Monthly Archives: January 2008

Humour

(For those who find the rest of the blog too dull)

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University Professor A: Have you read [insert book title]?

University Professor B: Read it? I haven’t even taught it.

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How many usability experts does it take to change a lightbulb?

Find out at http://www.webword.com/2006/01/31/usability-joke/

(The Jared Spool contributions are particularly entertaining.)

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A Heuristic Evaluation of the Usability of Infants by Scott McDaniel

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Twilight of the Books (Crain)

Crain, C. (2007, Dec. 24). Twilight of the books : what will life be like if people stop reading? In The New Yorker. Retrieved from
http://www.newyorker.com/arts/critics/atlarge/2007/12/24/071224crat_atlarge_crain/

In this article, Crain ponders the results from the recent N.E.A. report To Read or Not to Read “which showed correlations between the decline of readingand social phenomena as diverse as income disparity, exercise and voting” (p.1). He adds statistics from a number of other efforts to gauge people’s levels of reading ability and frequency (mainly in the U.S.), all of which indicate a decline in the reading of books. He also mentions research in the Netherlands, where having research participants keep time-budget diaries has shown that the time people spend reading is decreasing while the time spent watching television is still on the rise. Differences are clearly generational — whereas older generations tended to read more as they aged, this is no longer the case.

Crain referes to “some sociologists [ who ] speculate that reading books for pleasure will one day be the province of a special ‘reading class,’ much as it was before the arrival of mass literacy, in the second half of the nineteenth century. They warn that it probably won’t regain the prestige of exclusivity; it may just become ‘an increasingly arcane hobby” (p.4).

This topic is one that easily riles people at dinner tables and in university settings. For the most part, aside from the self-imposed devil’s advocate, in my experience everyone tends to have the same general position – a nostalgic longing for reading books and an aversion to the idea of reading from an electronic book tablet. But then again, I have not tried having this discussion with a group of nineteen-year-olds, and I do tend to spend time with people who could be described as ‘bookish’. My main confrontation is with myself. I feel that I am perfectly wedged in both the older and the newer generation. My reading hey-dey was in my pre-teen youth, and I am extremely nostlagic about that time, about the ability to focus and delve deeply into a book to the point of total absorption. These days, I tend to acquire masses of books that interest me, but more often than not leave them behind in favour of the television (more often than not for the mindless stuff, not for the infrequent quality programming that I acknowledge exists) or short, summary online articles (i.e. reading this article instead of the articles and books it references). I wonder if the people in these surveys share my longing for books, feel torn at all, or regret the amount of television they watch. (Where is that survey?)

Reading (Ch.20 in Handbook of Psychology)

Rayner, K., Pollatsek, A., and Starr, M. S. (2003). Reading. In Healy, A. F., and Proctor, R. W. (Eds.), Handbook of psychology (Vol. 4, pp. 549-574). New York: Wiley.

This chapter from the ‘Experimental Psychology’ volume of this collection gives a clear overview of the main areas of investigation related to reading within the field of psychology. It focuses on five central types of experimentation:

  • Word identification — where words are presented to participants and a response is illicited; response time is observe
  • Sound coding in word identification
  • Eye movements in reading (in which we observe first-fixation duration, gaze duration and the probability of skipping a word)
  • Word identification in context
  • Models of eye movement control

No definitive model has been accepted thus far for object recognition, let alone for word recognition. Models of pattern recognition generally fall into one of two classes. The first are template models, whereby incoming visual input is compared to existing memory-based object templates that enable object identification. There could potentially be a number of templates per category, depending on viewpoint or viewing condition. The second class involves feature models, where an object is identified based on its combination of visual features. Although we do not understand the process completely, we do know that words are identified very rapidly (in 150-200 ms) through an automatic process.

The extent to which sound coding is key in the reading process is controversial, but has been identified as highly significant, especially in research on dyslexia. According to this model, the written language would mainly be a means to accessing the spoken form of the language, and knowledge of the spoken language would facilitate the learning of the written form. Sound coding has been linked to how we access a visual word’s meaning(s), and in assisting short-term memory while reading.

Eye movements during reading have been studied at length. Through a series of alternating saccades (discrete jumps) and fixations (periods where the eyes remain fixed), our eyes focus on a small area of text (called the fovea) while also drawing limited information from the word immediately to the right of the fovea (which will be identified fully on the next fixation). Our eyes “typically move forward approximately eight characters at a time” (p.558) but not all eye movements are forward — “10-15% of eye movements shift backwards in text and are termed regressions” (p.558).These eye-tracking studies have also identified that short words are skipped completely most of the time, while longer words that are highly predictable (i.e. if they recur in the same passage) are sometimes skipped. Words seen more regularly have shorter fixations, while unusual words are fixated on for longer periods of time.

A key component in word and meaning identification is the context in which it occurs. Although the reading process is one that acts on an unconscious level, a word that is a predictable part of a sentence (dictated by context and meaning cues) will require a shorter fixation time that one that is less predictable. Syntactic ambiguity (as in the sentence While Mary was mending her sock fell off her lap) can also interfere with the automated reading process.

Also briefy mentioned is the increasing use of brain imaging (i.e. MRIs) to “study issues related to which parts of the brain are activcated when different types of words are processed” (p.550). I will be seeking more recent articles that focus on this aspect of reading research.

Although the research outlined in this chapter is helpful in understanding the cognitive processes applied in reading, it is also clear that many areas are not fully understood. For example, reading depends on much more than the mere recognition of words. The construction of meaning is in itself a huge area to be explored.

Future Reading: Digitization and its Discontents (Grafton)

Future reading : digitization and its discontents / Anthony Grafton
New Yorker, Nov. 5/07
http://www.newyorker.com/reporting/2007/11/05/071105fa_fact_grafton

This article puts the Google Book Search and Google Library Project digitization efforts in a historical context, pointing out that efforts to “accumulate, store, and retrieve information efficiently” are nothing new. From Mesopotamian scribes’ clay tablets to Erasmus’s “Adages” to Fremont Rider’s microphotography, the history around the organization of written information has deep roots.

G rafton, while recognizing and appreciating the many uses of digitized collections, points out a few areas where they fall short:

  • Google’s use of OCR (optical character recognition) for indexing purposes inevitably results in retrieval errors;
  • Efforts such as Google’s include the scanning of books for which copyright has not yet been cleared or ascertained;
  • Access to these collections is still far from seamless, and in many cases requires special charges or permissions even if retrieval can be achieved;
  • Due to the poor indexing, it is difficult for most scholars (let alone the rest of us) to keep on top of what documents are available electronically and through which portal;

and perhaps most interestingly:

  • Access to original documents can offer information (through annotations and other markings) that is often unavailable via electronic means, and that describes the readings others have had of a particular item.

Grafton refers to John Seely Brown and Paul Duguid’s “social life of information“, which posits that “the form in which you encounter a text can have a huge impact on how you use it,” which clearly does not merely distinguish between electronic and book forms, but also illustrates the difference between encountering a poem in a 1000-page anthology as opposed to a deftly bound book of verse. Grafton suggests that, following Seely Brown and Duguid’s theory, in order to understand the impact of a text, one should find as many copies of a book as possible and study all their many annotations. (I get the feeling I will be looking up these fellows’ writing. I will also find some suitable studies on annotation to bring into this discussion.)

Information Scent

The following is the wiki entry I produced for Dr. Jamie Blustein’s Human Factors in Computer Systems class last fall:

Overview

Information scent is the perceived relevance of a piece of information to a particular user’s information need. It is a sub-concept of information foraging theory, in which scent motivates navigation via on ongoing on-the-fly assessment of the value and cost of different information sources.

Motivation

Information scent and information foraging are part of the larger study of how people search for information, and is directly related to the areas of cognitive psychology and human behaviour. Unlike other modes of relevance assessment, the foundation of this theory is the perception of relevance based on context and factors relating to individuals or groups of users.

In terms of its applications to the Web, attempts to track and predict patterns in information scent are part of a larger effort to build a cognitive model of how users find information on the Web. Such a model can offer guidance in the area of Web and computer interface design—or in the words of one of the originators of information foraging theory, Peter Pirolli, information scent could enable a “move beyond design by good intuition” [6]. If design can ensure that the scent in a site is strong, the user should be more likely to make the best possible choices and be led to appropriate results [7].

As well as fostering the original research in this area [6] [7], to this day Palo Alto Research Center (PARC) supports ongoing research in this field. The PARC website lists 58 publications on the topic of information scent, including several recent additions that describe models for assessing scent and predicting user scent-following as well as systems that assist (or enhance) scent-following in virtual environments.

Chronology

1957 Nobel winning psychologist Herb Simon proposes Bounded Rationality theory; Chi describes it in 2003 as follows: “an agent behaves in a manner that is nearly optimal with respect to its goals as its resources will allow” (Chi, 2003)
1960’s Evolutionary-ecological optimal foraging theory is developed in anthropology
1991 Dennett coins term informavores [7]
1994 Sandstrom makes connection between optimal foraging theory and library science [7]
1995 Pirolli and Card develop Information foraging theory out of Xerox PARC; uses ecological metaphor to understand how users make decisions around information
1999 Pivotal article on information foraging [7] is published; includes prominent use of information scent concept
2000 Chi et al [2] [3] apply concept of information scent to the Web; develop information foraging into predictive model for Web surfing [1]
2003 Development at PARC of InfoScent™ Bloodhound Simulator, “a push-button navigation analysis system, which automatically analyzes the information cues on a Web site to determine the probability of user task success” [5]
2005 Development at PARC of ScentHighlights, a system that highlights relevant sentences in a text in response to keywords provided by the user [2]
2006 Development at PARC of ScentIndex, a system that produces an on-the-fly index specific in response to keywords provided by the user [2]

Details of Theory

Information foraging theory is built on adaptationist evolutionary-ecological explanations of food-foraging from the field of anthropology. Just as the practice of foraging for food is a constantly developing strategy based on an ongoing assessment of value and cost, people’s strategies when seeking out information are based on what they perceive to be information sources’ relative costs and value [7]. Costs of an information source can be resource costs (money, time, energy expended, cognitive use) or opportunity costs (benefits that may not be gained if one selects a particular information source) [7].

Optimal information foraging means “maximizing the rate of valuable information gained per unit cost, given the constraints of the task environment” [7]. This can be done via enrichment (improving your environment to fit your strategy) or by scent-following (using available proximal cues to follow a scent trail to a distal source that will hopefully satisfy your need) [7].

Although not initially modeled on the Web, a great deal of research at PARC [2] [3] has applied this theory to people’s trajectory within Web sites and from one site to another. With Simon’s bounded rationality theory as a starting point, they have extrapolated that in the realm of information searching humans will not always make the best choices, depending on their individual context. When considering the Web, the overwhelming availability of choices and difficulties linked to allocation of attention means that choices will often be suboptimal [1]. Indeed, Pirolli and Card (1999) stress from the onset that information scent is necessarily dynamic and imperfect due to constantly changing environmental conditions [7].

Jakob Nielsen’s simmered down version of information foraging and Web design is often quoted by other sources in the Web design community. His 2003 Alertbox entry Information Foraging: Why Google Makes People Leave Your Site Faster uses the information foraging concept of between-patch behaviour (propensity and motivation for moving from one information source to another) to describe changes in user patterns on the Web. He posits that Google’s reliability in returning relevant hits is contributing to users’ increased confidence about moving to another high-ranking site. He therefore recommends designing for “information snacking” and implementing means designed to lure users back to the site later on [4].

Method

The basis of Pirolli and Card’s information foraging methodology is an adapted form of the complex mathematical formula initially designed to accurately predict animals’ patterns regarding within-patch and between-patch selection of food sources.

The original study by Pirolli and Card (1999), as well as that discussed in Pirolli (1997), makes use of a system they developed called the Scatter/Gather browser. This tool uses clustering to assist a user in sifting through a large number of documents. In the study, the user is assigned a retrieval task query, then shown a number of thematic clusters on screen, of which he or she can gather as many of these as are relevant. The system then scatters a new sampling of clusters before the user, who repeats the gathering process. The clusters offered are at first very large, then increasingly narrow, until the number of remaining clusters is manageable enough for the user to scan or read [6]. The computation behind the system’s scattering is built on the ACT-R production system (later replaced by the ACT-IF model), a model of human cognition designed to recognize the spreading activation network (word representation and inter-word memory in users’ long-term memory) and interword correlation (IWC), “users’ conception of word synonymy” [6].

In later studies by Chi et al (2000, 2001), where information scent and information foraging are applied to the Web, two sets of algorithms are applied. The first, Web User Flow by Information Scent (WUFIS), is a behavioral prediction algorithm, while the second, Inferring User Need by Information Scent (IUNIS), is a need prediction algorithm. WUFIS simulates a large number of agents making their way from link to link and throughout the content in a Web site. For each of these agents, the model computes the information scent at each step, using spreading activation and comparing the pages’ content (i.e. words in the link itself, words in the text surrounding the link, graphics on or around the link, position of the link on the page) with the agent’s original information goals. By comparing agents’ random treks through a site and comparing these to the information goal, the highest-scent trail can be uncovered. [1]. As for the IUNIS algorithm, it is in effect simply a reversal of the WUFIS scent flow trajectory. Instead of having a known goal and an unknown destination, it starts from the end destination, and by applying the spreading activation to the path that is followed by a user, the original information goal is uncovered [1].

Applications

Predictive Model of Web Surfing

Chi (2003) describes the development of a predictive model for Web surfing. The InfoScent™ Bloodhound Simulator is a service designed to automatically infer the usability of a Website by way of an adaptation of the WUFIS algorithm, with simulated users that surf for specific goals. Basically, the Bloodhound needs to be given a Website address and a set of user tasks, and it will produce a usability report. [1]

Scent-friendly Web Design Solutions

Jakob Nielsen prescribes Web design strategies conducive to increased information scent, such as links and category text that accurately describes the content to be found at the destination, use of plain language rather than slogans, and built-in feedback to confirm to users that they are still on the right path towards their desired information goal [4]. Also useful to Web designers, Withrow (2003) points out user behaviours that indicate poor information scent: indecision or hesitation between two or more links, frustration or confusion during browsing, random clicking and over-use of the ‘Back’ button [8]. Withrow also recommends the construction of broader hierarchies, pointing out that by limiting headers to too small a number of terms will result in vaguer terms designed to suit a greater number of sub-headings [8].

Scent-based Information Retrieval Tools

In the last few years, the Palo Alto team has developed a number of practical tools that use information scent as a basis for improving computerized information retrieval. ScentIndex is a system that produces an on-the-fly index specific in response to keywords provided by the user. Similarly, ScentHighlights highlights relevant sentences in a text in response to keywords provided by the user. In both cases the computation for these systems rely on two components: spreading activation (a cognitive model of human memory retrieval used in cognitive psychology research) and word co-occurrence, which models “relatedness of concepts” and is used in statistical language processing [2].

Concluding Summary

As seen above, the tools that have been devised thus far using the principles of information scent and information foraging offer much promise for providing predictive methods of Web analytics and assistance to users in improving information retrieval.

Further Reading

  • Chi, E. H. (2003). Scent of the Web. In Ratner, J. (Ed.) Human factors and web development, pp. 265-285. Mahwah, NJ: Lawrence Erlbaum.
  • Chi, E. H., Pirolli, P., & Pitkow, J. (2000). The scent of a site: A system for analyzing and predicting information scent, usage, and usability of a Web site. In Proceedings of the ACM Conference on Human Factors in Computing Systems, CHI 2001, pp. 161-168. The Hague, Netherlands: Association for Computing Machinery.
  • Pirolli, P. (2007). Information foraging theory: Adaptive interaction with information. Oxford: Oxford University Press.
  • Pirolli, P., & Card, S. (1999). Information foraging. Psychological review, 106(4), 643-675. Retrieved 10/20/2007 from PsycArticles database.

References

[1] Chi, E. H. (2003). Scent of the Web. In Ratner, J. (Ed.) Human factors and web development, pp. 265-285. Mahwah, NJ: Lawrence Erlbaum.

[2] Chi, E. H., Hong, L., Gumbrecht, M., Card, S. K. (January 2005). ScentHighlights: Highlighting conceptually-related sentences during reading. In Proc. of the 10th International Conference on Intelligent User Interfaces, pp. 272-274. San Diego: ACM Press. Retrieved October 22, 2007, from http://www-users.cs.umn.edu/~echi/papers.html

[3] Chi, E. H., Pirolli, P., Chen, K. & Pitkow, J. (2001). Using information scent to model user information needs and actions on the Web. In Proceedings of the ACM Conference on Human Factors in Computing Systems, CHI 2001, pp.490-497. Seattle, WA: Association for Computing Machinery.

[4] Nielsen, N. (2003). Alertbox: Information foraging: Why Google makes people leave your site faster. Retrieved October 22, 2007, from http://www.useit.com/alertbox/20030630.html

[5] Palo Alto Research Center. (n. d.). The Bloodhound project: Automating discovery of Web usability issues using the InfoScent™ simulator. Retrieved from http://www.parc.com/research/publications/details.php?id=4747

[6] Pirolli, P. (1997). Computational models of information scent-following in a very large browsable text collection. In Proceeding of the ACM Conference on Human Factors in Computer Systems, CHI ‘97, pp.3-10. Retrieved October 15, 2007, from ACM database.

[7] Pirolli, P., & Card, S. (1999). Information foraging. Psychological review, 106(4), 643-675. Retrieved 10/20/2007 from PsycArticles data base.

[8] Withrow, J. (2002). Do your links stink? techniques for good web information scent. Bulletin of the American Society for Information Science and Technology, 28(5), 7. Retrieved 10/20/2007 from Research Library Complete database.

Motivations

This is where I come clean about my motivations for taking on this reading course:

I am well aware of the data (such as in the recent NEA report To Read or Not to Read: A Question of National Consequence) which presents seemingly alarming changes in children and adults’ reading behaviour. One of these trends is that reading for pleasure has decreased significantly. This finding saddens me, but does not surprise me. In fact, I fit the profile to a T.

I come from a family where reading was the chief source of entertainment, and my three siblings and I mutually encouraged this behaviour. Now, however, despite a continued interest in books, I struggle to sit down and read when I am not absolutely required to, not even to read a magazine article. I continue to buy books, but rarely get through them. When reading is required for a course, I can do it and gain enjoyment from the activity, which makes me question the phrase “reading for pleasure.” In my case the distinction is more between “voluntary” and “prescribed” reading. It is almost as though the enjoyable act of reading has become tainted by too much banal reading in my day-to-day life (i.e. memos, schedules, emails I don’t want to read but feel compelled to scan).

It is because of the situation described above that I have set up this reading course. If I am but a symptom of a larger systemic ailment, perhaps I can try out some possible cures on myself? In any case, although the topic fascinates me, I knew I wasn’t going to read any of this voluntarily, so I had no choice but to make my degree contingent on it…

Questioning Reading

My supervisor, Dr. Lawson, and I had an initial discussion this week about the possible directions my readings could take. A number of questions surfaced, which I expect will re-occur throughout the term. Some notable ones are the following:

  • Is the pursuit of reading an elitist construct?
  • How prevalent is the belief that reading for pleasure is inferior to reading for improvement/edification?
  • Is an advanced reading level still necessary for most people, in most situations, or is it merely the measure of academic success that we are most accustomed to using?
  • Will we continue to read if we don’t need to?
  • Could the broadening gap in reading ability between boys and girls be due to the fact that there are fewer books out there that interest teenage boys than girls?