Posts by Chinien & Boutin

Cognitive Styles and Cognitive Controls

Posted by on Oct 10, 2014 in Blog, Cognitive Control Regulating Information Processing

This entry is part 1 of 3 in the series Cognitive control regulating information processing

Cognitive Styles

The first part of this section presents a discussion on cognitive styles. The second part examines the cognitive style construct field-dependent and field-independent. The third part reviews the importance of this cognitive style dimension for the acquisition and deployment of digital skills.

For thousands of years educators have been preoccupied with individual differences among learners (Keef, 1982). Zhang (2005) traced back the foundation of the theory of cognitive style research and development to Kurt Lewin’s work in the 1920s: A Dynamic Theory of Personality. In his attempt to set the foundation for a theory of differentiation among human beings, Lewin (1923) defined differentiation as: “a function of the conditions of the environment as well as the individual peculiarities of the person” (p. 226). Zhang (2005) summarized Kevin’s general law of psychology, which she argued had a significant influence on research related to individual differences among individuals: “a person’s behavior B is a function of a person’s personality P an environmental situations E (B= f (PE))” (Zhang, 2005, p. 11). 

Researchers’ interest in cognitive styles can also be traced back to C. Jung in 1923 when he advocated a theory of psychological types, which differentiated individuals along two types of attitudes (extraversion and introversion); two perceptual functions (intuition and sensing); and two judgment functions (thinking and feeling) (Sternberg & Grigorenko, 2001). Herman Witkin is credited as being the father of cognitive style and his works have been a catalyst for the exponential growth in cognitive style research. Cognitive styles are the information processing habits representing the learner’s typical mode of perceiving, thinking, problem solving, and remembering (Messick, 1985). They are also described as: “high-level heuristics that organize and control behavior across a wide variety of situations” (Dufresne & Turcotte, 1997, p. 1). A panel of experts reached a consensus on a definition of cognitive styles through a Delphi study:

Cognitive styles are individual differences in processing that are integrally linked to a person’s cognitive system. More specifically, they are a person’s preferred way of processing (perceiving, organizing and analyzing) information using cognitive brain-based mechanisms and structures (Peterson & Rayner, 2009, p. 520).

These styles constitute important dimensions of individual differences among students. In an attempt to explain the cognitive style construct, Cross (1976) notes:

People see and make sense of the world in different ways. They give their attention to different aspects of the environment; they approach problems with different methods for solution; they construct relationships in distinctive patterns; they process information in different but personally consistent ways. … Style has a broad influence on many aspects of personality and behavior: perception, memory, problem solving, interest, and even social behaviors and self-concepts. (p. 115-116).

There have been thousands of research studies conducted on cognitive styles over the years, through which a large number of cognitive style dimensions have been identified. The Learning & Skills Research Centre (2004) made an inventory, which included 71 models (Kirby, 1979); (Zhang, Sternberg, & Rayner, 2012) also provide also a comprehensive summary of several cognitive style constructs that have been identified and researched. The Learning & Skills Research Centre (2004) has noted that the “enormous size of these literatures presents very particular problems for practitioners, policy-makers and researchers who are not specialists in this field” (p .2).

Gregoroc (1982) indicates that brain behavior research provides “strong evidence that individual differences do indeed exist and that some of our instructional approaches are inappropriate for many individuals” (p. 7). Ginsburg (1985) states: “individual differences is at the heart of education. To a large degree education is or should be concerned with developing meaningful forms of learning for individuals who differ in important ways” (p. 57).

In discussing the issues and concerns related to individual differences among learners and learning, Belland, Taylor, Canelos, Dwyer, & Baker (1985) note that:

Accommodating learners’ individual differences remains a concern for all teachers at all levels. Whether the individual difference is defined as genetic intelligence, as a cognitive style or as an attitude, these individual difference variables have a significant influence upon learning and overall academic progress (p. 185).

Cognitive styles are psychological constructs “usually conceptualized as characteristic modes of perceiving, remembering, thinking, and problem solving, reflective of information processing regularities that develop in congenial ways around underlying personality trends” (Messick, 1985, p. 90). Ragan et al.(1979) argue that since cognitive style determines the way we acquire and process information, the “individual may encounter tasks that require the processing of information in a way that they are unable to accomplish, simply because their cognitive style restrict the availability of the processing technique” (p. 2). This argument suggests that instructional tasks can be style-biased, and is supported by research findings showing differential effects of matching and mismatching instructional tasks to cognitive styles. Several researchers have criticized the cognitive control FD-FI because they have found significant relations between the construct and measures of academic achievement (Learning & Skills Research Centre, 2004b). Others have acknowledged that there might be an interaction of field-independence and achievement and directed their effort to assist field-dependent individuals to overcome their information processing deficit (Learning & Skills Research Centre, 2004b).

Despite the importance of cognitive styles as a framework for address significant issues related to individual differences among people, this field of research has been: “constantly repeatedly criticized for the myriad of tests; contested, confused and overlapping definitions and terminology; inappropriate measurement and lack of independent evaluation” (Peterson, Rayner, & Armstrong, 2009, p. 18). After conducting a comprehensive review of research on 13 major cognitive constructs, the Learning & Skills Research Centre (2004) arrived at similar conclusions. The major problem in the field of cognitive style research relates to the inappropriate conceptualization and operationalization of the construct for research purposes. The work of Jonassen and Grabowski (1993) has contributed to bring conceptual clarity among the four main categories of constructs that they have identified through their research. Following is a brief description of each:

Table 1. Categories of constructs

Categories of constructs Description
Cognitive controls Because of the rapidity with which this flow of information takes place, a person needs to have highly developed cognitive controls to be able to cope with this information processing demands effectively and efficiently. Cognitive controls influence and regulate perception.
Cognitive styles Cognitive styles describe learner traits.
Learning styles Learning styles refers to preferred modes of acquiring knowledge in a learning environment.
Personality types Personality type refers to learner’s attention, engagement, and expectations.
Source: Adapted from (Ayersman & Minden, 1995).


While it has been demonstrated that all these categories of cognitive constructs contribute in one way or another to learning and performance, the Witkin cognitive control field-dependent and field-independent is the primary interest of this project because it is by far the most researched and most influential construct (Learning & Skills Research Centre, 2004). As Messick (1986) noted earlier, many unfulfilled promises had been made in the name of cognitive styles. He stressed that these false promises “may be true for some cognitive styles, perhaps even most of them, but it is not true for field dependence-independence. Its early promise has been fulfilled, and its potential continues to offer ample collateral for exciting new forays” (Messick, 1986, p. 117).

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Efforts to attenuate information processing difficulties

Posted by on Oct 8, 2014 in Blog, Human Computer Information Interaction and Information Processing

Efforts to Attenuate Information Processing Difficulties

Human capacity to process information is limited by the capacity of the working memory. Overloading the working memory interferes with information processing. The Cognitive Load Theory (CLT) has been proposed as a framework to control and adjust the cognitive demands imposed upon an individual exposed to print materials or electronic materials accessible through complex digital environments. Hollender, Hoftmann, Deneke, & Schmitz (2010) identified three types of cognitive loads:

  • Intrinsic cognitive load: The intrinsic complexity of the information to be processed;
  • Extraneous cognitive load: The extraneous cognitive load is the result of being exposed to too much superficial information from various sources; as a result, the performer must devote considerable effort to extract and integrate the relevant information since “information from one source has to be maintained in working memory in order to integrate it with information from the other sources (Hollender, Hofmann, Deneke, & Schmitz, 2010, p. 1279) citing (Ayres & Sweller, 2005);
  • Germane cognitive load: Cognitive load is increased when providing too much variation in work example (Hollender et al., 2010).

The ICT usability standard is designed to ascertain that a satisfied user can use the technology to perform a task effectively and efficiently. There are five common dimensions of usability: learnability, memorability, efficiency, low error rate, and user satisfaction. Over the years, User Experience has evolved as a more holistic concept of usability, which encapsulates four desirable dimensions for a digital tool: enjoyable, motivating, aesthetically pleasing, and supportive of creativity (Hollender et al., 2010).

Given the increasing use of technology for learning, the human-computer interface research focused on the learner as a user. The priority of HCI and learning is on learnability and the effectiveness and efficiency of technology-based instructional materials (Hollender et al., 2010). Instructional design and development efforts are directed to the adaptation of instructional materials to fit learners’ needs. The Aptitude by Treatment Interaction (ATI) research indicates that instructional treatments differ in the information processing demand they place on learners. A learner may fail to master an instructional task, simply because of a deficit in information processing skills (Chinien & Boutin, 1993). Instructions are often adapted to circumvent low ability learners on the basis of a fundamental assumption that learnability is improved as instruction takes over more of the information processing burden (Crono & Snow, 1986). Robertson (1985) has suggested that in order to achieve successful human-computer interface: “both the information-processing systems and the strategies used by the machine and also the cognitive systems and strategies deployed by human need to be appropriate” (Robertson, 1985, p. 19).

Technology and software engineers and researchers have for a long time realized the importance of human information processing for the human-computer interface:

“The major problems that confront users of advanced information technology are not legibility and keyboard design but instead concern information management, problem description, process representation and the like…that this interaction takes place through computers and their peripheral devices should not be allowed to obscure the fact that it is essentially cognitive and that the most important issues are cognitive” (Storrs, Rivers, & Canter, 1984, p. 62), cited by (Robertson, 1985, p.19).

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Information Technology Revolution

Posted by on Sep 8, 2014 in Blog, Information Technology Revolution

New Economy Time Capsule

The world economy has been reshaped and significantly transformed in recent years. While we tend to refer to this transformed economy as the new economy, according to the theory of Kondratieff cycles, this economy is not so radically new. It is essentially a reborn economy; this transformation takes place about every 40 to 60 years. Global development has unfolded through the succession of new economies. The new economy may be represented as a comparable economic transition driven particularly by revolution in technology. The first new economy (1770 to 1830) was characterized by water mechanization. The second new economy (1820 to 1880) was focused on steam mechanization, which led to the development of railroads. The third new economy (1870 to 1930) brought major development in electricity. The fourth new economy (1910 to 1970) contributed to the growth of the automobile industry. The fifth new economy, which began around 1960, kick-started the development in defence, television, mainframe computers, personal computers, telecommunications, and entertainment (Norton, 1999).

Information Technology Revolution

The information and communication technology (ICT) revolution is characterized by three major trends. The foremost characteristic of the digital era was marked by the omnipresence of microchips. Secondly, there was a dramatic decrease in the cost of computing. Thirdly, there was significant reduction in data costs. The term digital economy was coined to describe this new economy, which is driven by ICT. This term emerged from the observation that the relatively smooth transition from the old economy to the new economy was facilitated by the emergence of information goods, which can be digitized. This prominence of information commodities also entailed three landmark events: the invention of the microprocessor in 1971, the introduction of the personal computer (PC) in 1981 and the commercialization of the Internet in 1994 (Norton, 1999).

The microprocessor switched the world from an analog to a digital mode in which virtually every person, company, and government is a customer for technology products, mostly because of the introduction of PCs in 1981. The invention of the PC thus rendered anything and everything subject to the power of the computer, while retaining the crucial dimensions of human scale, decentralized decision making, customized design, and creativity (Chinien, Moratis, Boutin, & Baalen, 2002). Countries that were early adopters of the digital economic paradigm have deployed technology to create wealth and social progress. According to a recent OECD report, China has become the largest exporter of ICT goods, while India is now the largest exporter of ICT infrastructure and services (OECD, 2010). The e-skills UK Sector Skills Council noted that: “Digital technology is the single biggest lever for productivity and competitiveness across every sector of the economy” (e-skills UK Sector Council, 2009a, p. 5).

The imperative for Canada to embrace the digital economy was stated in the Speech from the Throne on March 3, 2010, and concrete action to create a national digital economic strategy was launched in May 2010 by a broad consultation of Canadians. The consultation paper on a Digital Economy Strategy for Canada defined digital technologies as: “tools, capacities or knowledge assets that can be embedded in business processes, products and services to help firms and individuals in all sectors of the economy become more productive, innovative and competitive” (Government of Canada, 2010, p. 11).

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Give your brain an unfair advantage. Play NeuroLudus for a smarter brain! 3

Give your brain an unfair advantage. Play NeuroLudus for a smarter brain!

Posted by on May 9, 2014 in Feature

Neuro-Ludus is a brain training game designed to improve the information processing skills of youth and adults so that can use digital technology more effectively and efficiently. Try it now for FREE and share it with friends!

The game is wrapped around a scenario to capture players’ interest and maintain their engagement:

Following an ecological disaster, some primitive bacteria have attacked the human brain and threaten to erode the superiority of the human race over other living organisms. Save the human race! Play Neuro-Ludus and collect neuro-energy to rewire human brains.

The objective is to find a simple shape hidden in a complex figure using various game plays: drag and drop, point and click, flip drag and drop, and rotate, drag and drop. The game has 30 levels ordered according to game play, task complexity and play time. The players collect “neuroenergy” for each level successfully completed. The strength of the neuroenergy depends on the amount of time taken by the player to complete each level. The neuroenergy is applied for rewiring 1, 2 or 3 human brains depending upon its srength.

After playing the game once we suggest that you keep on playing it on a regular basis in order to maintain and further improve your gains: PRACTICE MAKES PERFECT. Neuro-Ludus is accessible in English and French through various digital platforms, including computers, tablets and cellular phones.

Visit our web site ( to learn more about the scientific foundations supporting the design and development of Neuro-Ludus.


To take full advantage of digital technology we must be able to process huge amount of complex information quickly and effectively.

Only a few decades ago, we were unable to help people to improve their information processing skills because of a constraining belief that the brain was “hard-wired” to function in predetermined ways, and could not be modified. However, recent revolutionary research in neuroplasticity has demonstrated that the brain is “soft-wired” and can be modified with appropriate training. We have applied cutting-edge development in interactive multimedia software technology, and recent important discoveries in neuroplasticity and cognitive modifiability in an innovative way to develop the Neuro-Ludus brain training game.


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