Two Ways of Learning Brand Associations

Four studies show that consumers have not one but two distinct learning processes that allow them to use brand names and other product features to predict consumption benefits. The first learning process is a relatively unfocused process in which all stimulus elements get cross-referenced for later retrieval. This process is backward looking and consistent with human associative memory (HAM) models. The second learning process requires that a benefit be the focus of prediction during learning. It assumes feature-benefit associations change only to the extent that the expected performance of the product does not match the experienced performance of the product. This process is forward looking and consistent with adaptive network models. The importance of this two-process theory is most apparent when a product has multiple features. During HAM learning, each feature-benefit association will develop independently. During adaptive learning, features will compete to predict benefits and, thus, feature-benefit associations will develop interdependently. We find adaptive learning of feature-benefit associations when consumers are motivated to learn to predict a benefit (e.g., because it is perceived to have hedonic relevance) but find HAM learning when consumers attend to an associate of lesser motivational significanc

Intelligent Association Exploration and Exploitation of Fuzzy Agents in Ambient Intelligent Environments

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant and important associations between embedded-agent based services that form Ambient Intelligent Environments (AIEs). The embedded agents are used in two ways; first they monitor the inhabitants of the AIE, learning their behaviours in an online, non-intrusive and life-long fashion with the aim of pre-emptively setting the environment to the users preferred state. Secondly, they evaluate the relevance and significance of the associations to various services with the aim of eliminating redundant associations in order to minimize the agent computational latency within the AIE. The embedded agents employ fuzzy-logic due to its robustness to the uncertainties, noise and imprecision encountered in AIEs. We describe unique real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture and methods

Explicit and Implicit Processes in Human Aversive Conditioning

The ability to adapt to a changing environment is central to an organism’s success. The process of associating two stimuli (as in associative conditioning) requires very little in the way of neural machinery. In fact, organisms with only a few hundred neurons show conditioning that is specific to an associated cue. This type of learning is commonly referred to as implicit learning. The learning can be performed in the absence of the subject’s ability to describe it. One example of learning that is thought to be implicit is delay conditioning. Delay conditioning consists of a single cue (a tone, for example) that starts before, and then overlaps with, an outcome (like a pain stimulus). In addition to associating sensory cues, humans routinely link abstract concepts with an outcome. This more complex learning is often described as explicit since subjects are able to describe the link between the stimulus and outcome. An example of conditioning that requires this type of knowledge is trace conditioning. Trace conditioning includes a separation of a few seconds between the cue and outcome. Explicit learning is often proposed to involve a separate system, but the degree of separation between implicit associations and explicit learning is still debated. We describe aversive conditioning experiments in human subjects used to study the degree of interaction that takes place between explicit and implicit systems. We do this in three ways. First, if a higher order task (in this case a working memory task) is performed during conditioning, it reduces not only explicit learning but also implicit learning. Second, we describe the area of the brain involved in explicit learning during conditioning and confirm that it is active during both trace and delay conditioning. Third, using functional magnetic resonance imaging (fMRI), we describe hemodynamic activity changes in perceptual areas of the brain that occur during delay conditioning and persist after the learned association has faded. From these studies, we conclude that there is a strong interaction between explicit and implicit learning systems, with one often directly changing the function of the other.</p

Los estudiantes y la propagación de las señales mecánicas : dificultades de una situación de varias variables y procedimientos de simplificación

This paper analyses the general ways of reasoning used by students when learning the propagation of a signal on a string. Tendencies towards a reduction of the number of variables can be brought out. On the one hand, students tend to combine the variables into a single notion. Their reasoning seems to be based on the idea of a material object (here the bump on the string), described through this single notion. On the other hand, they consider simultaneously never more than two variables so that relations they have learned are changed into binary associations. From this analysis, some pedagogical implications are derived

Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies

Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries to relevant document identifiers. The key idea behind DSI is to fully parameterize traditional ``index-retrieve'' pipelines within a single neural model, by encoding all documents in the corpus into the model parameters. In essence, DSI needs to resolve two major questions: (1) how to assign an identifier to each document, and (2) how to learn the associations between a document and its identifier. In this work, we propose a Semantic-Enhanced DSI model (SE-DSI) motivated by Learning Strategies in the area of Cognitive Psychology. Our approach advances original DSI in two ways: (1) For the document identifier, we take inspiration from Elaboration Strategies in human learning. Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning. Specifically, we select fine-grained semantic features from a document as Rehearsal Contents to improve document memorization. Both the offline and online experiments show improved retrieval performance over prevailing baselines.Comment: Accepted by KDD 202

Biological learning and artificial intelligence

It was once taken for granted that learning in animals and man could be explained with a simple set of general learning rules, but over the last hundred years, a substantial amount of evidence has been accumulated that points in a quite different direction. In animal learning theory, the laws of learning are no longer considered general. Instead, it has been necessary to explain behaviour in terms of a large set of interacting learning mechanisms and innate behaviours. Artificial intelligence is now on the edge of making the transition from general theories to a view of intelligence that is based on anamalgamate of interacting systems. In the light of the evidence from animal learning theory, such a transition is to be highly desired

A Wizard Hat for the Brain: Predicting Long-Term Memory Retention Using Electroencephalography

Learning is a ubiquitous process that transforms novel information and events into stored memory representations that can later be accessed. As a learner acquires new information, any feature of a memory that is shared with other memories may produce some level of retrieval- competition, making accurate recall more difficult. One of the most effective ways to reduce this competition and create distinct representations for potentially confusable memories is to practice retrieving all of the information through self-testing with feedback. As a person tests themself, competition between easily-confusable memories (e.g. memories that share similar visual or semantic features) decreases and memory representations for unique items are made more distinct. Using a portable, consumer-grade electroencephalography (EEG) device, I attempted to harness competition levels in the brain by training a machine learning classifier to predict long- term retention of novel associations. Specifically, I compare the accuracy of two logistic regression classifiers: one trained using existing category-word pairings (as has been done previously in the literature), and one trained using new episodic image-name associations developed to more closely model memory competition. I predicted that the newly developed classifier would be able to more accurately predict long-term retention. Further refinements to the predictive model and its applications are discussed

Ad hoc categories

People construct ad hoc categories to achieve goals. For example, constructing the category of “things to sell at a garage sale” can be instrumental to achieving the goal of selling unwanted possessions. These categories differ from common categories (e.g., “fruit,” “furniture”) in that ad hoc categories violate the correlational structure of the environment and are not well established in memory. Regarding the latter property, the category concepts, concept-to-instance associations, and instance-to-concept associations structuring ad hoc categories are shown to be much less established in memory than those of common categories. Regardless of these differences, however, ad hoc categories possess graded structures (i.e., typicality gradients) as salient as those structuring common categories. This appears to be the result of a similarity comparison process that imposes graded structure on any category regardless of type