In this paper, a new computational model of associative learning is proposed, which is based on the Ising model. Application of the stochastic gradient descent algorithm to the proposed model yields an on-line learning rule. Next, it is shown that the obtained new learning rule generalizes two well-known learning rules, i.e., the Hebbian rule and the Oja's rule. Later, the fashion of incorporat...

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 ...

This article argues that mirror neurons originate in sensorimotor associative learning and therefore a new approach is needed to investigate their functions. Mirror neurons were discovered about 20 years ago in the monkey brain, and there is now evidence that they are also present in the human brain. The intriguing feature of many mirror neurons is that they fire not only when the animal is per...

Systematicity commonly means that having certain cognitive capacities entails having certain other cognitive capacities. Learning is a cognitive capacity central to cognitive science, but systematic learning of cognitive capacities—second-order systematicity—has received little investigation. We proposed associative learning as an instance of second-order systematicity that poses a paradox for ...

Two studies, involving a total of nearly 500 Ss, were conducted to determine the mechanisms by which processing speed contributes to the relations between adult age and associative learning. Results of both studies indicated that increased age was related to poorer associative learning largely because of a failure to retain information about previously correct responses. This in turn was relate...

This paper proposes a limited kernel associative memory, where the number of kernels is limited to a certain number. This model aims to be used on embedded systems with a small amount of storage space. The learning algorithm for the kernel associative memory is an improved version of the limited general regression neural network, which was proposed by one of the authors. In the experiments, we ...

How do people come to assign symbolic labels to continuous dimensions? Previous work has shown that prediction-errordriven models are sensitive to the order of labels and exemplars during training; similar patterns of learning are found found in adult learners trained to associate labels with discrete visual stimuli. Here we provide further evidence in support of the hypothesis that an error-dr...

Associative emotional learning, which is important for the social emotional functioning of individuals and is often impaired in psychiatric illnesses, is in part mediated by dopamine and glutamate pathways in the brain. The protein DARPP-32 is involved in the regulation of dopaminergic and glutaminergic signaling. Consequently, it has been suggested that the haplotypic variants of the gene PPP1...

Both adults and young children possess powerful statistical computation capabilities--they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a ...

An understanding of associative learning is facilitated if it can be analyzed in a simple animal like the fruit fly Drosophila. Here, we introduce the first visual associative learning paradigm for larval Drosophila; this is remarkable as larvae have an order of magnitude fewer neurons than adult flies. Larvae were subjected to either of two reciprocal training regimes: Light+/Dark- or Light-/D...