Psyc355 p5 ip cognitive psychology applied

Running Head: COGNITIVE PSYCHOLOGY APPLIED 1
Cognitive Psychology Applied
Cherilyn Formanek
Colorado Technical University Online
PSYC355-1304B-01

COGNITIVE PSYCHOLOGY 2
Cognitive Psychology Applied
Introduction
Human cognition can be described as the conscious and unconscious mental processes of
the human brain; mental processes that motivate an organism toward action (Ashcraft &
Radvansky, 2010). These mental processes consist of attention, language, memory, perception,
reasoning and solving problems (Ashcraft & Radvansky, 2010). The field of cognitive
psychology studies human cognition by applying scientific principles (the scientific method)
toward understanding human mental processes in an variety of environments and conditions;
such as in personal and workplace life (Ashcraft & Radvansky, 2010). The following paper will
examine how cognitive psychology can be applied to personal and workplace issues and
specifically a new employee training program.
I. Introduction to Cognitive Psychology
Application of Cognitive Psychology to Personal and Workplace Issues
Cognitive psychology can have many applications to both personal and workplace issues;
first because the field of cognitive psychology studies the mental processes; how they function;
what areas of the brain may be engaged in each one; how they may differ and how they may be
the same between individuals; and the impact of nature and nurture on each of the processes
(Ashcraft & Radvansky, 2010; Eraut, 2007; Le Roy, Bastounis & Minibas-Poussard, 2012;
Robotti, 2012). Less important is the question of specific issues as personal and workplace
related; human beings may be consistent in their individual differences; but generally speaking
personal issues and workplace issues are not mutually exclusive; personal issues and workplace
issues often overlap (Ashcraft & Radvansky, 2010; Eraut, 2007; Le Roy, Bastounis & Minibas-
Poussard, 2012; Robotti, 2012). Four examples of how cognitive psychology can be applied to

both personal and workplace issues would be in the areas of learning, motivation, emotion and
behavior all of which impact and are impacted by attention, language, and memory, perception,
reasoning and problem-solving (Ashcraft & Radvansky, 2010; Eraut, 2007; Le Roy, Bastounis &
Minibas-Poussard, 2012; Robotti, 2012). Cognitive psychology (with many other disciplines in
particular the neurosciences) can be applied in terms of understanding development and function;
improving methods of learning and motivation; and helping individuals better understand and
control their emotions and behavior appropriately (Ashcraft & Radvansky, 2010; Eraut, 2007; Le
Roy, Bastounis & Minibas-Poussard, 2012; Robotti, 2012).
How Do You Know When Learning Has Occurred
First, learning can be defined as the altering of behavior in relation to one's experience
directly or indirectly (observational); there are many ways in which individuals can learn;
including classical and operant conditioning; insight learning and latent learning (Ashcraft &
Radvansky, 2010; Colorado Technical University, 2010). One can generally tell when learning
has occurred in others when they observe a change in a particular behavior; such as when a
young child learns to avoid touching a hot stove (Ashcraft & Radvansky, 2010; CTU, 2010;
2011). One generally knows when learning has occurred within oneself when one alters a
behavior and usually has a pretty good idea of why (such as previous behavior resulted in an
unpleasant outcome) (Ashcraft & Radvansky, 2010; CTU, 2010; 2011). For instance when one
chooses not to study and complete university course assignments one knows one is not learning
much; one's grades suffer and one may fail the class or pass with a very low grade. However
when one studies and completes university course assignments; one will tend to know which
concepts one feels they have a good grasp of; which ones they may be more uncertain of and
may need to study more; and one will be more likely to pass a class with a much higher grade.

An Example of Successful Learning
Successful learning requires all of the cognitive processes; attention, perception, memory,
language, reasoning and problem-solving skills (Ashcraft & Radvansky, 2010; CTU, 2010;
2011). An example of successful learning that many individuals have is learning to read which
usually begins first with learning language (Ashcraft & Radvansky, 2010; CTU, 2010; 2011;
Eraut, 2007; Robotti, 2012). One usually begins with the sensory sensation of hearing; one learns
to associate sounds with different stimuli including family members such as voice differences;
tone; differences in syllabic sound of individual words (Ashcraft & Radvansky, 2010; CTU,
2010; 2011; Eraut, 2007; Robotti, 2012). One pays attention and slowly over time learns to
imitate the sounds; make associations between objects and language words; often as such
behaviors are rewarded by others (Ashcraft & Radvansky, 2010; CTU, 2010; 2011; Eraut, 2007;
Robotti, 2012). The individual may need to reason and problem-solve how to use new language
skills such as remembering the right words to communicate with others so that meaning is
conveyed correctly (Ashcraft & Radvansky, 2010; CTU, 2010; 2011; Eraut, 2007; Robotti,
2012).
Likewise the individual also learns to express words in conjunction with tone of voice to
express intent and meaning; often imitating vocal patterns and tone of voice of those who are
modeling language speech (Ashcraft & Radvansky, 2010; CTU, 2010; 2011; Eraut, 2007;
Robotti, 2012). These are subsets of information one learns prior to being introduced to the
alphabet of the language one has learned; then one is introduced to the associated alphabet; must
provide attention to the individual letter and associated sound; and commit each to memory
(Ashcraft & Radvansky, 2010; CTU, 2010; 2011; Eraut, 2007; Robotti, 2012). One must then
learn how to sound out each letter in accordance with its representative sound; identify the word,

and understand it's meaning; and then learn the rules of grammar for the language where sets of
word symbols are put together to express sentences (Ashcraft & Radvansky, 2010; CTU, 2010;
2011; Eraut, 2007; Robotti, 2012). In learning to recognize individual words and their associated
meanings in sentences; one uses reasoning and problem-solving to determine the meaning of the
combined set of words within a sentence (Ashcraft & Radvansky, 2010; CTU, 2010; 2011;
Eraut, 2007; Robotti, 2012). In learning the process of reading in terms of sentence structure and
grammar one learns that one can also determine elements of meaning in unknown words by
considering the context of the written material; through each element of the learning process
attention, perception, memory; language, reasoning and problem-solving are integral (Ashcraft &
Radvansky, 2010; CTU, 2010; 2011; Eraut, 2007; Robotti, 2012).
II. Key Cognitive Psychology Concepts: the individual, workplace, adult learning; creating
successful learning organizations
Perception Attention and Memory
Factors that influence visual and auditory perceptual knowledge: there are several
factors that influence visual and auditory perceptual knowledge; including attentional processes;
short term, working and long-term memory; pattern recognition processes; feature detection and
top-down and bottom-up processing (Ashcraft & Radvansky, 2010; CTU, 2011; Hall, Fussell &
Summerfield, 2005; Talmsa, et al, 2006). Attentional processes influence what stimuli receive
focus and recording by visual and audio memory processes (Ashcraft & Radvansky, 2010; CTU,
2011; Hall, Fussell & Summerfield, 2005; Talmsa, et al, 2006). They can be reflexive; automatic,
selective and unconscious; we often take in more information that is stored in memory; what is
stored is usually because attention was given to it; and then depending on degree of importance
will often depend how much of the information will be placed in working and long-term memory

(Ashcraft & Radvansky, 2010; CTU, 2011; Hall, Fussell & Summerfield, 2005; Talmsa, et al,
2006). The more attention a stimuli receives particularly if emotional content is also involved;
the stronger information from stimuli is likely to be remembered and can then influence
perception (Ashcraft & Radvansky, 2010; CTU, 2011; Hall, Fussell & Summerfield, 2005;
Talmsa, et al, 2006). Likewise information stored in working and long-term memory is often
associated with other information; creating a knowledge base of data from learning and
experience that can be applied to novel situational and object stimuli in cognitive processing
2006).
In pattern recognition, objects perceived by one's eyes are processed and stored
automatically and usually unconsciously into memory as part of a problem-solving process
2006). The memory storage essentially records patterns to objects that are seen; in bits and pieces
and fills in the blank areas (Ashcraft & Radvansky, 2010). A set of principles describing how
visual perception is organized is called the Gestalt grouping principles; these principles help
explain how perceptual ambiguities are sorted out and resolved (Ashcraft & Radvansky, 2010).
Figure-ground explains the foreground-background relationship between objects and our ability
to perceive one object more prominently than others (Ashcraft & Radvansky, 2010). Closure is a
process that accounts for gaps in object perception; particularly when one object is blocking the
full view of another (such as a chair might block the full view of a television set from one visual
perspective in a room; one knows the full image of the television set from a record of viewings
from several different perspective in memory) (Ashcraft & Radvansky, 2010). Proximity groups
objects close to one another; similarity groups objects that are similar causing a blending effect

(pixels on a screen to create a larger image); good continuation is an assumption of a blocked
edge of an object continuing and common fate describes objects moving together being grouped
together (headlights of cars moving together in the same direction at night) (Ashcraft &
Radvansky, 2010).
Other important elements are top-down and bottom-up processing and feature detection.
Feature analysis or detection is essentially recognition of feature pattern components; horizontal,
vertical, diagonal lines, curves and arcs within objects (Ashcraft & Radvansky, 2010; Hall,
Fussell & Summerfield, 2005; Talmsa, et al, 2006). We learn to recognize and distinguish
between such components in an object; such as the vertical position and double curve in opposite
directions, first to the left on the top; then to the right on the bottom; that distinguishes the letter s
from the letter t (Ashcraft & Radvansky, 2010; Hall, Fussell & Summerfield, 2005; Talmsa, et al,
2006). Bottom up processing is also referred to as data-driven processing and refers to a
processing mechanism where information comes in and is interpreted in a low to high direction
until all patterns have been recognized and the object resolved (Ashcraft & Radvansky, 2010;
Hall, Fussell & Summerfield, 2005; Talmsa, et al, 2006). Top-down processing is also referred to
as conceptually driven processing; meaning that context influences perception from higher levels
down to lower levels of perception (Ashcraft & Radvansky, 2010; Hall, Fussell & Summerfield,
2005; Talmsa, et al, 2006). As an example a red light flickering on a panel can mean many
different things depending on the context; a red light flickering on a panel inside a break room of
a bank means a silent alarm has been activated and there may be a dangerous situation on the
other side of the break room door; the context shapes the direction of the processing from the top
downward and influences the perception of meaning (Ashcraft & Radvansky, 2010; Hall, Fussell
& Summerfield, 2005; Talmsa, et al, 2006).

Another important element of visual and audio perceptual knowledge is object
recognition; the ability to see or hear an object stimuli; identify it and assignment meaning to it
(Ashcraft & Radvansky, 2010; Hall, Fussell & Summerfield, 2005; Talmsa, et al, 2006).
Pertinent research in studying object recognition is the study of agnosia; a condition in which
there is a disruption in an individual's ability to identify and assign meaning to an object
(Ashcraft & Radvansky, 2010). First object recognition consists of a process of identifying
objects by component elements such as shape; edges and other elements help to further identify
the object in much the same was as pattern recognition (Ashcraft & Radvansky, 2010). Agnosia
is a condition in which the individual is unable to recognize object patterns and put them together
into a whole; or they are unable to match the object to its meaning; and it can be in the following
forms: prosopagnosia (disruption of facial recognition); apperceptive (inability to perceive
object pattern; or assign meaning); and associative (when the object can be recognized but not be
able to be associated with meaning from memory) (Ashcraft & Radvansky, 2010). It is from
studying agnosia that we understand the relationship of object recognition in visual perception
and association within memory (Ashcraft & Radvansky, 2010).
Learning Remembering and Knowing
Human beings are, generally speaking, always learning and committing new information
to memory (Ashcraft & Radvansky, 2010). There are different kinds of memory; explicit and
implicit memory for instance differs on whether or not an individual is conscious of the memory
retrieval (Ashcraft & Radvansky, 2010). Explicit memory consists of semantic and episodic
memory; semantic memory is described as general world knowledge and facts while episodic is
described as more personal, autobiographical memory (Ashcraft & Radvansky, 2010). Implicit
memory is also called procedural memory and consists of information we know but don't

necessarily need to be consciously aware of thinking about; we know how to walk; we do not
need to think about walking in order to walk (Ashcraft & Radvansky, 2010). Learning is a
process in which information is encoded; retained and retrieved from memory; some of it
implicit (procedural) and some of it explicit (semantic and episodic) (Ashcraft & Radvansky,
2010).
Learning through chunking or mnemonic devices as a child: most American children
memorize the alphabet using a rhythmic mnemonic device; a sing-song recitation of each
alphabet character in correct order (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013;
CTU, 2011, 2010; Congos, 2006). Chunking is used to teach children to differentiate between
consonant letters and vowel letters; and also to associate vocal sounds with each letter and some
letter combinations (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010;
Congos, 2006). Word spelling is essentially chunking; and children encounter vocabulary and
spelling lessons through most if not all of their academic school careers (Ashcraft & Radvansky,
2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006). Likewise chunking and
mnemonics are used in a whole slew of other subjects; please excuse my dear aunt sally is a
mnemonic that is taught to children (and retained by most adults) to help them keep the correct
order of mathematical operations in mind; and many of them are retained in memory well into
adulthood (for instance I before e except after c; or else I'd constantly be misspelling words like
receive) (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos,
2006).
Effectiveness of chunking and mnemonic devices: chunking and mnemonic devices are
effective enough that chunking is used extensively in teaching in general; and mnemonic devices
are still taught even at a university level (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013;

CTU, 2011, 2010; Congos, 2006). Regardless of the topic of course curriculum students are
generally going to be taught small chunks of material first in order to build up to a broader
understanding of the whole topic (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU,
2011, 2010; Congos, 2006). When teaching students of psychology for instance; some of the first
undergraduate classes would (or should) be on research methods(Ashcraft & Radvansky, 2010;
Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006). Such information is chunked in
terms of what is taught first; such as the Scientific method; and the meaning and context of
quantitative research versus qualitative research; and then finally on research methods that fall
under each category (such as a randomized survey method in quantitative; or a field study under
qualitative) (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos,
2006). In this way a general knowledge is established in semantic memory to which later
knowledge can be associated (such as the case study of HM was primarily exploratory;
qualitative; however many of the testing measurements were quantitative measurements used
with other participant populations, such as the I.Q. measurements) (Ashcraft & Radvansky,
2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006).
Mnemonic devices are also very effective in teaching information that should be
memorized due to relevant importance (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013;
CTU, 2011, 2010; Congos, 2006). One example seen repeatedly in classes especially when there
is an overlap between business administration topics and psychology is a model mnemonic;
Abraham Maslow's Hierarchy of Needs; this is a pyramid image that lists from bottom to top the
following: physiological needs; safety needs; affiliation needs; self-esteem needs; and self-
actualization needs (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010;
Congos, 2006). Another example is a word mnemonic used to help students memorize the Big

Five personality traits considered critically important to personality trait theory; open-
mindedness, conscientiousness, extraversion, agreeableness and neuroticism become much easier
to remember if one remembers the word ocean(Ashcraft & Radvansky, 2010; Brabeck & Jeffrey,
2013; CTU, 2011, 2010; Congos, 2006). Even the concepts of quantitative and qualitative can be
remembered accurately through the use of connection mnemonics; quantitative, quantity and
statistical analysis; qualitative, small groups, detailed and in-depth analysis (Ashcraft &
Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006).
Are chunking and mnemonic devices as effective for adults as they are for children:
chunking and mnemonic devices are effective for both adults and for children as tools for
memorization and skill building; but ultimately it also depends on how much an individual (child
or adult) pays attention to the information, rehearses the information and engages in repetition of
the information (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010;
Congos, 2006). Chunking and use of mnemonic devices may be slightly more effective for adults
when adults understand that chunking and mnemonic devices constitute strategies in learning and
memorization (Brabeck & Jeffrey, 2013). Children can learn as effectively when they perceive
the strategy is fun (such as singing the ABC song repeatedly); but can also be taught that these
are techniques that aid in memorization of important information (Ashcraft & Radvansky, 2010;
Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006). It will largely depend on individual
differences in terms of motivation to learn; how well an individual (child or adult) wants to learn
information and how much attention an individual spends on the information, how much practice
(rehearsal and repetition) an individual is willing to participate in with the information (Ashcraft
& Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006).

Learning techniques used as an adult: there are a few other techniques I use as an adult
to learn and remember something; first I read and reflect on what I am reading (Ashcraft &
Radvansky, 2010; Brabeck & Jeffrey, 2013). I take notes; often verbatim; and then paraphrased
(so I can double-check my understanding against the verbatim wording) (Ashcraft & Radvansky,
2010; Brabeck & Jeffrey, 2013). I think about how the material relates to personal experience
and semantic memory knowledge; and I will write about the associations (usually in a personal
journal first; then very often in class assignments) (Ashcraft & Radvansky, 2010; Brabeck &
Jeffrey, 2013). I re-organize the information if necessary and explain it to myself; and usually on
account of an assignment; to other people in the course of that assignment (Ashcraft &
Radvansky, 2010; Brabeck & Jeffrey, 2013). I think about how I can apply the information later;
usually in my personal life; often in terms of a professional context; and I even find myself
discussing information I've learned about with others outside of the class (Ashcraft &
Radvansky, 2010; Brabeck & Jeffrey, 2013).
Are there key differences in learning techniques used as children and adults: there
are key differences in how one might apply learning techniques used with children and adults
that are based on age, level of skill development and length of attention span but otherwise the
learning techniques themselves are not really all that different (Ashcraft & Radvansky, 2010;
Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006). Children from a very young age are
taught using chunking methods and mnemonic devices; as are older children and adults of all
ages (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006).
Likewise an individual who is motivated can also apply other learning techniques that essentially
do the same thing; encourage attention on a subject; employ rehearsal of the information;
enhance the movement of information from working memory into long-term memory by

associating the information more broadly in both semantic and even episodic memory (Ashcraft
& Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos, 2006). For individuals
with deficits in cognition or attention; information may need to be presented for learning in
smaller chunks (including use of mnemonics); for shorter durations of time; in more novel ways;
and with more frequency; but one would still provide information in chunks that build on prior
knowledge (Ashcraft & Radvansky, 2010; Brabeck & Jeffrey, 2013; CTU, 2011, 2010; Congos,
2006).
How can these concepts be applied successfully to learning organizations: first, what
is a learning organization? Theoretically it is an organization that has stepped away from older
hierarchical organizational structures in which there is a focus on control; especially information
control (CTU, 2013; Columbia University, n.d.). Learning organizations attempt to focus on
creating a competitive edge based on collective learning and knowledge; an ability to be more
efficient and flexible as an organization and also in meeting market demands (CTU, 2013;
Columbia University, n.d.). Learning organizations attempt to create and maintain an
environment in which all individuals acquire and share knowledge across all levels of the
organization rather than consolidating knowledge at only executive levels or in key departments
(CTU, 2013; Columbia University, n.d.). For the most part learning organizations rely heavily on
communication and database technology to disburse information across the organization and
between individuals (CTU, 2013; Columbia University, n.d.).
However organizations do not learn per se; they rely on the collective knowledge
retention and learning of the individuals who make up the organization (CTU, 2013; Columbia
University, n.d.). Organizations can only learn by acquiring individual members with the
necessary knowledge; and by relying on their willingness and ability to share that knowledge

with other individuals (CTU, 2013; Columbia University, n.d.). The individuals to whom such
information must be shared must likewise be able and willing to learn; and in this respect, yes the
previous concepts can be applied within a learning organization, successfully (CTU, 2013;
Columbia University, n.d.). Information can be encoded, retained and retrieved in both explicit
(semantic and episodic) and implicit memory (procedural) by individuals who can then transmit
such information to other individuals; as well as encode information into technology (computer)
based storage systems for later retrieval by yet other individuals (CTU, 2013; Columbia
University, n.d.). Chunking and mnemonics can certainly be used by such organizations in
training and development programs for employees at all levels; and even transmitting
information through interpersonal communication follows a pattern of organization in terms of
the information being transmitted; regardless of the medium used (CTU, 2013; Columbia
University, n.d.).
Knowledge Language and Comprehension
The mental processes involved in language, metamemory and comprehension:
The first thing to know about the mental processes involved in language, metamemory
and comprehension is that they are processes; they develop incrementally and in an interrelated
fashion (Ashcraft & Radvansky, 2010; StateUniversity.com, 2013; Swinney, 1981). Next,
linguistics is the study of language; language is a "shared symbolic system of communication"
(Ashcraft & Radvansky, 2010; p. 322); and psycholinguistics is the study of language as it is
learned and used by people (Ashcraft & Radvansky; p. 321). Important factors involved in
comprehension of information and communication itself is planning and memory; one must
know language grammar; phonology; syntax, semantics, conceptual and belief elements of
language interpretation (memory) and be able to communicate in comprehensible ways with

others by using the same rules (planning) (Ashcraft & Radvansky, 2010; StateUniversity.com,
2013; Swinney, 1981). Courtesy of research on aphasia patients we know that two areas of the
brain are primarily involved in language processing and comprehension and for which
metamemory is complicit; these are Broca's area and Wernicke's area respectively (Ashcraft &
Radvansky, 2010). Aphasia is described as interference in language; in Broca's aphasia; there is
interference in producing speech; in Wernicke's aphasia the interference involves comprehension
of words and associated concepts (Ashcraft & Radvansky, 2010).
Metamemory is an individual's ability to self-assess one's own memory; it is self-
awareness of what one knows and remembers and consists of both implicit and explicit memory
(Ashcraft & Radvansky, 2010; StateUniversity.com, 2013; Swinney, 1981). Metamemory allows
the individual to select strategies and plan; distribute cognitive resources (energies) between
tasks; pay attention to what and how one comprehends information (metacomprehension) and
self-evaluate task performance (Ashcraft & Radvansky, 2010; StateUniversity.com, 2013;
Swinney, 1981). Metacomprehension is self-assessment of what one has learned and understood;
research indicates that for information to be more fully comprehended; individuals need to
engage in metacomprehension as a means of knowing and reinforcing the information (Ashcraft
& Radvansky, 2010; StateUniversity.com, 2013; Swinney, 1981). Language is instrumental in
expressing concepts relating to memory and metamemory; comprehension and
metacomprehension; and individuals begin the process of acquiring language and language
comprehension in early childhood (Ashcraft & Radvansky, 2010; StateUniversity.com, 2013;
Swinney, 1981). Metamemory and metacomprehension appear to develop over time without
much self-awareness (and is continuous); individuals do not appear to be aware of metamemory
processes until around approximately age ten; and may also reflect increased competence in

adults in certain areas of expertise (Ashcraft & Radvansky, 2010; StateUniversity.com, 2013;
Swinney, 1981). In short; metamemory and language are integral components that appear to
build on one another to directly impact comprehension and metacomprehension components in
cognition (Ashcraft & Radvansky, 2010; StateUniversity.com, 2013; Swinney, 1981).
Helpful strategies for judging an individual's learning, knowledge acquisition and
comprehension:
Helpful strategies for judging an individual's learning, knowledge acquisition and
comprehension would be relatively similar to how one might assess one's own memory and
comprehension in learning (Ashcraft & Radvansky, 2010; StateUniversity.com, 2013; Swinney,
1981). When we engage in the learning process we can plan and engage in strategies to learn
(like chunking and use of mnemonic devices); we can monitor how well we understand what we
learn; and take steps to increase our understanding (Ashcraft & Radvansky, 2010;
StateUniversity.com, 2013; Swinney, 1981). We can self-assess our memory (procedural,
semantic; even episodic memory); and make associations in order to better remember concepts
(Ashcraft & Radvansky, 2010; StateUniversity.com, 2013; Swinney, 1981). We can explain what
we have learned either vocally or in written communications to others; or we can demonstrate
learning through completion of tasks (Ashcraft & Radvansky, 2010; StateUniversity.com, 2013;
Swinney, 1981). Likewise we can judge the learning, knowledge acquisition and comprehension
of other individuals by observing procedural demonstration of tasks or by observing how other
individuals communicate in terms of language; orally or in written forms; particularly we can
observe how competently an individual may have comprehended and acquired knowledge by the
ease in which they can explain learned concepts in their own words by way of language
(Ashcraft & Radvansky, 2010; StateUniversity.com, 2013; Swinney, 1981).

As an example of knowledge, language and comprehension in action and how one can
demonstrate knowledge and be judged in terms of comprehension of a subject; consider that a
class in cognitive psychology has been taught; information transmitted between an instructor and
a learner completely by way of English language rules and symbols (Ashcraft & Radvansky,
2010; StateUniversity.com, 2013; Swinney, 1981). Course content was shown using a textbook
full of symbols and icons; oral lectures accompanied visual symbols and icons on visual
mediums to transmit concepts and ideas between individuals (Ashcraft & Radvansky, 2010;
StateUniversity.com, 2013; Swinney, 1981). Course content was presented incrementally;
building up to a fuller understanding between concepts as time in the class progresses; the
learners take steps to learn; and self-monitor their comprehension of the course content (Ashcraft
& Radvansky, 2010; StateUniversity.com, 2013; Swinney, 1981). Individuals transmitted their
own knowledge and comprehension of the course content, using the same language rules in a
written format; actively demonstrating how well the information was understood by how well
they were able to transmit an explanation of it to their peers and instructor (how comfortable
were they; how easy was it to put the information into their own words) (Ashcraft & Radvansky,
2010; StateUniversity.com, 2013; Swinney, 1981). The assignments are then judged (graded) in
terms of knowledge acquired; how much learning and comprehension of the course content can
be demonstrated by organizing the information into a comprehensive written paper that meets
assignment parameters; in a way that demonstrates originality and yet also represents the
information accurately both in terms of the content and language expression (Ashcraft &
Radvansky, 2010; StateUniversity.com, 2013; Swinney, 1981).
Decision-Making Problem-Solving and Modern and Future Technology

Decision-making:decision-making is essentially choosing between options; when the
options differ significantly; choices are easiest; when options are minimal, choices are more
difficult(Ashcraft & Radvansky, 2010; CTU, 2012; 2009). Judgments are usually influenced by
knowledge and mental representation; can be made of physical or symbolic stimuli (objects,
situations); and can be prone to errors depending on choices of decision-making
strategies(Ashcraft & Radvansky, 2010; CTU, 2012; 2009). Two approaches often used by
individuals in the process of decision-making are heuristics and algorithms(Ashcraft &
Radvansky, 2010; CTU, 2012; 2009). Algorithms are sets of procedures or formulas that if
followed; will produce accurate results; heuristics are casual short-cuts in decision-making that
are prone to errors therefore often produce poor results(Ashcraft & Radvansky, 2010; CTU,
2012; 2009). Heuristics (such as representativeness, availability and simulation) tend to be
inaccurate because of problems with mental representation in areas such as knowledge domain,
working memory, logic and deficiency of evidence; which can result in biases (confirmation;
familiarity, hindsight; salience); fallacies (conjunction) and errors (attribution and anchoring
and adjustment)(Ashcraft & Radvansky, 2010; CTU, 2012; 2009).
Problem-solving: problem solving is the behavior or act of resolving difficulties in
various aspects of life; and can include decision-making strategies such as use of algorithms and
heuristics(Ashcraft & Radvansky, 2010; CTU, 2012, 2011). Problem-solving is a process of
steps such as identification of the problem, analysis of the problem including various methods
for overcoming the problem; decision-making regarding the best available solution; planning the
steps of overcoming the problem and then evaluating the outcome of the problem-solving
process(Ashcraft & Radvansky, 2010; CTU, 2012, 2011). Required within the problem-solving
process are components such as goal directions and use of sub-goals throughout the problem-

solving process; a planned sequence of steps; and engagement of multiple cognitive
processes(Ashcraft & Radvansky, 2010; CTU, 2012, 2011). Issues that individuals can have for
the problem-solving process are an absence of knowledge on how to problem solve (no training;
no natural talent) fear of decision-making; incorrect analysis of the problem; personal biases;
inability to translate a problem for solving by a computer; and improperly using the steps of the
problem-solving process(Ashcraft & Radvansky, 2010; CTU, 2012, 2011).
Modern and future technology: Essentially, decision-making and problem-solving
especially regarding modern and future technology should involve an emphasis on the use of
algorithms especially when it involves computer model technologies(Ashcraft & Radvansky,
2010; CTU, 2012, 2011; Durning & Artino, 2011; Nielsen, 2007; Wielenga-Meijer, Taris,
Kompier, & Wigboldus, 2010). Individuals should be trained to be aware of heuristic problem-
solving strengths (quick and simple decisions; also means-end analysis) and weaknesses;
including biases, fallacies and errors; training in problem-solving process steps; including goal
and subgoal setting and planning is also critical(Ashcraft & Radvansky, 2010; Durning & Artino,
2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). Of particular importance regarding
decision-making and problem-solving processes where it involves modern and future
technologies is innovation; creating new ways of using present technology (likewise future
technology)(Ashcraft & Radvansky, 2010; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-
Meijer, et al, 2010). Rather than use a particular tool for its most familiar purpose only; learn to
think creatively and find new ways for how the tool can be used to solve new problems(Ashcraft
& Radvansky, 2010; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). In
this way current technology (modern) can be used to create future technology; and the
underlying skill for developing new technologies is further developed for future use in creating

still newer future technologies(Ashcraft & Radvansky, 2010; Durning & Artino, 2011; Nielsen,
2007; Wielgenga-Meijer, et al, 2010).
Personal example of means-end analysis: when I decided to enroll in Colorado
Technical University's online Associate of Science in Business Administration degree program;
(later the Bachelor of Science in Psychology: Organizational Behavior); it was with the goal to
learn; not just earn a degree. When I started out; I felt my knowledge domain in business
administration was very limited; and that I should pay close attention to the university's teaching
methods in order to be able to make my own active evaluation of how well I was learning the
material. I recognized early on that taking a college course is not about showing what you
already know about a subject (which is often very little or full of errors) but what you learn
throughout the course; the overall goal is to develop knowledge about a topic and become
competent (Ashcraft & Radvansky, 2010; CTU, 2012; Durning & Artino, 2011; Nielsen, 2007;
Wielgenga-Meijer, et al, 2010). The content and course work within each phase represents sub-
goal knowledge; to reach the overall goal; one must master each sub-goal and build one's
competency from one sub-goal to the next until at the end of the course one can evaluate how
much one knows at the end; that one did not know in the beginning (Ashcraft & Radvansky,
2010; CTU 2012; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). There
are several operators in the course of learning in CTU's classes; use of textbooks and other
course materials; use of live chats and the professional expertise of instructors; use of algorithms
in terms of research, citation and references; all of which are designed to minimize the
differences between current states and sub-goal and goal states (Ashcraft & Radvansky, 2010;
CTU, 2012). In this way my educational process is itself an on-going means-end analysis and
how well I apply the various operators and actively participate in the learning process is reflected

in not just my own sense of learning the information it is also reflected in the consistency of my
grades and my ability to continue to learn still newer information as I go (Ashcraft & Radvansky,
2010; CTU, 2012; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010).
So far the means-end analysis has been highly successful; I earned my Associate of
Science in Business Administration in the fall of 2011; with a graduating G.P.A. of 4.0. I often
experienced the feeling of self-appraisal of learning; noting that in the course of trying to
understand complex concepts; what was difficult to grasp one day; would be grasped by the next
day (often with dream recall of course content in between; the brain processing information into
long-term memory) (Ashcraft & Radvansky, 2010). Likewise I have often been amazed at the
amount of recall from previous classes I have been able to have as a result of the effort of
learning information initially; and from periodic reinforcement of information through the course
of other classes and just simple reflection on course concepts(Ashcraft & Radvansky, 2010;
CTU, 2012; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). The means-
end analysis has pretty much become my standard algorithm of choice; if I want to continue
learning at the level to which I have been able to thus far; I continue to use the operators
provided; I continue to reflect on course materials; and this has continued to work as a means of
problem-solving in various courses (Ashcraft * Radvansky, 2010; CTU, 2012). I am at the
halfway point of my current degree program (Bachelor of Science, Psychology: Organizational
Behavior) and I still maintain a 4.0 G.P.A.; I no longer worry about not being able to learn a
complex topic; I know I will go back the next day and it will be easier to understand; likewise
the day after that (Ashcraft & Radvansky, 2010; CTU, 2012; Durning & Artino, 2011; Nielsen,
2007; Wielgenga-Meijer, et al, 2010).

Helpful suggestions for improving problem-solving abilities: first and foremost be
motivated to improve one's problem-solving abilities(Ashcraft & Radvansky, 2010; CTU, 2012;
Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). All of the helpful
suggestions in the world are essentially worthless if individuals are not motivated to act on
improving their problem-solving abilities(Ashcraft & Radvansky, 2010; CTU, 2012; Durning &
Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). Second, think critically about one's
own judgments and biases; question the ways in which we draw our conclusions about things;
are they based on faulty heuristics; fallacies; biases and errors in thinking(Ashcraft &
Radvansky, 2010; CTU, 2012; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al,
2010). Analyze a problem carefully and be sure one's understanding of a problem is accurate;
then consider pros and cons of the possible solutions (Ashcraft & Radvansky, 2010; CTU, 2012;
Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). Plan; plan how one will
go about accomplishing not just goals; but also sub-goals (Ashcraft & Radvansky, 2010; CTU,
2012; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). Have an accurate
assessment of one's own knowledge domain; take steps to add information to one's knowledge
domain; and practice one's skills and knowledge in order to increase one's competencies
(Ashcraft & Radvansky, 2010; CTU, 2012; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-
Meijer, et al, 2010).
How can the knowledge of human problem-solving be applied to the use of modern
and future technologies: Human problem-solving has been the root of all technologies; from
prehistoric tool development to the modern day use of both tools and technologies to solve all
manner of problems in general (Ashcraft & Radvansky, 2010). At some point humans noted that
lightning striking a nearby tree resulted in fire; which was useful for keeping warm; and set

about solving the problem of losing access to fire by learning to start fires. Humans have been
actively solving problems ever since; and in modern times can now create and innovate
technology to a point where some problem-solving tools can create other problem-solving tools
(such as robots working in an assembly factory building ball peen hammers) (Ashcraft &
Radvansky, 2010). However this does not mean that modern technology (or future technology)
will ever evolve past a point where problems will no longer require solving; at least initially by
human beings even when using tools to do the problem solving(Ashcraft & Radvansky, 2010;
CTU, 2012; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al, 2010). Humans
can program computers with algorithms to use in problem-solving; but problem-solving
processes do not rely on algorithms alone; but usually on multiple problem-solving strategies
working together along with analogies, insight, planning, knowledge and practice (Ashcraft &
Radvansky, 2010; CTU, 2012; Durning & Artino, 2011; Nielsen, 2007; Wielgenga-Meijer, et al,
2010).
Recommendations for Adult Learning and Successful Learning Organizations
Recommendations for adult learning and successful learning organizations with regard to
employee training programs are a combination of in-person classroom instruction, online course
instruction; on-the-job training (procedural memory related job tasks) and mentorship (Ashcraft
& Radvansky, 2010; Columbia University, n.d.; Fuller, Unwin, Felstead, Jewson & Kakavelakis,
2007; Murdoch-Eaton & Whittle, 2012). Instruction should include organizational policies,
procedures and culture; time and stress management; interpersonal communication skills;
critical-thinking; problem-solving skills; and diversity (Ashcraft & Radvansky, 2010; Columbia
University, n.d; Fuller, et al, 2007; Murdoch-Eaton & Whittle, 2012). Likewise instruction
should also cover job or task specific instruction; equipment and safety training; and training

should be conducted in a variety of ways; to instruct employees and to also best facilitate
judgment of learning and comprehension (Ashcraft & Radvansky, 2010; Columbia University,
n.d.; Fuller, et al, 2007; Murdoch-Eaton & Whittle, 2012). Employees of successful learning
organizations must be able to communicate clearly with one another; and be willing to divulge
information rather than collect it and use it for their own benefit (Ashcraft & Radvansky, 2010;
Columbia University, n.d; Fuller, et al, 2007; Murdoch-Eaton & Whittle, 2012). Successful
learning organizations need to be able to train employees for general skills such as critical-
thinking and problem-solving; and evaluate employee learning and comprehension as well as
evaluate individual differences in skill levels and train accordingly (Ashcraft & Radvansky,
2010; Columbia University, n.d.; Fuller, et al, 2007; Murdoch-Eaton & Whittle, 2012).

References
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(Ashcraft & Radvansky, 2010; Durning & Artino, 2011; Wielgenga-Meijer, et al, 2010)

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