Running head: MEANTAL ABILITY & SENSORY DISCRIMINATION
Mental Ability and Sensory 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
____________________________________
Alexandra Smith
April 2012
1
MENTAL ABILITY & SENSORY DISCRIMINATION
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Table of Contents
Acknowledgements……………………………………………………………………...3
Abstract……………………………………………………………………………….....4
Introduction……………………………………………………………………………...5
Method………………………………………………………………………………….15
Results…………………………………………………………………………………...17
Discussion……………………………………………………………………………….19
References……………………………………………………………………………….23
MENTAL ABILITY & SENSORY DISCRIMINATION
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Acknowledgements
The past year has presented me with the opportunity of working with many people who have
made the completion of my honours thesis possible. I would like to take this opportunity to thank
them. First and foremost, I would like to thank my advisor, Dr. Michael Houlihan, for his
continuous support and expertise. I would also like to thank my reader, Dr. Del Brodie, for his
advice and helpful recommendations. To Dr. Kim Fenwick- thank you for your guidance
throughout the year, that kept us all on track. Kris DesRoches, Ian Davidson, and Lauren
Morrisey, I cannot express how much I appreciated your help with data collection. I would also
like to thank all the other volunteers who provided their assistance in the lab. Finally, to all the
participants who took the time to make this research possible.
MENTAL ABILITY & SENSORY DISCRIMINATION
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Abstract
The study of intelligence has always been popular in the field of psychology. A rather recent
approach in measuring cognitive abilities involves its assessment in the absence of attention. The
aim of the present study examines the relationship between intelligence and auditory sensory
discrimination abilities. Fluid intelligence represents an individual’s ability to comprehend and
manipulate patterns, as well as to adapt to novel situations. Fluid intelligence, which is the most
accurate predictor of general intelligence, was measured using the Advanced Matrices Test.
Sensory discrimination was assessed using the mismatch negativity, an even-related potential
(ERP) reflected in the EEG recording. This was recorded while the participants were presented
with a series of sounds varying in intensity. The mismatch wave demonstrates one’s ability to
discriminate between the sounds, without attending to them. The amplitude of the mismatch
negativity has been found to predict sensory discrimination abilities and discrimination abilities
are found to predict mental abilities. Thus the brain’s innate ability to discriminate between
varying intensities of auditory paradigms at an unconscious level is hypothesized to predict
mental abilities.
MENTAL ABILITY & SENSORY DISCRIMINATION
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Mental Ability & Sensory Discrimination
Advancements in the study of intelligence contest sensory discrimination capabilities are
an apposite index of mental abilities. Higher cognitive abilities are congruent with better sensory
discrimination accuracy. The understanding of mental abilities and their assessment is highly
valuable, as intelligence tests are determined the most accurate indication of academic and career
achievement (Haavisto & Lehto, 2004). As cognitive abilities are frequently measured through
fluid intelligence assessments, an overview of the encompassing three-stratum intelligence
model will be discussed. Further, a review of the biological determinants of fluid intelligence and
the psychometric measurement of sensory discrimination through the ERP is in order. Frequency
but not duration discrimination accurately correlate with mental abilities (Troche, Houlihan,
Stelmack, & Rammsayer, 2010). Thus, the aim of this research seeks to identify the parameters
of sensory discrimination as a predictor of intelligence through the manipulation of auditory
intensity.
Fluid and Crystallized Intelligence
A descriptive model of intelligence that has been researched over the past 100 years, is
the model of general intelligence, as proposed by Spearman in 1904. Through factor analysis,
Spearman (1904) outlined specific cognitive tasks and specialized abilities correlated with the
concept of general intelligence (Haavisto & Lehto, 2004). These specialized abilities that are
predictive of general intelligence include skills such as visual perception, spatial reasoning and
nonverbal capabilities (Haavisto & Lehto, 2004), however these abilities do not all share the
same correlations with general intelligence. A positive manifold effect is observed within the
three stratum hierarchy, as all cognitive abilities correlated with the construct of general
intelligence; several predictors more accurately anticipate general intelligence than do others
MENTAL ABILITY & SENSORY DISCRIMINATION
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(van der Maas et al., 2006). These abilities are measured through intelligence batteries, and are
organized in a hierarchical model, where general intelligence was the highest order strata. The
second stratum consists of the specific cognitive abilities, and the tests used to assess these
abilities consist of the first stratum.
Cattell and Horn contest that the third stratum of general intelligence does not fully
encompass all cognitive loadings; rather fluid and crystallized intelligence better served this
model. The Cattell-Horn-Carroll (CHC) model, however, is generally the most accepted model
of intelligence by all within the field, which encompasses the ideas of Spearman’s general
intelligence as the highest order of intelligence (McGrew, 2009). Fluid and crystallized
intelligence are both original subsets of general intelligence, as proposed by Cattell (1963). Both
are described as second-order functions of general intelligence in the CHC model. Fluid
intelligence is the ability to easily adapt to novel environments, and to create inferences, thus it is
the ability think fluidly. Crystal intelligence involves the use of previously acquired information
and skills. Fluid skills, however, are not dependant on the acquisition of facts, rather reliant upon
an individual’s ability to think abstractly. Preusse et al. (2011) suggest greater fluid intelligence
is the proficiency of integrating previous knowledge and applying it to the present. Tasks
exercising fluid intelligence require cognitive processing in a manner to create inferences to
solve in a novel situation (Kan et al., 2011). Fluid capabilities involve the recognition and
comprehension of patterns (Preusse et al., 2011). Further, competency in mathematics is reported
in high fluid capabilities (Preusse et al., 2011) and fluid intelligence is the most accurate
indicator of general intelligence (Wooglar et al., 2010).
Crystallized intelligence requires the use of previous knowledge for comprehension,
whereas previously acquired knowledge is not imperative in exercising fluid intelligence.
MENTAL ABILITY & SENSORY DISCRIMINATION
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Crystallized intelligence is culturally based, often reflecting school curriculums among young
children (Cattell 1963). Further, crystallized intelligence is dependant on fluid intelligence, as
Kan et al. (2011) report individuals with high fluid intelligence abilities acquire higher levels of
crystallized intelligence. In time limited cognitive tasks, fluid intelligence is of greater advantage
than crystallized (Cattell, 1963), this is because fluid intelligence is the ability to create
inferences and adapt to novel situations, and the use previously acquired information is not
necessary. However, individuals dominant in crystallized intelligence better perform in tasks
where time is not limited, and the task involves knowledge such as history or geography, where
they are able to apply their existing knowledge to complete a task successfully (Cattell, 1963).
Biology of Mental Ability
Biological models that complement the factor analysis framework of intelligence purport
mental ability is in large due to neurons and specific areas of the brain. Galton (1883) proposed
intellect was a product of an individual’s ability to discriminate (as cited in Meyer, Hagmannvon Arx, Lemola, & Grob, 2010). Jenson’s (1982) neural oscillation theory predicts mental
abilities are determined by the conductance speed of neurons. Thus processing information at a
greater speed is indicative of higher cognitive abilities (as cited in Helmbold, Troche, &
Rammsayer, 2006). Current research maintains these theoretical views, and expand on the neural
oscillation models. For example, the degree of myelination of the neurons is proposed to play a
crucial role in intelligence (Jung & Haier, 2007). Jung and Haier (2007) purport myelin density
affects the size of the axon, and consequently the large axon size will increase the conduction
speed of neural impulses. Individuals whom process information more quickly because of this
neural efficiency are considered to have higher cognitive abilities (Jung & Haier, 2007).
Additionally, white matter was positively correlated with intelligence, which further reinforces
MENTAL ABILITY & SENSORY DISCRIMINATION
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the myelination findings from previous work (Jung & Haier, 2009; Jung & Haier, 2007). White
matter of the brain consists of myelinated neurons that are indicative of the speed of neural
processing. Therefore, the degree of white matter found in the brain makes is consistent with the
myelination of neurons.
Jung and Haier (2007) propose exercising cognitive abilities activates the parietal and
frontal areas of the brain, known as the parietal frontal integration theory (PFIT). Therefore, the
PFIT model indicates intelligence results from the interaction between these neural structures,
rather than centralized in one unique area (Preusse et al., 2011). Sensory information is
processed initially in the occipital and auditory cortexes, for visual and acoustic stimuli,
respectively (Preusse, van deer Meer, Deshpande, Krueger, & Wartenbuger, 2011). The encoded
information is then sent from the parietal to the frontal cortex for interpretation.
Cognitive processes exercising fluid capabilities are reported to activate the parietal and
frontal lobes of the brain (Preusse et al., 2011). This demonstrates cognitive processes activate
several neural pathways, and intellect results from the integration of multiple cortices (Preusse et
al., 2011). Colom et al. (2009) investigated fluid, crystallized and spatial intelligence and it’s
relationship with the PFIT model previously proposed (Jung & Haier, 2007). Specific areas of
the brain are active when general, crystallized and spatial intelligence are exercised, however no
significant area was determined for fluid intelligence. This suggests fluid intelligence is an
activation of multiple cortical areas. The frontal and parietal cortices are implicated with fluid
intelligence, as when completing a matrices task that requires fluid thinking, activation of these
areas, as measured by increase blood flow was observed (Kane & Engle, 2002).
The EEG and Event-Related Potential
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The electroencephalogram (EEG) is a functional research technique that allows for the
recording of differences in neural electrical activity (Coles & Rugg, 1995). This is achieved in
placing electrode sensors on the scalp (Coles & Rugg, 1995). In a research paradigm, stimuli
sequences are presented, and variations in the voltage of the EEG are observed respective to each
stimulus. This measure of cortical differences in voltage is recorded from the onset of a stimulus
to it’s neural processing, and therefore is time-locked to the neural processing of the stimuli.
Averaging is used to extract the event-related potential (ERP), and it is representative of the
neural processing of the stimuli (Coles & Rugg, 1995). Thus, the ERP is representative of the
processing of information (the stimuli) over a particular time period. Once the ERP is derived
from the EEG raw data, the amplitude and latency may be measured (Coles & Rugg, 1995). The
amplitude refers to the strength of the ERP, or how positive or negative the wave is at it’s peak.
Latency measures the time elapsed from stimulus onset to the processing of the event (Coles &
Rugg, 1995). The ERP is useful in many research paradigms, including ones of memory and
discrimination (Duncan et al., 2009).
The Mismatch Negativity Waveform
An oddball paradigm is the most reliable auditory sequence used when eliciting the
mismatch negativity waveform. This involves a sequence of standard and deviant tones, where
the deviant tones occur 20% of the time to violate a pattern created by the sequence of standard
ones. The deviant tones may differ in parameters such as pitch, frequency, and intensity (Duncan
et al., 2009). Pakarinen et al. (2010) reveal these standard tones do not need to be identical,
however may follow an abstract rule in which the mismatch will nonetheless be elicited. An
example of an abstract rule may consist of the standard tone following a pattern of increasing
degrees of intensity, therefore each standard tone differs from the previous, yet is not considered
MENTAL ABILITY & SENSORY DISCRIMINATION
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deviant as it follows the rules of the pattern (Paavilainen, Simola, Jaramillo, Naatanen, &
Winkler, 2003). Theses stimuli are presented while attention is directed elsewhere, an unrelated
task is completed, which stimulates a separate sensory system. Therefore, the mismatch
negativity is elicited in the absence of attention and represents the innate ability to detect change
among stimuli.
The mismatch negativity is derived from subtracting the standard from the deviant ERP
waveform (Pakarinen, Huotainen, & Naatanen, 2010). The mismatch negativity is identified as
the portion of the subtracted waveform between 150 and 250 ms after the onset of stimuli
(Pakarinen et al., 2010) and is largest at the frontal electrode sites, however is also distinct at the
site of the auditory cortex (Duncan et al., 2009; Naatanen 1990). The amplitude of the mismatch
negativity waveform increases and shorter latencies are observed when the standard and deviant
tones are more easily distinguished (Naatanen, 2008). Therefore, the accuracy in discriminating
between the standard and deviant tones is positively correlated with larger amplitudes (Naatanen,
2008). Reduced amplitude is demonstrated when a tone is obscure in nature and consequently the
amplitude of the mismatch negativity wave decreases as the degree of difficulty increases
(Naatanen, 2008). The latency of the mismatch negativity wave is shorter when the difference
between the frequent and deviant tone is largest. The amplitude of the waveform demonstrates a
concentration of frontal activity (Duncan et al., 2009). This frontal activation reflects the
attention switch in attending to the stimuli (Duncan et al., 2009).
The mismatch negativity is elicited from several source generators including the auditory
cortex, which in turn activates the frontal cortex (Naatanen, Tervaniemi, Sussman, Paavilainen,
& Winkler, 2001). The frontal lobe is responsible for attending, thus it maintains where (to
which stimulus) an individuals’ attention is held (Naatanen et al., 2011). The activation of the
MENTAL ABILITY & SENSORY DISCRIMINATION
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frontal cortex represents an individual’s shift in attention processing. However, the auditory
cortex must first process the acoustic information in order for the switch in cortical activation to
occur. The auditory cortex is responsible for categorizing all auditory stimulation; it must
differentiate between all streams unique in pitch, length, and tone, and coins each stream to its’
original source. Naatanen et al. (2001) termed this “auditory stream segregation” (p. 283), where
the auditory cortex functions to differentiate and organize all incoming acoustic streams, thus
creating an afferent model of the stimuli. This entire process occurs at an attention-independent
level. The mismatch negativity is elicited once the auditory cortex recognizes one sound stream
is split into two (Naatanen et al., 2001). Thus, the auditory cortex compares each stream
automatically, and when a tone infringes the past representation, the mismatch negativity is
elicited.
Further, the mismatch negativity is elicited when the expectation of the proceeding
auditory event is not met; rather the sequence deviated from the norm (Naatanen et al., 2001).
The expectation of the standard pattern is formed as a sensory memory trace in the auditory
cortex. The auditory cortex compares each tone to this trace, and therefore distinguishes the
deviant tone, as it violates the pattern held in echoic memory. The strength of this memory trace
is reflected in the amplitude of the waveform (Bazana & Stelmack, 2002). This further
demonstrates the auditory cortex’s ability to maintain several auditory streams in memory
simultaneously and the sensitivity of the mismatch negativity, in the brains’ ability to
discriminate when the standard is more obscure (Naatanen et al., 2001; Naatanen, Astikainen,
Ruusuvirta, Huotilainen, 2010). The elicitation of the mismatch negativity through abstract
standard paradigms represents the processing of the auditory stimuli occurs at a pre-attentive
level and is contested to be a measure of intelligence (Naatanen et al., 2001). Further, it is
MENTAL ABILITY & SENSORY DISCRIMINATION
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suggested that the auditory cortex is able to keep more permanent streams of auditory
information in memory. This is reflected through the use of language (Naatanen et al., 2001) and
is vital for the recognition of speech violations.
The mismatch negativity is an appropriate measure of discrimination as this waveform is
elicited in the absence of attention (Duncan et al., 2009). As the mismatch negativity is elicited
while attention is directed elsewhere, this demonstrates the sensory discrimination of the
unattended auditory stimuli occurs at an attention-independent level. Therefore, direct attention
is not needed to detect the differences of acoustic parameters. The detection of discrimination in
auditory stimuli has been elicited during REM sleep, while in a coma, in newborns and even in
fetuses (Nataanen, Kujala, & Winkler, 2011; Naatanen, Astikainen, Ruusuvirta, & Huotilainerf,
2010).
Mismatch Negativity and Cognitive Abilities
The mismatch negativity amplitude has been found to be predictive of mental abilities in
multiple reports. A relationship between response time and cognitive ability is demonstrated in
the report from Bazana and Stelmack (2002), where higher cognitive ability individuals
completed a cognitive task, more accurately and in less time than did lower ability individuals.
Mismatch negativity waves were elicited in a backward masking auditory oddball paradigm. The
inter-tone interval (ITI), the duration between the standard or deviant tone and the mask, was
manipulated. Shorter ITI were presumed to have a higher degree of difficulty, and therefore,
smaller mismatch negativity amplitudes and longer latencies would be reflected in the results, as
well as decreased accuracy among both high and low cognitive ability groups. Shorter response
times and mismatch negativity latencies were found. However, the manipulation of the ITI
displayed no significant effects for accuracy neither in discrimination, nor in the mismatch
MENTAL ABILITY & SENSORY DISCRIMINATION
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negativity amplitudes. Therefore, Bazana & Stelmack (2002) postulate the ITI did not interfere
with the memory trace of the paradigm, perhaps because the masking stimulus was compounded
with the standard or deviant tone, and thus perceived as a single unified tone. Shorter latencies,
however, were reflected for the mismatch negativity higher ability group. These findings support
the hypothesis of higher cognitive ability as a result of neural efficiency and speed (Beauchamp
& Stelmack, 2006).
ITI, frequency and type of the masking stimulus were manipulated in a subsequent report
(Beauchamp & Stelmack, 2006). Consistent with the findings of Bazana and Stelmack (2002),
shorter latencies were observed with shorter ITI. This contradicts the findings of Winkler and
Naatanen (1999), who reported shorter ITI would increase the degree of difficulty and decrease
accuracy in responses (as cited in Bazana and Stelmack, 2002). Shorter latencies of the mismatch
negativity among higher ability individuals found, and demonstrate sensory discrimination and
response speed is involved with cognitive abilities and intelligence. This is because shorter
response times for higher ability individuals are prevalent across all conditions of the
experimental paradigm (Beauchamp & Stelmack, 2006). The type of masking tone however does
effect the elicitation of the mismatch negativity. The mask tones used by Beauchamp and
Stelmack (2006) varied in degrees of difficulty and thus the elicitation of the mismatch
negativity varied accordingly. The use of white noise as a masking tone is suggested to have a
lower degree of difficulty because individuals are less likely to conceive this stimulus as
compounded with the preceding standard or deviant tone. Therefore, participants displayed
greater response accuracy and took less time to respond to the deviant to the target when the
mask type used was white noise. In the masking paradigm however, higher ability participants
MENTAL ABILITY & SENSORY DISCRIMINATION
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demonstrated shorter latencies, reflecting their ability to process the stimuli more efficiently than
lower-ability participants.
In a recent report of frequency and duration discrimination, Troche et al. (2010) observed
individuals of higher cognitive abilities displayed larger mismatch negativity amplitudes in an
auditory frequency discrimination task. Further, high cognitive abilities predicted larger
amplitudes in the mismatch negativity waveform across all conditions presented including
frequency, duration and near-threshold parameters. High cognitive abilities however, were not
indicative of discrimination abilities during the duration discrimination task. This may suggest
the mismatch negativity is reflective of the ability to access sensory memory rather than
discrimination ability as previous research has detailed (Troche et al., 2010). Because the
mismatch negativity amplitude did not predict sensory discrimination accuracy, it is suggested
the ability to access the memory trace of the auditory cortex that may be indicative of cognitive
ability. This suggests higher ability individuals have an easier time detecting change among the
parameters of the stimuli. The relationship between mental ability and sensory discrimination is
supported in the recent findings of Houlihan and Stelmack (2012). Positive correlations between
larger mismatch negativity amplitudes and mental ability scores were observed, in an auditory
paradigm following an abstract rule.
Mismatch negativity amplitudes are contested to be an index of sensory discrimination
abilities. Previous reports have demonstrated sensory discrimination is predictive of mental
abilities (Beauchamp & Stelmack, 2006; Troche et al., 2010; Houlihan & Stelmack, 2012). These
findings are consistent with the theory of neural oscillation, as individuals of higher mental
ability process information at a greater speed than those of low mental ability. It is expected
MENTAL ABILITY & SENSORY DISCRIMINATION
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larger mismatch negativity amplitudes and shorter latencies will be observed in high mental
ability individuals.
Method
Participants
Participants included female first year psychology, undergraduate students aged 18 to 24
(n= 47, m-18.6, SD=1.04). Data was eliminated from four participants due to poor signal to noise
ratios . Since larger amplitudes are observed in females (Ikezawa et al., 2008) males were
excluded from the sample. Only individuals with normal hearing were permitted to participate.
Individuals administrating centrally acting medication or with neurological disorders were
excluded from the study. The participants were required to abstain from consumption of alcohol
24 hours prior to their recording, and caffeine and nicotine one hour prior. Course credit and
monetary remuneration were awarded to the participants for their contribution. Ethics approval
was received from the St. Thomas Research Ethics Board.
Measures
The Adaptive Matrices Task test was designed using the Rasch dichotomous probabilistic
test model and the standard measurement error is set at .63 for form S3 corresponding to a
reliability of .87 (Hornke, Etzel, & Rettig, 2011) and provides the most precise estimate of
general intelligence. The test is computer based, and adapts to the abilities of the participant. The
format of this test is a 3 x 3 matrix, with a series of geometric patterns, where the bottom right
cell of the matrix is missing. Eight options are given, and the participant was required to select
the appropriate option that best fits the sequence presented. The test first presents a matrix of
medium difficulty, and the proceeding items are of either increasing or decreasing difficulty until
the participant gives an incorrect or correct response. The test stops once the participant answers
MENTAL ABILITY & SENSORY DISCRIMINATION
16
10 successive questions either incorrectly or correctly, although this is not probable. The test
will also stop if the participant has answered 30 matrices, which indicates the responses are not
consistent and therefore a reliable measure cannot be given.
Procedure
The participant attended two data collection sessions. Participants initially performed the
computer based Adaptive Matrices Task. This task was completed in a group setting. The
participants were told to take their time when selecting their response, and were advised to spend
at least one minute per question. Further, the participants were informed that if they responded
too quickly to the questions, their score would not provide a reliable measure of their abilities. If
this were the case, the participant would not be permitted to attend the second session.
The subsequent session was scheduled to occur 1 to 30 days following the cognitive
assessment. EEG data was recorded while the participant viewed a silent film with subtitles, and
was concurrently presented with a series of auditory stimuli. The participant selected one of the
following animated films to view: Toy Story 3, How to Train you Dragon, Monsters Inc., Shrek
2, Shrek 3, or Ice Age.
The participants were instructed to ignore the auditory stimuli during the recording. Six
sound sequences that followed an oddball sequence of standard and deviant sounds varying in
intensity, each of which consisted of 680 standard tones and 120 deviant tones, were presented to
each participant. In order to create a memory trace of the standard sound, the standard was
presented 20 times at the beginning of each sequence. This was to ensure the elicitation of the
mismatch negativity waveform, as this ERP component is measured at an attention independent
level. The standard white noise sounds in all sequences were threshold, 2x threshold and 3x
threshold in intensity, and 200 ms duration. This threshold was determined during a pilot study,
MENTAL ABILITY & SENSORY DISCRIMINATION
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with a 75dB standard. This was the minimal difference in intensity needed for the participant to
articulate and decide which sound from a selection was loudest. The inter-tone-interval was 600
ms from onset to onset, with a10 ms rise and fall time.
Electrophysiological recording
The EEG was recorded using the EasyCap electrode cap using 32 Ag/AgCl electrodes,
referenced to the nose, and AFz as the ground. Filter settings included Neuroscan NuAmp 0.5 to
100 Hz, with a sampling rate of 500Hz.
In an offline analysis, vertical EOG was measured from one electrode placed below the
right eye and FP2. Horizontal EOG was measured from F7 and F8. EOG was corrected using a
regression-based algorithm provided by NeuroScan. The mismatch negativity wave was derived
from the subtraction of the standard from the deviant ERP waveforms. Amplitude and latency
was quantified as the point of maximal amplitude between 145 and 250 ms following onset. The
data was visually reviewed for artifacts, filtered from 1 and 15Hz, and epoch averages were
computed separately for each condition. ERP epochs were created between-50-400ms. Separate
averages were calculated for standard and deviant tones in each condition, the mismatch
negativity calculated as difference between standard and rare.
Results
The relationship between auditory intensity and mismatch amplitude and latency were
evaluated using a mixed measures analysis of variance with a repeated measures factor of
electrode locus and a between subjects factor of IQ group. High and low IQ groups were created
based on median split of the Advanced Matrices test scores. No significant results were found at
the level of p< .05 [F(3, 123) = 1.906, p >.05, = .720] for latency. Null effects were observed
for the between subjects effects for IQ [F (1, 41) > 1, NS]. The interaction of locus and IQ group
MENTAL ABILITY & SENSORY DISCRIMINATION
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yielded no interaction [F (3, 123) = 1.974, p >.05, = .720]. P values were adjusted using
Greenhouse Geisser corrections for the factor of locus.
The relationships between cognitive ability and mismatch negativity amplitude, and
cognitive ability and mismatch negativity latency, were calculated using Pearson’s r correlations.
No correlations yielded significant results at p < .05 level (see table 1).
Table 1
Amplitude
r
Mean
Latency
SD
r
Mean
SD
FZ
.183
-.60
.632
.125
202.65
29.574
FC1
.086
-.67
.763
.125
201.21
28.838
FC2
.237
-.69
.767
.161
201.77
30.134
CZ
.164
-.67
.891
.138
200.79
29.462
Figure 1 Grand Average
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Figure 1. The grand mismatch negativity averages for all conditions of threshold, twice threshold
and thrice threshold.
Discussion
The present study examined the observed relationship between mental ability and
auditory sensory discrimination, manipulating the intensity of auditory stimuli. Previous reports
from Troche et al. (2010) and Houlihan & Stelmack (2012) found larger mismatch negativity
amplitudes elicited during frequency discrimination and paradigms following an abstract
deviance rule significantly correlated with cognitive ability scores. Thus the current study
predicted individuals of high mental ability would demonstrate superior sensory discrimination
facility, and consequently larger mismatch negativity amplitudes would be elicited. The findings
however, did not support the hypothesis in the present analysis of auditory intensity
manipulation, mismatch characteristics, and mental ability.
There are several explanations for the inability to find a relation between mental ability
and sensory discrimination. One possible conclusion from the lack of significant correlations
with mental ability is that there is no relation between the processes represented by the mismatch
negativity and intelligence. Previous research indicates frequency deviance in an oddball
patterns, and sequences following an abstract rule elicit larger mismatch negativity amplitudes
among higher cognitive ability individuals (Troche et al., 2010; Houlihan & Stelmack, 20120).
No interaction effect, however, was found between mental ability and mismatch negativity
amplitude or latency. Further, the correlations of mental ability scores and mismatch negativity
amplitude scores yielded no significant findings. Therefore, future research is required to
confirm whether intensity discrimination abilities may accurately predict mental abilities.
MENTAL ABILITY & SENSORY DISCRIMINATION
20
The observed null effects may infer the parameters of discrimination abilities do not
extend to intensity manipulation. The present mismatch negativity amplitude results, however, is
inconsistent with previous findings from Schroger et al. (1996), Pakarinen et al. (2007), and
Houlihan & Stelmack (2012), all of who reported significant results of larger mismatch
negativity amplitudes through intensity manipulation, in oddball, optimal and abstract
paradigms. This supports mismatch negativity amplitude as an reliable index of discrimination
abilities at an attention-independent level.
The mismatch negativity amplitudes observed in the present study are considerably
smaller (less negative) relative to previous research (Troche et al., 2010). This lends evidence
that the intensity deviance used did not elicit the intended mismatch negativity amplitudes. The
intensity thresholds that were used in the study were calculated from pilot data that used a
different computer and sound system. The differences in the two computer sound systems led to
overall differences in intensity and therefore the intended discriminability of the stimuli may not
have been achieved on the new computer. As the differences between the standard and deviant
stimuli increase, that is—unconscious deviance detection increases in facility, larger mismatch
negativity amplitudes are elicited (Naatanen et al., 2001). As the mismatch negativity amplitudes
were uncommonly small, and this was observed in both high and low mental ability participants,
suggests the intensity paradigm was entirely too difficult. Due to the hardware difficulties, the
threshold levels presented to the participant were not as indicated, the differences were much
smaller. Therefore, the null correlations reported may be caused by the poor, and unreliable
elicitation of the mismatch negativity. Moreover, because the amplitudes of the mismatch
negativity were so small, and therefore did not provide adequate measures of sensory
discrimination, it is plausible intensity discrimination is predictive of mental abilities. Figure 2
MENTAL ABILITY & SENSORY DISCRIMINATION
21
provides an illustration of the current mismatch negativity amplitudes relative to the amplitudes
observed in Troche et al. (2010) that yielded significant results. Future research is needed to
confirm this hypothesis.
Using ERP data to examine and predict mental abilities has proven beneficial and
valuable to many facets in the discipline of psychology. The mismatch negativity allocates
mental abilities to be measured without active attention, in a field dominated by intelligence
batteries all of which require participant attention to complete. Although no support for the
hypothesis of relation between mismatch negativity amplitude and intelligence was found, this
may be due to the discriminability of the standard and deviant stimuli used rather than the lack of
relation between the mismatch negativity and intelligence. Therefore, the current findings
promote future research, replicating this paradigm with more appropriate stimuli, to examine the
parameters of intelligence testing at an attention independent level.
Figure 2 Mismatch Negativity Amplitudes
Figure 2. Mismatch negativity amplitudes from current study and from previous findings of
Troche et al. (2010), which yielded significant correlations with mental ability. The amplitudes
MENTAL ABILITY & SENSORY DISCRIMINATION
22
from the current study are considerably smaller than previous findings, which may be due to the
discriminability of standard and deviant stimuli used.
MENTAL ABILITY & SENSORY DISCRIMINATION
23
References
Bazana, P. G., & Stelmack, R. M. (2002). Intelligence and information processing during an
auditory discrimination task with backward masking: An even-related potential analysis.
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