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 INTELLIGENCE AND AUDITORY DISCRIMINATION 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 2 INTELLIGENCE AND AUDITORY DISCRIMINATION 3 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. INTELLIGENCE AND AUDITORY DISCRIMINATION 4 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. INTELLIGENCE AND AUDITORY DISCRIMINATION 5 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 INTELLIGENCE AND AUDITORY DISCRIMINATION 6 testing system that allowed for a distinction to be made between children with varying degrees of intellectual difficulties. Building on this original concept of testing, the Stanford-Binet Intelligence Test was produced, relying on similar measures to score an individuals intelligence quotient. The test itself was the first to include the notion of IQ (Laurent, Swerdlik, & Ryburn, 1992). Several revisions have been made to the Stanford-Binet test and many other tests measuring intelligence have been developed in this time. In his extensive research into the nature of intelligence Spearman found that differentiating cognitive tests had positive correlations with one another and referred to this a ‘positive manifold effect’ (Spearman, 1904). Spearman accounted for this phenomenon using the concept of general intelligence or Spearman’s g, the underlying mental ability that determines how an individual will perform on different tests of mental ability. General intelligence is a broad aspect of intelligence, as it can be measured using many forms of intelligence testing. Therefore, by examining one factor of intelligence one is able to determine an individual’s general intelligence with significant accuracy. This construct of general intelligence that could be measured using a single and varying factor of intelligence is referred to as Spearman’s g. Cattell (1963) theorized that Spearman’s g was not a singular factor, but was broken down into two separate factors that accounted for diverse mental abilities, which are referred to as fluid and crystallized general ability. Crystallized ability are those abilities that have been previously learned from prior use and therefore, ‘crystallized’ as a result. This factor is relative to the information that is taught and therefore reflects diversity of cultural influences on learning (Cattell, 1963). Fluid ability pertains to reasoning skills and the ability to adapt to novel situations (Cattell, 1963; Gray, Chabris, & Braver, 2003). Fluid ability is the factor of INTELLIGENCE AND AUDITORY DISCRIMINATION 7 intelligence that is most related to natural influences. Therefore, the influence of heredity or injury sustained to the central nervous system is reflected in fluid ability (Horn & Cattell, 1966). Carroll (1993) expanded upon the general fluid (Gc) and general crystallized (Gc) abilities and introduced the three stratum theory, which includes both Spearmans g along with Gf and Gc abilities. These abilities are organized in relation to their higher order processing, placing the highest order factor, g, on the top strata. Further, the Cattell-Horn-Carroll (C-H-C) model is a hierarchical model upon which these abilities are placed. The C-H-C model consists of three strata, which encompass Horn & Catell’s (1966) general Gf and Gc as well as Carroll’s threestratum model (1993). In the C-H-C model proposed by Carroll, three layers or ‘stratum’ encompass the range and variability in individual differences of cognitive abilities. At the top of this stratum Spearman’s general intelligence represents the most broad and widespread aspect of intelligence (Carroll, 1993; 1997). Carroll (1993) examined 70 first order abilities and found the correlations that existed between them. From the correlations found between these first order factors, Carroll eight second-order factors. The second stratum holds the eight broad variables of cognitive ability that are considered narrower than that of general intelligence. These eight include the aforementioned variables of crystallized and fluid intelligence as well as general memory and learning, broad visual perception, broad auditory perception, broad retrieval ability, broad cognitive speed, and processing speed (Carroll 1993; 1997). Finally, the third stratum consists of seventy still narrower abilities including auditory attention, decision-making speed, and concept formation (Carroll, 1997). One of the crucial findings that came out of the extensive examination of intelligence and the tests thereof is that of the relationship between general intelligence and discrimination ability. Galton’s original proposal of the predictive nature of sensory discrimination has been confirmed INTELLIGENCE AND AUDITORY DISCRIMINATION 8 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, INTELLIGENCE AND AUDITORY DISCRIMINATION 9 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 INTELLIGENCE AND AUDITORY DISCRIMINATION 10 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 INTELLIGENCE AND AUDITORY DISCRIMINATION 11 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 INTELLIGENCE AND AUDITORY DISCRIMINATION 12 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). INTELLIGENCE AND AUDITORY DISCRIMINATION 13 MMN and Mental Ability The established relationship between discrimination abilities and intelligence has led to the use of the MMN to explore this relationship at a neurological level. Bazana and Stelmack (2002) found that individuals with higher mental abilities have shorter MMN latencies. The study included the presentation of an oddball paradigm with backward masking. The process of backward masking involves limiting the amount of time for the acquisition of information from a series of stimuli (Bazana & Stelmack, 2002). The stimulus is presented for a short time and is then followed by the masking stimulus, which is generally random in nature. Individuals with higher mental ability tended to display larger amplitude MMNs in the higher intensity conditions. However, this relationship was only observed in the highest intensity condition and was not evident throughout any other conditions (Bazana & Stelmack, 2002). Individuals with higher mental ability had shorter peak latencies than those of participants scoring lower in mental ability during conditions where attention was not directed towards the stimuli (Bazana & Stelmack, 2002). This indicates that the varying speed in information processing may be related to variances in mental ability, and therefore provides support for the neural efficiency hypothesis. Beauchamp and Stelmack (2006) conducted a study in which mask type and inter-tonal interval (ITI) were manipulated and individuals were exposed to the stimuli in both active and passive conditions. ITI is described as the interval of time between the presentation of deviant stimulus and the presentation of the mask (Beauchamp & Stelmack, 2006). Individuals with high mental ability displayed larger amplitudes when compared with that of individuals with that of a lower intelligence score (Beauchamp & Stelmack, 2006). However, these findings only extended to one condition. These individuals also displayed faster reaction time speed and greater accuracy with their responses. Further research into the relationship between MMN and INTELLIGENCE AND AUDITORY DISCRIMINATION 14 mental ability has been conducted, most often using the oddball paradigm to elicit the MMN while altering the physical characteristics of the stimuli for each study. The relationship between mental ability and MMN was recently examined using stimuli that differed in duration and frequency. Troche, Houlihan, Stelmack, and Rammsayer (2010) reported individuals with higher mental ability showed larger amplitudes for frequency but not duration MMNs. This particular study did not observe the same relationship between MMN latency and mental ability as previous studies. The lack of relationship between MMN latency and mental ability is explained through the lack of using auditory masks. In Bazana and Stelmack’s study, (2002) the MMN latency decreased as the intervals between stimuli and auditory masks decreased. The authors concluded that this may have been caused by a speedoriented processing of information that was not required in the Troche et al study (2009). A more recent study (Troche et al., 2010) resulted in similar findings. Larger MMN amplitudes were associated with higher mental abilities when the stimuli differed in frequency. These results indicate that the ability to access sensory memory, where the process of stimuli comparison occurs, is greater in individuals with higher mental ability (Troche et al., 2010). More recently, the relationship between intelligence and the MMN was explored by Houlihan and Stelmack (2012). Using stimuli that followed the rule of the higher the frequency, the lower the intensity, findings indicated further support of the MMN amplitude and intelligence relationship. These results indicate the greater facilitation of discrimination for individuals with higher intelligence levels and thus supports the higher intelligence, greater sensory discrimination hypothesis. Further findings from the study showed an insignificant relationship between intelligence and MMN latency. There have been many findings of the relationship between MMN amplitude and higher INTELLIGENCE AND AUDITORY DISCRIMINATION 15 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 INTELLIGENCE AND AUDITORY DISCRIMINATION 16 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 INTELLIGENCE AND AUDITORY DISCRIMINATION 17 participants, each one differing in deviance intensity, while the standard tone remains the same throughout. The standard and deviant tones followed an oddball paradigm and differed by a variation of 5 to 15 dB depending on which set is presented. Each set of tones consists of 20% deviant tones and 80% standard tones, as consistent with the general presentation of an oddball paradigm to elicit an MMN waveform. The standard intensity of the stimuli was 5dB, with the deviant stimuli ranging at 5, 10, or 15 dB above or below the standard. The standard stimuli were presented 20 times in succession at the beginning of each sequence. These 20 standard, successional tones were not included in the final averaging of responses to the tones. Each of the tones were presented for 200ms. The inter-tone-interval was 600ms from onset to onset, with 10ms as the rise and fall time. EEG Recording The EEG data was recorded using the EasyCap electrode cap using 32Ag/AgCl active electrodes, using the nose as a reference and AFz as the ground. Vertical EOG was measured using an electrode placed under the right eye and FP2. The mismatch negativity wave was derived by subtracting the standard waveforms from the deviant waveforms. Filter settings included Neuroscan NuAmp 0.5 to 100 Hz, with a sampling rate of 500Hz. Amplitude and latency were measured between 140-200ms after the onset of stimulus. The data was visually reviewed for artifacts, filtered between1 and 15Hz. Separate averages were calculated for each deviant intensity condition, from 60-95 dB. The ERP averages were calculated for the electrodes FP1, FP2, FZ, and CZ. Separate averages for the standard and deviant stimuli were calculated, with the MMN obtained by calculating the difference between the standard average. Active Tasks Once individuals completed the passive task of listening to the binaurally presented tones, INTELLIGENCE AND AUDITORY DISCRIMINATION 18 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 INTELLIGENCE AND AUDITORY DISCRIMINATION 19 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 INTELLIGENCE AND AUDITORY DISCRIMINATION 20 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. INTELLIGENCE AND AUDITORY DISCRIMINATION 21 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 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. 22 INTELLIGENCE AND AUDITORY DISCRIMINATION 23 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 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 Table 2. Indicates the correlations as well as the means for IQ and MMN latency in all conditions. 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. 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