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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

with

Honours in Psychology

____________________________________

Kristyn Kelsey

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Table of Contents

Acknowledgements……………………………………………………………………....3

Abstract………………………………………………………………………………......4

Introduction………………………………………………………………………….…...5

Method……………………………………………………………………………….….15

Results……………………………………………………………………………….….18

Discussion………………………………………………………………………....……19

Additional Tables ……………………………………………………………………....23

Figure 1…………………………………………………………………………………24

Figure 2…………………………………………………………………………………25

Figure 3…………………………………………………………………………………26

References………………………………………………………………………………27

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Acknowledgement

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.

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Abstract

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.

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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

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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

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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 three-

stratum 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

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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,

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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

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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

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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

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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).

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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

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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 speed-

oriented 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.

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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.

Method

Participants

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

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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.

Measures

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.

Procedure

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

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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

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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

calculated.

Results

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

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the g-score of individuals increased the amplitude of the MMN in the 5db condition increased

also.

Discussion

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

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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.

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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

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mental ability that do not require attention and, therefore, remove some of the confounds of

currently used psychometric measures of intelligence.

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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 conditions.

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Amplitude (5db)

Amplitude(10db)

Amplitude (15db)

r Mean R Mean r Mean

FZ -.41** -.68 -.03 -1.42 -.15 -1.91

FC1 -.37* -.63 -.07 -1.36 -.15 -1.77

FC2 -.40** -.68 -.09 -1.33 -.18 -1.88

CZ -.41** -.64 -.09 -1.17 -.15 -1.74

Latency (5db)

Latency(10db)

Latency(15db)

r Mean r Mean r Mean

FZ .02 189.78 .05 180.91 .10 177.65

FC1 -.07 186.91 .15 180.89 .06 176.30

FC2 -.13 191.69 -.00 181.69 .11 173.34

CZ -.10 186.08 .16 175.47 .13 176.82


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.

24


Figure 2. The evident elicitation of MMN in the louder than threshold conditions for the electrode FZ.

25


Figure 3. The evident elicitation of the MMN in the softer than threshold conditions for electrode FZ.

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