AIME and Learning
Amount of Invested Mental Effort and Learning from Media:
A Conceptual Review
Richard Clark (1983) reviewed research on learning from media and
concluded that media are "mere vehicles that deliver instruction but do not
influence student achievement any more than the truck that delivers our
groceries causes changes in our nutrition"(p. 445). The conclusion led to many
reactions and criticisms in literature related to media use in education.
Nonetheless, a decade later, Clark (1994) restated his conclusion, writing that
even his most outspoken critics agreed that "...there is no compelling evidence
in the past 70 years of published research that media cause learning increases
under any conditions" (p. 25).
However, the growing use of new media in society and education has
still raised many questions about how people learn from different sources.
Researchers asking questions such as "Is learning from different media
distinctive?" as Neuman did when she experimented with fifth-graders' learning
from text and video, are still finding results that "...the medium per se may
have little direct influence in cognition and learning" (119). Results with
computers are hardly different. Fletcher-Flinn and Gravatt (1995) tested the
efficacy of computer-assisted instruction (CAI) in a meta-analysis and found
that "...what accounts for the typical learning advantage of CAI in this
meta-analysis and others is the better quality instruction provided by CAI
materials," rather than any inherent characteristic of computers (p. 219). In
another example, Rice (1994) examined reading comprehension using two
presentation formats: paper and computers. No significant effects were found
for mode of presentation. This study concluded that "...reading comprehension
constructs appear to be the same between computer presentation and paper
presentation of text" (p. 153). These findings are all in line with Wilbur
Schramm's claim that learning is influenced more by the content and type of
instruction offered by a medium than by the medium itself (Schramm, 1977, cited
in Clark, 1994).
Clark agrees "wholeheartedly" with the views of Gavriel Salomon
and others who point to differences in both cognitive processes and motivation
which are attributable to "learners' beliefs and expectations about their
reactions to external events -- not to external events alone:"
There is compelling research evidence that students' beliefs
their chances to learn from any given media are different for
different students and
for the same students at different times. (Clark,1994, p. 23)
In the early 1980s, Salomon developed a model to examine the way
people learn from different media. His initial studies focused on the
differences in learning between print and television among sixth graders. The
central variable in Salomon's work is the amount of invested mental effort that
individuals expend in different situations. The amount of invested mental
effort (AIME) is defined in terms of mental elaboration on information and level
of automaticity employed in information processing. More elaboration and less
automaticity represent greater AIME. For the purposes of this review, AIME is
identified by self-reports, as it was in Salomon's original work. Salomon
posits that more AIME will mean better learning.
The next section of this paper will further explain Salomon's model
including definitions of key variables and the proposed relationships among
them. Then the method of collecting studies for this review, and its
limitations, will be discussed. Studies that were conducted in the initial
development of the model will be reviewed as will studies that have since
empirically tested parts of the model. Since each study builds on earlier work,
this review is organized in a chronological manner. The purpose of this paper
is to determine the utility of Salomon's model and its components for future
research on learning from media.
AIME and Learning; An Overview of Salomon's Model
Salomon (1981a) reasoned that the amount of mental effort invested
is not dependent on the stimulus alone. He cited Katz, Blumler, and Gurevitch
(1974) to explain how individuals perceive different media in different ways.
Katz and his colleagues found that many people look to newspapers to connect to
a larger society, but use books introspectively to "know themselves" better.
In an attempt to explain this phenomenon, Salomon wrote that "Sources are
composed of different parts, but we selectively anticipate certain parts that
are distinctive to a source, often underweighing everything that is not within
the selected parts" (1981a, p127). He extends his explanation by mentioning how
schools often become considered exclusively print-oriented, art museums only
aesthetic, and newspapers as connections to society. Although each source
serves other functions, these other functions become overlooked. Audience
members weigh the attributes of different media unevenly, processing some
messages more deeply than others. The idea of AIME was born from this idea of
variable levels of information processing across different media.
The stimulus, then, does not necessarily dictate the amount of
invested mental effort it requires. For example, a facial expression, which
could easily be perceived as a simple stimulus, could also be perceived as a
complex means of communication requiring extensive elaboration for proper
interpretation. To the contrary, a complex poem could be perceived as
meaningless and generate almost no inferences or elaboration.
Salomon's position that increased mental effort will increase
learning is based on a wide body of cognitive, educational and social psychology
literature. Some processes, according to Salomon, are automatic and require
little mental effort while other, deeper processes involve more purposeful
mental elaboration. Craik and Lockhart (1972), Kane and Anderson (1978), Bobrow
and Collins (1975), and Kintsch (1977) are cited in support of the idea that
information processed with more elaboration comes into greater contact with
mental schemata, "...thus leaving more memory traces and enriching the meanings
arrived at" (Salomon, 1983a, p. 43). Salomon also cites empirical studies by
Langer and her colleagues (e.g., 1978) in several of his explanations of AIME
(e.g. 1981a, 1981b, 1983a, 1984). Langer's construct of "mindlessness" is used
to explain the way in which individuals sometimes rely on the structure of a
situation to represent underlying meaning rather than processing information at
a deeper level, or more "mindfully." Salomon (1983a) points out the
similarities of the mindful/mindless processing construct with the shallow/deep
It appears that mindlessness (vs. mindfulness) and shallow
(vs. deep) are closely related to each other. For Langer,
processing means ignoring information which is perceived to be
Shallow processing, as dealt with by Craik, Kintsch and
others, means automatic
processing of well rehearsed features, while deep processing
employment of non-automatic elaborations (p. 44).
When familiar material is encountered that is perceived to be easily
understood (regardless of whether it actually is easily comprehended),
individuals tend to invest less mental effort in processing the information.
But when material perceived to be new, unfamiliar, or complex is encountered,
higher AIME is likely. Hence, better learning will result. Individuals,
according to Salomon's model, base their perceptions of different stimuli
largely on past experiences with the stimuli.
AIME is described by Salomon as a product of an individual's mental
elaborations and level of automaticity in information processing. These
elaborations and level of automaticity, in turn, affect learning outcomes.
Salomon defines AIME with the following equation: AIME = number of elaborations
X 1/automaticity. More elaborations and less automaticity equals greater AIME.
In his early work on AIME (1981a, 1981b, 1983a, 1983b, 1984),
Salomon presented a model of learning from media which had four major components
-- perceived demand characteristic (PDC), perceived self-efficacy (PSE), amount
of invested mental effort (AIME), and finally learning. Salomon's general
argument is that "...learning from different sources greatly depends on the
differential way in which these sources are perceived, for these perceptions
determine to an important extent the mental effort expended in the learning
process" (1983a, p.42). Individuals' perceptions (PDC and PSE) "jointly" affect
AIME, which in turn affects learning (1983a, p. 47).
Nominal definitions of variables
The perceived demand characteristic of a particular stimulus, task,
or context is a term used by Salomon to describe the amount of effort people
perceive as necessary to process information. PDC is based on two key aspects.
The first is perceived realism. "When a mode of presentation is perceived as
undistorted, unbiased, true, familiar, or real, it needs the investment of only
little mental effort" (Salomon, 1981b, p.94). The second is the perceived
"easiness" of the information, context or task. Media which are seen as easy
are, by definition, perceived to be less demanding. Salomon posits that
television, although potentially just as demanding as other media, has a lower
minimum of effort required for enjoyment and therefore is often seen as
demanding much less effort overall by viewers. He attributes this perception
both to the perceived realism (especially for children) of television's explicit
visual nature and its perceived easiness due to its low demand on effort for
minimal understanding of content. On the other hand, he hypothesizes that print
is often seen as less real, and also less "easy" because it requires more effort
than television for minimal understanding. In other words, print media
generally have a higher PDC than television.
The other major factor affecting AIME in Salomon's model is
perceived self-efficacy (PSE), "...one's belief in one's ability to perform
certain activities" (Salomon, 1981a, p. 135). The construct of PSE is derived
from Bandura's (1977a) social learning theory. Some children, for example, may
perceive their reading skills as low, but assume that they have mastered
television viewing. These children would have greater PSE with televised
messages than with the same information in print.
Learning follows AIME in Salomon's model. Of course, there are
countless ways of defining and measuring learning. In most of Salomon's early
tests of the AIME model, learning is examined in terms of either post-exposure
tests of factual recognition of explicitly-presented information from the
presentations, or tests of inferences resulting from the same presentations, or
both. As with the other variables in this model, operational definitions of
learning have sometimes differed in the array of studies that have applied
If a person is exposed to the same information through two
different media, and the person considers one medium significantly "easier" than
the other, from which medium will the person learn more? At first glance, the
answer to this question would seem to be easy -- the person will learn more from
the easy medium. But, as Salomon has proposed, the easy answer may not always
be the best one. The above variables may be related in the following manner.
First, the perceived demand characteristic of a medium may be negatively
correlated with perceived self-efficacy. The more effort a person thinks a
particular message, medium, or situation demands, the less confident they will
be in meeting those demands. Second, the correlation between perceived
self-efficacy and the amount of invested mental effort may be curvilinear
(Salomon, 1981a). If an individual's perceived self-efficacy is very low, or
approaching zero, their amount of invested mental effort will likely also be
very low. That is, since they perceive such a low probability of success in
processing the information, they most likely will not think it is worthwhile to
even try. However, most media people encounter on a daily basis do not pose
such overwhelming challenges. In situations of moderate difficulty, Bandura
(1977b) found that higher self-efficacy generally results in greater investment
of mental effort and greater persistence in overcoming difficulties. But
Salomon found in his early studies (to be discussed next) that the relationship
between self-efficacy and amount of invested mental effort can be negative in
some cases. Salomon hypothesized that the positive relationship between
self-efficacy and the amount of invested mental effort "...exists only up to a
certain point; beyond that, people relinquish the investment of effort, as they
are sure the messages or tasks are familiar and easy" (1981a, p.136). So the
relationship resembles an inverted "u" -- when perceived self-efficacy is very
low or very high, AIME is hypothesized to be low, and when PSE is moderate, AIME
is hypothesized to be higher.
Third, based on the literature Salomon cited in defining AIME as a
concept related to deeper information processing, "mindfulness," and more
non-automatic elaboration, AIME should correlate positively with learning.
Proposed Relationships of Variables
AIME PDC=Perceived demand characteristic
AIME=Amount of invested mental effort
Limitations and Methods of this Conceptual Review
By 1984, Salomon had developed his ideas into a slightly more
complex model which included variations on the different interrelationships of
the four main variables: PDC, PSE, AIME, and learning. In addition, he (1984)
proposed a feedback loop to indicate the cyclical nature of the relationships.
The present review will focus on the three key relationships hypothesized: a
negative correlation between PDC and PSE, a curvilinear correlation between PSE
and AIME, and a positive correlation between AIME and learning. The remainder
of this paper will document the results of an extensive search of available
literature for studies pertaining to Salomon's concept of AIME, and relevant
findings in these studies will be discussed.
To locate research related to AIME, a systematic literature search
was conducted. Two computer databases, Educational Resources Information Center
(ERIC) and PsychInfo, were searched using the keywords, "invested mental
effort." The abstracts were then scanned and articles that were obviously
unrelated were eliminated. The remaining articles were reviewed and
bibliographies scanned for other related articles, which were then also
gathered. At this step, it became apparent that nearly all related articles
published after 1984 cited Salomon's (1984) "Television is 'easy' and print is
'tough'..." which was published in Journal of Educational Psychology. So the
final step in searching the literature involved searching the Social Science
Citation Index for any articles which cited Salomon's (1984) article in each
year from 1984 to 1996. The only limitation on the overall search process was
availability. Any articles that were not available on ERIC microfiche or in the
University library system were not included. There were very few studies (one
published and two unpublished) that could not be gathered and therefore could
not be reviewed for relevance to this conceptual review.
Initial Studies and Measures
Salomon's model was first developed and tested in the early 1980s
with two of his own experiments and one other study which was a combination of
survey and experimental research. The first experiment (discussed in Salomon,
1981a, 1981b, and 1984) was conducted with 124 sixth-grade children in the San
Francisco Bay area. Salomon (1984) describes the different measures used in
detail. This paper will review them briefly. PDC was operationalized with two
measures on a questionnaire administered a week in advance of exposure. The
first measures (six questions) concerned children's perceptions of the realism
of TV and print materials. Replies to these six questions ranged from low
realism to high realism on a five-point scale. An average score was computed
for each respondent (Cronbach Alpha = .71). The second set of measures of PDC
pertained to children's causal attributions of success or failure to learning
from television or print. The causal attributions available for these four
forced-choice questions were based on the internal causes of ability or
inability, and effort or no effort (i.e. "because that student was smart,"
"because that student tried hard"). Other questions focused on external causes
for success in learning (i.e. "TV is always easy stuff," "that book was easy.").
PSE was determined by asking students, "How easy would it be for
you to learn how to from a book (or TV program)?" Ten items were
plugged in to this question including "solve a math problem," "build a model,"
and "know the life of a famous person." Each question could be answered on
five-point scale ranging from very easy to very difficult. Scores were then
added to calculate the overall PSE score which yielded a Cronbach Alpha of .89
(Salomon, 1984, p. 652). This index was adapted for studying media use from
AIME was measured with a four-question, self-report questionnaire
administered subsequent to exposure to TV or print. The four questions follow:
How hard did you try to understand the film (story)?
How hard did your friends in the room try to understand the film
How much did you concentrate while watching (reading)?
How easy to understand was the story?
A scale of effort ranging from one (lowest) to four (highest)
accompanied the questions and an average score for each subject was calculated
(Cronbach Alpha = .81). The issue of the limitations of this self-report
measure was addressed by Salomon and will be discussed below.
The final variable, achievement, was composed of two measures:
inferential learning and factual recognition. Inferential learning was measured
with four open-ended questions "... pertaining to possible motives and causes
neither explicitly shown in the film nor described in the text" (Salomon, 1984,
p.652). Scores of one to three were given by two independent graduate student
coders (interscorer reliability = .87, Cronbach Alpha = .76). Factual
recognition was measured with ten multiple-choice questions pertaining to
specific facts made explicit in the story (Cronbach Alpha = .92). Salomon found
in this study that children definitely had different perceptions of TV and
...most subjects perceived successful comprehension of print
reside in readers but success in comprehending TV was
attributed mainly to the ease
of the medium. And whereas failure with print was attributed
by many to its
difficulty, failure in comprehending TV was perceived to
reside mainly in the
viewers...(Salomon, 1984, p. 653)
Children also reported television to be more realistic than print.
PDC was lower for TV than print, and PSE was higher for television than print.
That is, children perceived television as "easy" and print as relatively "tough"
and, as expected, their perceived self efficacy was significantly lower for
print than TV.
PSE positively correlated with AIME in the print group (r=.37), but
PSE negatively correlated with AIME in the TV group (r=-.49). The print group
findings were consistent with previous research by Bandura where he found that
higher perceived self-efficacy leads to greater effort in the face of obstacles.
However, the very high PSE for TV correlated negatively with AIME. Although
Salomon explained the relationship between PSE and learning in separate terms
for "easy" and "tough" media, this finding is in line with the hypothesized
curvilinear, inverted-"u" relationship in which moderate levels of PSE correlate
with the highest levels of AIME while extreme levels correlate with lower AIME.
Apparently, children perceived TV as very easy with little mental effort
necessary to process information and they perceived books as tough enough to
warrant additional mental effort. However, neither source was perceived as so
tough that children relinquished mental effort.
Finally, the San Francisco study showed positive relationships
between AIME and overall achievement for both print and TV groups (r=.64 and
r=.67, respectively at p<.01). Thus, Salomon's theoretical model surrounding
the AIME construct was introduced. But limitations on the model were soon
evident based on the findings in similar early studies testing it.
Salomon carried out his second AIME experiment with sixth-graders
in Israel. He modeled it after the San Francisco study. Only highlights of
this study were discussed (in Salomon 1981b). An interesting finding was the
lack of difference between children's levels of perceived self-efficacy with
television and print. Salomon also found that Israeli children invested similar
amounts of mental effort in both TV and text sources. He proposed that Israeli
children took television more seriously than American children, again suggesting
that the differences do not lie in the medium alone. He pointed to cultural
differences in viewing experiences that led American children to perceive
television as easier than print while Israeli children perceived both sources as
Furthermore, the relationship between PSE and AIME was negative
(-.28) in the print condition and this relationship was "...strongest (-.53) for
that subsample of poor readers who were also the ones to learn least from either
film or text" (Salomon, 1981b, p.97). Salomon found that the "least skilled
subjects" falsely perceived themselves as most efficacious. Thus they invested
the least effort and learned the least. This finding highlights the importance
of individual perceptions regardless of individual accuracy.
In support of his model, Salomon found in the Israel study that
AIME positively correlated with "learning outcomes" for "the good readers" in
both the television condition (.68) and in the text condition (.58)" (Salomon,
1981b, p. 97). Here he discusses how children who have mastered certain skills
are less likely to relinquish AIME. Children who have not yet learned to read
well would not be expected to demonstrate learning from text. Nonetheless,
correlations between AIME and learning for the entire group of students were not
In another variation, Salomon (1983b) induced mental effort by
having subjects try to make sense of information presented in jumbled video
clips. In this study Salomon found little or no correlation between mental
effort and achievement. Cennamo (1993) suggests that the extra mental effort
may have been used to "search memory for related schemata," rather than
processing information. This finding, in conjunction with Salomon's discussion
of the Israeli study, supports the idea that AIME leads to learning via its
relationships to pre-existing schemata and ability to process information.
Kunkel and Kovaric (1983) were mainly concerned with the
relationship between PDC and AIME and, in turn, the relationship between AIME
and learning. As opposed to previous studies, however, they tested the effects
within the medium of television rather than across different media. They first
surveyed 57 undergraduates and found a significant difference between the mean
AIME score for Public Broadcasting (PBS) programming and commercial television
programming (4.35 and 2.98, respectively on a ten-point scale, p<.001). They
then worked under the assumption that people tend to use more AIME when viewing
PBS than when viewing commercial TV.
Kunkel and Kovaric then conducted an experiment with 85 new
undergraduate subjects randomly assigned to one of four treatments. Half the
students were told they would see a program made for PBS while the other half
were told they would see a program made for commercial TV. In each of those
conditions, demand was manipulated by telling half they would be tested on the
content which was serious and educational (higher PDC). The other half did not
anticipate testing (lower PDC). The subjects were then all shown the exact same
program. Afterwards, they were given a questionnaire which, among other things,
measured their learning from the program.
The main effect for learning was slightly higher for the PBS
(higher AIME) viewers than the commercial TV (lower AIME) viewers. An
interesting result, however, was that among those who thought they would be
tested, there was no difference in learning based on differences in AIME. The
authors suggest that this may be due to a ceiling effect for AIME. To explain
how the results may apply to other situations, they suggest that the conditions
in which no test was anticipated "...can be considered to represent most closely
the viewing environment that people typically experience during everyday TV
viewing" (p. 23). Indeed, the test manipulations likely altered PDC and its
effects on PSE and AIME.
Additional analysis of the data from the San Francisco study
revealed differences in the types of learning that resulted as a function of
different levels of AIME assumed to occur in everyday media use. When Salomon
(1984) calculated the results of learning tests separately for inferential and
factual/recognition measures, he found that the correlation between AIME and
recognition scores for TV and print (.04 and .24, respectively) were not very
impressive. Essentially, factual information was retained equally well
regardless of AIME. On the other hand, AIME and inferential learning correlated
positively for both TV and print (.69 and .72, p<.01). As expected, scores on
the inference test were significantly higher for children exposed to print
rather than TV. Salomon (1984) reasoned that higher AIME and non-automatic
processing would be expected to improve inferential learning, but that AIME
"...would be expected to have no influence on such learning outcomes as
incidental, unguided acquisition of facts that are involuntarily, automatically,
and episodically carried out (e.g. Kane & Anderson, 1978; Kintsch, 1977)"
(p.648). In other words, AIME affects inferential learning more than factual
recognition, which may occur just as well via automatic processes.
Validity and Reliability of AIME Scale
The question of the validity and reliability of self -report
measures of AIME has been addressed in these initial studies, as well as in many
following studies. In introducing the concept and its self-report measurement
method, Salomon (1984, p.648) suggested that even though individuals may "...not
be valid sources of information pertaining to the factors that affect their
decisions (Wilson & Nisbett, 1978)," they should be able to report with relative
accuracy how much mental effort they expend. The validity of this assumption
was tested mainly by examining whether the predicted relationships between AIME
and learning were found based on the theoretical connection of the two
variables. As noted above, the early studies of AIME did yield significant
positive correlations between AIME and learning, especially inferential
learning. Cronbach Alpha measures were also applied to the AIME scale. The
AIME averages computed for the San Francisco study yielded .81. Earlier,
Salomon (1981b) reported that he had found "reasonable reliability over time (up
to .60)..." for AIME measures, although the specific statistic yielding that
number was not described (p. 98). At that time, however, he wrote that he was
testing alternative measures.
Salomon (1983a) compared his measure of mental effort to Kerr's
(1973) secondary-task technique in which subjects are asked to respond to an
outside stimulus (i.e. a flashing light) as a secondary task while working on a
primary task such as reading. A longer response time to the secondary task
represents more mental effort. However, Salomon asserts that this technique is
"...inappropriate for cases where subjects are to perform the primary task as
they would under normal conditions" (1983a, p. 44). Indeed, this way of
measuring all but eliminates the possibility of subjects mindlessly performing
the primary task since they must be instructed that such a task is 'primary' and
to prioritize their effort expenditure. Salomon concludes this time that
"Trying out various methods, my students ...and I have finally settled on the
use of self-reports" (1983, p. 45).
Studies Empirically Testing Concepts and Relationships
Salomon & Leigh (1984)
Salomon and Leigh consider, and rule out, two alternate measures of
AIME. The first measure infers mental effort from the time taken by subjects to
perform tasks. The second measure is cognitive capacity usage inferred from
performance on primary and secondary tasks. "The first measure is inadequate for
our purposes, as it yields no direct measure of effort" and "....the secondary
task technique is equally inappropriate for our purposes, as it requires the
direction of subjects' attention to the primary task and therefore inhibits the
treatment of effort expenditure" (p.121). They also report that although AIME
self-report measures do have some limitations, their past research has shown
self-reports to be "sufficiently valid" for their purposes (p.121). Two studies
were reported in this article.
Measures of AIME included a pre-test questionnaire asking about
specific types of material in both TV and print (i.e. sports, detective, etc.)
and a general measure of AIME for TV or print "in general" (p. 123).
Post-exposure AIME was measured in the same manner as Salomon's earlier studies.
Inferential learning was measured with eight questions pertaining to causes for
characters' behaviors, characters' thoughts, and how events were related. Six
multiple choice questions pertaining to explicitly-presented facts made up the
Salomon and Leigh (1984) supported earlier findings that, according
to children, print generally commands more mental effort than TV. Pre- and
post-viewing AIME measures correlated significantly with each other indicating
that children's initial perceptions of AIME may be correct. Both pre-test
reports of general AIME and post-exposure reports of AIME correlated positively
with learning measured in terms of both recall and inferences. These
correlations varied in magnitude and significance (.11 to .42, see appendix).
They do not convincingly refute the hypothesized positive relationship between
AIME and learning, but they do encourage further testing.
The authors measured two types of perceived self-efficacy: one the
PSE in generating inferences from TV or print, the other measured PSE in
recalling details. The strongest PSE-AIME correlation found was between
inferential PSE and AIME with text (.66). Presumably, inferring information
from print would be a relatively challenging situation for children. In
moderately challenging situations such as inferring information from TV or
recalling details from print, PSE correlated somewhat less strongly with AIME
(.22 and .12). While in the easiest task, recall from TV, the relationship
between PSE and AIME was negative (-.39). These findings seem to fit well with
the hypothesis that the relationship between PSE and AIME is an inverted "u"
where more challenging tasks result in rising mental effort, while tasks
perceived as extremely easy, like recalling details from TV, result in less
mental effort. The other extreme, where the task is seen as difficult
enough, and PSE low enough that subjects will become discouraged and relinquish
AIME, was not presented in this experiment.
In their second experiment, PDC was manipulated by instructing
children to watch or read the story for fun (low PDC), or "to see how much you
can learn from it" (high PDC). This manipulation made little difference in the
print conditions since children already perceived reading as requiring high
levels of AIME. In contrast, AIME scores rose significantly for TV viewers when
they thought they were supposed to learn from the program. The PDC manipulation
brought AIME up from TV's initially low levels to an apparent ceiling level
which was already present in print conditions. This finding is in line with the
findings of Kunkel and Kovaric (1983).
As expected, recall of details was not affected by the PDC
manipulation. However, inference learning scores were increased significantly
by the PDC manipulation in the TV groups. This finding suggests that greater
perceived demand led to higher AIME, perhaps by lowering perceived self-efficacy
with TV into the range where perceived self -efficacy correlates positively with
mental effort. In other words, children weren't so sure that TV was "easy'"
when told it was for serious learning and hence invested more mental effort.
The outcome, at any rate, was greater inferential learning.
This study investigated variables related to learning from
computer-presented expository text with 27 college students. Learning was
measured with five factual and five comprehensive multiple-choice test items on
a post test for each text passage provided. Chu cited Salomon (1984) in defining
AIME and apparently measured mental effort in the same way. Chu also measured
"perceived task difficulty" and "perceived reading ability" on a post test.
Although these variables were measured differently than PDC and PSE, their
relationships with other variables support Salomon's model as hypothesized.
Perceived task difficulty, like PDC in previous studies, correlated negatively
with perceived reading ability (-.27). In turn, perceived reading ability, like
PSE with books in previous studies, correlated positively with AIME (.42).
Perceived task difficulty also correlated positively with AIME "...weakly, but
significantly" at .27 and p<.01 suggesting that students thought the computer
texts were tough enough to warrant extra effort, but not so tough that they
relinquished AIME. Finally in a multiple regression analysis, AIME predicted
reading performance with a standardized B value of .73. Again, Salomon's model
was basically supported.
Krendl proposed using Salomon's model to explain the process by
which people learn from media. Her study involved a panel of 611 students in
grades three through ten who completed a questionnaire which measured several
variables: including demographics and media preferences such as watching TV,
using a computer, reading, or writing.
Krendl found that "...in general, the more an activity is
preferred, the less difficult it is perceived to be, but the more likely one is
to think one will learn from it" (p. 229). This finding corroborates with
previous work which shows that individuals are often incorrect in assuming that
they learn more from easier media. It reflects the idea that "easier" media
(which are more enjoyable) often lead to less AIME, and therefore less learning.
Reiser, Williamson and Suzuki (1988)
The authors pretested and posttested three- and four-year olds on
their recognition of letters and numbers before and after watching a "Sesame
Street" tape. They cited Salomon in predicting that children would learn more
in the conditions where they watched with adults who asked questions and
provided feedback on the content (higher AIME condition) than in a control
condition in which adults passively watched with the child. "If Salomon is
correct, then children's perceptions regarding the information presented via
"Sesame Street" are likely to be affected by the types of tasks adults ask them
to perform while viewing the show" (p. 16). Indeed, children in the
question-and-feedback condition performed significantly better on the post test
than children in the control group (means of 7.1 and 5.4 of 10, respectively).
In addition, another group, in which children's attention was merely directed to
the program, did not perform significantly better than the control group
(mean=6.0), indicating that mere attention to TV's images may not be enough to
increase AIME or learning.
These results, however, are not as supportive of Salomon's model as
they first appear. The test of learning was referred to as "comprehension," but
actually measured recognition. Salomon's model predicts increase in inferential
learning as a result of higher AIME. But the very young subjects in this study
likely differed from the older subjects involved in previous studies in what
would be considered automatic and non-automatic learning processes.
This study replicated Salomon's original studies with 140
sixth-graders in the Netherlands. Beentjes tested the AIME and PSE instruments
and found their levels of consistency to be acceptable. Beentjes questioned the
PDC measurements as well, but did not find satisfactory results for the
perceived realism scale or the attribution questions for either medium. The
only part of the PDC scale which worked as predicted was the question asking why
a child understands a book or TV program well. The "reverse question, in which
the failure to comprehend was to be explained," did not result in higher PDC for
TV than print as expected. Beentjes suggests that "all optional alternative
explanations that the child gives for the failure to understand a program or
book can be interpreted as indicating high PDC" (p. 55). Responses that the
child "isn't smart," or "didn't try hard" could also indicate high PDC. These
choices, according to Beentjes, implicitly indicate that a certain amount of
intelligence is needed to understand the program or book. He also convincingly
argues against Salomon's assumption that easiness is equated to realism. He
gives the example of a cartoon compared to a detective show. The cartoon,
although easier, is perceived as less real.
Beentjes did find, like Salomon, that children invest more mental
effort on average in books than in TV. However, the Dutch children, like the
Israeli children (Salomon, 1981b), did not have higher PSE for learning from TV
rather than books. The small relationships between PSE and AIME (-.01 and -.13)
can be accounted for with two possible explanations. First, Beentjes'
questionnaire gauged mental effort used in various types of content presented in
programs and books rather than media in general. Beentjes suggests an
interaction effect between content and medium on AIME and PSE that may have
occurred. For example, children may feel more efficacious learning how to make
plastic from TV, but would perceive less self efficacy in learning grammar from
TV than books. Beentjes also points to a difference in cultures as a possible
explanation for the lack of clarity in the correlation between AIME and PSE.
Dutch children, like Israeli children, may have had fewer opportunities "to
develop the perception that television is a medium from which information can
quite easily be obtained" (p. 57).
In sum, this replication seriously challenges the PDC construct,
both empirically and theoretically, and encourages further study of the other
elements and relationships in Salomon's model. He recommends considering the
topic of content, the medium and the interaction of topic and medium in
measuring perceived self-efficacy in future studies.
Salomon, Globerson & Guterman (1989)
This study tested the effectiveness of a computer program called
"Learning Partner" on increasing learners' reading competence. One of the
hypotheses tested was that subjects using "Learning Partner" would invest more
mental effort during the reading process. Of interest to the present review is
Salomon et al.'s modified measurement of AIME. In this study, the actual
concept of mental effort was introduced to students at the beginning of the post
test questionnaire. They "...illustrated this by two activities -- to tie one's
shoes and to struggle through a math problem -- the former receiving a low
position on a mental effort expenditure scale and the latter a high one"
(p.623). Three questions followed with Likert-type responses. Reliability of
this measure was found to be acceptable.
Furthermore, Salomon et al. reported that effort expenditure
"correlated as high as .45 with inference generation after initial ability was
partialled out." Apparently, the assumption made here (as was made in
reporting learning results for only the "good readers" in Salomon 1981b) is that
variability in learning related to AIME can be demonstrated best if the subjects
are able to read in the first place. Nonetheless, if very low self-efficacy, be
it perceived or actual, leads to little AIME and little learning, Salomon's
model is still supported. Unfortunately, these relationships are not reported
in this study.
Cennamo, Savenye & Smith (1991)
Cennamo et al. examined learners' preconceptions of interactive
video, instructional television and traditional television with 71
undergraduates. They compared the three types of media by their effects on
AIME, recall, and inferential learning.
Cennamo et al. were skeptical of Salomon's AIME measure (Cronbach
Alpha of .55 in this study), but did find significant correlations between AIME
and inferential learning (.27). They also found an unexpected correlation
between AIME and recall of .33. One of the possible explanations given for the
AIME-recall correlation was that the instructional materials used in this study
provided practice questions which encouraged factual recall, not inferential
The role of PDC, referred to as preconceptions of difficulty in
this study, was again seriously challenged. Based on earlier studies, Cennamo
et al. expected to find that learners would invest the least effort in media
perceived as the least demanding, "easiest." They found the opposite. For
example, subjects perceived interactive video as the least difficult of the
three media to learn from, but also perceived it as requiring more mental
effort. The undergraduate education majors in this study, as opposed to small
children in other studies, "...may be aware that an "easy" lesson is not
necessarily easy to "learn from""(p. 13). However, perceived self-efficacy in
learning was not examined in this study.
Bordeaux & Lange (1991)
This study used interviews with parents and children to assess AIME
with children's home viewing as opposed to laboratory activity. The focus of
their study was how much mental effort was reported for different types of
television programs (i.e., children or adult shows) among different age groups.
For example, they expected, and found, that AIME for child-oriented programming
dropped with increasing age.
The authors expected that the AIME scores would be low "...both for
highly unfamiliar and highly familiar programs, but high for moderately familiar
programs" (p. 629). Although their focus was programs and not media, this
expectation parallels the proposed inverted "u" relationship between PSE and
AIME. However, familiarity explained little of the variance in AIME and the
results did not support the predictions which were partly based on the concepts
of self-efficacy and PDC. Perhaps familiarity did not range from very low to
Mevarech, Shir, & Moshovitz-Hadar (1992)
This study tested the extent to which a two-media educational
environment (i.e., video combined with computers) would affect achievement more
than either one of its components alone. "Mindfulness" was measured at four
points during the study of learning achievement in geometry. The self-report
mindfulness measure was derived from Salomon and Globerson (1987). It yielded a
reliability coefficient of .80. Although the relationship between mindfulness
and math achievement was not made explicit, a comparison of mindfulness levels
at four points in this study showed a steady increase for computer-only and
video-only groups, relatively stable levels across time for the no-media group,
and a pattern of rising mindfulness with sharp decline at the last measure for
the two-media group. Perhaps over the course of the study, the two-media
condition became perceived as too difficult (very high PDC/very low PSE),
causing the drop in mindfulness (AIME) at the end. This study draws attention
to the importance of length of exposure in examining AIME.
Sherman experimented with various combinations of narrator type and
on-screen character type in a junior high school-level science film. He studied
the effects on achievement, AIME and attitude. Sherman concluded that even
though he showed the same visual images to all subjects and manipulated only
aspects of the voice-over, "...the visual images corresponding to the
information may have meant something different to the viewers depending on the
version [of narration, i.e., story format, same-age narrator, first-person
narrator]" (p.7). In other words, differences in learning, AIME and attitude in
these findings were likely due to different viewer perceptions. A significant
correlation was found between AIME and post-test performance (.24, p<.01).
Beentjes & van der Voort (1993)
Beentjes and van der Voort designed this study to test three
hypotheses based on Salomon's model:
...(a) children invest more mental effort in processing print
stories compared to television stories; (b) story recall is
not affected by the
medium through which the story is presented; and (c) print
stories lead to more
inferential learning than television stories. (p. 191)
The authors modified Salomon's methodology in several key ways.
First, the experiment was conducted with fourth-graders (n=44) and sixth graders
(n=44). Second, they provided two stories, one print and one video, to each
child with a counter-balanced design so that the 2X2 conditions of media (print
or TV) and story (story A or B) were distributed evenly. Furthermore, they used
an elaborate five-step design to develop equivalent print versions of TV
stories. Third, they not only measured retention and inference immediately
following exposure, but they also used a delayed instrument to measure retention
two to three weeks later. Fourth, they assessed AIME in two ways: using
Salomon's self-report method after exposure, and using a secondary-task measure
during reading and viewing.
Regarding the secondary-task measure, children were instructed as
follows: "the story is more important than the bleeps, so read/watch the story
and whenever you hear a bleep you react as soon as possible" (p. 195). Twenty
bleeps occurred during the 10-13 minute exposure. Children were equipped with
headphones to shut out outside noise. During reading/viewing the experimenter
was seated diagonally behind the child.
Three of four of Salomon's AIME questions were used. The one using
the word "concentrate" was dropped because Dutch children are not accustomed to
the term. Cronbach Alphas ranged from .60 to .64. Correlations between the two
AIME measures were low, mostly negative and mostly insignificant.
All three hypotheses were more or less rejected. Children's mental
effort was higher in reading than for watching TV, but only according to the
AIME measure. Reaction-time data rejected this hypothesis. Recall from
television and print versions of a story was only equal on the immediate
retention tests but not on the delayed retention tests where viewer scores were
higher than reader scores. Finally, performance on the inference test for TV
viewers was higher than for readers and "considerably" higher than for
The authors cite several possible reasons for the contradictions.
They felt that Salomon's print version of stories in previous studies may have
been too "rich," offering better information in some way and leading to the
resulting increase in inferential learning for print. Also criticized was
Salomon's test for learning only immediately after exposure and for only one
story and one age group. Furthermore, they question the AIME measurement in
stating that Salomon's hypotheses "...may hold true only if a retrospective
measure is used" (p. 202).
However, it is arguable whether the findings in this study are
comparable to previous studies. Beentjes and van der Voort "...did not
investigate children's perceptions of television and print" (p. 193). If the
secondary task measurement method changed children's perceptions by its
stressful and demanding nature, it may have made TV viewing more demanding and
reading simply too tough, which, by shifting positions on the inverted "u" of
AIME, may well have led to opposite results.
This study was designed to examine the effects of computer-assisted
instruction in individualized (one child to a computer) and cooperative (a pair
of children to a computer) situations on AIME, math achievement and social
acceptance of high and low achievers. AIME was measured according to Salomon's
model at the beginning, middle, and end of the study which lasted several
No correlation was given for the relationship of AIME and
achievement. However, analyses of AIME showed that "low achievers" who worked
individually gradually decreased AIME over time, while the low achievers who
worked with partners gradually increased AIME. "High achievers" improved at
approximately the same rate in both conditions. Mevarech suggests that the
difference for low achievers may be due to the perceptions of accountability and
attentiveness that may have been fostered by the presence of a partner.
Unfortunately, these perceptions were not measured in terms of PSE or PDC.
Verhagen & Breman (1995)
Verhagen et al. studied instructional format and segment length in
interactive video programs in experiments with university freshmen in the
Netherlands. One of Verhagen and Breman's objectives was to examine the
relationships between PSE, PDC, AIME and learning performance. Verhagen and
Breman, however, only used one post-test question to measure AIME, one pre-test
question to measure PSE, and one question asked twice (pre- and post-test) to
measure PDC. Learning was measured by comparing scores on two multiple-choice
tests (pre- and post-tests) of factual knowledge.
Verhagen and Breman found significant (p<.001) positive
correlations between AIME and pre-test PDC (.39), post-test PDC (.48), and PSE
(.68). These results are in line with Salomon's early findings with print.
However, "no significant relationships were discovered" between AIME and test
performance (p.15). This result is consistent with Salomon's findings regarding
AIME and factual recall, as should have been expected since factual knowledge
was measured. Nonetheless, Verhagen and Breman's measures of these four
variables significantly differ from earlier measures and operations. Therefore,
this data on its own cannot be used in strong support of Salomon's model, but
should be taken into consideration in conjunction with other studies.
Of the relationships between variables that this review focused on,
one seems most supported -- the relationship between AIME and learning. This
relationship seems to be clearest in studies in which AIME measurement methods
were least obtrusive and learning was measured in terms of inferences. The
correlation was not clear in studies in which individual ability was low, as
when children classified as poor readers were tested on learning from texts, or
when subjects were asked to read for ten to thirteen minutes and, at the same
time, respond to twenty bleeps. Studies measuring learning in terms of recall
or recognition had mixed results, but AIME's hypothesized correlation with
inferential learning was generally supported. This support for the expected
correlation between AIME and learning also helps demonstrate the validity of the
Reliability of Salomon's AIME measure was found to be acceptable.
This is not to say that the measure cannot be improved, but alternate measures
introduced so far do not appear to be suitable to measure mental effort with
media consistent with natural conditions. This is a vital aspect of measurement
if research results are to be applied to learning outside the laboratory.
Although the idea of perceived demand characteristic is central to
Salomon's explanations of how different individuals in different situations vary
in their perceptions of media, research to this point fails to offer a suitable
operational definition of PDC. Perhaps PDC is too global a construct to be
useful in models of the specific processes which lead to learning from media.
Measuring aspects of (or closely related to) perceived demand such as perceived
self-efficacy may be a more productive approach to determining the antecedents
So far, research on the relationship between PSE and AIME has
yielded results which do not refute the hypothesized curvilinear correlation.
However, these findings are often best explained in a post-hoc manner.
Furthermore, explanations of findings are hardly parsimonious. Salomon himself
normally reports the findings in terms of two separate correlations rather than
one curvilinear one. For example, he (1984) reports a positive correlation
between PSE and AIME for "demanding" material, but a negative correlation when
"easy" material is to be processed (p.649). Nonetheless, the relationship
between PSE and AIME is certainly worthy of further investigation. Future
studies clearly proposing the curvilinear relationship and experimenting with
situations that span the entire range of PSE among subjects will help clarify
Further research using Salomon's model of learning from media are
certainly in order. Questions still remain regarding the curvilinear
relationship between perceived self-efficacy and the amount of invested mental
effort. Future research should also seek to identify other factors which
influence AIME. Theoretically, the best place to begin looking for AIME's
antecedents is in the area of individual perceptions of various media and
content. For example, new communication technology, compared to television and
traditional print, may elicit the widest range of PSE among individuals and
therefore may be a productive starting point for further investigation of the
PSE-AIME relationship. In addition, future investigators should pay close
attention to the specific type of learning affected by AIME. Understanding
these relationships will help us understand learning from media.
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Correlations between AIME and Learning
STUDY MEDIUM CORRELATION/ LEARNING
achievement (inferential learning and recognition)
124 6th graders
factual recognition and inferences? (not clear)
Statistics limited to certain subjects -- "those for whom
learning outcomes were the good readers..." (p. 97).
Kunkel & Kovaric (1983)
mean learning scores
Std. Dev. =2.65
learning (recall and comprehension)
85 college students
Based on significant (p < .001) differences of mean AIME scores
in pre-test survey (4.35/10 for PBS vs. 2.98/10 for Comm TV) and in post-test
self-report measure (PBS=5.5, Comm TV=3.14). See study for further details on
manipulations of AIME and interaction effects.
r=.24 not significant
r=.04 not significant
124 6th graders
from same data as (1981a)
Salomon & Leigh (1984, first experiment)
print (general AIME)
print (post-exposure AIME)
64 6th graders
standardized B = .73
factual and comprehensive multiple-choice measure
27 college students
(graduates and undergrads)
Reiser et al. (1988)
TV "Sesame Street"
compared adjusted posttest means "using pretest scores as
recognition of letters and numbers (delayed post-test)
95 three- and four-year olds
See study for details of AIME manipulation.
Salomon et al. (1989)
r= "as high as .45" *
Correlation figured after initial ability was partialled out.
Cennamo et al. (1991)
Interactive video, instructional TV and traditional TV
not separated by media for these stats.
71 undergraduate students
Junior high school-level science film
recall and comprehension
441 7th graders
Varied voiceover-character relationship in film
Beentjes & van der Voort (1993)
AIME for print over TV
Recall for TV over print
Inference scores for TV over print
F (1,84) = 23.51 p<.001
F (1,84) = 5.05
F (1,84) = 28.40
44 4th graders
44 6th graders
Verhagen & Breman (1995)
Interactive video programs
"no significant relationships" (p. 15)
factual knowledge (multiple choice)
73 university freshmen
Conclusion based on a Kruskal-Wallis one-way analysis of
variance. See study (p.15) for details.
* indicates that no p was given
**not clear which study this data results from
 Salomon (1981) cites previous work including Anderson, et al.
(1977) and Kintsch (1977) in making this assertion. See pages 93-95 for more
 Few specific statistics were published from this study.
 Perhaps the revised PSE measure which specifically addressed
efficacy in inferential learning and recall helped clear up the misperceptions
that the Israeli sixth-graders in Salomon's (1981b) study experienced with print
 It is not clear which one of Salomon's studies this statistic
 Bordeaux and Lange's specifically addressed of the questionable
AIME construct. Computation of AIME scores, developed from Salomon's (1984)
questions, resulted in Cronbach Alphas of .80 for child-type programs and .73
for adult-type programs. Forty-eight of the 116 subjects were interviewed twice
with test-retest Pearson correlations of .88, .90, and .92 for child-, adult-,
and total-program AIME scores.
 Performance included recall and comprehension measures.
 The self-report measure yielded a Cronbach Alpha of .70, but
"Using a Cronbach procedure for inferring the reliability coefficient by
doubling the number of items provides an alpha coefficient of .825" (p. 454).
Mevarech suggests that any limitation on the self-report AIME measure "...may
apply more to automatic processes which by their very nature are unavailable to
introspection than to intentional control processes" (p.461). Indeed, by
Salomon's original definition, AIME is an indicator of non-automatic processes.
 With the exception of Salomon's introductory work.
 This type of inquiry can be found in organizational behavior
literature dealing with factors affecting motivation and effort (e.g., Tang,
1990; Bandura & Cervone, 1986). Bandura and Cervone (1986) found that
"motivation is perhaps best maintained by a strong sense of self-efficacy to
withstand failure, coupled with some uncertainty..."(p.110). They explained how
increases in PSE lead to more motivation and effort, but cited Salomon (1984) in
showing how greater PSE also may lead to decreases in effort, for many of their
subjects became "overcomplacent" and relinquished motivation and effort.
Amount of Invested Mental Effort and Learning from Media:
A Conceptual Review
This conceptual review examines a model of the effects of
amount of invested mental effort (AIME) on learning from
media. The model
proposes that individual perceptions including perceived
(PDC) and perceived self-efficacy (PSE) affect AIME, and
that AIME affects
learning. The review shows that attempts to operationalize
PDC have met little
success; the relationship between PSE and AIME is
questionable, but encourages
further research; and the relationship between AIME and
inferential learning has
generally been supported.
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