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 1
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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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INTELLIGENCE AND AUDITORY DISCRIMINATION
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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INTELLIGENCE AND AUDITORY DISCRIMINATION
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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INTELLIGENCE AND AUDITORY DISCRIMINATION
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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INTELLIGENCE AND AUDITORY DISCRIMINATION
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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INTELLIGENCE AND AUDITORY DISCRIMINATION
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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INTELLIGENCE AND AUDITORY DISCRIMINATION
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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INTELLIGENCE AND AUDITORY DISCRIMINATION
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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INTELLIGENCE AND AUDITORY DISCRIMINATION
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
INTELLIGENCE AND AUDITORY DISCRIMINATION
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.
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INTELLIGENCE AND AUDITORY DISCRIMINATION
Figure 2. The evident elicitation of MMN in the louder than threshold conditions for the electrode FZ.
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INTELLIGENCE AND AUDITORY DISCRIMINATION
Figure 3. The evident elicitation of the MMN in the softer than threshold conditions for electrode FZ.
References
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INTELLIGENCE AND AUDITORY DISCRIMINATION
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 event-
related potential analysis of an auditory oddball discrimination task. Intelligence, 34, 571-
586.
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
27
INTELLIGENCE AND AUDITORY DISCRIMINATION
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.).
MacMillan.
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.
28
INTELLIGENCE AND AUDITORY DISCRIMINATION
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 Stanford-