Integrating constraints for learning word-referent mappings

Learning word-referent mappings is complex because the word and its referent tend to co-occur with multiple other words and potential referents. Such complexity has led to proposals for a host of constraints on learning, though how these constraints may interact has not yet been investigated in detail. In this paper, we investigated interactions between word co-occurrence constraints and cross-situational statistics in word learning. Analyses of child-directed speech revealed that when both object-referring and non-referring words occurred in the utterance, referring words were more likely to be preceded by a determiner than when the utterance contained only referring words. In a word learning study containing both referring and non-referring words, learning was facilitated when non-referring words contributed grammatical constraints analogous to determiners. The complexity of multi-word utterances provides an opportunity for co-occurrence constraints to contribute to word-referent mapping, and the learning mechanism is able to integrate these multiple sources of information.     

An associative model of rat serial pattern learning in three-element sequences

Hypotheses ranging from subsymbolic to symbolic have been proposed to account for rat sequential behaviour, and in the subsymbolic domain alone there are multiple proposed subsymbolic processes or factors thought to affect serial behaviour. A behavioural study and computer simulations were conducted to evaluate these hypotheses, and a new computational associative model based on pairwise associations and generalization was evaluated. Seven 3-element sequences were selected for study that systematically (1) varied sequence discriminability, (2) varied reward magnitude, and (3) manipulated the order of food quantities. Neither element discriminability nor response enhancement subsymbolic processes in isolation were able to account for the behavioural data; however, simulations from the computational model known as the sequential pairwise associative memory (SPAM) model with a log-linear mapping of stimulus dimension items to food quantities correlated well with the behavioural data. SPAM accounts for differential element anticipation in different sequences by appealing to pairwise association of sequence events and generalization between cues as the principal factors mediating pattern tracking in three-element sequences.     

An associative model of geometry learning: a modified choice rule

In a recent article, the authors (Miller & Shettleworth, 2007) showed how the apparently exceptional features of behavior in geometry learning ("reorientation") experiments can be modeled by assuming that geometric and other features at given locations in an arena are learned competitively as in the Rescorla-Wagner model and that the probability of visiting a location is proportional to the total associative strength of cues at that location relative to that of all relevant locations. Reinforced or unreinforced visits to locations drive changes in associative strengths. Dawson, Kelly, Spetch, and Dupuis (2008) have correctly pointed out that at parameter values outside the ranges the authors used to simulate a body of real experiments, our equation for choice probabilities can give impossible and/or wildly fluctuating results. Here, the authors show that a simple modification of the choice rule eliminates this problem while retaining the transparent way in which the model relates spatial choice to competitive associative learning of cue values.     

An adaptive linear filter model of procedural category learning

Cognitive Processing - We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to...     

Simulation of associative learning with the replaced elements model

Associative learning theories can be categorized according to whether they treat the representation of stimulus compounds in an elemental or a configural manner. Since it is clear that a simple elemental approach to stimulus representation is inadequate, there have been several attempts to produce more elaborate elemental models. One recent approach, the replaced elements model (Wagner, 2003), reproduces many results that have until recently been uniquely predicted by Pearce's configural theory (Pearce, 1994). Although it is possible to simulate the replaced elements model using "standard" simulation programs, the generation of the correct stimulus representation is complex. The present article describes a method for simulation of the replaced elements model and presents the results of two example simulations that show differential predictions of replaced elements and Pearce's configural theory.     

An associative account of the development of word learning

Word learning is a notoriously difficult induction problem because meaning is underdetermined by positive examples. How do children solve this problem? Some have argued that word learning is achieved by means of inference: young word learners rely on a number of assumptions that reduce the overall hypothesis space by favoring some meanings over others. However, these approaches have difficulty explaining how words are learned from conversations or text, without pointing or explicit instruction. In this research, we propose an associative mechanism that can account for such learning. In a series of experiments, 4-year-olds and adults were presented with sets of words that included a single nonsense word (e.g. dax). Some lists were taxonomic (i.,e., all items were members of a given category), some were associative (i.e., all items were associates of a given category, but not members), and some were mixed. Participants were asked to indicate whether the nonsense word was an animal or an artifact. Adults exhibited evidence of learning when lists consisted of either associatively or taxonomically related items. In contrast, children exhibited evidence of word learning only when lists consisted of associatively related items. These results present challenges to several extant models of word learning, and a new model based on the distinction between syntagmatic and paradigmatic associations is proposed.     

An attention-based associative account of adjacent and nonadjacent dependency learning

In 5 experiments, results showed that when participants are faced with materials embedding relations between both adjacent and nonadjacent elements, they learn exclusively the type of relations they had to actively process in order to meet the task demands, irrespective of the spatial contiguity of the paired elements. These results are consonant with current theories positing that attention is a necessary condition for learning. More important, the results provide support for a more radical conception, in which the joint attentional processing of 2 events is also a sufficient condition for learning the relation between them. The well-documented effect of contiguity could be a by-product of the fact that attention generally focuses on contiguous events. This reappraisal considerably extends the scope of approaches based on associative or statistical processes.     

Humour in advertising: An associative processing model

This chapter reviews a research programme on the effects of humour in advertising on positive and negative brand associations and brand choice, and integrates the findings into a single overarching model. Based on the Associative and Propositional Processes Model of Evaluation (Gawronski & Bodenhausen, 2006, 2007, 2011), we propose that repeated pairings of a novel brand with brand-unrelated humour forms positive brand associations, which mediate spontaneous brand choice. This associative process was found to be independent from the level of distraction posed by humour and from awareness of the stimulus pairings. In fact the distraction posed by humour benefits persuasion by preventing negative brand associations. Previous marketing research, which mainly viewed humour as a cue in peripheral processing, was rather pessimistic about the persuasive impact of humour. In contrast, this research programme suggests that a repeated pairing of a brand with humour affects the brand’s underlying associative structure, which may lead to stable attitude changes that guide overt spontaneous brand choice. Theoretical and practical implications are discussed.     

The role of associative history in models of associative learning: A selective review and a hybrid model

Associative learning theories strive to capture the processes underlying and driving the change in strength of the associations between representations of stimuli that develop as a result of experience of the predictive relationships between those stimuli. Historically, formal models of associative learning have focused on two potential factors underlying associative change, namely processing of the conditioned stimulus (in terms of changes in associability) and processing of the unconditioned stimulus (in terms of changes in error). This review constitutes an analysis of the proper role of these two factors, specifically with regard to the way in which they are influenced by associative history (the prior training undergone by cues). A novel “hybrid” model of associative learning is proposed and is shown to provide a more satisfactory account of the effects of associative history on subsequent learning than any previous single-process theory.     

HMS: A MATLAB simulator of the Harris model of associative learning

Harris (2006) recently proposed a new elemental model of the processes involved in associative learning. Although Harris explicated all relevant mathematical and conceptual details of the model in his article, implementing a computer simulation of his model requires considerable programming expertise and work. We therefore present the Harris model simulator (HMS), a MATLAB simulator of Harris's model. HMS provides a graphical user interface for manipulating all essential parameter values and for controlling the simulation process, the graphical visualization of the simulation course, and the numerical results. HMS is available free of charge from www.staff.uni-marburg.de/(tilde)lachnit/harris/. HMS allows researchers to easily derive and evaluate predictions for the Harris model, and it will therefore facilitate insights into the mechanisms of associative learning.     

Filial imprinting and associative learning

Evidence is reviewed that is relevant to an interpretation of filial imprinting as a form of classical or Pavlovian conditioning. For such an interpretation it has to be assumed that the imprinting object provides both the unconditioned stimulus (UCS) and the conditioned stimulus (CS). The difficulty for an experimental approach is that CS and UCS are always presented simultaneously and cannot be manipulated independently, making it virtually impossible to test phenomena such as latent inhibition and extinction. Existing evidence is consistent with a conditioning theory of imprinting, for instance with regard to the reversibility of filial preferences. Some hitherto unexplained findings can be accounted for in terms of overshadowing and blocking. Results of an experiment are discussed in which the occurrence of blocking was explicitly tested. The findings support an associative learning interpretation of imprinting, but further experiments are needed to be able to rule cut alternative explanations.     

The 28th Bartlett Memorial Lecture Causal learning: An associative analysis

The concordance between performance and judgements of the causal effectiveness of an instrumental action suggests that such actions are mediated by causal knowledge. Although causal learning exhibits many associative phenomena—blocking, inhibitory or preventative learning, and super-learning—judgements of the causal status of a cue can be changed retrospectively as a result of learning episodes that do not directly involve the cue. In order to explain retrospective revaluation, a modified associative theory is described in which the learning processes for retrieved cue representations are the opposite to those for presented cues, and this theory is evaluated by studies of the role of within-compound associations in retrospective revaluation and blocking. However, this modified theory only applies when the within-compound association represents a contiguous rather than a causal cue relationship.     

Syntactic facilitation of children's associative learning: An instructional effect

An experiment is reported in which connective recall, as well as noun recall, was obtained in a pair-associate learning, syntactic facilitation paradigm. Dfferences in the pattern of noun and connective recall, and a partial independence in the two types of recall, were obtained. These results were interpreted as consistent with the hypothesis that experimenter-supplied verb links facilitate noun pair learning by serving an instructional role, indicating to the subject how a pair might be coded.     

Retrospective revaluation as simple associative learning

Backward blocking, unovershadowing, and backward conditioned inhibition are examples of retrospective revaluation phenomena that have been suggested to involve more than simple associative learning. Models of these phenomena have thus used additional concepts, for example, appealing to attentional effects or more elaborate learning mechanisms. The author shows that a suitable representation of stimuli, paired with a careful analysis of the discriminations faced by animals, leads to an account of these and other phenomena in terms of a simple elemental model of associative learning, with essentially the same learning mechanism as the R. A. Rescorla and A. R. Wagner (1972) model. The author concludes with a discussion of some implications for theories of learning.     

Stimulus control and associative learning.

Interest in operant research on stimulus control has declined at the same time that much interest has burgeoned in nonoperant areas. Several examples of this shift toward traditional learning theory are considered, all of which have sponsored theoretical approaches that attempt to characterize the underlying associative units. These theoretical approaches are defended on the grounds that they have generated a deeper understanding of a variety of often puzzling phenomena. My projection is that future research will be determined even more strongly by theories about the structure of associations. Particular issues for which such discussion will have major impact include (1) whether conditional stimulus control is qualitatively different than simpler forms of stimulus control, (2) whether stimulus control is organized hierarchically, and (3) the origin of categories of stimulus equivalence.     

Combining predictors for multi-layer architecture of adaptive fuzzy inference system

To solve problems with a Sugeno adaptive fuzzy neural network using training data, it is necessary to select the appropriate combination of input characteristics of the sub-adaptive neuro-fuzzy inference system (ANFIS) and to determine the appropriate topology. The multi-layer architecture of a sub-ANFIS (MLA-ANFIS) is a good model for prediction problems and solves them modularity. Since, the combination of several predictors is the current focus in the construction of hybrid intelligent systems; we created many solutions to combine machine learning methods, namely ANFIS, support vector machine (SVM), deep neural network (DNN), naive Bayes (NB), linear regression (LR), extreme learning machine (ELM), and decision tree (DT) mixed predictors, and ensemble bootstrap aggregation based on MLA-ANFIS in order to discover the optimal model of combined predictors based on the MLA-ANFIS with a combination of input features entered in the MLA-ANFIS. We implemented our approaches on 365-day concrete compressive strength, thoracic surgery, fertility diagnosis, breast, energy, and glass identification datasets from UCI. The experimental results prove that the combining predictors for the MLA-ANFIS show performance improvements compared to the pure MLA-ANFIS method.     

Conflict adaptation by means of associative learning

Cognitive control is responsible for adapting information processing in order to carry out tasks more efficiently. Contrasting global versus local control accounts, it has recently been proposed that control operates in an associative fashion, that is, by binding stimulus-response associations after detection of conflict (Verguts & Notebaert, 2009). Here, this prediction is explicitly tested for the first time. In a task-switching study where both tasks use the same relevant information, we previously reported conflict adaptation over tasks (Notebaert & Verguts, 2008). In the current experiment, we demonstrate that this is restricted to conditions where both tasks use the same effectors, thereby supporting the associative control account.