognitive systems face a tension between stability and plasticity. The maintenance of long‐term representations that reflect the global regularities of the environment is often at odds with pressure to flexibly adjust to short‐term input regularities that may deviate from the norm. This tension is abundantly clear in speech communication when talkers with accents or dialects produce input that deviates from a listener's language community norms. Prior research demonstrates that when bottom‐up acoustic information or top‐down word knowledge is available to disambiguate (...) speech input, there is short‐term adaptive plasticity such that subsequent speech perception is shifted even in the absence of the disambiguating information. Although such effects are well‐documented, it is not yet known whether bottom‐up and top‐down resolution of ambiguity may operate through common processes, or how these information sources may interact in guiding the adaptive plasticity of speech perception. The present study investigates the joint contributions of bottom‐up information from the acoustic signal and top‐down information from lexical knowledge in the adaptive plasticity of speech categorization according to short‐term input regularities. The results implicate speech category activation, whether from top‐down or bottom‐up sources, in driving rapid adjustment of listeners' reliance on acoustic dimensions in speech categorization. Broadly, this pattern of perception is consistent with dynamic mapping of input to category representations that is flexibly tuned according to interactive processing accommodating both lexical knowledge and idiosyncrasies of the acoustic input. (shrink)

This essay explores the universal cognitive bases of biological taxonomy and taxonomic inference using cross-cultural experimental work with urbanized Americans and forest-dwelling Maya Indians. A universal, essentialist appreciation of generic species appears as the causal foundation for the taxonomic arrangement of biodiversity, and for inference about the distribution of causally-related properties that underlie biodiversity. Universal folkbiological taxonomy is domain-specific: its structure does not spontaneously or invariably arise in other cognitive domains, like substances, artifacts or persons. It is plausibly an innately-determined (...) evolutionary adaptation to relevant and recurrent aspects of ancestral hominid environments, such as the need to recognize, locate, react to, and profit from many ambient species. Folkbiological concepts are special players in cultural evolution, whose native stability attaches to more variable and difficult-to-learn representational forms, thus enhancing the latter's prospects for regularity and recurrence in transmission within and across cultures. This includes knowledge that cumulatively enriches (folk expertise), overrides (religious belief) or otherwise transcends (science) the commonsense ontology prescribed by folkbiology. Finally, the studies summarized here indicate that results gathered from “standard populations” in regard to biological categorization and reasoning more often than not fail to generalize in straightforward ways to humanity at large. This suggests the need for much more serious attention to cross-cultural research on basic cognitive processes. (shrink)

We address the problem of predicting how people will spontaneously divide into groups a set of novel items. This is a process akin to perceptual organization. We therefore employ the simplicity principle from perceptual organization to propose a simplicity model of unconstrained spontaneous grouping. The simplicity model predicts that people would prefer the categories for a set of novel items that provide the simplest encoding of these items. Classification predictions are derived from the model without information either about the number (...) of categories sought or information about the distributional properties of the objects to be classified. These features of the simplicity model distinguish it from other models in unsupervised categorization (where, for example, the number of categories sought is determined via a free parameter), and we discuss how these computational differences are related to differences in modeling objectives. The predictions of the simplicity model are validated in four experiments. We also discuss the significance of simplicity in cognitive modeling more generally. (shrink)

Seeking parallels among disciplines can have both risks and benefits. Finding parallels may be a vacuous exercise in categorization, generating no new insights. And pointing to analogous functions may cause us to treat them as homologous. Hull et al. have provided a basis for the generation of insights in different selectionist areas, without confusing analogy with homology.

The computational notion of “trading spaces” is highly relevant to the psychological domain of categorization. The “theory” view of concepts can be interpreted as a recoding view. A design principle for exploiting learned recodings in order to handle the type-2 problem of forming sophisticated concepts is outlined.

Multiple acoustic dimensions signal speech categories. However, dimensions vary in their informativeness; some are more diagnostic of category membership than others. Speech categorization reflects these dimensional regularities such that diagnostic dimensions carry more “perceptual weight” and more effectively signal category membership to native listeners. Yet perceptual weights are malleable. When short-term experience deviates from long-term language norms, such as in a foreign accent, the perceptual weight of acoustic dimensions in signaling speech category membership rapidly adjusts. The present study investigated (...) whether rapid adjustments in listeners’ perceptual weights in response to speech that deviates from the norms also affects listeners’ own speech productions. In a word recognition task, the correlation between two acoustic dimensions signaling consonant categories, fundamental frequency and voice onset time, matched the correlation typical of English, and then shifted to an “artificial accent” that reversed the relationship, and then shifted back. Brief, incidental exposure to the artificial accent caused participants to down-weight perceptual reliance on F0, consistent with previous research. Throughout the task, participants were intermittently prompted with pictures to produce these same words. In the block in which listeners heard the artificial accent with a reversed F0 × VOT correlation, F0 was a less robust cue to voicing in listeners’ own speech productions. The statistical regularities of short-term speech input affect both speech perception and production, as evidenced via shifts in how acoustic dimensions are weighted. (shrink)

The many constraints of outer space experience challenge the human ability to coexist. Paradoxically, astronauts assert that on the international space station there are no conflicts or, at least, that they are able to manage their differences, behavioral as well as cognitive, in full respect of human rights and the imperatives of cooperative living. The question is: Why? Why in those difficult, a-terrestrial, and therefore almost unnatural conditions do human beings seem to be able to peacefully and collaboratively live together? (...) What is there beyond terrestrial boundary conditions that allows for such a result? And what can we learn from the astronauts’ experience about the effectiveness of human rights on Earth? My proposal is that the a-terrestrial dimension deeply alters the mind/body indexical framework and, in this way, disentangles the human inclination to semiosis from the cognitive and behavioral habits of categorization and territorialization inherent in the experience on Earth. If analyzed through the spectrum of an interdisciplinary approach involving anthropology, semiology, law, and human geography, I think that outer space enterprises can offer many insights into the cognitive and ethical/political hindrances to the effectiveness of human rights and their intercultural uses. Meanwhile the compulsive greed for a possessive territorialization of outer space and celestial bodies is growing by leaps and bounds. It haunts and imbues both astropolitics and space law. The astronauts’ semio-anthropological experience of human rights and cooperative coexistence seems to have been left in orbit. The future requires awareness and action by anthropologists, semioticians, cognitive scientists, geographers and lawyers, working all together in an interdisciplinary effort to move beyond approaching the experiential with a territorial mindset. The hope is that the “dark dream” of human exploitation/colonization of outer space will not turn from a political and legal speculation into a future reality. (shrink)

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words. We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: the acquisition of features that discriminate among categories, and the grouping of concepts into categories based on those features. (...) Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (shrink)

Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error‐driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of categories better than learners exposed to input where labels preceded objects. We sought to replicate this finding in two online experiments employing the same tests used originally: A four pictures test (match a label (...) to one of four pictures) and a four labels test (match a picture to one of four labels). In our study, only findings from the four pictures test were consistent with the original result. Additionally, the effect sizes observed were smaller, and participants over‐generalized high‐frequency category labels more than in the original study. We suggest that although Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) feature‐label order predictions were derived from error‐driven learning, they failed to consider that this mechanism also predicts that performance in any training paradigm must inevitably be influenced by participant prior experience. We consider our findings in light of these factors, and discuss implications for the generalizability and replication of training studies. (shrink)

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human (...) class='Hi'>categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. (shrink)

Humans learn new concepts extremely fast. One or two examples of a new concept are often sufficient for us to grasp its meaning. Traditional theories of concept formation, such as symbolic or connectionist representations, have problems explaining the quick learning exhibited by humans. In contrast to these representations, I advocate a third form of representing categories, which employs geometric structures. I argue that this form is appropriate for modeling concept learning. By using the geometric structures of what I call “conceptual (...) spaces,” I define properties and concepts. A learning model that shows how properties and concepts can be learned in a simple but naturalistic way is then presented. This model accounts well for the role of similarity judgments in concept learning. Finally, as an application, the concept representations are used to give an analysis of nonmonotonic reasoning. (shrink)

The acquisition of complex motor, cognitive, and social skills, like playing a musical instrument or mastering sports or a language, is generally associated with implicit skill learning . Although it is a general view that SL is most effective in childhood, and such skills are best acquired if learning starts early, this idea has rarely been tested by systematic empirical studies on the developmental pathways of SL from childhood to old age. In this paper, we challenge the view that childhood (...) and early school years are the prime time for skill learning by tracking age-related changes in performance in three different paradigms of SL. We collected data from participants between 7 and 87 years for a Serial Reaction Time Task testing the learning of motor sequences, an Artificial Grammar Learning task testing the extraction of regularities from auditory sequences, and Probabilistic Category Learning in the Weather Prediction task , a non-sequential categorization task. Results on all three tasks show that adolescence and adulthood are the most efficient periods for skill learning, since instead of becoming less and less effective with age, SL improves from childhood into adulthood and then later declines with aging. (shrink)