Intelligence and Auditory Discrimination

Intelligence and Auditory Discrimination
A Thesis
Presented to
St. Thomas University
In Partial Fulfillment
of the Requirements for the Degree of
Bachelor of Arts
Honours in Psychology
Kristyn Kelsey
Table of Contents
Additional Tables ……………………………………………………………………....23
Figure 1…………………………………………………………………………………24
Figure 2…………………………………………………………………………………25
Figure 3…………………………………………………………………………………26
I would like to firstly thank my thesis supervisor, Dr. Houlihan, for allowing me to work with
him for this study. He has greatly inspired me to continue to achieve all that I can through hard
work and perseverance. Second, I would like to thank my reader, Dr. Perunovic for the time and
consideration that has been taken to examine this thesis. The guidance and inspiration provided
by my predecessor to this study, Alexandra Smith, is also greatly appreciated. I would also like
to thank Lauren Morrissey for her continued assistance in the psychophysiology lab. Thanks is
also extended to Christian Morin, who has also been a help in the lab. Finally, I would like to
thank the volunteers who continue to make the long process of data collection possible.
Individual differences in intellectual abilities have been studied in the field of psychology
for over one hundred years. A greater understanding of intelligence has come with the discovery
of the relationship between intelligence and sensory discrimination. Recently, a relationship
between event-related potentials (ERP) and these individual differences has been discovered.
The mismatch negativity (MMN) ERP is an accurate representation of an automatic brain
response to changes in the auditory environment. Auditory discrimination abilities have long
since shown to be indicative of general discrimination abilities. In turn, these discrimination
abilities are highly related to intelligence. The current study examines the relationship between
intelligence and MMN. A strong, positive relationship between these two could point towards a
new, attention independent method of measuring intelligence.
Intelligence and Auditory Discrimination
Intelligence and individual differences therein have long been studied in the field of
psychology. More recently, the relationship between sensory discrimination and intelligence has
been examined, finding that higher intelligence is linked with better sensory perception. Further
understanding of intelligence is necessary, as intelligence has been shown to have predictive
qualities. These predictions extend to behaviours as well as the likelihood of success in various
aspects of life such as academics, work, and even social aspects. Intelligence tests themselves
were developed for the purpose of determining the likelihood of a child’s success in school,
therefore, it is unsurprising that the tests are still used for this purpose (Binet, 1905). Intelligence
tests have also been used to predict the length of time that an individual will remain in school.
Those who tend to receive higher scores on tests are more likely to remain in school for longer
years. If an individual obtains good grades, they will likely be encouraged to continue on with
their education, and to take college and university preparatory courses thus encouraging them
further to continue (Rehberg & Rosenthal, 1978). It is likely that in such situations, individuals
with higher intelligence scores will find education more enjoyable and rewarding, another
contributing factor to the desire to continuing education. Often in line with the amount of time
spent receiving education is social status and income. Although there are various factors that
influence status and income, intelligence has a significant influence on these aspects of life.
Further still, intelligence tests have also been shown to predict success within the work place as
well as more favourable social outcomes (for a review see: Neisser et al., 1996).
The examination of intelligence has changed significantly over time, with the focus
centering upon psychometric testing as a measure of intelligence. The beginnings of
psychometric testing can be seen in the work of Alfred Binet (1905, 1916). Binet developed a
testing system that allowed for a distinction to be made between children with varying degrees of
intellectual difficulties. Building on this original concept of testing, the Stanford-Binet
Intelligence Test was produced, relying on similar measures to score an individuals intelligence
quotient. The test itself was the first to include the notion of IQ (Laurent, Swerdlik, & Ryburn,
1992). Several revisions have been made to the Stanford-Binet test and many other tests
measuring intelligence have been developed in this time.
In his extensive research into the nature of intelligence Spearman found that
differentiating cognitive tests had positive correlations with one another and referred to this a
‘positive manifold effect’ (Spearman, 1904). Spearman accounted for this phenomenon using
the concept of general intelligence or Spearman’s g, the underlying mental ability that
determines how an individual will perform on different tests of mental ability. General
intelligence is a broad aspect of intelligence, as it can be measured using many forms of
intelligence testing. Therefore, by examining one factor of intelligence one is able to determine
an individual’s general intelligence with significant accuracy. This construct of general
intelligence that could be measured using a single and varying factor of intelligence is referred to
as Spearman’s g.
Cattell (1963) theorized that Spearman’s g was not a singular factor, but was broken
down into two separate factors that accounted for diverse mental abilities, which are referred to
as fluid and crystallized general ability. Crystallized ability are those abilities that have been
previously learned from prior use and therefore, ‘crystallized’ as a result. This factor is relative
to the information that is taught and therefore reflects diversity of cultural influences on learning
(Cattell, 1963). Fluid ability pertains to reasoning skills and the ability to adapt to novel
situations (Cattell, 1963; Gray, Chabris, & Braver, 2003). Fluid ability is the factor of
intelligence that is most related to natural influences. Therefore, the influence of heredity or
injury sustained to the central nervous system is reflected in fluid ability (Horn & Cattell, 1966).
Carroll (1993) expanded upon the general fluid (Gc) and general crystallized (Gc)
abilities and introduced the three stratum theory, which includes both Spearmans g along with Gf
and Gc abilities. These abilities are organized in relation to their higher order processing, placing
the highest order factor, g, on the top strata. Further, the Cattell-Horn-Carroll (C-H-C) model is
a hierarchical model upon which these abilities are placed. The C-H-C model consists of three
strata, which encompass Horn & Catell’s (1966) general Gf and Gc as well as Carroll’s threestratum model (1993). In the C-H-C model proposed by Carroll, three layers or ‘stratum’
encompass the range and variability in individual differences of cognitive abilities. At the top of
this stratum Spearman’s general intelligence represents the most broad and widespread aspect of
intelligence (Carroll, 1993; 1997). Carroll (1993) examined 70 first order abilities and found the
correlations that existed between them. From the correlations found between these first order
factors, Carroll eight second-order factors. The second stratum holds the eight broad variables
of cognitive ability that are considered narrower than that of general intelligence. These eight
include the aforementioned variables of crystallized and fluid intelligence as well as general
memory and learning, broad visual perception, broad auditory perception, broad retrieval ability,
broad cognitive speed, and processing speed (Carroll 1993; 1997). Finally, the third stratum
consists of seventy still narrower abilities including auditory attention, decision-making speed,
and concept formation (Carroll, 1997).
One of the crucial findings that came out of the extensive examination of intelligence and
the tests thereof is that of the relationship between general intelligence and discrimination ability.
Galton’s original proposal of the predictive nature of sensory discrimination has been confirmed
in many instances since its introduction (Spearman, 1904; Acton & Schroeder, 2001; Stelmack &
Beauchamp, 2006; Troche, Houlihan, Stelmack, & Rammsayer, 2009).
Neural Efficiency
Galton was the first to propose that efficiency with which cognitive tasks could be
performed was related to individual differences in intelligence (Galton, 1883). Expanding upon
the idea that efficiency of performance is related to intelligence, Haier (1992) put forth the
hypothesis of neural efficiency. Within the neural efficiency hypothesis, individual differences in
intelligence are viewed as the result of more efficiently functioning brains. Therefore, individuals
with a higher level of intelligence expend less energy when processing information when
compared to those of a lower intelligence (Haier et al., 1992). One method of studying the
underlying neurological correlates of the neural efficiency hypothesis comes in the form of
electroencephalography (EEG). The use of EEGs to determine individual differences in speed of
processing yield results that support the neural efficiency hypothesis. Acton and Schroeder
(2001) also found correlations within their recent study that gave significant support to the neural
efficiency hypothesis.
The parieto-frontal integration theory is based on the assumption that multiple brain
systems converge together to determine the underlying biological mechanisms that are associated
with intelligence. This convergence is highly dependent upon white matter density to link these
systems together (Jung & Haier, 2007). Specifically, sensory information is taken in primarily
through visual or auditory pathways, and is the integrated in the parietal cortex. Following this,
the parietal cortex interacts with frontal regions of the brain to allow for the process of problem
solving. These areas include the dorsolateral prefrontal cortex, the inferior and superior parietal
lobule, the anterior cingulate, and regions within the temporal and occipital lobes (Jung & Haier,
2007). A negative correlation between intelligence levels and energy consumption by the brain
supports the neural efficiency hypothesis, in that individuals with high intelligence may need less
neurons or do not require the extensive functioning of brain systems (Haier et al., 1998).
Further, Jensen’s (1982) neural oscillation theory postulates that individual differences in
information processing speed and therefore intelligence depend on the rate of oscillation between
the refractory and excitatory states of neurons. The oscillation rate determines the speed at
which an individual can transmit neurally encoded information (Troche & Rammsayer, 2009).
Therefore, individuals who have shorter refractory periods will be able to process information
more quickly and as a result, display higher levels of intelligence. A theory that further involves
the concept of neural oscillations determining information processing speed is that of the
temporal resolution power (TRP) hypothesis (Rammsayer & Brandler, 2007). Individuals with
higher neural temporal resolution are able to process and transmit information at a faster rate. As
has been stated, this increased speed equates to higher intelligence levels. Using psychophysical
timing tasks, temporal resolution ability can be determined (Rammsayer & Brandler, 2002).
These psychophysical timing tasks imply sensory discrimination and, as is indicated by
Spearman (1904) and more recently Acton and Schroeder (2001), sensory discrimination is
strongly related to levels of intelligence. There are three models of the TRP hypothesis, the first
posits that TRP is amodal and that an individuals timing task performance on both auditory and
visual tasks can be related to one singular factor, specifically TRP which is strongly related to
intelligence (Haldemann, Stauffer, Troche, & Rammsayer, 2012). However, a second model
proposes that TRP is modality specific and that TRP can be split into an auditory TRP (aTRP)
and a visual TRP (vTRP). A third model indicates a hierarchy of TRP, with the aTRP and vTRP
controlled by a higher level, modality independent factor. The theory of a modality sensitive
TRP was supported in a recent study by Haldemann et al (2012), which found a relationship
between both aTRP and intelligence as well as vTRP and intelligence. The process was not
entirely modality sensitive, however, supporting the theory that there could be a higher order
amodal processing system that controls the modality sensitive TRP.
The importance of these findings within the context of the current study is the
relationship between intelligence and differentiating abilities in speed and accuracy. The
evidence that supports the neural efficiency hypothesis also indicates that individuals with higher
levels of intelligence can process information more quickly and with a greater amount of
accuracy. Further examination of efficiency and accuracy in relation to intelligence can be done
using the medium of EEGs (Nebauer & Fink, 2009).
Electroencephalography and Event Related Potentials
Electroencephalography (EEG) allows for the recording of electrical activity produced
from the firing of neurons within the brain (Neidermeyer & Lopes da Silva, 2004). EEGs also
allow for the extraction of event related potentials (ERPs) using signal averaging (Duncan et al.,
2009). ERPs reflect the processing of sensory information as well as the occurrence of higher
order processing (Duncan et al., 2009). The sensory information that is processed is often
presented in the form of a stimulus. The stimulus may be presented in different modalities,
including visual and auditory stimulation. The ability to measure ERP’s and the knowledge of
their originating structures allows for the use of ERP’s in clinical settings. ERP’s can provide
information about damage sustained to the structure or an abnormality in the structure (Duncan
et al., 2009). An ERP is characterized by its polarity, along with its latency. For example, the
ERP component P300 has a positive polarity as indicated by the P and generally peaks around
300ms after the presentation of a stimulus. Because of its non-invasive nature, the ERP is an
ideal measure of cognitive and higher order processes that occur in the brain. ERPs also provide
significant temporal resolution and therefore provide the best indication of the speed of
information processing.
Mismatch Negativity
A particular ERP that is related to discrimination ability is the mismatch negativity wave.
The mismatch negativity (MMN) waveform is a response to any discriminable difference
between present stimuli and the preceding stimuli (Naatanen, Jacobsen, & Winkler, 2005). The
MMN often reaches its peak at around 150-200ms after the presentation of a stimulus, the peak
represented by the highest point of amplitude within the 50-200ms time frame. A typical
paradigm consists of a sequence of repeated, standard sounds that are sporadically interrupted by
less frequent tones, often termed the ‘deviant’ (Duncan et al., 2009). The deviant stimuli may
differ from the standard tones by frequency, intensity, location, and pitch (Coles & Rugg, 1995;
Duncan et al., 2009). As the differences between the standard and deviant stimuli increase, the
amplitude of the MMN also increases, indicating that the underlying mechanisms of the MMN
are sensitive to the distance between standard and deviant stimuli. This sensitivity to change has
been interpreted in terms of discrimination ability. The changes between the standard and deviant
tones in the aforementioned paradigms can also vary between paradigms.
The MMN is generated by an automatic change detection process that identifies the differences
between the presented stimuli and the sensory-memory representation of the standard stimulus
(Naatanen, Jacobsen, & Winkler, 2005). To elicit the MMN, attention is often directed
elsewhere in order to examine the abilities of the brain to respond to stimuli without attention
being focused on the presented stimuli (Naatanen et al., 2007). Therefore, attention is often
directed towards another task, such as viewing a silent film or reading a book, as to prevent
attention dependent ERP’s from masking the presentation of an MMN (Naatanen et al., 2007).
The MMN is generated from two specific brain areas, with both the auditory cortex and right
frontal cortex responsible for MMN generation (Duncan et al., 2009).
During EEG recording, participants are told to ignore the stimuli as to allow for the
automatic processes to occur without attending to the stimuli. The changes between the standard
and deviant tones can also vary between paradigms. The central auditory system must first
recognize the standard stimuli as such, therefore allowing for the deviant stimuli to violate the
predetermined standard (Nataanen et al., 2007). The strength of the MMN wave can be
determined through amplitude, the highest peak of the waveform. In MMN, the amplitude
indicates the strength of the response to the deviant stimuli. The latency of the MMN is an
indication of the speed of response to the deviant stimuli. As the differences between the
standard and deviant stimuli increase, the latency decreases, indicating a faster recognition of the
change (Naatanen et al., 2007). This supports the theory that the MMN wave can also indicate
differences in processing speed, as the speed of recognition of change increases along with the
magnitude in difference between stimuli. An increase in the distance between the deviant and
standard tones also results in an increase in the amplitude of the MMN (Beauchamp & Stelmack,
2006). Therefore, the MMN is indicative of processes that discriminate between the standard
and deviant stimuli.
The presentation of the MMN during instances of diverted attention also shows the
ability of the brain to perform complex tasks such as comparing multiple sounds, without the
necessity of attention (Naatanen et al., 2007). By using multiple forms of auditory changes, the
MMN can indicate the structures that are most affected by specific forms of auditory changes
(Nataanen et al., 2007).
MMN and Mental Ability
The established relationship between discrimination abilities and intelligence has led to
the use of the MMN to explore this relationship at a neurological level. Bazana and Stelmack
(2002) found that individuals with higher mental abilities have shorter MMN latencies. The
study included the presentation of an oddball paradigm with backward masking. The process of
backward masking involves limiting the amount of time for the acquisition of information from a
series of stimuli (Bazana & Stelmack, 2002). The stimulus is presented for a short time and is
then followed by the masking stimulus, which is generally random in nature. Individuals with
higher mental ability tended to display larger amplitude MMNs in the higher intensity conditions.
However, this relationship was only observed in the highest intensity condition and was not
evident throughout any other conditions (Bazana & Stelmack, 2002). Individuals with higher
mental ability had shorter peak latencies than those of participants scoring lower in mental ability
during conditions where attention was not directed towards the stimuli (Bazana & Stelmack,
2002). This indicates that the varying speed in information processing may be related to
variances in mental ability, and therefore provides support for the neural efficiency hypothesis.
Beauchamp and Stelmack (2006) conducted a study in which mask type and inter-tonal
interval (ITI) were manipulated and individuals were exposed to the stimuli in both active and
passive conditions. ITI is described as the interval of time between the presentation of deviant
stimulus and the presentation of the mask (Beauchamp & Stelmack, 2006). Individuals with
high mental ability displayed larger amplitudes when compared with that of individuals with that
of a lower intelligence score (Beauchamp & Stelmack, 2006). However, these findings only
extended to one condition. These individuals also displayed faster reaction time speed and
greater accuracy with their responses. Further research into the relationship between MMN and
mental ability has been conducted, most often using the oddball paradigm to elicit the MMN
while altering the physical characteristics of the stimuli for each study.
The relationship between mental ability and MMN was recently examined using stimuli
that differed in duration and frequency. Troche, Houlihan, Stelmack, and Rammsayer (2010)
reported individuals with higher mental ability showed larger amplitudes for frequency but not
duration MMNs. This particular study did not observe the same relationship between MMN
latency and mental ability as previous studies. The lack of relationship between MMN latency
and mental ability is explained through the lack of using auditory masks. In Bazana and
Stelmack’s study, (2002) the MMN latency decreased as the intervals between stimuli and
auditory masks decreased. The authors concluded that this may have been caused by a speedoriented processing of information that was not required in the Troche et al study (2009). A
more recent study (Troche et al., 2010) resulted in similar findings. Larger MMN amplitudes
were associated with higher mental abilities when the stimuli differed in frequency. These
results indicate that the ability to access sensory memory, where the process of stimuli
comparison occurs, is greater in individuals with higher mental ability (Troche et al., 2010).
More recently, the relationship between intelligence and the MMN was explored by Houlihan
and Stelmack (2012). Using stimuli that followed the rule of the higher the frequency, the lower
the intensity, findings indicated further support of the MMN amplitude and intelligence
relationship. These results indicate the greater facilitation of discrimination for individuals with
higher intelligence levels and thus supports the higher intelligence, greater sensory
discrimination hypothesis. Further findings from the study showed an insignificant relationship
between intelligence and MMN latency.
There have been many findings of the relationship between MMN amplitude and higher
mental ability (Troche et al., 2010; Bazana & Stelmack, 2002; Beauchamp & Stelmack 2006).
These findings support the biological theories of intelligence, namely that of the neural
efficiency hypothesis. Individuals with higher levels of intelligence are said to process
information quickly, as theorized by neural efficiency, thus high mental ability individuals
should process the sensory information in the current study more quickly than those of low
mental ability.
The current study will examine the relationship between MMN and mental ability using
standard and deviant stimuli that vary in intensity. The expected finding is that individuals with
higher mental ability will display larger MMN amplitudes and shorter MMN latencies, indicating
a faster speed of information processing as well as a greater discrimination ability. These
findings could lead to the use of ERP’s to measure mental ability, therefore providing a new,
attention independent method of intelligence testing.
Participants that took part in the study were female, first year psychology students aged
18-24 (n=49). Females have been show to display larger amplitude ERP’s, therefore males were
excluded from the study (Barrett & Fulfs, 1998; Ikezawa et al., 2008). Participants were required
to have normal hearing. Individuals who were taking centrally acting medication as well as
those who had a neurological disorder were excluded from the study. The individuals
participating were required to abstain from alcohol 24 hours prior to the EEG recording, as well
as abstain from caffeine and nicotine one hour before the recording. Course credit was given for
participation, as well as a monetary compensation of 10 dollars. Individuals who did not require
course credit were invited to participate and were offered a monetary compensation in place of
course credit at the rate of 10 dollars per hour.
The Intelligence Structure Battery-Short Form (INSSV) was used to determine mental
ability (Arendasy, Hornke, Sommer, & Gittler, 2010). The test itself is computer based and is
adaptive therefore, depending upon the number of correct answers the participants gave, the test
would increase or decrease in difficulty. For the factor at the top of the hierarchy, g, the test’s
reliability is .91. Five secondary structure factors were also tested in the INSSV. General fluid
intelligence was examined using a word association and verbal reasoning task. The fluid
intelligence task involved both a figurative and numerical reasoning task. Quantitative reasoning
was tested using math competence and flexibility, while long-term memory and visual
processing abilities were also tested. The participants overall g-score was calculated using the
results from these five secondary factor tasks.
There were two separate data collection sessions. Participants were first required to
perform the computer based cognitive testing. This task was completed in a group of up to 25
participants. The participants were required to attend the second session, which was the EEG
recording session, within 1 to 30 days after the cognitive test. In order to divert attention away
from the sounds being presented, the individual viewed an animated film during the EEG session.
The recording took place while participants viewed a film without sound in a separate room
while being presented with auditory stimuli. The attention of the participants was held by the act
of reading the subtitles, which accompany the silent film.
The stimuli were presented binaurally, with six sets of 420 tones presented to the
participants, each one differing in deviance intensity, while the standard tone remains the same
throughout. The standard and deviant tones followed an oddball paradigm and differed by a
variation of 5 to 15 dB depending on which set is presented. Each set of tones consists of 20%
deviant tones and 80% standard tones, as consistent with the general presentation of an oddball
paradigm to elicit an MMN waveform. The standard intensity of the stimuli was 5dB, with the
deviant stimuli ranging at 5, 10, or 15 dB above or below the standard. The standard stimuli
were presented 20 times in succession at the beginning of each sequence. These 20 standard,
successional tones were not included in the final averaging of responses to the tones. Each of the
tones were presented for 200ms. The inter-tone-interval was 600ms from onset to onset, with
10ms as the rise and fall time.
EEG Recording
The EEG data was recorded using the EasyCap electrode cap using 32Ag/AgCl active
electrodes, using the nose as a reference and AFz as the ground. Vertical EOG was measured
using an electrode placed under the right eye and FP2. The mismatch negativity wave was
derived by subtracting the standard waveforms from the deviant waveforms. Filter settings
included Neuroscan NuAmp 0.5 to 100 Hz, with a sampling rate of 500Hz. Amplitude and
latency were measured between 140-200ms after the onset of stimulus. The data was visually
reviewed for artifacts, filtered between1 and 15Hz. Separate averages were calculated for each
deviant intensity condition, from 60-95 dB. The ERP averages were calculated for the electrodes
FP1, FP2, FZ, and CZ. Separate averages for the standard and deviant stimuli were calculated,
with the MMN obtained by calculating the difference between the standard average.
Active Tasks
Once individuals completed the passive task of listening to the binaurally presented tones,
they were instructed to conduct an active task of responding to the tones. The tones are similar
to those presented to them during the passive tasks. They remained in the room they already
occupied and were given instructions to attend to the tones. They were told that there would be a
set of tones, initially, that were identical. Once they began to detect different tones they were
asked to respond upon hearing a different tone. Individuals were instructed to respond by
selecting the button on the far left of the keypad when they encounter a tone that is stronger in
intensity or weaker in intensity than the tone presented at the beginning of the sequence. They
were instructed to respond on the keypad with their dominant hand.
Statistical Analysis
To examine the relationship between mental ability and MMN latency as well as the
relationship between mental ability and MMN amplitude, Pearson’s r correlations were
Within each of the conditions (threshold, twice, and thrice the threshold) an MMN was
elicited using the oddball paradigm. For each condition within the current study, there was an
elicitation of the MMN (See figures 2 and 3). The overall increase of MMN amplitude in
relation to the increase in difference between the standard and deviant stimuli can be seen in
Figure 1. The relationship between MMN amplitude and intelligence, as well as the relationship
between MMN latency and intelligence were calculated using Pearson’s correlations. Three
participants were excluded from these results as their recording sessions did not provide any
usable data, leaving N = 46. A negative correlation was observed between IQ and amplitude in
the x1 threshold condition, at all electrode positions (see table 1). These findings indicate that as
the g-score of individuals increased the amplitude of the MMN in the 5db condition increased
The current study examined the relationship between the MMN and mental ability,
hypothesizing that participants with higher mental ability scores would produce larger MMN
amplitudes. This would be seen as a measure of participants discrimination ability and also
indicate that participants with higher mental ability also showed greater discrimination ability.
Further, it was hypothesized that participants with higher mental ability scores would have
shorter MMN latencies, providing support for the neural efficiency hypothesis. Findings of the
current study indicate that the MMN in an accurate marker of discrimination ability. This can be
seen in the increase in MMN amplitude with the increase in difference between standard and
deviant stimuli, which is evident in figure 1 and figure 2. Some support for the relationship
between intelligence and discrimination ability was found in the current study. Higher scores of
mental ability were related to larger MMN amplitudes, indicating that participants with higher
mental ability also displayed better sensory discrimination. However, mental ability scores were
not related to MMN latency. This does not provide support for the neural efficiency hypothesis,
as participants with higher intelligence did not indicate a faster automatic response than those
with lower intelligence.
The MMN has been shown to be indicative of discrimination ability, as can be seen by
the sensitivity of the MMN to even slight auditory changes (Nataanen et al., 2007). The current
study has provided support for this, as the grand averages of MMN amplitude changed
significantly with an increase in difference between the standard and deviant tones. This
relationship can be seen in Figure 1. These findings provide support for the use of the MMN as
the only objective measure of sound-discrimination accuracy (Nataanen, Tervaniemi, Sussman,
Paavilainen & Winkler, 2001). Therefore, the MMN provides the opportunity to examine certain
aspects of auditory learning and discrimination abilities, such as a measure of effectiveness of
training and rehabilitation programs for individuals with dyslexia (Nataanen et al., 2001).
Current and previous research of intelligence has indicated a relationship between sensory
discrimination and intelligence. As the MMN is considered to be an indication of discrimination
ability, it is a useful tool for discovering aspects of intelligence that are not attention dependant.
The lack of necessity of attention for elicitation of the MMN has also made it a useful
tool in clinical studies of auditory processing in instances of a deficit in attentional abilities
(Naatanen et al., 2007; Pakarinen, Takegata, Rinne, Huotilainen & Naatanen, 2007). Using an
oddball paradigm, Bazana and Stelmack (2002) reported a relationship between MMN latency
and mental ability. A more recent study indicated a negative correlation between MMN latency
and mental ability (Beauchamp & Stelmack, 2006). Though a relationship was found between
latency and mental ability, there was no relationship found between amplitude and mental ability
in the aforementioned study. Troche et al. (2010) reported opposing results both in terms of
latency as well as amplitude. Similar to Troche et al., within the current study, a relationship
between latency and mental ability was not observed in any conditions. This does not support
the speed of information processing aspect of the neural efficiency hypothesis, as individuals
with higher intelligence did not demonstrate a quicker MMN response. The findings of this
study, as well as the similar findings of Troche et al. (2010), may indicate that the relationship
between intelligence and MMN extends only to discrimination ability and not information
processing speed.
MMN amplitude and mental ability provided support for the hypothesis that individuals
with higher mental ability display larger amplitudes. This relationship occurred at a significant
level within the smallest difference condition, which was the 5db condition. This finding could
indicate that the higher intensity difference conditions were not difficult enough therefore
creating a ceiling effect that did not allow for the prediction of mental ability from MMN
amplitude within the other conditions. While including additional conditions with difficulty
greater than that of a 5db and 10db difference may provide further support for the hypothesis it
also presents similar issues as the current findings have encountered. The inclusion of more
difficult conditions could create a floor effect within the data, providing similar results to the
current study.
The use of the MMN waveform in examining the speed of information processing as well
as its use in examining sensory discrimination has provided great insight into the relationships
between discrimination, efficiency, and intelligence. However, the current study has not
provided support for the speed of information processing aspect of the neural efficiency
hypothesis. Therefore, postliminary studies should focus on the aspects of discrimination ability
and intelligence, which was supported within the current study. Further, in the event that
continuing research provides additional support for the relationship between intelligence and
sensory discrimination determinable through the MMN, there is potential for the use of ERPs to
predict mental ability. There is a large argument that has been made regarding the inseparability
of attention and cognitive processes. However, the elicitation of an MMN during passive, ignore
conditions indicates that attention may not be as significant in cognitive processes.
Using the MMN to predict mental ability provides unique opportunities for determinants of
mental ability that do not require attention and, therefore, remove some of the confounds of
currently used psychometric measures of intelligence.
Additional Tables and Figures
Table 1. Indicates the correlations as well as the means for IQ and MMN amplitude in all
conditions. ** Indicates a significant relationship at .01 significance level. * Indicates a
significant relationship at .05 significance level
Table 2. Indicates the correlations as well as the means for IQ and MMN latency in all
Figure 1. For each rise in difference between standard and deviant stimuli, there is a rise in
amplitude. This figure represents the amplitude changes averaged in all conditions using the
electrode FZ.
Figure 2. The evident elicitation of MMN in the louder than threshold conditions for the
electrode FZ.
Figure 3. The evident elicitation of the MMN in the softer than threshold conditions for electrode
Aboitiz, F. (1992). Brain connections: Interhemispheric fiber systems and anatomical brain
asymmetries in humans. Biological Research, 25, 51−61.
Acton, G. S., & Schroeder, G. H. (2001). Sensory discrimination as related to general
intelligence. Intelligence, 29, 263-271.
Arendasy, M., Hornke, L.F., Sommer, M., & Gittler, J. G. (2010). INSSV-Short Form.
Bazana, P. G., & Stelmack, R. M. (2002). Intelligence and information processing during an
auditory discrimination task with backward masking: An event-related potential analysis.
Journal of Personality and Social Psychology, 83, 998-1008.
Beauchamp, C. M., & Stelmack, R. M. (2006). The chronometry of mental ability: An eventrelated potential analysis of an auditory oddball discrimination task. Intelligence, 34, 571586.
Binet, A. (1905). New methods for the diagnosis of the intellectual levels of sub-normals. L’anee
Psychologie, 12, 191-244. Translation by Elizabeth S. Kite (1916) The development of
intelligence in children.
Carroll, J.B. (1993). Human cognitive abilities: A survey of factor-analytical studies. New York:
Cambridge University Press.
Carroll, J.B. (1997). The three-stratum theory of cognitive abilities. In D.P. Flanagan, J.L.
Genshaft, & P.L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests,
and issues (pp. 122-130). New York : The Guilford Press.
Cattell, J.M. (1890). Mental tests and measurements. In D. Wayne (Ed.) Readings in the history
of psychology (pp. 347-354). East Norwalk, CT, USA: Appleton-Century-Crofts
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal
of Educational Psychology, 54, 1-22.
Coles, M. G. H. & Rugg, M. D. (1995, eds). Electrophysiology of Mind: Event Related
Potentials and Cognition. New York: Oxford University Press.
Duncan, C. C., Barry, R. J., Connolly, J. F., Fischer, C., Michie, P. T., Naatanen, R., Polich, J.,
Reinvang, I., and Petten, C. V. (2009). Event-related potentials in clinical research:
Guidelines for eliciting. recording, and quantifying mismatch negativity, P300, and
N400. Clinical Neurophysiology, 120, 1883-1908.
Galton, F. (1883). Inquiries into human faculy and its development. Gavin Tredaux (Eds.).
Gray, J. R.., Chabris, C. E., & Braver, T. S. (2003). Neural mechanisms of general fluid
intelligence. Nature Neuroscience, 6, 316-322.
Haier, R.J., Siegel, B.V., Nuechterlein, K. H., Hazlett, E., Wu, J.C., Paek, J., & Browning, H.L.
(1998) Cortical glucose metabolic rate correlates of abstract reasoning and attention
studied with positron emission tomography, Intelligence, 199 – 218.
Haldemann, J., Stauffer, C., Troche, S., Rammsayer, T. (2012). Performance on auditory and
visual temporal information processing is related to psychometric intelligence.
Personality and Individual Differences, 52, 9-14.
Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized
general intelligences. Journal of Educational Psychology, 57, 253-270.
Jung, R. E., & Haier, R. J. (2007). The parieto-frontal integration theory (PFIT) of intelligence:
Converging neuroimage evidence. Behavioral and Brain Sciences, 30, 135-187.
Laurent, J., Swerdlik, M. & Ryburn, M. (1992). Review of validity research on the StanfordBinet Intelligence Scale: Fourth edition. Psychological Assessment, 4, 102-112.
Nataanen, R., Tervaniemi, M., Sussman, E., Paavilainen, P. & Winkler, I. (2001). Primitive
intelligence in the auditory cortex. TRENDS in Neuroscience, 24, 283-288.
Naatanen, R., Jacobsen, T., & Winkler, I. (2005). Memory based or afferent processes in
mismatch negativity: A review of the evidence. Psychophysiology, 42, 25-32.
Naatanen, R., Paavilainen, P., Rinne, T., and Alho, K. (2007). The mismatch negativity (MMN)
in basic research of central auditory processing: A review. Clinical Neurophysiology, 118,
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and
Biobehavioral Reviews, 33, 1004-1023.
Niedermeyer E., & Lopes da Silva, F. (2004). Electroencephalography: Basic Principles,
Clinical Applications, and Related Fields. Lippincot Williams & Wilkins.
Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Halpern D. F., and Turkheimer, E. (2012).
Intelligence: New findings and theoretical developments. American Psychologist, 67,
Pakarinen, S., Takegata, R., Rinne, T., Huotilainen, M., and Naatanen, R. (2007). Measurement
of extensive auditory discrimination profiles using the mismatch negativity (MMN) of
the auditory event related potential (ERP). Clinical Neurophysiology, 118, 177-185.
Rammsayer, T. H., & Brandler, S. (2002). On the relationship between general fluid intelligence
and psychophysical indicators of temporal resolution in the brain. Journal of Research in
Personality, 36, 507−530.
Rammsayer, T. H., & Brandler, S. (2007). Performance on temporal information processing as
an index of general intelligence. Intelligence, 35, 123–139.
Rehberg, R. A., & Rosenthal, E. R. (1978). Class and merit in the American high school. New
York: Longman.
Spearman, C. (1904). General intelligence objectively determined and measured. The
American Journal of Psychology, 15, 201-292.
Troche, S. J., Houlihan, M. E., Stelmack, R. M., & Rammsayer, T. H. (2009). Mental ability,
P300, and mismatch negativity: Analysis of frequency and duration discrimination.
Intelligence, 37, 365-373.
Troche, S. J., Houlihan, M. E., Stelmack, R. M., & Rammsayer, T. H. (2010). Mental ability and
the discrimination of auditory frequency and duration change without focused attention:
An analysis of mismatch negativity. Personality and Individual Differences, 49, 228-233.
Related flashcards


25 cards

Alcoholic drinks

19 cards

Dissociative drugs

53 cards

Create Flashcards