Children improve at word learning during the 2nd year of life—sometimes dramatically. This fact has suggested a change in mechanism, from associative learning to a more referential form of learning. This article presents an associative exemplar-based model that accounts for the improvement without a change in mechanism. It provides a unified account of children's growing abilities to (a) learn a new word given only 1 or a few training trials (“fast mapping”); (b) acquire words that differ only slightly in phonological (...) form; (c) generalize word meanings preferentially along particular dimensions, such as object shape (the “shape bias”); and (d) learn 2nd labels for already-named objects, despite a persisting resistance to doing so (“mutual exclusivity”). The model explains these improvements in terms of increased attention to relevant aspects of form and meaning, which reduces memory interference. The interaction of associations and reference in word learning is discussed. (shrink)

Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...) the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations. (shrink)

This paper deals with the problem of understanding semiosis and meaning in cognitive systems. To this aim we argue for a unified two-factor account according to which both external and internal information are non-independent aspects of meaning, thus contributing as a whole in determining its nature. To overcome the difficulties stemming from this approach we put forward a theoretical scheme based on the definition of a suitable representation space endowed with a set of transformations, and we show how it can (...) be implemented, in the case of a single agent, by a neural network architecture. Numerical experiments conducted on different instances of the latter show that similar representations are developed as a consequence of the fact that these instances are facing a similar semantic task. This allows to model social and environmental influences through a system of interacting agents, each described by a specific implementation of this model architecture. (shrink)

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...) and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects. (shrink)

In Book I, Part I, Section VII of the Treatise, Hume sets out to settle, once and for all, the early modern controversy over abstract ideas. In order to do so, he tries to accomplish two tasks: (1) he attempts to defend an exemplar-based theory of general language and thought, and (2) he sets out to refute the rival abstraction-based account. This paper examines the successes and failures of these two projects. I argue that Hume manages to articulate a plausible (...) theory of general ideas; indeed, a version of his account has defenders in contemporary cognitive science. But Hume fails to refute the abstraction-based account, and as a result, the early modern controversy ends in a stalemate, with both sides able to explain how we manage to speak and think in general terms. Although Hume fails to settle the controversy, he nevertheless advances it to a point from which we have yet to progress: the contemporary debate over abstract ideas in cognitive science has stalled on precisely this point. (shrink)

Deep neural networks (DNNs) provide a unique opportunity to move towards a generic modelling framework in psychology. The high representational capacity of these models combined with the possibility for further extensions has already allowed us to investigate the forest, namely the complex landscape of representations and processes that underlie human cognition, without forgetting about the trees, which include individual psychological phenomena.

People can support abstract reasoning by using mental models with spatial simulations. Such models are employed when people represent elements in terms of ordered dimensions (e.g. who is oldest, Tom, Dick, or Harry). We test and find that the process of forming and using such mental models can influence the liking of its elements (e.g. Tom, Dick, or Harry). The presumed internal structure of such models (linear-transitive array of elements), generates variations in processing ease (fluency) when using the model in (...) working memory (see the Symbolic Distance Effect, SDE). Specifically, processing of pairs where elements have larger distances along the order should be easier compared to pairs with smaller distances. Elements from easier pairs should be liked more than elements from difficult pairs (fluency being hedonically positive). Experiment 1 shows that unfamiliar ideographs are liked more when at wider distances and therefore easier to process. Experiment 2 replicates this effect with non-words. Experiment 3 rules out a non-spatial explanation of the effect while Experiments 4 offers a high-powered replication. Experiment 5 shows that the spatial effect spontaneously emerges after learning, even without a task that explicitly focuses on fluency. Experiment 6 employed a shorter array, but yielded no significant results. (shrink)

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct (...) aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability. (shrink)

How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.

Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance imaging, a novel (...) neuroimaging method whose significance for current debates on psychological models and mechanistic explanation has yet to be explored. (shrink)

The functionalist approach to kinds has suffered recently due to its association with law-based approaches to induction and explanation. Philosophers of science increasingly view nomological approaches as inappropriate for the special sciences like psychology and biology, which has led to a surge of interest in approaches to natural kinds that are more obviously compatible with mechanistic and model-based methods, especially homeostatic property cluster theory. But can the functionalist approach to kinds be weaned off its dependency on laws? Dan Weiskopf has (...) recently offered a reboot of the functionalist program by replacing its nomological commitments with a model-based approach more closely derived from practice in psychology. Roughly, Weiskopf holds that the natural kinds of psychology will be the functional properties that feature in many empirically successful cognitive models, and that those properties need not be localized to parts of an underlying mechanism. I here skeptically examine the three modeling practices that Weiskopf thinks introduce such non-localizable properties: fictionalization, reification, and functional abstraction. In each case, I argue that recognizing functional properties introduced by these practices as autonomous kinds comes at clear cost to those explanations’ counterfactual explanatory power. At each step, a tempting functionalist response is parochialism: to hold that the false or omitted counterfactuals fall outside the modeler’s explanatory aims, and so should not be counted against functional kinds. I conclude by noting the dangers this attitude poses to scientific disagreement, inviting functionalists to better articulate how the individuation conditions for functional kinds might outstrip the perspective of a single modeler. (shrink)

The cell assembly is an important concept for cognitive psychology. Cognitive processing will to a large extent depend on the relations that can exist between different assemblies. A potential relation between assemblies can already be seen in the occurrence of (classical) conditioning. However, the resulting associations between assemblies only produce behavioristic processing or so-called regular computation. Higher-level cognitive abilities most likely result from nonregular computation. I discuss the possibility of this form of computation in terms of cell assemblies.

Cortical reverberations may induce synaptic changes that underlie developmental plasticity as well as long-term memory. They may be especially important for the consolidation of synaptic changes. Reverberations in cortical networks should have particular significance during development, when large numbers of new representations are formed. This includes the formation of representations across different sensory modalities.

Interpreting the Miyashita et al. experiments in terms of a cellassembly representation does not adequately explain the performance of Miyashita's monkeys on novel stimuli. We will argue that the latter observations point to acompositionalrepresentation and suggest a dynamics involving rapid and reversible binding of distinct activity patterns.

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 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)

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data (...) can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. (PsycINFO Database Record (c) 2012 APA, all rights reserved). (shrink)

We argue that atomistic learning?learning that requires training only on a novel item to be learned?is problematic for networks in which every weight is available for change in every learning situation. This is potentially significant because atomistic learning appears to be commonplace in humans and most non-human animals. We briefly review various proposed fixes, concluding that the most promising strategy to date involves training on pseudo-patterns along with novel items, a form of learning that is not strictly atomistic, but which (...) looks very much like it ?from the outside? (shrink)

Penn et al. argue that the complexity of relational learning is beyond animals. We discuss a model that demonstrates relational learning need not involve complex processes. Novel stimuli are compared to previous experiences stored in memory. As learning shifts attention from featural to relational cues, the comparison process becomes more analogical in nature, successfully accounting for performance across species and development.

This thesis consists of five main chapters including three independent studies, focusing on reward-based distractor interference and reward-association. In particular, the thesis addresses at which attentional processing stages the reward-based distractor interference takes place, as well as whether and how the reward association is learned on different levels. In the first chapter, I introduced a general background of attention, associative learning, and relations between reward associative learning and attention. In the end, I highlighted the open issues that this thesis aimed (...) to address. Chapter II aimed to answer the question at which processing stage the reward-based distractor interferes with the ongoing task. The study adopted the visual additional singleton search task but extended it to visual-tactile search. A classical finding of the reward-based distractor interference is that a reward-associated color feature, which appears as a task-irrelevant distractor, albeit it is not salient, captures attention and impedes task-relevant response. However, it is unclear at which attentional processing stage the reward-driven capture effects occur. To identify the distractor interference at the pre-attentional searching stage or post/focal- attentional decision-making stage, three experiments were conducted. Instead of a circular search display used in a standard attentional capture paradigm, this study used a horizontal search display to maximize potential attentional capture and disengagement. The results revealed that the reward-based distractor interference was only observed when the target and reward-based distractor were on the same side, indicating the interference likely occurred at the post/focal- attentional decision-making stage. To confirm this, Experiments 2 and 3 adopted the crossmodal search paradigm, in which the search stage and post-selective identification stage took place in different modalities. Such design enabled us to distinguish whether the interference effect occurs at the searching stage or the post/focal-attentional decision-making stage. The results further corroborated that the reward-driven capture effects occur at the post/focal-attention stage. Chapter III focused on the question related to the level of reward associative learning. Most previous studies on reward-based attentional capture implicitly used feature-based reward association, particularly on the feature level, e.g., color-reward association. This leaves the question open regarding whether the associative reward could be established at the level of response mapping or task-set mapping. Two experiments were designed to address this question. In Experiment 1, high/low rewards were associated with both the two colors and the left/right response in the training phase, and the reward-associated color became one of the task-irrelevant distractors in the test phase. We failed to find any evidence on the reward-response association. However, the results showed the distractor with previously reward-associated features facilitated search, indicating a better distractor handling in the test phase. In Experiment 2, the reward was associated with task-sets in the training phase and the task-sets kept the same in the test phase, while the search task was changed. The results suggested that reward-associated learning facilitated distractor handling and task-set learning. Chapter IV tackled the open research question - whether reward associative learning could take place at the multi-conjunction level. The question is important, given that previous studies on reward-based attentional capture only investigated associative learning of a single feature. As reward association in daily life is usually complex, Study 3 aims to explore whether reward learning can be established through reward assignments with conjunction features and whether the interference of distractors with previously reward-associated features can be observed in a similar way as previous studies shown on single-feature-based reward-driven capture effects. Reward learning was found locally trial-by-trial throughout the whole learning process, however, it was hard to establish a reliable conjunction reward association. Furthermore, there was no reward-based distractor interference in the test phase. The findings suggest that conjunction reward-association is difficult, though a short-lived trial-to-trial conjunction reward learning can be established. The final Chapter V discussed the findings of these three studies and their inter-relations. And I summarized how these findings could contribute to the debate of reward-based distractor interference. In summary, this thesis helps to improve our knowledge about reward-based distractor interference, particularly on the identification of the processing stage of distractor interference and what can be associated with reward in reward associative learning. (shrink)

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as ahierarchy, ranging from the concrete to the abstract; however, we argue that (...) there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged,diversity.Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into:modality-specificrepresentations, instantiated on perceptual similarity;multimodalrepresentations, instantiated primarily on the discovery of spatiotemporal contiguity; andcategoricalrepresentations, instantiated primarily on social interaction. These elements serve as the building blocks ofcomplex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e.,predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology. (shrink)

The concept of an attractor in a mathematical dynamical system is reviewed. Emphasis is placed on the distinction between a cell assembly, the corresponding attractor, and the attractor dynamics. The biological significance of these entities is discussed, especially the question of whether the representation of the stimulus requires the full attractor dynamics, or merely the cell assembly as a set of reverberating neurons. Comparison is made to Freeman's study of dynamic patterns in olfaction.

Further tests of Amit's model are indicated. One strategy is to use the apparent coding sparseness of the model to make predictions about coding sparseness in Miyashita's network. A second approach is to use memory overload to induce false positive responses in modules and biological systems. In closing, the importance of temporal coding and timing requirements in developing biologically plausible attractor networks is mentioned.

Invariance principles underlie many key theories in modern science. They provide the explanatory and predictive framework necessary for the rigorous study of natural phenomena ranging from the structure of crystals, to magnetism, to relativistic mechanics. Vigo (2008, 2009)introduced a new general notion and principle of invariance from which two parameter-free (ratio and exponential) models were derived to account for human conceptual behavior. Here we introduce a new parameterized exponential “law” based on the same invariance principle. The law accurately predicts the (...) subjective degree of difficulty that humans experience when learning different types of concepts. In addition, it precisely fits the data from a large-scale experiment which examined a total of 84 category structures across 10 category families (R-Squared =.97, p < .0001; r= .98, p < .0001). Moreover, it overcomes seven key challenges that had, hitherto, been grave obstacles for theories of concept learning. (shrink)

Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ‘‘sampling’’ assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, (...) data are assumed to have been generated without any restrictions. We develop a more general account of sampling that allows for an intermediate mixture of these two extremes, and we test its usefulness. In two experiments, we show that most people complete simple one-dimensional generalization tasks in a way that is consistent with their believing in some mixture of strong and weak sampling, but that there are large individual differences in the relative emphasis different people give to each type of sampling. We also show experimentally that the relative emphasis of the mixture is influenced by the structure of the available information. We discuss the psychological meaning of mixing strong and weak sampling, and possible extensions of our modeling approach to richer problems of inductive generalization. (shrink)

Learning depends on attention. The processes that cue attention in the moment dynamically integrate learned regularities and immediate contextual cues. This paper reviews the extensive literature on cued attention and attentional learning in the adult literature and proposes that these fundamental processes are likely significant mechanisms of change in cognitive development. The value of this idea is illustrated using phenomena in children's novel word learning.

In this prcis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (...) (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed-forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed. (shrink)

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...) single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be (...) described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity. Key Words: categorization; cognitive explanation; language; learning; reasoning; rules; similarity. (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)