Précis of Semantic Cognition: A Parallel Distributed Processing Approach

Toward the Emergence of Intelligent Control: Episodic Generalization and Optimization

Human cognition is unique in its ability to perform a wide range of tasks and to learn new tasks quickly. Both abilities have long been associated with the acquisition of knowledge that can generalize across tasks and the flexible use of that knowledge to execute goal-directed behavior. We investigate how this emerges in a neural network by describing and testing the Episodic Generalization and Optimization (EGO) framework. The framework consists of an episodic memory module, which rapidly learns relationships between stimuli; a semantic pathway, which more slowly learns how stimuli map to responses; and a recurrent context module, which maintains a representation of task-relevant context information, integrates this over time, and uses it both to recall context-relevant memories (in episodic memory) and to bias processing in favor of context-relevant features and responses (in the semantic pathway). We use the framework to address empirical phenomena across reinforcement learning, event segmentation, and category learning, showing in simulations that the same set of underlying mechanisms accounts for human performance in all three domains. The results demonstrate how the components of the EGO framework can efficiently learn knowledge that can be flexibly generalized across tasks, furthering our understanding of how humans can quickly learn how to perform a wide range of tasks-a capability that is fundamental to human intelligence.

Semantic-to-autobiographical memory priming causes involuntary autobiographical memory production: The effects of single and multiple prime presentations

A number of studies (Mace et al., Memory & Cognition, 47, 299-312, 2019; Mace & Unlu, Memory & Cognition, 48, 931-941, 2020) have demonstrated that the activation of semantic memories leads to the activation of autobiographical memories on an involuntary memory task (the vigilance task; Schlagman & Kvavilashvili, Memory & Cognition, 36, 920-932, 2008), suggesting that this form of priming (semantic-to-autobiographical) plays a role in the production of involuntary autobiographical memories in everyday life. In the current study, we investigated the effects of prime repetition on involuntary memory production in the vigilance task. Primed participants were either treated to one priming session, where they judged the familiarity of words (e.g., parade), or three priming sessions, where they also judged the familiarity of words as well as decided whether sentences containing the words made sense (e.g., the parade dragged on for hours), and if their corresponding images were sensible (e.g., an image of a parade). The results showed that primed participants produced more involuntary memories with primed content on the vigilance task than control participants, and three-session primed participants produced more memories than one-session primed participants. Similar to other areas where prime repetition has been investigated (e.g., implicit memory, semantic priming), the results show that prime repetition enhances semantic-to-autobiographical memory priming. The results also further support the idea that semantic-to-autobiographical memory priming may play a significant role in the production of involuntary memories in everyday life, as concept repetition is a likely part of everyday experience. These implications, as well as others, are discussed.

Decoding the information structure underlying the neural representation of concepts

The ability to identify individual objects or events as members of a kind (e.g., “knife,” “dog,” or “party”) is a fundamental aspect of human cognition. It allows us to quickly access a wealth of information pertaining to a newly encountered object or event and use it to guide our behavior. How is this information represented in the brain? We used functional MRI to analyze patterns of brain activity corresponding to hundreds of familiar concepts and quantitatively characterized the informational structure of these patterns. Our results indicate that conceptual knowledge is stored as patterns of neural activity that encode sensory-motor and affective information about each concept, contrary to the long-held idea that concept representations are independent of sensory-motor experience.

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional “semantic hubs” contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

Perceptual Connectivity Influences Toddlers' Attention to Known Objects and Subsequent Label Processing

While recent research suggests that toddlers tend to learn word meanings with many “perceptual” features that are accessible to the toddler’s sensory perception, it is not clear whether and how building a lexicon with perceptual connectivity supports attention to and recognition of word meanings. We explore this question in 24–30-month-olds (N = 60) in relation to other individual differences, including age, vocabulary size, and tendencies to maintain focused attention. Participants’ looking to item pairs with high vs. low perceptual connectivity—defined as the number of words in a child’s lexicon sharing perceptual features with the item—was measured before and after target item labeling. Results revealed pre-labeling attention to known items is biased to both high- and low-connectivity items: first to high, and second, but more robustly, to low-connectivity items. Subsequent object–label processing was also facilitated for high-connectivity items, particularly for children with temperamental tendencies to maintain focused attention. This work provides the first empirical evidence that patterns of shared perceptual features within children’s known vocabularies influence both visual and lexical processing, highlighting the potential for a newfound set of developmental dependencies based on the perceptual/sensory structure of early vocabularies.

Common and distinctive cognitive processes between categorization and category-based induction: Evidence from event-related potentials

Categorization involves forming equivalence classes of discriminable entities, whereas category-based induction (CBI) involves employing categorical knowledge to generalize novel properties. Previous studies have suggested either common or distinctive cognitive processing between categorization and CBI. However, no study has compared cognitive processes with the same stimuli sets using event-related potentials (ERPs), which help to determine the cognitive processes with a high temporal solution. In this study, we compared the ERP responses to categorization and CBI using two separate experiments (i.e., generic and specific conclusions), with the same task materials. Results from both experiments identified distinctive cognitive processing between categorization and CBI based on a greater proportion of "definitely" responses and smaller amplitudes of sustained negativity during categorization. These observations suggest that categorization involves decreased conflict monitoring and control than CBI under single-premise conditions. Contrastingly, categorization and CBI elicited similar FN400 amplitudes in both experiments, which suggests a common cognitive process between them. These findings present the common and distinctive cognitive processes between categorization and CBI.

Neural Population Dynamics and Cognitive Function

Representations in the brain are encoded as patterns of activity of large populations of neurons. The science of population encoded representations, also known as parallel distributed processing (PDP), achieves neurological verisimilitude and has been able to account for a large number of cognitive phenomena in normal people, including reaction times (and reading latencies), stimulus recognition, the effect of stimulus salience on attention, perceptual invariance, simultaneous egocentric and allocentric visual processing, top-down/bottom-up processing, language errors, the effect of statistical regularities of experience, frequency, and age of acquisition, instantiation of rules and symbols, content addressable memory and the capacity for pattern completion, preservation of function in the face of noisy or distorted input, inference, parallel constraint satisfaction, the binding problem and gamma coherence, principles of hippocampal function, the location of knowledge in the brain, limitations in the scope and depth of knowledge acquired through experience, and Piagetian stages of cognitive development. PDP studies have been able to provide a coherent account for impairment in a variety of language functions resulting from stroke or dementia in a large number of languages and the phenomenon of graceful degradation observed in such studies. They have also made important contributions to our understanding of attention (including hemispatial neglect), emotional function, executive function, motor planning, visual processing, decision making, and neuroeconomics. The relationship of neural network population dynamics to electroencephalographic rhythms is starting to emerge. Nevertheless, PDP approaches have scarcely penetrated major areas of study of cognition, including neuropsychology and cognitive neuropsychology, as well as much of cognitive psychology. This article attempts to provide an overview of PDP principles and applications that addresses a broader audience.

Semantic fluency difficulties in developmental dyslexia and developmental language disorder (DLD): poor semantic structure of the lexicon or slower retrieval processes?

BACKGROUND

Children with dyslexia and/or developmental language disorder (hereafter children with DDLD) have been reported to retrieve fewer words than their typically developing (TD) peers in semantic fluency tasks. It is not known whether this retrieval difficulty can be attributed to the semantic structure of their lexicon being poor or, alternatively, to words being retrieved more slowly despite semantic structure being intact.

AIMS

To test two theoretical models that could potentially account for retrieval difficulties in semantic fluency tasks, namely, the Poor Lexical-Semantic Structure Model and the Slow-Retrieval Model. Both models predict that children with DDLD will retrieve fewer items compared with TD children. However, while the Poor Lexical-Semantic Structure Model predicts a less sophisticated network of semantic connections between words in the lexicon, as evidenced by smaller clusters of related items in children with DDLD, the Slow-Retrieval Model predicts intact inter-item associations in the lexicon, as evidenced by the two groups' clusters being of a similar size. The groups' semantic fluency performance was therefore compared. How semantic fluency performance related to children's language, literacy, and phonological skills was also investigated.

METHODS & PROCEDURES

A total of 66 children with DDLD aged 7-12 years and 83 TD children aged 6-12 years, all monolingual Greek speakers, were tested on semantic fluency, using the categories 'animals', 'foods' and 'objects from around the house'. The numbers of correct and incorrect responses, clusters and switches, and the average cluster size were computed. Children were also assessed on non-verbal IQ, language, literacy and phonological tasks.

OUTCOMES & RESULTS

In both groups, productivity in semantic fluency tasks correlated strongly with the numbers of clusters and switches, but not with average cluster size. The DDLD group produced significantly fewer correct responses and fewer clusters compared with the TD group, but the two groups showed similar switching and average cluster size. Children's language, literacy and phonological skills significantly predicted the number of correct responses produced, beyond the significant effect of age.

CONCLUSIONS & IMPLICATIONS

We conclude that poorer semantic fluency performance in children with DDLD results not from a lexicon with poor semantic structure, but rather from slower retrieval processes from a lexicon with intact semantic structure. The underlying causes of slow lexical retrieval still need further investigation.

Neural Oscillation Profiles of a Premise Monotonicity Effect During Semantic Category-Based Induction

A premise monotonicity effect during category-based induction is a robust effect, in which participants are more likely to generalize properties shared by many instances rather than those shared by few instances. Previous studies have shown the event-related potentials (ERPs) elicited by this effect. However, the neural oscillations in the brain underlying this effect are not well known, and such oscillations can convey task-related cognitive processing information which is lost in traditional ERP analysis. In the present study, the phase-locked and non-phase-locked power of neural oscillations related to this effect were measured by manipulating the premise sample size [single (S) vs. two (T)] in a semantic category-based induction task. For phase-locked power, the results illustrated that the premise monotonicity effect was revealed by anterior delta power, suggesting differences in working memory updating. The results also illustrated that T arguments evoked larger posterior theta-alpha power than S arguments, suggesting that T arguments led to enhanced subjectively perceived inductive confidence than S arguments. For non-phase-locked power, the results illustrated that the premise monotonicity effect was indicated by anterior theta power, suggesting that the differences in sample size were related to a change in the need for cognitive control and the implementation of adaptive cognitive control. Moreover, the results illustrated that the premise monotonicity effect was revealed by alpha-beta power, which suggested the unification of sentence and inference-driven information. Therefore, the neural oscillation profiles of the premise monotonicity effect during semantic category-based induction were elucidated, and supported the connectionist models of category-based induction.

Curiosity-based learning in infants: a neurocomputational approach

Infants are curious learners who drive their own cognitive development by imposing structure on their learning environment as they explore. Understanding the mechanisms by which infants structure their own learning is therefore critical to our understanding of development. Here we propose an explicit mechanism for intrinsically motivated information selection that maximizes learning. We first present a neurocomputational model of infant visual category learning, capturing existing empirical data on the role of environmental complexity on learning. Next we "set the model free", allowing it to select its own stimuli based on a formalization of curiosity and three alternative selection mechanisms. We demonstrate that maximal learning emerges when the model is able to maximize stimulus novelty relative to its internal states, depending on the interaction across learning between the structure of the environment and the plasticity in the learner itself. We discuss the implications of this new curiosity mechanism for both existing computational models of reinforcement learning and for our understanding of this fundamental mechanism in early development.

Semantic richness and word learning in children with autism spectrum disorder

Semantically rich learning contexts facilitate semantic, phonological, and articulatory aspects of word learning in children with typical development (TD). However, because children with autism spectrum disorder (ASD) show differences at each of these processing levels, it is unclear whether they will benefit from semantic cues in the same manner as their typical peers. The goal of this study was to track how the inclusion of rich, sparse, or no semantic cues influences semantic, phonological, and articulatory aspects of word learning in children with ASD and TD over time. Twenty-four school-aged children (12 in each group), matched on expressive vocabulary, participated in an extended word learning paradigm. Performance on five measures of learning (referent identification, confrontation naming, defining, phonetic accuracy, and speech motor stability) were tracked across three sessions approximately one week apart to assess the influence of semantic richness on extended learning. Results indicate that children with ASD benefit from semantically rich learning contexts similarly to their peers with TD; however, one key difference between the two groups emerged - the children with ASD showed heightened shifts in speech motor stability. These findings offer insights into common learning mechanisms in children with ASD and TD, as well as pointing to a potentially distinct speech motor learning trajectory in children with ASD, providing a window into the emergence of stereotypic vocalizations in these children.

Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0–3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM) and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system.

The Theory of Localist Representation and of a Purely Abstract Cognitive System: The Evidence from Cortical Columns, Category Cells, and Multisensory Neurons

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings - in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.

Oscillatory Dynamics Supporting Semantic Cognition: MEG Evidence for the Contribution of the Anterior Temporal Lobe Hub and Modality-Specific Spokes

The "hub and spoke model" of semantic representation suggests that the multimodal features of objects are drawn together by an anterior temporal lobe (ATL) "hub", while modality-specific "spokes" capture perceptual/action features. However, relatively little is known about how these components are recruited through time to support object identification. We used magnetoencephalography to measure neural oscillations within left ATL, lateral fusiform cortex (FC) and central sulcus (CS) during word-picture matching at different levels of specificity (employing superordinate vs. specific labels) for different categories (manmade vs. animal). This allowed us to determine (i) when each site was sensitive to semantic category and (ii) whether this was modulated by task demands. In ATL, there were two phases of response: from around 100 ms post-stimulus there were phasic bursts of low gamma activity resulting in reductions in oscillatory power, relative to a baseline period, that were modulated by both category and specificity; this was followed by more sustained power decreases across frequency bands from 250 ms onwards. In the spokes, initial power increases were not stronger for specific identification, while later power decreases were stronger for specific-level identification in FC for animals and in CS for manmade objects (from around 150 ms and 200 ms, respectively). These data are inconsistent with a temporal sequence in which early sensory-motor activity is followed by later retrieval in ATL. Instead, knowledge emerges from the rapid recruitment of both hub and spokes, with early specificity and category effects in the ATL hub. The balance between these components depends on semantic category and task, with visual cortex playing a greater role in the fine-grained identification of animals and motor cortex contributing to the identification of tools.

Natural Language Acquisition: State Inferring and Thinking

Natural language understanding plays an important role in our daily life. It is very significant to study how to make the computer understand the human language and produce the corresponding action or response. Most of the prior language acquisition models adopt handcrafted internal representation, and they are not sufficiently brain-based and not sufficiently comprehensive to account for all branches in psychology and cognitive science. An emergent developmental network (DN) is used to learn, infer and think a knowledge base represented as a finite automaton, from sensory and motor experience grounded in this operational environments. This work is different in the sense that we emphasize on the mechanism that enable a system to develop its emergent representations from its operational experience. By emergent, we mean a pattern of responses of multiple elements that corresponds to an event outside the closed skull but each element (e.g. pixel, muscle, neuron) of the pattern typically does not have a meaning. In this work, internal unsupervised neurons of the DN are used to represent short contexts, and the competitions among internal neurons enable them to represent different short contexts. By internal, we mean that all the neurons inside a brain are not directly supervised by the external environment — outside the brain skull. In this work, we analyze how internal neurons represent temporal contexts and how the feature neurons of the DN represent earlier contexts. Accuracy of Z state inferring and $X$ thinking of a relative complex training sequence (denoted as DN-2 in this work) can reach 100% and 75%, respectively. Comparative experiment results between this emergent method and the symbolic method, their corresponding Z state inferring and $X$ thinking accuracy are 100% and 82.1%, 85.7% and 75%, respectively (taking DN-6 in this work as the example), demonstrate the efficiency of the DN on natural language inferring and thinking. Complexity of the finite automaton is low and so is the temporal contexts, but the same principle is potentially applicable to more complex cases.

The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions-a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment.

A Model of Emergent Category-specific Activation in the Posterior Fusiform Gyrus of Sighted and Congenitally Blind Populations

Theories about the neural bases of semantic knowledge tend between two poles, one proposing that distinct brain regions are innately dedicated to different conceptual domains and the other suggesting that all concepts are encoded within a single network. Category-sensitive functional activations in the fusiform cortex of the congenitally blind have been taken to support the former view but also raise several puzzles. We use neural network models to assess a hypothesis that spans the two poles: The interesting functional activation patterns reflect the base connectivity of a domain-general semantic network. Both similarities and differences between sighted and congenitally blind groups can emerge through learning in a neural network, but only in architectures adopting real anatomical constraints. Surprisingly, the same constraints suggest a novel account of a quite different phenomenon: the dyspraxia observed in patients with semantic impairments from anterior temporal pathology. From this work, we suggest that the cortical semantic network is wired not to encode knowledge of distinct conceptual domains but to promote learning about both conceptual and affordance structure in the environment.

The representation of category typicality in the frontal cortex and its cross-linguistic variations

When asked to judge the membership of typical (e.g., car) vs. atypical (e.g., train) pictures of a category (e.g., vehicle), native English (N=18) and native Chinese speakers (N=18) showed distinctive patterns of brain activity despite showing similar behavioral responses. Moreover, these differences were mainly due to the amount and pervasiveness of category information linguistically embedded in the everyday names of the items in the respective languages, with important differences across languages in how pervasive category labels are embedded in item-level terms. Nonetheless, the left inferior frontal gyrus and the bilateral medial frontal gyrus are the most consistent neural correlates of category typicality that persist across languages and linguistic cues. These data together suggest that both cross- and within-language differences in the explicitness of category information have strong effects on the nature of categorization processes performed by the brain.

O paradigma conexionista aplicado às pesquisas em linguagem

O estudo trata da Teoria Conexionista e suas vertentes e de como este modelo teórico pode contribuir para a prática fonoaudiológica. O objetivo do trabalho foi reunir e discutir diversos estudos sobre a teoria conexionista aplicada à aprendizagem da linguagem, com o intuito de promover uma reflexão sobre as contribuições que esta teoria pode trazer à prática fonoaudiológica. A partir disso, verificou-se que os estudos baseados na teoria conexionista relacionados à Fonoaudiologia são predominantes na área de aquisição e terapia da linguagem, processamento auditivo e de leitura. Porém, os mesmos são escassos e pouco aplicados. Além disso, não há uma relação direta com as técnicas que podem ser utilizadas, mas sim com a análise da evolução terapêutica, especialmente na interpretação de como se dá o processo de aprendizagem da língua falada e/ou escrita.

Abstracting Grammar from Social–Cognitive Foundations: A Developmental Sketch of Learning

Although the understanding of the development of infants’ social cognition and cooperative reasoning has progressed significantly, to date, it has yet to be worked through in any detail how this knowledge interacts with and constrains emerging syntactic representations. This review is a step in that direction, aiming to offer a more integrated account of the learning mechanisms that support linguistic generalizations. First, I review the developmental literature that suggests social–cognitive foundations get linguistic constructions “off the ground.” Second, I focus on building layers of abstractions on top of this foundation and the kind of cognitive processes that are involved. Crucially important in this explanation will be the fact that humans possess a unique set of social–cognitive and social motivational-skills that allows language to happen. Furthermore, early linguistic categories are formed around the underlying functional core of concepts and on the basis of their communicative discourse function. This, combined with powerful pattern-detection skills, enables distributional regularities in the input to be paired with what the speakers intend to communicate.

Categorical implicit learning in real-world scenes: Evidence from contextual cueing

The present study examined the extent to which learning mechanisms are deployed on semantic-categorical regularities during a visual searching within real-world scenes. The contextual cueing paradigm was used with photographs of indoor scenes in which the semantic category did or did not predict the target position on the screen. No evidence of a facilitation effect was observed in the predictive condition compared to the nonpredictive condition when participants were merely instructed to search for a target T or L (Experiment 1). However, a rapid contextual cueing effect occurred when each display containing the search target was preceded by a preview of the scene on which participants had to make a decision regarding the scene's category (Experiment 2). A follow-up explicit memory task indicated that this benefit resulted from implicit learning. Similar implicit contextual cueing effects were also obtained when the scene to categorize was different from the subsequent search scene (Experiment 3) and when a mere preview of the search scene preceded the visual searching (Experiment 4). These results suggested that if enhancing the processing of the scene was required with the present material, such implicit semantic learning can nevertheless take place when the category is task irrelevant.

Associative processes in intuitive judgment

Dual-system models of reasoning attribute errors of judgment to two failures: the automatic operations of a 'System 1' generate a faulty intuition, which the controlled operations of a 'System 2' fail to detect and correct. We identify System 1 with the automatic operations of associative memory and draw on research in the priming paradigm to describe how it operates. We explain how three features of associative memory--associative coherence, attribute substitution and processing fluency--give rise to major biases of intuitive judgment. Our article highlights both the ability of System 1 to create complex and skilled judgments and the role of the system as a source of judgment errors.

Domain-creating constraints

The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A learning system must be constrained to learn efficiently, but some of these constraints are themselves learnable. To know how something will behave, a learner must know what kind of thing it is. Although this has led previous researchers to argue for domain-specific constraints that are tied to different kinds/domains, an exciting possibility is that kinds/domains themselves can be learned. General cognitive constraints, when combined with rich inputs, can establish domains, rather than these domains necessarily preexisting prior to learning. Knowledge is structured and richly differentiated, but its "skeleton" must not always be preestablished. Instead, the skeleton may be adapted to fit patterns of co-occurrence, task requirements, and goals. Finally, we argue that for models of development to demonstrate genuine cognitive novelty, it will be helpful for them to move beyond highly preprocessed and symbolic encodings that limit flexibility. We consider two physical models that learn to make tone discriminations. They are mechanistic models that preserve rich spatial, perceptual, dynamic, and concrete information, allowing them to form surprising new classes of hypotheses and encodings.

Symbol interdependency in symbolic and embodied cognition

Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information.

A computational neural model of goal-directed utterance selection

It is generally agreed that much of human communication is motivated by extra-linguistic goals: we often make utterances in order to get others to do something, or to make them support our cause, or adopt our point of view, etc. However, thus far a computational foundation for this view on language use has been lacking. In this paper we propose such a foundation using Markov Decision Processes. We borrow computational components from the field of action selection and motor control, where a neurobiological basis of these components has been established. In particular, we make use of internal models (i.e., next-state transition functions defined on current state action pairs). The internal model is coupled with reinforcement learning of a value function that is used to assess the desirability of any state that utterances (as well as certain non-verbal actions) can bring about. This cognitive architecture is tested in a number of multi-agent game simulations. In these computational experiments an agent learns to predict the context-dependent effects of utterances by interacting with other agents that are already competent speakers. We show that the cognitive architecture can account for acquiring the capability of deciding when to speak in order to achieve a certain goal (instead of performing a non-verbal action or simply doing nothing), whom to address and what to say.

An algebra for the analysis of object encoding

The encoding of the objects from the world around us is one of the major topics of cognitive psychology, yet the principles of object coding in the human brain remain unresolved. Beyond referring to the particular features commonly associated with objects, our ability to categorize and discuss objects in detailed linguistic propositions implies that we have access to generic concepts of each object category with well-specified boundaries between them. Consideration of the nature of generic object concepts reveals that they must have the structure of a probabilistic list array specifying the Bayesian prior on all possible features that the object can possess, together with mutual covariance matrices among the features. Generic object concepts must also be largely context independent for propositions to have communicable meaning. Although, there is good evidence for local feature processing in the occipital lobe and specific responses for a few basic object categories in the posterior temporal lobe, the encoding of the generic object concepts remains obscure. We analyze the conceptual underpinnings of the study of object encoding, draw some necessary clarifications in relation to its modality-specific and amodal aspects, and propose an analytic algebra with specific reference to functional Magnetic Resonance Imaging approaches to the issue of how generic (amodal) object concepts are encoded in the human brain.