Attention mechanisms in the CHREST cognitive architecture

In this paper, we describe the attention mechanisms in CHREST, a computational architecture of human visual expertise. CHREST organises information acquired by direct experience from the world in the form of chunks. These chunks are searched for, and verified, by a unique set of heuristics, comprising the attention mechanism. We explain how the attention mechanism combines bottom-up and top-down heuristics from internal and external sources of information. We describe some experimental evidence demonstrating the correspondence of CHREST’s perceptual mechanisms with those of human subjects. Finally, we discuss how visual attention can play an important role in actions carried out by human experts in domains such as chess

The Influence of Social Priming on Speech Perception

Speech perception relies on auditory, visual, and motor cues and has been historically difficult to model, partially due to this multimodality. One of the current models is the Fuzzy Logic Model of Perception (FLMP), which suggests that if one of these types of speech mode is altered, the perception of that speech signal should be altered in a quantifiable and predictable way. The current study uses social priming to activate the schema of blindness in order to reduce reliance of visual cues of syllables with a visually identical pair. According to the FLMP, by lowering reliance on visual cues, visual confusion should also be reduced, allowing the visually confusable syllables to be identified more quickly. Although no main effect of priming was discovered, some individual syllables showed the expected facilitation while others showed inhibition. These results suggest that there is an effect of social priming on speech perception, despite the opposing reactions between syllables. Further research should use a similar kind of social priming to determine which syllables have more acoustically salient features and which have more visually salient features

Learning perceptual schemas to avoid the utility problem

This paper describes principles for representing and organising planning knowledge in a machine learning architecture. One of the difficulties with learning about tasks requiring planning is the utility problem: as more knowledge is acquired by the learner, the utilisation of that knowledge takes on a complexity which overwhelms the mechanisms of the original task. This problem does not, however, occur with human learners: on the contrary, it is usually the case that, the more knowledgeable the learner, the greater the efficiency and accuracy in locating a solution. The reason for this lies in the types of knowledge acquired by the human learner and its organisation. We describe the basic representations which underlie the superior abilities of human experts, and describe algorithms for using equivalent representations in a machine learning architecture

Combining low-level perception with expectations in CHREST

The ability of humans to reliably perceive and recognise objects relies on an interaction between information seen in the visual image and prior expectations. We describe an extension to the CHREST computational model which enables it to learn and combine information from multiple input modalities. Simulations demonstrate the presence of quantitative effects on recognition ability due to cross-modal interactions. Our simulations with CHREST illustrate how expectations can improve classification accuracy, reduce classification time, and enable words to be reconstructed from noisy visual input

A tutorial on cue combination and Signal Detection Theory: Using changes in sensitivity to evaluate how observers integrate sensory information

Many sensory inputs contain multiple sources of information (‘cues’), such as two sounds of different frequencies, or a voice heard in unison with moving lips. Often, each cue provides a separate estimate of the same physical attribute, such as the size or location of an object. An ideal observer can exploit such redundant sensory information to improve the accuracy of their perceptual judgments. For example, if each cue is modeled as an independent, Gaussian, random variable, then combining Ncues should provide up to a √N improvement in detection/discrimination sensitivity. Alternatively, a less efficient observer may base their decision on only a subset of the available information, and so gain little or no benefit from having access to multiple sources of information. Here we use Signal Detection Theory to formulate and compare various models of cue-combination, many of which are commonly used to explain empirical data. We alert the reader to the key assumptions inherent in each model, and provide formulas for deriving quantitative predictions. Code is also provided for simulating each model, allowing expected levels of measurement error to be quantified. Based on these results, it is shown that predicted sensitivity often differs surprisingly little between qualitatively distinct models of combination. This means that sensitivity alone is not sufficient for understanding decision efficiency, and the implications of this are discussed

Abstraction and context in concept representation

This paper develops the notion of abstraction in the context of the psychology of concepts, and discusses its relation to context dependence in knowledge representation. Three general approaches to modelling conceptual knowledge from the domain of cognitive psychology are discussed, which serve to illustrate a theoretical dimension of increasing levels of abstraction

More than skin deep: body representation beyond primary somatosensory cortex

The neural circuits underlying initial sensory processing of somatic information are relatively well understood. In contrast, the processes that go beyond primary somatosensation to create more abstract representations related to the body are less clear. In this review, we focus on two classes of higher-order processing beyond somatosensation. Somatoperception refers to the process of perceiving the body itself, and particularly of ensuring somatic perceptual constancy. We review three key elements of somatoperception: (a) remapping information from the body surface into an egocentric reference frame (b) exteroceptive perception of objects in the external world through their contact with the body and (c) interoceptive percepts about the nature and state of the body itself. Somatorepresentation, in contrast, refers to the essentially cognitive process of constructing semantic knowledge and attitudes about the body, including: (d) lexical-semantic knowledge about bodies generally and one’s own body specifically, (e) configural knowledge about the structure of bodies, (f) emotions and attitudes directed towards one’s own body, and (g) the link between physical body and psychological self. We review a wide range of neuropsychological, neuroimaging and neurophysiological data to explore the dissociation between these different aspects of higher somatosensory function

Consumers’ Demand for Pork Quality: Applying Semantic Network Analysis, May 2006

Abstract Consideration of consumers’ demand for food quality entails several aspects. Quality itself is a complex and dynamic concept, and constantly evolving technical progress may cause changes in consumers’ judgment of quality. To improve our understanding of the factors influencing the demand for quality, food quality must be defined and measured from the consumer’s perspective (Cardello, 1995). The present analysis addresses the issue of food quality, focusing on pork—the food that respondents were concerned about. To gain insight into consumers’ demand, we analyzed their perception and evaluation and focused on their cognitive structures concerning pork quality. In order to more fully account for consumers’ concerns about the origin of pork, in 2004 we conducted a consumer survey of private households. The qualitative approach of concept mapping was used to uncover the cognitive structures. Network analysis was applied to interpret the results. In order to make recommendations to enterprises, we needed to know what kind of demand emerges from the given food quality schema. By establishing the importance and relative positions of the attributes, we find that the country of origin and butcher may be the two factors that have the biggest influence on consumers’ decisions about the purchase of pork