Context constraints, prior vocabulary knowledge and on-line inferences in reading

Resumen tomado de la publicaci??n

Effect of initial status and growth in pseudoword reading on Spanish reading comprehension at the end of first grade

Resumen tomado de la publicaci??n

The improvement of reading skills of L1 and ESL children using a Response to Intervention (RtI) Model

Resumen tomado de la publicaci??n

ISIS reading group

Reading group on diverse topics of interest for the Information: Signals, Images, Systems (ISIS) Research Group of the School of Electronics and Computer Science, University of Southampton.

Reading and research skills

University of Southampton, Dyslexia Services have developed a range of academic study skills resources available to download. This resource supports reading and research skills.

Navigating the page: an academic guide to effective reading

This guide introduces you to the various reading styles that will help you get through your research most effectively. It gives tips on how to skim read, and also how to read critically.

Reading Other File Formats.doc (A1)

Notes on how to get from Excel, Access and Textfiles into SPSS. Used in Research Skills for Biomedical Science

Smart Meetings - background reading

Background reading for coursework to prepare a technical report as part of the orientation phase. These items are business documents (i.e. grey literature) which might be read as a prelude or complement to finding information in peer reviewed academic publications. grey literature links and articles to be used in preparation of technical report. See also overview guidance document for this assignment http://www.edshare.soton.ac.uk/8017/

Green IT (includes High Powered Computing) - background reading

Background reading for coursework to prepare a technical report as part of the orientation phase. These items are business documents (i.e. grey literature) which might be read as a prelude or complement to finding information in peer reviewed academic publications. grey literature links and articles to be used in preparation of technical report. See also overview guidance document for this assignment http://www.edshare.soton.ac.uk/8017/

Technical Report Background Reading Guidance and Links

Handout which contains a set of links to a variety of background resources associated with the topics for a technical report coursework. Resources are clustered into three overview areas, but contain links which be used to address each of the six questions scenarios.

COMP1205 Writing summaries: reading list

small set of selected papers from which to choose two papers to read and summarise

Disciplinary Differences: directed reading 1

Two articles outlining some early work on interdisciplinarity. Interesting as much for the style of the papers and the contents. You can download copies of the papers from this share. You can also access the full text via TDNET through the university library web site. Instructions provided.

The Age of Social Machines

Many of the most successful and important systems that impact our lives combine humans, data, and algorithms at Web Scale. These social machines are amalgamations of human and machine intelligence. This seminar will provide an update on SOCIAM, a five year EPSRC Programme Grant that seeks to gain a better understanding of social machines; how they are observed and constituted, how they can be designed and their fate determined. We will review how social machines can be of value to society, organisations and individuals. We will consider the challenges they present to our various disciplines.

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.