Iconic memory, location information, and partial report

It has been suggested that the systematic decline of partial report as the delay of the partial-report cue increases is due to a time-related loss of location information. Moreover, the backward masking effect is said to be precipitated by the disruption of location information before and after identification. Results from three experiments do not support these claims when new indices of location information and of item information are used. Instead, it was found that (a) the systematic decline in partial report was due to a time-related loss of item information, and (b) location information was affected neither by the delay of the partial-report cue nor by the delay of backward masking. Subjects adopted the "select-then-identify" mode of processing

Teacher cognition in language teaching: A review of research on what language teachers think, know, believe, and do

This paper reviews a selection of research from the field of foreign and second language teaching into what is referred to here as teacher cognition – what teachers think, know, and believe and the relationships of these mental constructs to what teachers do in the language teaching classroom. Within a framework suggested by more general mainstream educational research on teacher cognition, language teacher cognition is here discussed with reference to three main themes: (1) cognition and prior language learning experience, (2) cognition and teacher education, and (3) cognition and classroom practice. In addition, the findings of studies into two specific curricular areas in language teaching which have been examined by teacher cognition – grammar teaching and literacy – are discussed. This review indicates that, while the study of teacher cognition has established itself on the research agenda in the field of language teaching and provided valuable insight into the mental lives of language teachers, a clear sense of unity is lacking in the work and there are several major issues in language teaching which have yet to be explored from the perspective of teacher cognition

Distributed Cognition: Cognizing, Autonomy and the Turing Test

Some of the papers in this special issue distribute cognition between what is going on inside individual cognizers’ heads and their outside worlds; others distribute cognition among different individual cognizers. Turing’s criterion for cognition was individual, autonomous input/output capacity. It is not clear that distributed cognition could pass the Turing Test

Modeling Life as Cognitive Info-Computation

This article presents a naturalist approach to cognition understood as a network of info-computational, autopoietic processes in living systems. It provides a conceptual framework for the unified view of cognition as evolved from the simplest to the most complex organisms, based on new empirical and theoretical results. It addresses three fundamental questions: what cognition is, how cognition works and what cognition does at different levels of complexity of living organisms. By explicating the info-computational character of cognition, its evolution, agent-dependency and generative mechanisms we can better understand its life-sustaining and life-propagating role. The info-computational approach contributes to rethinking cognition as a process of natural computation in living beings that can be applied for cognitive computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201

Distributed cognition: cognizing, autonomy and the Turing Test

Some of the papers in this Special Issue distribute cognition between what is going on inside individual cognizers’ heads and their outside worlds; others distribute cognition among different individual cognizers. Turing’s criterion for cognition was for individual, autonomous input/output capacity. It is not clear that distributed cognition could pass the Turing Tes

Institutional Cognition

We generalize a recent mathematical analysis of Bernard Baars' model of human consciousness to explore analogous, but far more complicated, phenomena of institutional cognition. Individual consciousness is limited to a single, tunable, giant component of interacting cogntivie modules, instantiating a Global Workspace. Human institutions, by contrast, seem able to multitask, supporting several such giant components simultaneously, although their behavior remains constrained to a topology generated by cultural context and by the path-dependence inherent to organizational history. Surprisingly, such multitasking, while clearly limiting the phenomenon of inattentional blindness, does not eliminate it. This suggests that organizations (or machines) explicitly designed along these principles, while highly efficient at certain sets of tasks, would still be subject to analogs of the subtle failure patterns explored in Wallace (2005b, 2006). We compare and contrast our results with recent work on collective efficacy and collective consciousness

Face Cognition: A Set of Distinct Mental Abilities

Perceiving, learning, and recognizing faces swiftly and accurately is of paramount importance to humans as a social species. Though established functional models of face cognition<sup>1,2</sup> suggest the existence of multiple abilities in face cognition, the number of such abilities and the relationships among them and to other cognitive abilities can only be determined by studying individual differences. Here we investigated individual differences in a broad variety of indicators of face cognition and identified for the first time three component abilities: face perception, face memory, and the speed of face cognition. These component abilities were replicated in an independent study and were found to be robustly separable from established cognitive abilities, specifically immediate and delayed memory, mental speed, general cognitive ability, and object cognition. The analysis of individual differences goes beyond functional and neurological models of face cognition by demonstrating the difference between face perception and face learning, and by making evident the distinction between speed and accuracy of face cognition. Our indicators also provide a means to develop tests and training programs for face cognition that are broader and more precise than those currently available).<sup>3,4</sup&#x3e

Tacit Representations and Artificial Intelligence: Hidden Lessons from an Embodied Perspective on Cognition

In this paper, I explore how an embodied perspective on cognition might inform research on artificial intelligence. Many embodied cognition theorists object to the central role that representations play on the traditional view of cognition. Based on these objections, it may seem that the lesson from embodied cognition is that AI should abandon representation as a central component of intelligence. However, I argue that the lesson from embodied cognition is actually that AI research should shift its focus from how to utilize explicit representations to how to create and use tacit representations. To develop this suggestion, I provide an overview of the commitments of the classical view and distinguish three critiques of the role that representations play in that view. I provide further exploration and defense of Daniel Dennett’s distinction between explicit and tacit representations. I argue that we should understand the embodied cognition approach using a framework that includes tacit representations. Given this perspective, I will explore some AI research areas that may be recommended by an embodied perspective on cognition

Blended Cognition

The central concept of this edited volume is "blended cognition", the natural skill of human beings for combining constantly different heuristics during their several task-solving activities. Something that was sometimes observed like a problem as “bad reasoning”, is now the central key for the understanding of the richness, adaptability and creativity of human cognition. The topic of this book connects in a significant way with the disciplines of psychology, neurology, anthropology, philosophy, logics, engineering, logics, and AI. In a nutshell: understanding better humans for designing better machines. It contains a Preface by the editors and 12 chapters