Cognitive Fluidity

Steven Mithen, Deputy Vice Chancellor of the University of Reading, and author, writes about cognitive fluidity in an essay titled Out of the mind: material culture and the supernatural in Becoming Human. (2009).

He discusses two cognitive developments which resulted in religion and art; the emergence of cognitive fluidity and the extension of the human mind beyond the brain into ‘material culture’.

In defining cognitive fluidity Steven Mithen compares our early mind with that of Neaderthals. He discusses how they had domain-specific ‘mentalities’, meaning that they had botanical and zoological knowledge and so on, but they lacked the “ability for metaphor and had limited imagination”. He continues; “the mental conception of a supernatural being requires cognitively fluid thought – that which makes such connections between cognitive domains.”

My interpretation of this is the human ability to change and modify though across domains.

In reference to interactive text and symbol manipulation, it is worth reflecting on how writing slowed down cognitive fluidity by making nodes more fixed and did not allow for flexibility in their rearrangement.

Visual-Syntactic Text Formatting: Theoretical Basis and Empirical Evidence for Impact on Human Reading

Visual-Syntactic Text Formatting: Theoretical Basis and Empirical Evidence for Impact on Human Reading

Randall C. Walker, MD, Walker Reading Tech., Inc. Bloomington, Minnesota, Adam S. Gordon, Walker Reading Tech., Inc. Bloomington, Minnesota, Phil Schloss, BSEE, Walker Reading Tech., Inc. Bloomington, Minnesota, Charles R. Fletcher, PhD, University of Minnesota Minneapolis, Minnesota, Charles A. Vogel, PhD, Eagle Valley School District Eagle, Colorado, Stan Walker, MD, Northwest Eye Clinic Minneapolis, Minnesota


Visual-Syntactic Text Formatting (VSTF) algorithms first analyze, then reformat a sentence into cascading patterns that cue syntactic structure and assist visual- processing.

VSTF was evaluated in yearlong, classroom-based, randomized controlled trials, with in-class reading sessions (25 minutes per session, twice a week), using electronic textbooks for high school students.

Pretest- posttest analysis showed that, in each grade, VSTF students had significantly higher scores on nationally standardized (and conventionally formatted) reading proficiency tests over controls; effect sizes ranged from .41 to .69 standard deviations, and the one-year growth in reading proficiency with VSTF was equivalent to 2 to 3 years’ of additional growth in study and national controls. VSTF groups also significantly increased scores, with medium effect sizes, on standardized quizzes and exami- nations for comprehension and retention of the material in the textbooks.

Key words: Text formatting; Syntactic Processing; Reading Comprehension 

Example, from screenshot


Theoretical Background

A wide range of neurocognitive, linguis- tic, and psychological research affirms that an important dimension for the representation of meaning in natural spoken language is syntax.

However, syntax is more complex than a simple, concatenated sequence of one phrase after another; rather, it is hierarchical, much like a set of Russian dolls, in which smaller dolls, or phrase- groups, are “nested” inside ever-larger ones. The human mind’s capacity to build sentences through the recursive process of nesting language units inside other units, and thereby transforming them, is the essential feature that enables human language to represent an infinite number of meanings.

Layout to serve as Prosodic cues

When natural language is spoken, it is produced, perceived, and interpreted as a linear structure — through time — which limits its capacity for conveying the multi-dimensional, hierarchical structures of syntax. Nevertheless, this linear structure can be enriched with prosodic cues, which in turn can denote syntactic relation- ships to enhance the efficiency of a listener’s comprehension of a spoken sentence. Prosodic cues, (In linguistics, prosody (from Ancient Greek: προσῳδίᾱ prosōidíā “song sung to music; tone or accent of a syllable”, is concerned with those elements of speech that are not individual phonetic segments (vowels and consonants) but are properties of syllables and larger units of speech. These contribute to linguistic functions such as intonation, tone, stress, and rhythm. wikipedia) which give speech a highly differentiated acoustic structure beyond the acoustic representations of the words themselves, are more subtle and multidimensional than the simple pauses that occur at major phrase boundaries, and have been shown to be powerful enough to enable listeners to accurately predict the syntactic categories of about-to-be uttered phrases, based on prosodic patterns leading up to the not-yet-uttered phrase.

Up to the present time, the transcription of natural language sentences has also been linear. Within such linear scripts, some specific cues, (such as punctuation marks), denote syntactic boundaries; some (but not all) of these punctuation marks correspond to pauses and prosodic variations in spoken language.However, when sentences become longer and more complex, working memory is overloaded, and the effi- ciency of comprehension can break down.

The purpose of VSTF is to make a sentence diagram that can similarly increase reading comprehension of the sentence itself;
but it does not simply make a diagram of a sentence —
rather, it makes a diagram with (and only with)
the words of a sentence,
positioning the words and
segments into specific locations relative to one another to create
“extra- linear” structural information.


The Seven Tasks for Visual Information Environments

The Seven Tasks for Visual Information Environments By Ben Shneiderman (1996):

  • Overview Gain an overview of the entire collection
  • Zoom Zoom in on items of interest
  • Filter Filter out uninteresting items
  • Details-On-Demand Select an item or group and get details when needed
  • Relate View relationships among items
  • History Keep a history of actions to support undo, replay, and progressive refinement
  • Extract Allow extraction of sub-collections and of the query parameters

“Overview first, zoom and filter, then details-on-demand”