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1 CHAPTER 3 ERP Components: Th e Ups and Downs 2 1 4 of Brainwave Recordings

5 Emily S. Kappenman and Steven J. Luck

6 Abstract

7 This chapter provides a framework for understanding, interpreting, and using event-related potential 8 (ERP) components in the broad domain of mind, brain, and behavior sciences. The fi rst section defi nes 9 the term ERP component , describing the neural events that give rise to ERP components and explaining 10 how multiple components sum together to form the observed ERP waveform. The next section 11 describes the problems involved in isolating individual ERP components from the observed waveform, 12 which is often much more diffi cult than researchers realize. This is followed by a discussion of the 13 challenges involved in linking an ERP component with a specifi c neural or psychological process 14 and then using this link to answer broader questions about the mind and brain. The chapter concludes 15 with a discussion of what types of questions are most easily answered with ERPs and the approaches 16 that have proven effective in overcoming the challenges of the technique.

17 Keywords : event-related potential, ERP component , peaks , waves , reverse inference

18 Th e goal of this chapter is to provide a framework Th e fi rst section of the chapter is aimed at defi n- 38 19 for understanding, interpreting, and using event- ing the term ERP component, describing the neural 39 20 related potential (ERP) components in the broad events that give rise to ERP components and explain- 40 21 domain of mind, brain, and behavior sciences. ing how multiple components sum together to 41 22 Researchers in other areas such as political science, form the observed ERP waveform. Th e next section 42 23 economics, law, and medicine may also fi nd this describes the problems involved in isolating indi- 43 24 overview useful as a guide to a broad understanding vidual ERP components from the observed wave- 44 25 of ERP components. Event-related potentials have form, which is often much more diffi cult than 45 26 been used for decades to uncover aspects of the sen- researchers realize. Th is is followed by a discussion of 46 27 sory, cognitive, and motor processes that underlie the challenges involved in linking an ERP compo- 47 28 human thought and behavior. Th e excellent tempo- nent with a specifi c neural or psychological process 48 29 ral resolution of the technique provides a narration and then using this link to answer broader questions 49 30 of neural processes as they unfold millisecond by about the mind and brain. Th ese challenges may 50 31 millisecond, adding whole pages to the story of the seem insurmountable, but researchers have devel- 51 32 mind that behavioral and imaging techniques leave oped experimental and analytic approaches that can 52 33 blank. However, the ERP technique is not without overcome them in many cases. Th e key to using 53 34 limitations. As refl ected in the title of this chapter, ERPs eff ectively is to understand what questions can 54 35 there are both advantages and limitations of the be answered by ERP experiments and how the limi- 55 36 ERP technique, and we will explore both the ups tations of the technique can be avoided. Indeed, 56 37 and the downs of ERPs in this chapter. despite its limitations, the ERP technique is often 57

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1 the best one for answering certain types of questions. waveform as a depiction of the changes in scalp- 53 2 Th e chapter therefore ends with a discussion of what recorded voltage over time that refl ect the sensory, cogni- 54 3 types of questions are most easily answered with tive, aff ective, and motor processes elicited by a stimulus . 55 4 ERPs and the approaches that have proven eff ective We can defi ne an ERP peak as a reliable local positive 56 5 in overcoming the challenges of the technique. or negative maximum in the observed ERP waveform 57 6 Although a number of the issues we address are (the term reliable allows us to disregard local maxima 58 7 discussed elsewhere in the literature (e.g., see Luck, that result from high-frequency noise). 59 8 2005 ), this chapter provides a comprehensive and Th e term ERP component is more challenging to 60 9 concise overview of the nature and use of ERP com- defi ne. Th is term gets bandied about in the litera- 61 10 ponents from a vantage point that is readily accessi- ture very frequently, but it is rarely defi ned or con- 62 11 ble to researchers from a wide range of backgrounds. ceptualized beyond the peaks in the observed ERP 63 12 Readers who have no familiarity at all with the ERP waveform. In some sense, the term ERP component 64 13 technique may wish to fi rst read the more basic is analogous to the concept of attention: Just 65 14 introduction provided by Luck (in press). as “everyone knows what attention is” (James, 1890 , 66 p. 381), everyone knows what an ERP component 67 15 Th e Nature of ERP Components is (at least everyone in the ERP world). Moreover, 68 16 What Is an ERP Component? despite the fact that attention researchers all believe 69 17 Th e ERP waveform appears on the scalp as a series they know what attention is, they vary substantially 70 18 of positive and negative peaks1 that vary in polarity, in how they use the term attention (Luck & Vecera, 71 19 amplitude, and duration as the waveform unfolds 2002 ), and ERP researchers similarly vary in how 72 20 over time. However, the actual waveform is continu- they use the term component . Th erefore, just as it 73 21 ous, with no sudden transitions between one peak is diffi cult to elicit agreement on the term attention 74 22 and the next, and division of the ERP waveform in a room full of attention experts, it is no easy task 75 23 into discrete peaks is somewhat arbitrary. Indeed, to fi nd a simple, concise, and widely accepted defi - 76 24 this peak-centered view of the ERP waveform may nition of the term ERP component . Furthermore, 77 25 refl ect an intrinsic predisposition of the human there is an important distinction between how these 78 26 visual system to use minima of curvature (places terms have evolved: although attention researchers 79 27 where orientation reverses direction) to defi ne the frequently debate the fundamental nature of atten- 80 28 parts of complex real-world objects (Hoff man & tion, ERP researchers rarely discuss the nature of 81 29 Richards, 1984 ). Although the peaks are visually ERP components. 82 30 salient, there is no a priori reason to believe that Th ere are, of course, counterexamples to this 83 31 each peak refl ects a specifi c brain process. However, sweeping generalization about the nature of ERP 84 32 early ERP researchers tended to make this assump- components. For example, Manny Donchin has 85 33 tion, and this has had a major infl uence on the ter- written extensively and explicitly throughout his 86 34 minology and analytical techniques used in ERP career about ERP components and their existence 87 35 research. Sophisticated ERP researchers have recog- beyond the peaks in the observed ERP waveform 88 36 nized for decades that the peaks are somewhat arbi- (e.g., see Donchin & Heffl ey, 1978 ). More recently, 89 37 trary, and they make a distinction between peaks Luck ( 2005 ) provided a comprehensive discussion 90 38 (local voltage maxima) and components (discrete of the distinction between components and peaks. 91 39 intracranial sources of voltage that refl ect specifi c Th e concept of a component has also been discussed 92 40 neurocognitive processes, defi ned further below). in the context of mathematical techniques for isolat- 93 41 Nonetheless, it is still common for researchers to ing components, such as principal component anal- 94 42 assume that a peak in the observed ERP waveform is ysis (Donchin & Heffl ey, 1978 ) and independent 95 43 equal (or approximately equal) to an underlying component analysis (see Chapter 3, this volume). 96 44 ERP component. Perhaps the most important goal However, this important issue is often ignored in the 97 45 of this chapter will be to encourage readers to look ERP literature and warrants continued discussion. 98 46 beyond the visually salient peaks to the underlying In a general sense, we can defi ne the term ERP 99 47 components; it is the underlying components rather component as a scalp-recorded voltage change that 100 48 than the peaks that directly refl ect the neural and refl ects a specifi c neural or psychological process . 101 49 psychological processes we wish to study. Although most researchers understand and use 102 50 To clarify the relationships among peaks and words such as refl ect and process, such terms them- 103 51 components, it is important to begin with some selves refer to loose concepts without clear defi ni- 104 52 clear defi nitions. We can defi ne the observed ERP tions. Consequently, it will be necessary to fi ll out 105

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1 the details of this defi nition over the course of this can be considered an electrical dipole , with positive 53 2 chapter. However, this concise defi nition does pro- on one end and negative on the other end. When 54 3 vide a reasonable approximation of the way the term PSPs occur simultaneously in many neurons that are 55 4 ERP component is usually used by ERP researchers. spatially aligned, such that their dipoles point in the 56 5 We will illustrate the relationship between the ERP same direction, the dipoles sum together to form 57 6 waveform and the underlying ERP components in a large dipole known as an equivalent current dipole . 58 7 the following sections, fi rst discussing the neural If a suffi ciently large number of spatially aligned 59 8 events that give rise to the observed ERP waveform neurons are simultaneously active, the equivalent 60 9 and the process of isolating the ERP waveform from current dipole is large enough to be reliably recorded 61 10 other electrical activity. We will then illustrate the on the surface of the scalp. Th is requires the simul- 62 11 diff erences between the peaks in the ERP waveform taneous activation of thousands of neurons, due in 63 12 and the underlying ERP components through the part to the many layers of tissue that separate the 64 13 use of simulated waveforms. scalp electrodes from the neurons. Th is is most likely 65 to occur in groups of pyramidal cells in cerebral 66 14 Where Do ERP Components Come From? cortex, which are lined up together perpendicularly 67 15 Event-related potentials are voltage fl uctuations in with respect to the cortical surface and are often 68 16 the ongoing electroencephalogram (EEG) that are active in unison. In other words, ERPs are almost 69 17 time-locked to an event, such as the onset of a stim- always the result of PSPs in large groups of cortical 70 18 ulus or the execution of a manual response. Electro- pyramidal cells. It should be noted that, except in 71 19 encephalographic research began long before a few unusual cases, scalp ERPs do not refl ect action 72 20 laboratory computers were available, and early potentials. Th us, ERPs represent the inputs to a 73 21 researchers were able to observe only large ERPs that group of neurons rather than the outputs of those 74 22 were visible on single trials (Davis, 1939 ) prior to neurons. Also, due to the necessity for such large 75 23 the advent of computer averaging in the early 1960s numbers of spatially aligned neurons to be simulta- 76 24 (Galambos & Sheatz, 1962 ). However, most ERPs neously active in scalp recordings, much of the 77 25 are rather small in comparison with the ongoing neural activity in the brain that gives rise to cogni- 78 26 EEG activity and usually become visible only when tion and behavior is not visible to an electrode 79 27 multiple EEG epochs are combined together to form placed on the scalp. 2 80 28 an average ERP waveform. Th is averaging process For a given equivalent current dipole or neural 81 29 proved extremely benefi cial to the fi eld of ERPs and generator source, the specifi c distribution of posi- 82 30 was the fi rst occurrence in which signal averaging tive and negative voltages recorded on the scalp is 83 31 “revealed the existence of novel, previously unknown, determined by the position of the dipole in the head 84 32 neural processes” (Donchin et al., 1978 , p. 349). and its orientation with respect to the scalp (although 85 33 To understand the intricate mixture of signals we it should be noted that the choice of reference elec- 86 34 record on the surface of the scalp, we must fi rst trode can also play a factor in the voltage distribu- 87 35 understand where and how these signals arise neu- tion; see Luck, 2005 , chap. 3). In other words, each 88 36 rally. Although it is diffi cult to know with certainty equivalent current dipole will produce both positive 89 37 how scalp-recorded voltage changes originate at the and negative voltages on the head, with a band 90 38 neural level, the following represents the best esti- of zero separating the positive and negative voltage 91 39 mate based on our understanding of both biophys- halves. Th is voltage reversal on the opposite side 92 40 ics and the properties of neural communication. of the equivalent current dipole is often not very 93 41 Th e changes in scalp-recorded voltage that give noticeable, because electrodes are not generally 94 42 rise to the ERP waveform refl ect the summation of placed over the entire head, but the reversal is easily 95 43 postsynaptic potentials (PSPs) that occur simultane- observed for some components (such as the N170; 96 44 ously in large numbers of cortical pyramidal cells see Chapter 5, this volume). Th e positive or nega- 97 45 that are orientated in a similar manner with respect tive polarity of an ERP component at a given elec- 98 46 to the scalp (see Luck, 2005 , chap. 1). Th ese PSPs trode site is related to several factors, including the 99 47 are a result of changes in electrical potential that orientation of the equivalent current dipole with 100 48 occur when ion channels open or close in response respect to the electrode, and it is not usually possible 101 49 to neurotransmitters binding with receptors on the to link the polarity to the type of neural processing 102 50 postsynaptic cell membrane, which leads to the fl ow (such as inhibition versus excitation). For a more 103 51 of ions into or out of the cell. When a PSP occurs at detailed discussion of the factors that aff ect the 104 52 one end of a cortical pyramidal neuron, the result polarity of an ERP, see Luck ( 2005 , chap. 1). 105

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1 Because electrical potential travels close to the do we determine whether the same mental process 53 2 speed of light, the transmission through the brain, is occurring in two individual neurons, and on a 54 3 meninges, skull, and scalp is essentially instanta- larger scale, in groups of neurons? Th is is a complex 55 4 neous. In other words, the voltages measured on the issue that remains to be resolved by future research. 56 5 scalp at a particular time refl ect synaptic activity at Th e combination of multiple ERP components 57 6 that particular instant, with no measurable delay. on the scalp leads to the superposition problem , which 58 7 Th us, ERPs provide a direct and instantaneous mil- is depicted in Figure 1.1 . When multiple ERP com- 59 8 lisecond-resolution measure of activity related to ponents are simultaneously active, the recorded 60 9 neurotransmission. voltage at the scalp is based on the sum of the volt- 61 ages from all the individual components. Th is is a 62 10 Summation of Components in the simple additive process. Th at is, if you knew the 63 11 Observed ERP Waveform true waveform for each individual component, you 64 12 It is important to note that although the ERP wave- could add all the component waveforms together to 65 13 form at a particular instant refl ects synaptic activity get the ERP waveform at each electrode site (scaling 66 14 at that moment, it does not refl ect only the neural each component by a weighting factor that refl ects 67 15 activity that began at that particular instant. the contribution of the component to the voltage 68 16 Specifi cally, the PSPs that give rise to ERPs last on measured at a specifi c electrode site). Unfortunately, 69 17 the order of tens or even hundreds of milliseconds.3 the true waveform for each component is not known 70 18 Th erefore, as new mental processes are unfolding, in real recordings, and it is quite diffi cult to reduce 71 19 the previous neural activations persist. In other the sum of the components in the observed data to 72 20 words, multiple groups of neurons are active simul- the individual components. However, understand- 73 21 taneously in diff erent regions in the brain. If we ing with simulated data how the voltage recorded at 74 22 think of this neural activity in terms of dipoles, this a particular electrode site refl ects the various inter- 75 23 means that multiple equivalent current dipoles are nal generator sources can help us understand the 76 24 active simultaneously. In fact, source localization properties and intricacies of the ERP signals. 77 25 studies have shown that as many as 10 separate Th e propagation of voltage from a single genera- 78 26 equivalent current dipoles may be active at a given tor site to a particular electrode site depends on the 79 27 time (Di Russo et al., 2002 ; Picton et al., 1999 ). position and orientation of the ERP generator 80 28 If we return to our conception of ERP components, source with respect to the electrode, along with the 81 29 in which we defi ne an ERP component as a signa- conductance of the brain, skull, and scalp. Th is can 82 30 ture of an individual neural process, each equivalent be quantifi ed with a weighting factor: Th e contribu- 83 31 current dipole is essentially a separate ERP compo- tion of a given generator to the voltage recorded 84 32 nent. In other words, when we say that multiple from a given electrode site is simply the waveform at 85 33 equivalent current dipoles are active simultaneously, the generator multiplied by the weighting factor 86 34 this really means that multiple ERP components are (see Figure 1.1 ). Th ere will be a separate weighting 87 35 generated simultaneously. factor specifying the relationship between each elec- 88 36 In some cases, neurons engaged in one mental trode site and each internal neural generator source. 89 37 process may be distributed in diff erent areas of the Together, the set of weighting values between each 90 38 brain, such as the simultaneous processing of a source and each electrode site provides a mixing 91 39 single auditory signal in both the left and right tem- matrix that defi nes how the diff erent components 92 40 poral lobes. Th is would essentially lead to two mix together at each site. Some mathematical tech- 93 41 equivalent current dipoles. Should we consider these niques for recovering the underlying components 94 42 two dipoles as two separate ERP components or as work by computing an unmixing matrix that reverses 95 43 a single ERP component? Th ey are typically treated this process, passing the observed data through the 96 44 as parts of a single component under the assump- unmixing matrix to compute the component wave- 97 45 tion that both hemispheres are engaging in essen- forms (see Chapter 3, this volume). 98 46 tially the same mental process. However, this is a When multiple ERP components are simultane- 99 47 fi ne detail of the defi nition of an ERP component, ously generated in diff erent brain areas, the voltages 100 48 with little practical signifi cance for the use of ERP from these components sum together. Th e voltage 101 49 components. Furthermore, resolution of this issue recorded at each site will therefore be the sum of each 102 50 would require a precise defi nition of what is meant of the internally generated ERP components, with 103 51 by mental process in terms of the behavior of neu- each scaled by the weight between that electrode 104 52 rons, both individually and as a group. Th at is, how site and each of the generator locations. Th e value at 105

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Fig. 1.1. Relation between the underlying component waveforms and the observed scalp waveforms. In this example, three components are present (C1, C2, C3), each of which has a waveform (shown at the bottom left) and a generator location (represented by the arrows in the head). Th e contribution of each component waveform to the observed waveform at a given electrode site is determined by a weighting factor that refl ects the location and orientation of the generator relative to that electrode, along with the conductivity of the tissues that form the head. Th e observed waveform at a given electrode site (shown at the bottom right) is equal to the sum of each of the component waveforms, multiplied by the weighting factor between each component and that electrode site. Th e weights are indicated by the w ’s on the arrows between the component waveforms and the observed

waveforms (e.g., w 2,3 represents the weighting factor between component 2 and electrode 3).

1 a given electrode site at a particular moment in time and the high resistance of the skull relative to the 20 2 is equivalent to the magnitude of each component at low resistance of the underlying brain and overlying 21 3 that time, scaled by the appropriate weighting factor scalp causes the voltage to spread laterally as it trav- 22 4 and then summed together. Consequently, the ERP els. Th e signals are therefore blurred together by the 23 5 waveform at each electrode site contains information head, which further distorts the relationship between 24 6 about all of the neural generators in the brain, not the voltage at a particular electrode site and the 25 7 just the generator sources located close to the elec- cortex directly under that site. 26 8 trode (although nearby sources will usually have a Of course, anyone who has seen the ERP wave- 27 9 greater weight). forms from multiple electrode sites knows that dif- 28 10 Th e inability to relate the ERP waveform at a ferences exist in the shape and size of the ERP 29 11 particular electrode site to the neural tissue directly waveform across electrode sites. In other words, 30 12 below the electrode site is made even more severe by although the waveform at each electrode site refl ects 31 13 the properties of the head. Specifi cally, as electrical neural signals from all over the brain, the summated 32 14 activity travels from the brain to the surface of the signals are not identical at each site. It is tempting to 33 15 scalp, the activity must pass through layers of skull use the scalp distribution information to estimate 34 16 and scalp. Although these constituents of the head the location of the neural generator source by, for 35 17 are suffi ciently conductive to allow the electrical example, determining at which electrode site the 36 18 activity generated in the brain to appear on the sur- signal is largest. However, the superposition of mul- 37 19 face of the head, they are not perfect conductors, tiple components and the blurring of the voltages 38

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1 across the head make it impossible to determine the rather than passing the component waveforms 53 2 locations of the generator sources solely from the through the weights shown in Figure 1.1 to obtain 54 3 observed waveforms. In fact, an infi nite number of the observed waveforms at each electrode (moving 55 4 internal generator confi gurations could produce any from left to right in the fi gure), these techniques pass 56 5 observed distribution of ERP activity over the scalp the waveforms observed at each electrode site through 57 6 (see Luck, 2005 , chap. 7). Th us, there is no tech- an unmixing matrix to obtain the component wave- 58 7 nique that can determine, with certainty, the loca- forms (moving from right to left). Unfortunately, 59 8 tions of the sources and the waveform at each source there is no unique solution to the problem of deter- 60 9 without bringing in diffi cult-to-verify assumptions mining the underlying component waveforms from 61 10 or other sources of evidence. the observed scalp waveforms, and these three tech- 62 11 To summarize, the ERP waveform refl ects ongo- niques use diff erent assumptions to pick a single 63 12 ing synaptic activity related to mental processing solution to this problem (without any guarantee 64 13 as it unfolds millisecond by millisecond. However, that the correct solution will be found). 65 14 because scalp-recorded signals require the simulta- In source localization techniques, a component 66 15 neous activation of large groups of spatially similar is equivalent to a neural generator source. Th ese 67 16 oriented neurons, only a portion of the neural activ- techniques use biophysical assumptions about the 68 17 ity that occurs in response to a stimulus will be fl ow of current through the conductive tissues of 69 18 measurable from electrodes on the surface of the the head to defi ne the scalp distribution of each 70 19 scalp. Furthermore, the ERP waveform at a given component (and thereby compute a unique unmix- 71 20 electrode site refl ects the contribution of many ing matrix). To obtain a unique solution, these tech- 72 21 simultaneously active ERP components that overlap niques must also rely on additional assumptions, 73 22 in time, and it is diffi cult to mathematically unmix such as a specifi c number of discrete dipoles or max- 74 23 the observed waveforms and determine the original imal smoothness in the distribution of current fl ow 75 24 component waveforms. over the cortical surface. Th at is, these techniques 76 fi nd the set of single-component scalp distributions 77 25 Other Approaches to Defi ning that can sum together to provide the best fi t to the 78 26 ERP Components observed scalp distribution as it varies over time 79 27 In this section, we will consider the relationship while also being consistent with a variety of assump- 80 28 between the defi nition of the term ERP component tions (for a review and critique, see Luck, 2005 , 81 29 that we have proposed in this chapter and the way chap. 7). Th us, source localization techniques defi ne 82 30 that components are defi ned by four other app- a component as activity arising from a region of 83 31 roaches: source localization , principal component cortex, which is similar to our defi nition of an ERP 84 32 analysis (PCA; see Donchin & Heffl ey, 1978 ), inde- component as refl ecting a specifi c brain process (on 85 33 pendent component analysis (ICA; see Chapter 3, this the assumption that most brain processes occur in 86 34 volume), and time-frequency analysis (see Chapter 2, discrete areas4 ). However, our defi nition of the term 87 35 this volume). We will concentrate on the spatial ERP component goes further, because more than one 88 36 variants of PCA and ICA, in which components are brain process may occur in a given region of cortex. 89 37 defi ned on the basis of scalp distribution informa- Moreover, source localization approaches diff er con- 90 38 tion (see Spencer et al., 2001 , for a discussion of siderably from the traditional approach to defi ning 91 39 temporal and spatiotemporal PCA). components in the procedures used to discover 92 40 We will begin by considering the source localiza- and defi ne individual components. Whereas source 93 41 tion, ICA, and PCA approaches. In these three localization techniques use a variety of assumptions 94 42 approaches, a component is defi ned solely by its to select a set of scalp distributions that together 95 43 scalp distribution, which is assumed to remain provide a quantitative account of the data from 96 44 stable over the course of a single experimental ses- a given experiment, traditional approaches to defi n- 97 45 sion (this is a reasonable assumption given that ing components are based on using experimental 98 46 brain geometry is unlikely to undergo major changes manipulations to test hypotheses about the link 99 47 within a few hours). As mentioned in the previous between a voltage defl ection and an underlying 100 48 section, these techniques provide an unmixing neural or psychological process (as discussed further 101 49 matrix that refl ects the estimated scalp distributions in a later section). 102 50 of the individual components; the waveform for Principal component analysis and ICA make 103 51 each component is computed by passing the no biophysical assumptions, but instead use the sta- 104 52 observed waveforms through this matrix. Th at is, tistical properties of the data to derive the scalp 105

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1 distributions of the components. Th at is, the obser- Th ere are, however, some practical problems 54 2 ved scalp distribution changes from moment to associated with linking ICA components to ERP 55 3 moment and from condition to condition as the components as we have defi ned them here. First, 56 4 underlying components wax and wane, and the sta- ICA is applied to single-subject data, and it can be 57 5 tistical relationships between the values observed at diffi cult to determine the correspondence between 58 6 the diff erent electrode sites are used to determine the ICA components obtained for the diff erent sub- 59 7 the scalp distributions of the individual compo- jects. Th e same problem arises when comparing 60 8 nents. In PCA, for example, two electrode sites will components across experiments. Second, the ICA 61 9 tend to contribute strongly to the same component computational approach requires that the number 62 10 if they tend to covary in voltage. Principal compo- of ICA components is always equal to the number 63 11 nent analysis is designed to fi nd an unmixing matrix of electrodes, and this means that multiple true 64 12 in which a small number of components— each components may be lumped together into a single 65 13 with its own scalp distribution — can sum together ICA component or that a single true component 66 14 to explain most of the variations in the observed may be distributed across multiple ICA compo- 67 15 scalp distribution. It reduces a large and complex set nents. It remains to be seen how well the traditional 68 16 of observed scalp distributions (for each time point, approach and the ICA approach to defi ning and 69 17 condition, etc.) to a small number of component isolating components can be combined. 70 18 scalp distributions. In contrast, ICA is designed to Th e time-frequency approach is very diff erent 71 19 fi nd an unmixing matrix that maximizes the inde- from the source localization, ICA, and PCA app- 72 20 pendence of each component so that every indivi- roaches (although it can be combined with them). 73 21 dual component represents the largest possible In the time-frequency approach, the EEG is decom- 74 22 amount of information. Th e scalp distributions of posed into the sum of a set of oscillations, and the 75 23 the components in ICA may be correlated with each power in each frequency band is estimated at each 76 24 other (as would be expected for two independent moment in time (with varying degrees of temporal 77 25 but nearby neural sources), but the strength of acti- precision; see Chapter 2, this volume, for details). 78 26 vation of each component varies independently of Th e results of this approach can be related in con- 79 27 the strength of the other components over time ventional ERP components in two main ways. 80 28 points and over conditions. Whereas PCA attempts First, if the oscillations vary randomly in phase 81 29 to lump as much information as possible into a small from trial to trial, they will ordinarily disappear 82 30 number of components, ICA attempts to split apart when the single-trial EEG epochs are averaged 83 31 the information into diff erent components (for a together; oscillations of this sort are completely 84 32 detailed comparison, see Chapter 3, this volume). invisible in conventional averaged ERP waveforms 85 33 Because it is a “lumping” technique, spatial PCA (for an exception, see Mazaheri & Jensen, 2008 ). 86 34 by itself is unlikely to produce components that are In such cases, oscillations within a given frequency 87 35 related to individual neural and psychological pro- band are often considered as being analogous to 88 36 cesses. However, the essence of ICA corresponds ERP components, refl ecting a specifi c neural or psy- 89 37 well with a reasonable assumption about these pro- chological process. However, many diff erent pro- 90 38 cesses. Specifi cally, for something to count as a cesses might lead to oscillations in a given frequency 91 39 unique process, it must be dissociable from other band, so it is problematic to assume that power in 92 40 processes. Th is is largely identical to saying that a given frequency band in one experiment refl ects 93 41 the process must sometimes vary independently of the same process refl ected by power in that same 94 42 other processes, and this is exactly the type of inde- frequency band in another experiment (e.g., theta- 95 43 pendence that ICA uses to defi ne components. band activity in one experiment may refl ect very 96 44 Th us, although ICA uses a mathematical approach diff erent processes than theta-band activity in a dif- 97 45 rather than a hypothesis-testing approach to derive ferent experiment). Assuming that a given band 98 46 the components, it shares much with the defi nition refl ects a specifi c process would be analogous to 99 47 of the term ERP component that we have proposed assuming that any positive defl ection in the P3 100 48 in this chapter. Moreover, the components isolated latency range refl ects a single process. 101 49 by ICA often have a scalp distribution that matches A second possibility is that a stimulus might 102 50 what would be expected for a single dipole, even perturb the phase of an ongoing oscillation, causing 103 51 though the technique makes no biophysical assump- the phase to become consistent across trials during 104 52 tions about dipoles (see, e.g., Figure 3.9 in Chap- the period immediately after the stimulus. In such 105 53 ter 3, this volume). cases, the phase consistency across trials will allow 106

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1 the oscillation to survive the averaging process neural processing. However, the problem of overlap- 52 2 (see Figure 2.2 in Chapter 2, this volume). When ping components makes it diffi cult to ascertain 53 3 this happens, a component in an averaged ERP may whether a change in a peak in the observed ERP 54 4 actually consist of a portion of an ongoing oscilla- waveform is due to a change in one component, 55 5 tion rather than refl ecting a discrete voltage defl ec- a change in a diff erent component, or changes in a 56 6 tion that is elicited by the stimulus. combination of multiple components. In the lan- 57 guage literature, for example, it is not always clear 58 7 Challenges in Isolating ERP Components whether a putative increase in N400 amplitude might 59 8 We have defi ned the term ERP component as scalp- actually be a decrease in P3 amplitude, and a great 60 9 recorded neural activity that is associated with a par- deal of work was needed to determine that the P600 61 10 ticular neural or psychological process. It is the component elicited by syntactic anomalies was diff er- 62 11 nature of the underlying process that we are seeking ent from the P3 wave (see Chapter 15, this volume). 63 12 to uncover with ERP research; however, as discussed Figure 1.2 illustrates some of the measurement 64 13 in the preceding section, the ERP waveform that we problems that arise due to the overlap of ERP com- 65 14 can record contains a mixture of many diff erent ponents. In this simulated example, the observed 66 15 ERP components. Deconstructing the ERP wave- waveform shown in Figure 1.2A is the sum of the 67 16 form into its ERP components is no trivial task. three underlying components shown in Figure 1.2B . 68 17 An infi nite number of combinations of underlying In other words, Figure 1.2B is the observed ERP 69 18 components could sum together to give rise to a waveform and Figure 1.2A shows the underlying 70 19 given ERP waveform. Th is section is devoted to components (which we cannot observe directly in 71 20 illustrating the diffi culty in assessing changes in real experiments). Looking at the observed wave- 72 21 a component from the observable ERP waveform. form, the ERP appears to consist of a positive com- 73 22 To illustrate these points, we will use simulated data ponent from 0 to 90 ms, a negative component 74 23 for which the underlying ERP components are from 90 to 180 ms, and a positive component from 75 24 known and modifi able. Th is section is primarily 180 to 450 ms. However, the underlying compo- 76 25 aimed at pointing out the limitations of ERP com- nents are much longer in duration, with the fi rst 77 26 ponent research. Although this section may make positive component active from 0 to 200 ms, the 78 27 ERP research seem dismal, you should not become negative component active from 50 to 325 ms, and 79 28 disheartened with ERPs. Th e fi nal section of this the second positive component active from 100 to 80 29 chapter will provide some tools that have been suc- 450 ms. Th us, one cannot easily determine the dura- 81 30 cessful for using ERPs to answer questions about tion of an underlying component from the duration 82 31 the mind, brain, and behavior. of the peak in the observed waveform. Th e diffi culty 83 of assessing component duration from the ERP 84 32 ERP Peaks ≠ ERP Components waveform is a problem in experimental contexts 85 33 As discussed earlier, the ERP waveform looks like as well, particularly when a smaller component is 86 34 a succession of distinct and easily separable peaks, preceded or followed by a much larger component. 87 35 but these peaks do not map onto distinct ERP com- For example, it is diffi cult to assess the duration 88 36 ponents in a simple one-to-one manner. Th e neural of the N2 component when it is followed closely by 89 37 activation associated with each distinct mental pro- the much larger P3 component. Although it is often 90 38 cess persists for tens or hundreds of milliseconds, the case that evaluating the length of a peak in the 91 39 which means that the ERP signature from one pro- waveform minimizes the apparent duration of a 92 40 cess will overlap with the ERP signature for subse- component, the waveform can also make a compo- 93 41 quent processes either in part or in whole. Even if nent seem longer in duration than it is in actuality. 94 42 these neural processes occur in separate parts of the For example, the late positive potential (LPP) in the 95 43 brain, the ERP waveform at a given electrode site emotion literature appears as a single component 96 44 will be the weighted sum of all of the underlying that is hundreds of milliseconds in duration; how- 97 45 components. In other words, each peak in the wave- ever, the LPP may actually be composed of several 98 46 form is usually determined by more than one, and distinct shorter-duration components (see Chap- 99 47 often several, separate ERP components. ter 16, this volume). Th erefore, the duration of 100 48 Much ERP research has centered on evaluating peaks in the ERP waveform is often quite diff erent 101 49 diff erences in the size or timing of an ERP compo- from the duration of the underlying components. 102 50 nent across conditions or across groups of subjects. Changes in the timing or size of components 103 51 Such changes can speak volumes about diff erences in across experimental conditions or groups of subjects 104

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Fig. 1.2. Example of how the peaks in an observed waveform can misrepresent the underlying components. Panel A shows the observed waveform, and Panel B shows the underlying components that sum together to produce the observed waveform. Note that Peak 1 is much earlier than the peak of component C1, and the shape of Peak 2 is very diff erent from the shape of component C2. Panel C shows the original waveform overlaid with a waveform in which the amplitude of component C2 has been decreased. Note that this change in C2 causes an increase in the amplitude of Peak 3, even though component C3 does not diff er between these waveforms. Panel D shows the original waveform overlaid with a waveform in which the amplitude of component C1 has been increased. Note that this changes the amplitude and latency of Peak 2, even though component C2 does not diff er between these waveforms.

1 can also be diffi cult to assess from the ERP wave- positive component also increased the apparent 31 2 form. Figure 1.2C shows the eff ect of an experimen- latency of the negative peak, even though no latency 32 3 tal manipulation that decreases the amplitude of the shift occurred for any of the underlying compo- 33 4 negative component. In addition to decreasing nents. In other words, a change in the amplitude 34 5 the measured amplitude of the negative peak in the of one component can in some cases masquerade as 35 6 observed waveform, this manipulation greatly a shift in the latency of a diff erent component. 36 7 increases the amplitude of the second positive peak Th erefore, it is often diffi cult to determine whether 37 8 (even though the manipulation did not change the a specifi c type of modulation of the ERP waveform 38 9 amplitude of the second positive component). Th is is related to the same type of change in the underly- 39 10 is one clear example of how changes in the ampli- ing components. In other words, measured shifts in 40 11 tude of one component (the negative component) peak latency can sometimes be caused merely by 41 12 can result in an amplitude change in a subsequent changes in component amplitude, and measured 42 13 part of the waveform (the second positive peak). changes in peak amplitude can sometimes result 43 14 Based on a superfi cial evaluation of the waveform, from shifts in component latency. 44 15 these changes would lead to the erroneous conclu- Although we have shown a few cases of the dif- 45 16 sion that the diff erence between conditions was the fi culty in linking changes in the ERP waveform 46 17 result of modulations in two underlying ERP com- with changes in particular underlying ERP compo- 47 18 ponents; however, in this case, both peak modula- nents, this is by no means an exhaustive description 48 19 tions were caused by a change in a single underlying of the ways in which changes in underlying compo- 49 20 ERP component. Th erefore, researchers may draw nents can aff ect the observed ERP waveform. We 50 21 substantially incorrect conclusions if they assume encourage anyone interested in exploring these 51 22 that a change in the size of a peak refl ects a change eff ects to create simulated data and see how modu- 52 23 in the size of a particular component. lations in the underlying components aff ect various 53 24 Similarly, Figure 1.2D shows the eff ect of a parts of the ERP waveform (this is easy to do in a 54 25 manipulation that increases the amplitude of the spreadsheet program, such as Excel). Furthermore, 55 26 fi rst positive component. In addition to increasing it should be noted that the simulation shown in 56 27 the measured amplitude of the fi rst positive peak Figure 1.2 may actually underestimate the severity 57 28 in the observed waveform, this manipulation of the problem of measuring amplitudes and laten- 58 29 decreased the measured amplitude of the negative cies from the ERP waveform, because modeling 59 30 peak. Th e manipulation of the amplitude of the first eff orts suggest that 6–10 generators may be active 60

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1 within a given 150 ms period (Di Russo et al., 2002 ; each trial. However, this is often not the case. 33 2 Picton et al., 1999 ), in contrast to the 3 neural gen- Specifi cally, just as the behavioral reaction time 34 3 erators used in the simulation shown in Figure 1.2 . varies substantially from trial to trial in an experi- 35 4 On the other hand, considerable information about ment, the timing of the underlying neural processes 36 5 the underlying component structure can often be that give rise to the ERP components may also vary 37 6 obtained by examining the waveforms from multi- from trial to trial. Th e variability in the timing of 38 7 ple electrode sites, because diff erent components a component across trials is known as latency jitter , 39 8 will be weighted diff erently at each electrode. and it can actually be quite problematic to the inter- 40 pretation of an average waveform. When latency 41 9 Variability in ERPs jitter is present for a component, as depicted in 42 10 Amplitudes and latencies are almost always mea- Figure 1.3 , the average ERP waveform will contain 43 11 sured from the average of multiple EEG segments a “smeared-out” version of the component. Specif- 44 12 but separately for each individual subject. In other ically, the average ERP waveform will refl ect both 45 13 words, all of the trials in a condition are averaged the earliest onset and latest off set times of the com- 46 14 together for a given subject, and the amplitude and ponent, as opposed to refl ecting the average onset 47 15 latency measures are computed for each subject and off set times. In addition, latency jitter can 48 16 from this average waveform. Each subject then con- greatly reduce the measured peak amplitude (dis- 49 17 tributes a value to the statistical test for diff erences cussed more fully later in the chapter). Furthermore, 50 18 across conditions or groups, with the variance across although this variation in timing across trials is 51 19 subjects contributing to the ability to detect a sig- informative about the nature of the process refl ected 52 20 nifi cant experimental eff ect. Th is process of signal by the component, it can make the comparison of 53 21 averaging is incredibly important and integral to the the size and timing of a component across condi- 54 22 utilization of ERPs; averaging across multiple EEG tions or across groups of subjects more diffi cult. 55 23 epochs reveals ERPs that are not visible on single Specifi cally, greater variability in the timing of a 56 24 trials, and data from multiple subjects provide a component across conditions may be incorrectly 57 25 measure of variance that is important to assessing interpreted as a change in the size or duration of the 58 26 statistically signifi cant changes. However, it is component. For example, a comparison of the two 59 27 important to understand distortions that can be conditions depicted in Figures 1.3A and 1.3B might 60 28 introduced by the averaging process. lead to the erroneous conclusion of a smaller com- 61 29 Th e process of averaging across multiple trials to ponent in condition A than in condition B, even 62 30 form an average ERP waveform relies on several though the only diff erence between the conditions 63 31 assumptions, the most important of which is that lies in the variability in the component timing across 64 32 the timing of the signal of interest is the same on trials. Th erefore, understanding how latency jitter 65

Fig. 1.3. Example of how diff erences in latency jitter (the amount of variability in component latency across trials) infl uence the average waveform. Th e green waveforms are the single-trial data, and the red waveforms are the averages across trials. The jitter in single-trial latency is greater in (A) than in (B), leading to a broader averaged waveform with a lower peak amplitude in A than in (B). Th at is, even though the amplitude of the single-trial waveforms is equivalent in (A) and (B), the peak amplitude of the averages diff ers between (A) and (B). In addition, the onset time and off set time of each average refl ect the earliest onset times and latest off set times of the single trials rather than the average of the single-trial onset and off set times.

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1 can impact the average waveform can be useful in there is substantial variability in the timing of the 20 2 interpreting experimental eff ects. components across subjects, the grand average ERP 21 3 Measures of amplitude and latency are almost will refl ect that variability. 22 4 always taken from individual subject waveforms. One of the most salient factors when measuring 23 5 By contrast, most ERP papers show the grand aver- the amplitudes and latencies of ERP components 24 6 age ERP waveform across subjects, as opposed to from the individual subject waveforms is the quite 25 7 each of the individual subject waveforms. Th erefore, substantial variation in shape across waveforms. For 26 8 the characterizations we can make of the compo- example, consider the waveforms in Figure 1.4A . 27 9 nents in a particular experiment are generally taken Th e bottom waveform is the grand average across 28 10 from an average representation of all the subject subjects, and the other waveforms refl ect 8 ran- 29 11 waveforms in the study. It is tempting to think that domly selected subjects from the 20 individuals 30 12 the grand average ERP waveform would refl ect the who contributed to the grand average. Th e high- 31 13 average of all of the individual subject waveforms lighted portion of the fi gure corresponds to the time 32 14 that make up the average; however, just as the aver- period one might select to measure the P2 wave, 33 15 age of multiple EEG segments within a subject because it covers almost the entire duration of the 34 16 refl ects the range of the epochs, a grand average wave in the grand average. However, the activity 35 17 across subjects actually refl ects the earliest onset and within this time window varies considerably across 36 18 latest off set times and not the average of the onsets the individual subject waveforms. For some of the 37 19 and off sets of the components. In other words, if subjects, the fi rst positive wave peaks prior to the 38

A B

Subject 1

Subject 2 Overall sum points upward Subject 3

Subject 4

Subject 5

Subject 6

Subject 7 Overall sum points downward

Subject 8

+5mV Average −200 200 400 600 800 −5mV

Fig. 1.4. (A) Single-subject ERP waveforms from 8 of 20 subjects in an oddball paradigm, along with the grand average of all 20 subjects (data from the study of Luck et al., 2009 ). (B) Example of how small diff erences between two subjects in the position of an active area of cortex within a sulcus could lead to opposite polarities at the electrode shown on the surface of the head. Each arrow represents the equivalent current dipole in a small patch of cortex, with positive at the arrowhead end and negative at the opposite end. Many of these dipoles will cancel each other, and the surface voltage will refl ect the activity in the noncanceling dipoles.

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1 beginning of the window (e.g., subjects 3, 4, and 7), overall activity at a given scalp electrode will be pos- 54 2 and one subject’s waveform is entirely negative itive for one subject and negative for the other. 55 3 during this window (subject 3). Although this variability can be problematic for 56 4 Th e between-subject variations in the ERP studies designed to assess individual diff erences, 57 5 waveform can be quite disconcerting when measur- there is considerable similarity in the grand average 58 6 ing a component from the single-subject waveforms. ERP waveforms from diff erent experiments that uti- 59 7 It is very unlikely that the same process reaches lize similar tasks. Th is gives us some confi dence that 60 8 maximal activity at 145 ms in one healthy adult reliable conclusions can be drawn by comparing rea- 61 9 (e.g., subject 7) and at 220 ms in another (e.g., sub- sonably sized groups of subjects, even if the indi- 62 10 ject 6), so it does not seem appropriate to use a vidual subjects within a group vary considerably in 63 11 window that is broad enough to include peaks at waveform shape. For example, there is great similar- 64 12 such diff erent latencies. And it is hard to understand ity across P3 oddball studies in grand average ERP 65 13 how the negative defl ection exhibited by subject 3 waveforms, despite the fact that these waveforms are 66 14 could represent the same functional brain activity as made up of diff erent underlying individual-subject 67 15 the positive defl ection exhibited by subjects 1 and 2 waveforms. Consider the ERP waveforms in Fig- 68 16 in this same interval. However, as discussed above, ure 1.5 . Th e top left panel shows all 20 individual- 69 17 peaks in the ERP waveform do not correspond subject waveforms from a P3 oddball task, subdivided 70 18 directly to the underlying components. So, how at random into two separate groups of 10 subjects 71 19 problematic are these individual-subject waveform each. Th ere is enormous variability bet ween subjects 72 20 diff erences? in the amplitude and shape of the ERP waveform, 73 21 To understand whether the diff erences among with much larger P3 waves in some subjects than in 74 22 individual-subject waveforms adversely aff ect our others. However, as can be seen at the bottom of 75 23 characterization of the components, we must fi rst Figure 1.5 , the grand averages across these two sub- 76 24 understand the source of the diff erences. For later groups of subjects are quite similar in amplitude, 77 25 periods of the waveform that refl ect higher cogni- timing, and shape, despite the large diff erences in 78 26 tive processes, diff erences in size and shape may the underlying individual-subject ERP waveforms 79 27 refl ect diff erences in the strategies subjects engage in that make up those grand averages. In other words, 80 28 during cognitive processing. Th erefore, individual the individual-subject diff erences do not alter the 81 29 diff erences in the size and shape of the waveform overall experimental eff ect when the sample size is 82 30 may refl ect actual processing diff erences. However, suffi cient. However, it is important to remember 83 31 for the sensory processing that occurs within ~200 that some measurement techniques may be more 84 32 ms after the onset of the stimulus, it is unlikely that aff ected by this between-subjects variance than other 85 33 diff erences in waveform size and shape refl ect diff er- techniques. We will address the issue of measure- 86 34 ences in strategy or processing, at least in healthy ment later in the chapter. It should also be noted 87 35 subjects. Instead, the waveform diff erences most that statistical techniques can be applied that allow 88 36 likely arise from diff erences across subjects in non- measurements to be made from grand averages 89 37 functional “nuisance” factors such as skull thickness rather than from single-subject waveforms, capital- 90 38 and cortical folding patterns. izing on the stability of the grand averages (Kiesel 91 39 Just as fi ngerprints are unique to each individual, et al., 2008 ; Miller et al., 1998 , 2009 ). 92 40 so is the intricate pattern of sulci and gyri in the 41 human brain. Such changes in folding pattern could How to Identify and Defi ne an 93 42 easily lead to diff erences in ERP waveforms across ERP Component 94 43 subjects like those illustrated in Figure 1.4 A. For Given how diffi cult it is to isolate a specifi c ERP 95 44 example, Figure 1.4 B shows how a relatively small component from the ERP waveform, you may be 96 45 diff erence between two subjects in the location wondering how we even know that a specifi c ERP 97 46 of an active strip of cortex within a sulcus could lead component exists. For example, how do we know 98 47 to opposite polarities for those two subjects at a that there is an N1 wave, a P3 wave, an N400, and 99 48 given electrode site. More of the active region is so on? Of course, there is a voltage defl ection in 100 49 on one side of the sulcus for one subject and more is a broad time range corresponding to each of these 101 50 on the opposite side of the sulcus for the other sub- components, but as we have already seen, there 102 51 ject, leading to an overall equivalent current dipole are usually multiple components active simultane- 103 52 pointing upward for one subject and pointing ously in a given time range. So, how do we know 104 53 downward for the other subject. Consequently, the that a voltage defl ection is caused by a specifi c ERP 105

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Fig. 1.5. Example of the similarity of grand average waveforms despite substantial diff erences among the single-subject waveforms. Waveforms from 20 subjects in an oddball experiment were randomly divided into two groups of 10. Th e single-subject waveforms for each group are shown at the top left and top right. Note the large variability in the amplitude and shape of the waveforms. Th e grand averages of these two subgroups of 10 subjects are shown at the bottom. Despite the large diff erences among the individual subjects, the grand averages from the two subgroups are quite similar.

1 component in one study, and how do we know that And because an ERP component is a scalp-recorded 23 2 that same ERP component is active in subsequent signature of a neural process, it stands to reason that 24 3 studies? In other words, how do we operationally the timing of an ERP component will vary across 25 4 identify and defi ne an ERP component? these same contexts. We can see timing variability 26 5 Event-related potential components are often quite clearly in studies of the P3 component, which 27 6 defi ned in terms of a combination of polarity, latency, can vary across conditions by hundreds of millisec- 28 7 and scalp distribution. Th is method of defi ning ERP onds, sometimes occurring before the manual 29 8 components is evident from the common naming response and sometimes appearing after the response. 30 9 scheme in which ERP components are named in Th is is one reason that the moniker P300 is often 31 10 terms of polarity and latency (given either in milli- shortened to P3, to eliminate the association with 32 11 seconds or as the ordinal position in the waveform). the time value of 300 ms. Although the timing of 33 12 However, as we will see below, these dimensions most ERP components is not nearly as variable as 34 13 describe the observed peaks and do not provide that of the P3, timing variability does occur for all 35 14 a stable and precise means of defi ning the underlying ERP components. Visual sensory components, for 36 15 ERP components. Th at is, the factors of polarity, example, increase in latency as stimulus brightness 37 16 timing, and scalp distribution can vary from context decreases for the simple reason that the amount 38 17 to context, rendering them unstable representations of time required for information to reach cortex 39 18 of a component. We will explore each of these factors increases as brightness decreases. In addition, most 40 19 in turn and will end with some strategies for defi ning components change in latency across early develop- 41 20 and isolating ERP components. ment (see Chapter 17, this volume) and across aging 42 21 As discussed above, the timing of a neural pro- (see Chapter 18, this volume). Examining the varia- 43 22 cess can vary across trials, subjects, and experiments. tion in time windows over which the components 44

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1 are measured in diff erent studies makes the variation ERP component may vary across experimental con- 54 2 in component latencies across experiments quite texts. For example, one subcomponent of the audi- 55 3 obvious. Th erefore, although a specifi c latency is tory N1 family arises from tonotopically mapped 56 4 often denoted by the name of an ERP component, auditory cortex, and its scalp distribution therefore 57 5 this latency is approximate and often specifi c to the changes according to the pitch of the stimulus 58 6 context in which the component was fi rst identifi ed, (Bertrand et al., 1991 ). Moreover, the scalp distribu- 59 7 and latency cannot be used as a direct means of tion in any given time range is infl uenced by all the 60 8 determining whether a component in a given study components active in that range, which makes it dif- 61 9 is the same as a component observed in previous fi cult to determine the true distribution of a single 62 10 studies, especially if the subjects, stimuli, or task component in a given experiment (unless that com- 63 11 diff er considerably across studies. ponent has been isolated using one of the approaches 64 12 Many ERP component names also make refer- described later in this chapter). Furthermore, the 65 13 ence to the polarity of the component, but polarity apparent scalp distribution can vary widely, depend- 66 14 may vary for a single component. For example, the ing on the choice of reference electrode (see Luck, 67 15 C1 wave reverses in polarity for stimuli in the upper 2005 , chap. 3). 68 16 visual fi eld compared with stimuli presented in the One additional variable that is often used to iden- 69 17 lower visual fi eld owing to the cortical folding pat- tify and defi ne ERP components is their sensitivity 70 18 tern of primary visual cortex. Both the positive- and to experimental manipulations or factors (see 71 19 negative-polarity C1 waves refl ect the same under- Donchin et al., 1978 , for a thorough discussion). 72 20 lying process and are therefore the same ERP com- Th at is, what are the tasks, stimuli, timing parame- 73 21 ponent by any reasonable defi nition. Although ters, and other factors that allow the component to 74 22 other ERP components do not reverse polarity so be observed, and how do changes in these various 75 23 dramatically, diff erences in cortical folding pattern factors modulate the timing, amplitude, and scalp 76 24 across subjects might occasionally lead to polarity distribution of the component? For example, the 77 25 diff erences from one subject to the next at a given N2pc is observed for a target stimulus surrounded 78 26 electrode site (see, e.g., subject 3 in Figure 1.4 A). by distractors but not for a target stimulus presented 79 27 Furthermore, as discussed above, all ERP compo- in isolation (see Chapter 12, this volume). Th is 80 28 nents are positive on one end of the dipole and dependence of the N2pc on the presence of distract- 81 29 negative on the other end, and all ERP components ing information in the display has played a large role 82 30 therefore reverse polarity at some place on the in shaping various theories of the component. 83 31 head. Furthermore, the N2pc has been shown to be largely 84 32 If the polarity and timing information cannot be unaff ected by the probability of the target item (see 85 33 used to identify a component, what about the scalp Chapter 12, this volume), in contrast to the P3b. 86 34 distribution? Scalp distribution is often used to dis- Th erefore, sensitivity to experimental factors can 87 35 tinguish between components that have the same help to identify the nature of a component and to 88 36 polarity and similar latencies, such as the “frontally distinguish among diff erent components. However, 89 37 distributed P3a” versus the “centroparietal P3b.” In just as discussed above with the variables of polarity, 90 38 these cases, researchers often refer to a family of timing, and scalp distribution, sensitivity to experi- 91 39 components (e.g., the N2 family of components) mental factors is not by itself a suffi cient method for 92 40 consisting of a set of subcomponents (e.g., the N2a, defi ning a component. For example, multiple ERP 93 41 N2b, N2c, and N2pc subcomponents). Each sub- components may be sensitive to the same experi- 94 42 component is actually a full-blown component, mental manipulation, such as the similar dependence 95 43 refl ecting a diff erent functionally and anatomically of P2 and P3b amplitude on the probability of the 96 44 defi ned process, and the diff erent subcomponents target stimulus. Furthermore, it is diffi cult to deter- 97 45 within a family are united only by their common mine if an experimental manipulation has modu- 98 46 polarity and similar timing. lated the strength or timing of a specifi c component, 99 47 Although adding the scalp distribution informa- or rather has resulted in a change in task strategy that 100 48 tion can help to defi ne a component, it will be inef- has aff ected some other overlapping component. 101 49 fective if multiple subcomponents have similar scalp Th at is, it is diffi cult to assess whether an experimen- 102 50 distributions (e.g., it seems likely that multiple dif- tal manipulation has made an impact on a specifi c 103 51 ferent brain processes will produce a positive voltage component, and it is also diffi cult to determine 104 52 defl ection over the frontal lobes between 300 and whether the experimental manipulation changed the 105 53 600 ms). Moreover, the scalp distribution for a single strength, location, or timing of the neural process. 106

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1 From these considerations, it should be clear that diff erent-race faces, because the voltage was positive, 54 2 it is not appropriate to formally defi ne an ERP com- peaked near 300 ms, and was maximal at parietal 55 3 ponent in terms of a combination of polarity, timing, electrode sites. However, 325 ms would be an unusu- 56 4 scalp distribution, and sensitivity to experimental ally early latency for a visual P3b component, espe- 57 5 manipulations. Th ese variables may be associated cially in 5-year-old children. Moreover, even if a P3b 58 6 with a given component, but they do not defi ne the were present in this latency range, the diff erence 59 7 component. We have instead argued that the term between conditions had a more central scalp distri- 60 8 ERP component is best defi ned in terms of the scalp- bution than is typical for the P3b component. Th us, 61 9 recorded activity generated by a specifi c neural or it would be unlikely that this experimental manipu- 62 10 psychological process, which in turn produces the lation primarily infl uenced P3b amplitude. 63 11 polarity, latency, and scalp distribution of the com- When this converging evidence approach is 64 12 ponent (which vary as that process varies), along taken, it is important to consider both the strength 65 13 with the sensitivity of the component to experimen- of the evidence that a given component has a specifi c 66 14 tal manipulations. Unfortunately, our preferred property and the degree to which other components 67 15 defi nition is not very useful as an operational defi ni- might have that same property (this is essentially an 68 16 tion (i.e., a defi nition that describes the operations application of Bayes’s theorem). For example, 69 17 necessary to determine whether a specifi c voltage although the N400 component is almost always 70 18 defl ection refl ects a specifi c component), because it present between 300 and 600 ms (see Chapter 15, 71 19 is not usually possible to determine from the obser- this volume), many other components are also active 72 20 ved waveforms the voltage that is attributable to a in this latency range, so the fi nding that a given volt- 73 21 specifi c known process. age defl ection occurs in this latency range is not 74 22 Th us, in practice, the best way to identify a spe- strong evidence that the defl ection is an N400 com- 75 23 cifi c component is to take a converging evidence ponent. In contrast, the lateralized readiness poten- 76 24 approach that intelligently combines various factors tial (LRP; see Chapter 9, this volume) and the N2pc 77 25 (including but not limited to polarity, latency, scalp component (see Chapter 12, this volume) have dis- 78 26 distribution, and sensitivity to experimental manip- tinctive lateralized scalp distributions that are not 79 27 ulations) that would be expected to be true of a present for many other components; the presence 80 28 given process in a given context. For example, imag- of this distinctive scalp distribution therefore pro- 81 29 ine that an oddball task was used in a study of vides strong (although not infallible) evidence that 82 30 elderly individuals, and a large positive voltage with an LRP or N2pc was present. 83 31 a parietal maximum was observed to peak at 500 ms With this approach, one is never completely cer- 84 32 for the oddball stimuli, with a much smaller voltage tain that a specifi c component has been identifi ed, 85 33 observed for the standard stimuli. Four pieces of and the strength of a conclusion will depend on 86 34 evidence converge on the conclusion that this volt- both the number of pieces of converging evidence 87 35 age consists predominantly of the P3b component: and the strength of each piece. Although it may be 88 36 (1) the voltage is positive at sites where the P3b is disappointing that one can never be certain that a 89 37 typically positive; (2) the latency is what we would specifi c component has been identifi ed, this kind of 90 38 expect given that cognition is typically slowed in uncertainty is common in all fi elds of science. More- 91 39 elderly individuals; (3) the scalp distribution is con- over, as discussed in the latter part of this chapter, it 92 40 sistent with previous studies of the P3b; and (4) the is sometimes possible to use component-independent 93 41 voltage shows the typical dependence on target experimental designs in which the conclusions of a 94 42 probability. Now consider an example in which study do not depend on identifying a specifi c ERP 95 43 5-year-old children are asked to passively view pic- component. 96 44 tures of same-race faces and pictures of diff erent- 45 race faces, and a greater positive voltage is observed Linking Components with Processes: Th e 97 46 for the diff erent-race faces with a peak latency Problems of Forward and Reverse Inference 98 47 of 325 ms. Imagine also that the voltage for both Up to this point, we have assumed that we already 99 48 same-race and diff erent-race faces was largest at know what neural or psychological process is 100 49 parietal electrode sites, but the diff erence in voltage refl ected by a given ERP component. In this sec- 101 50 between same-race and diff erent-race faces was larg- tion, we consider how one might create this link 102 51 est at central sites. Is this a P3b component? (which we call the problem of forward inference ) and 103 52 A superfi cial analysis might lead to the conclusion how one might use this information to draw con- 104 53 that a larger P3b component was observed for the clusions in new experiments (which Poldrack, 2006 , 105

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1 called the problem of reverse inference in the context process that is used to suppress inputs from distrac- 53 2 of neuroimaging). tor objects surrounding a potential target. Th is was 54 initially tested with the most obvious possible 55 3 Th e Problem of Forward Inference manipulations, such as removing the distractors to 56 4 It is more diffi cult than one might think to demon- see if the N2pc component would disappear. 57 5 strate that a given ERP component (or any other A second set of experiments tested more refi ned 58 6 physiological measure) refl ects a specifi c neural or manipulations based on fi ndings from monkey sin- 59 7 psychological process. Th e challenge arises from the gle-unit experiments (Luck et al., 1997 ). Th e results 60 8 fact that we are looking for a neural measure of of these experiments were consistent with the pro- 61 9 a given process because we do not fully understand posed link between N2pc and attentional fi ltering, 62 10 the process and wish to use the neural measure to and subsequent experiments assumed that this link 63 11 study the process. Because we do not fully under- was true and used it to test hypotheses about atten- 64 12 stand the process, it is diffi cult to design unambigu- tion. For example, one study asked whether the 65 13 ous tests of the hypothesis that a given component same putative fi ltering mechanism was used by tar- 66 14 refl ects this process. For example, imagine that com- gets defi ned by diff erent types of features (Girelli & 67 15 ponent A is hypothesized to refl ect the encoding of Luck, 1997 ), and another series of experiments 68 16 information in verbal working memory. We could asked whether this mechanism was applied in paral- 69 17 test this hypothesis by comparing the ERPs in a con- lel or in serial (Woodman & Luck, 1999 , 2003b ). 70 18 dition in which subjects are asked to encode words However, later evidence demonstrated that the 71 19 in working memory and a condition in which they N2pc does not refl ect fi ltering of the distractors per 72 20 passively view the same words. However, it is possi- se, instead refl ecting operations that must be applied 73 21 ble that working memory encoding is fairly auto- to the attended object itself when distractors are 74 22 matic and would occur in both conditions; thus, the present (Hickey et al., 2009 ). Th is is a modest change 75 23 absence of a diff erence in component A between in the process thought to be refl ected by the N2pc, 76 24 conditions might not be strong evidence against the but it was enough to slightly change the conclusions 77 25 hypothesis that this component refl ects working that can be drawn from the previous studies. 78 26 memory updating. Moreover, if component A is 27 found to diff er between conditions, this could refl ect Th e Problem of Reverse Inference 79 28 some other process that diff ers between these condi- Once the problem of forward inference has been 80 29 tions (see Shulman, 1996 , for an interesting discus- solved and a given component has been linked with 81 30 sion of a related set of issues in the context of some certainty to a given process, it is desirable to 82 31 neuroimaging). use this component as a measure of the presence, 83 32 Th is problem could potentially be solved with magnitude, and timing of that process in new exper- 84 33 a bootstrapping approach (the term bootstrapping iments. Th is leads to the problem of reverse infer- 85 34 refers to “pulling oneself up by one’s bootstraps”). ence: If a component is present at a particular time, 86 35 In this approach, one begins by trying the most can we conclude that the process was present at that 87 36 obvious and unassailable manipulations of a given time? In Poldrack’s ( 2006 ) analysis of this problem 88 37 process to see if the component is present under the in the context of neuroimaging, the question is 89 38 conditions in which everyone would agree that the framed as follows: If brain activity has previously 90 39 process should be present. If the hypothesis survives been observed in area X when process P is active, 91 40 multiple tests of this nature, it is tentatively accepted. can we use the presence of activity in area X in a new 92 41 Th e component is then used to test new hypotheses experiment as evidence that process P was active in 93 42 about the process it is thought to refl ect. If these that experiment? As an example, Poldrack cited 94 43 experiments yield results that are broadly consistent experiments using diff erences in activity in the 95 44 with evidence from other approaches, then confi - dorsal striatum across conditions, which had previ- 96 45 dence in the link between the component and the ously been associated with reward processing, as evi- 97 46 process continues to grow. If discrepancies arise, dence that reward mechanisms were diff erentially 98 47 then researchers must reappraise the link between active in these conditions. 99 48 the component and the process. However, one must be cautious about using 100 49 As an example, consider the N2pc component reverse inference. Reverse inference is actually a case 101 50 (for a detailed discussion, see Chapter 12, this of the well-known logical error of affi rming the conse- 102 51 volume). Luck and Hillyard ( 1994 ) proposed that quent. If the presence of P (e.g., reward) leads to the 103 52 this component refl ects an attentional fi ltering occurrence of X (activity in the striatum), this does 104

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1 not mean that the occurrence of X necessarily entails one could reasonably assume that the presence of an 54 2 the presence of P. For example, sleeping (P) causes increased BOLD signal necessarily refl ected the 55 3 the eyes to close (X), but eye closure (X) does not allocation of attention. 56 4 necessarily mean that someone is asleep (P). Reverse Two main problems must be solved for reverse 57 5 inference is valid only when it is possible inference to be used with a given ERP component 58 6 to say that X occurs if and only if P occurs (i.e., X to draw strong conclusions. First, it is necessary to 59 7 never occurs without P). In functional magnetic conduct a comprehensive set of experiments testing 60 8 resonance imaging (fMRI) this standard is diffi cult the hypothesis that the component of interest is 61 9 to meet, because it is likely that the thousands of present if and only if the corresponding process 62 10 neurons in a given voxel and the millions of neurons occurs. Th is is the problem of forward inference, 63 11 within a cortical area are involved in multiple pro- and it is made diffi cult by the fact that we do not 64 12 cesses (e.g., the same neurons in visual cortex that are usually know enough about the process that a com- 65 13 involved in perception are also involved in working ponent hypothetically refl ects to know whether this 66 14 memory). Consequently, it is not usually possible to process is present or absent in a given experimental 67 15 assert that activity in a given voxel occurs if and only condition. Second, once the problem of forward 68 16 if a single process occurred. inference has been solved, new experiments that 69 17 Fortunately, an if-and-only-if condition is not as attempt to use reverse inference must solve the pro- 70 18 diffi cult to achieve for ERP components, because blem of component identifi cation. Th at is, one must 71 19 scalp ERPs represent a subset of the activity occur- be able to demonstrate that voltage defl ections 72 20 ring within a given brain area. As described earlier, observed in the new experiments represent the same 73 21 ERPs refl ect the synchronous activity of cortical component observed in the earlier studies that esta- 74 22 pyramidal cells, and many processes that occur blished the link between the component and the 75 23 within a given brain region will not lead to an ERP process. 76 24 signature on the scalp. Consequently, whereas almost Th ese two challenges are suffi ciently diffi cult that 77 25 any process within a given brain region will change it may never be possible to use reverse inference 78 26 metabolic activity and therefore change the blood with complete certainty. However, as Poldrack 79 27 oxygen-dependent (BOLD) activity, only a subset of (2006 ) discussed in the context of neuroimaging, 80 28 processes within a given region will produce a mea- one can use a Bayesian approach to draw probabilis- 81 29 surable ERP on the scalp. Th is makes ERP compo- tic inferences on the basis of reverse inference. Th is 82 30 nents more likely than BOLD responses to be tied to involves assessing the probability that the ERP com- 83 31 a specifi c process, and makes it less likely that a ponent would be present even if the corresponding 84 32 change in a given ERP component refl ects diff erent process was not active and the probability that the 85 33 processes in diff erent experiments. In other words, it corresponding process would be active without elic- 86 34 is more plausible that a specifi c ERP component will iting the ERP component. Th ese probabilities are 87 35 be present if and only if a given process is present diffi cult to calculate, so this Bayesian approach is 88 36 than that a BOLD response in a specifi c voxel will be usually used informally. For example, we do not 89 37 present if and only if a given process is present. know the probability that an N2pc component 90 38 For example, the evidence to date indicates that would be present without the allocation of atten- 91 39 the N2pc component is present if and only if atten- tion, and we do not know the probability that the 92 40 tion is allocated to an object in the presence of dis- allocation of attention may occur without an N2pc 93 41 tractors. Of course, future research may demonstrate component. Th us, we cannot provide a precise 94 42 that the N2pc component can sometimes be elic- probability for the claim that the variety of atten- 95 43 ited under conditions that do not involve this atten- tion indexed by the N2pc component is needed for 96 44 tion process, but it is at least plausible that this conjunction targets but not for feature targets (based 97 45 component might be present if and only if this on the presence of an N2pc for the former but not 98 46 attention process occurs. For example, when Luck the latter). However, given that several experiments 99 47 and Ford ( 1998 ) found that an N2pc was pre- support the contention that N2pc is observed if and 100 48 sent for conjunction targets and not for feature tar- only if this particular mechanism of attention is 101 49 gets, they were reasonably justifi ed in using reverse present, and given that the N2pc can be isolated 102 50 inference to draw the conclusion that a specifi c quite well from other components because of its dis- 103 51 mechanism of attention was allocated to the con- tinctive contralateral scalp distribution, we can say 104 52 junction targets and not to the feature targets. something informal such as “Th e fi nding that an 105 53 In contrast, there is no area of the brain in which N2pc was present for conjunction targets but not 106

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1 feature targets provides good evidence that the Event-related potentials provide a unique window 52 2 attentional processes that were present in prior into ongoing processing in the brain, serving as a 53 3 N2pc experiments are needed for the detection of continuous play-by-play of processing as it unfolds 54 4 conjunction targets but not for the detection of fea- over time. It is this high temporal resolution of ERPs 55 5 ture targets.” that makes them so desirable as a measure of brain 56 6 Interestingly, the logic of reverse inference may processing. With ERPs, we can see processing before, 57 7 sometimes allow stronger conclusions to be drawn during, and after the execution of behavioral res- 58 8 from the absence of an ERP component than from ponses, providing us with additional insights that 59 9 its presence. If we can say that a given physiological cannot be gained with behavioral measures alone. 60 10 measure X is always present when process P occurs— However, the limitations of the ERP technique dis- 61 11 without the if-and-only-if restriction — then we can cussed in the previous sections mean that ERPs are 62 12 use the modus tollens argument from classical logic. only well suited for answering certain types of ques- 63 13 Th is argument says that if we know that the presence tions. Understanding the types of questions that can 64 14 of P entails X, then the absence of X entails the be readily answered with ERPs is essential for the 65 15 absence of P. Th at is, if previous experiments demon- successful application of the technique, and the 66 16 strate that process P always leads to physiological remainder of the chapter will focus on describing 67 17 measure X, then the absence of physiological mea- several types of questions that ERPs have proven 68 18 sure X in a new experiment can be used to deduce useful in answering. 69 19 that process P was not present. For example, Vogel Th e domains covered here may not encompass 70 20 and colleagues (1998 ) assumed that working memory every current or potential use of ERPs; for example, 71 21 encoding leads to the occurrence of a P3 wave (for ERPs may be useful as potential biomarkers in mental 72 22 supporting evidence, see Chapter 7, this volume). illness (Javitt et al., 2008 ; Luck et al., in press). 73 23 Th ey found that this component was absent under However, the topics covered here provide a broad 74 24 conditions that led to an “attentional blink,” and overview of the ways in which ERPs have been most 75 25 from this they concluded that no working memory commonly used to make scientifi c progress. Th ese 76 26 encoding occurred for stimuli presented during the can be broadly divided into four domains, which we 77 27 attentional blink. Th is is a logically valid conclusion. will explore in turn below: (1) determining which 78 28 However, its truth depends on the validity of the ini- cognitive or neural process diff ers across conditions 79 29 tial assumption that working memory encoding or across groups (e.g., perception, attention, response 80 30 leads to a P3 wave, which is not certain. Nevertheless, selection); (2) determining whether and when the 81 31 this general approach is less problematic than the brain has completed some set of processes; (3) uncov- 82 32 typical use of reverse inference, which is based both ering new mental processes and subdividing known 83 33 on the assumption that a component is present when processes; and (4) covert monitoring of processing in 84 34 the corresponding process occurs and on the further situations in which overt behavior is diffi cult to mea- 85 35 assumption that the component is absent when the sure or interpret (e.g., coma, infancy). We will exam- 86 36 process does not occur. Of course, it is important to ine each of these areas, providing specifi c examples of 87 37 ensure that the absence of a voltage defl ection in a how ERPs have been used to expand our understand- 88 38 given condition truly refl ects the absence of the com- ing in each domain. 89 39 ponent of interest rather than cancellation by an 40 opposite-polarity component, latency jitter, poor Using Specifi c Components 90 41 signal quality, low statistical power, and so on. to Index Specifi c Processes 91 One of the most notable and widely used applica- 92 42 Solving and Avoiding the Problems tions of ERPs is to determine which specifi c neural 93 43 Associated with ERP Components or psychological process is aff ected by the factors 94 44 We have now seen how diffi cult it can be to associ- of interest in the experiment. In other words, does 95 45 ate changes in the observed ERP waveform with a particular manipulation aff ect process A or pro- 96 46 changes in an underlying ERP component. You cess B or alternatively, do two groups of individuals 97 47 may fi nd yourself rightfully wondering, so what is diff er in process A or process B? Using ERPs in this 98 48 this technique good for? In this section, we explore manner usually requires that (1) the precise neural 99 49 methods and strategies that have proven successful or psychological process indexed by a component is 100 50 in using ERP components to answer questions known and understood and that (2) the component 101 51 about the mind and brain. can be successfully isolated from the surrounding 102

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1 and overlapping ERP components. As discussed demonstrating that the N2pc is a scalp-recorded 53 2 earlier in the chapter, both of these requirements are signature of covert shifts of visual attention and on 54 3 diffi cult to meet; therefore, this branch of research the ability to isolate the N2pc from the surrounding 55 4 typically relies on a number of assumptions con- ERP activity (which was achieved by using contral- 56 5 cerning the specifi c nature of the ERP component ateral-minus-ipsilateral diff erence waves, as descri- 57 6 of interest. Th ese assumptions about the nature bed in more detail below). Additionally, we can use 58 7 of the component are usually based on a wealth of ERPs to assess whether multiple stages of processing 59 8 previous research on the component and ideally are aff ected in a patient group. For example, is the 60 9 include both studies in which the experimental RT slowing exhibited by schizophrenia patients 61 10 manipulations that alter the component are explored caused by a generalized slowing of all cognitive and 62 11 and studies that are specifi cally aimed at elucidating neural processing or a combination of some subset 63 12 the functional nature of the component (termed of processes? 64 13 ERPology by Luck, 2005 ). We will fi rst give an 14 example of using components in this process- methods for isolating an 65 15 dependent manner to make the main issues facing erp component 66 16 researchers in this domain concrete, followed by As described above, the ability to use ERP compo- 67 17 some tips on how to successfully isolate and mea- nents as indexes of specifi c processes (reverse infer- 68 18 sure an ERP component. ence) depends on the ability to successfully isolate 69 19 Imagine that we wanted to understand why the component of interest from the surrounding 70 20 schizophrenia patients show prolonged reaction ERP components. Th is is not an easy task. It may 71 21 times (RTs) across a wide variety of behavioral tasks, even seem impossible. However, there are a number 72 22 an eff ect that has been observed for decades (see the of tricks that can be used to isolate a particular ERP 73 23 review by Nuechterlein, 1977 ). In other words, component of interest from all of the other ongoing 74 24 which stage or stages of processing are slowed in activity. Although the specifi c methods will depend 75 25 schizophrenia patients, producing the slowing of on the specifi c task, ERP component, question of 76 26 behavioral RTs? We can address this question by interest, and so on, the following strategies have 77 27 examining whether particular ERP components are proven successful in a number of diff erent contexts. 78 28 aff ected in the patient group compared to healthy One strategy is to focus the experimental design 79 29 controls. Th at is, is the scalp-recorded signature of a on ERP components that are large compared to the 80 30 particular cognitive process delayed in latency or surrounding components. For example, the P3 wave 81 31 decreased in amplitude in the patients compared to is often >10 microvolts, making it easy to distinguish 82 32 the controls? Th is general approach has been used in from the much smaller surrounding and overlapping 83 33 studies of schizophrenia to examine abnormalities ERP components. A second strategy is to focus the 84 34 in many components, including the mismatch neg- task design such that only one or two ERP compo- 85 35 ativity (MMN), the P1 wave, the N2pc component, nents diff er across conditions. When the design 86 36 the P3 wave, the lateralized readiness potential, and focuses on a small number of ERP components, it 87 37 the error-related negativity (Bates et al., 2002 ; is easier to avoid signifi cant component overlap, 88 38 Butler et al., 2007 ; Javitt, 2000 ; Jeon & Polich, making the measurement of a specifi c component 89 39 2003 ; Luck et al., 2006 , 2009 ; see Chapter 19, this much easier. A third strategy involves subtracting 90 40 volume, for a review). out overlapping ERP components by creating dif- 91 41 Th is approach — as typically applied — requires ference waves between conditions or between elec- 92 42 that previous experiments have already linked a trode sites. For example, the lateralized readiness 93 43 component to this process, and it requires deter- potential (LRP) is a diff erence wave created by sub- 94 44 mining that a newly observed diff erence between tracting the voltage at sites ipsilateral to the response 95 45 patients and controls refl ects a change in this spe- hand from the activity at sites contralateral to the 96 46 cifi c component and not some other component response hand. Th is subtraction process eff ectively 97 47 (see the earlier sections on forward and reverse infer- isolates only the activity related to res ponse selec- 98 48 ence). For example, the N2pc component was used tion, subtracting away the many other processes that 99 49 to assess whether prolonged behavioral RTs are do not diff er between the contralateral and ipsilat- 100 50 accompanied by delays in the allocation of covert eral hemispheres; indeed, any brain activity that 101 51 visual spatial attention in schizophrenia patients diff ers between the contralateral and ipsilateral elec- 102 52 (Luck et al., 2006 ), which relied on previous work trode sites (relative to the hand that responds) must 103

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1 be generated during or after the process that deter- methods for measuring an 53 2 mines which hand should respond (see Chapter 9, erp component 54 3 this volume). Similarly, by computing a rare-minus- Once a component has been successfully isolated 55 4 frequent diff erence wave in an oddball paradigm, it from the overlapping activity, some quantitative 56 5 is possible to isolate probability-sensitive ERP com- assessment of the component must be made in order 57 6 ponents such as the P3 wave (see, e.g., Luck et al., to compare it across conditions or across groups of 58 7 2009 ; Vogel et al., 1998 ). subjects. Th e most widely used quantitative charac- 59 8 Although diff erence waveforms can be an eff ec- terizations of ERP components include amplitude 60 9 tive tool in isolating specifi c ERP components, they and latency assessments. Despite the inherent dif- 61 10 are not a panacea. First, a diff erence waveform is ference between peaks and components described 62 11 eff ective in isolating a specifi c ERP component only above, it is common for ERP researchers to quantify 63 12 when all or most other components do no not vary ERP results by measuring the amplitude and latency 64 13 across the two conditions used in the subtraction. of the peaks. Peak amplitude and peak latency mea- 65 14 Second, when a diff erence wave varies in amplitude sures are generally computed by choosing a time 66 15 across groups or across conditions, it is diffi cult to window surrounding a peak in the waveform and 67 16 know which of the two waveforms used in the sub- fi nding the most positive point in that time win- 68 17 traction actually varies. For example, the LRP is dow (or the most negative point for a negative- 69 18 decreased in schizophrenia patients relative to con- going peak). Th e amplitude at this point is used as 70 19 trol subjects (Luck et al., 2009 ), but this could a representation of the magnitude of the compo- 71 20 refl ect less activation over the contralateral hemi- nent, and the latency of this point is used as a repre- 72 21 sphere or more activation over the ipsilateral hemi- sentation of the timing of the component. 73 22 sphere. Th ird, activity in a diff erence wave could Historically, peak measures were employed because, 74 23 refl ect latency diff erences between the two original as Donchin and Heffl ey ( 1978 ) so aptly stated, “it 75 24 waveforms rather than a diff erence in amplitude. requires nothing but an x-y plotter, a ruler, and 76 25 An additional class of strategies uses scalp distri- enough time” (p. 557). Th ese were often all that a 77 26 bution information to isolate components. A simple typical ERP researcher had at his or her disposal in 78 27 version of this strategy is simply to measure a given the early days of ERP research, but researchers today 79 28 component at an electrode site where this compo- have computers capable of performing much more 80 29 nent is relatively large and other components are advanced algorithms than those that a ruler can 81 30 relatively small. A somewhat more sophisticated accomplish, and we are no longer limited to such 82 31 approach is to use a vector fi lter, which combines the simple measurement techniques. 83 32 data across all scalp sites in a manner that refl ects Is there anything special about the amplitude or 84 33 the scalp distribution of a given component (see, timing of the peaks in the observed ERP waveforms? 85 34 e.g., Gehring et al., 1992 ). Event-related potential As Figure 1.2 illustrates, the amplitude and timing 86 35 source localization techniques go one step further, of the peaks in the observed waveform may be quite 87 36 providing a source waveform for each estimated diff erent from the amplitude and timing of the 88 37 generator site. In addition, ICA and PCA can use underlying components that sum together to pro- 89 38 scalp distribution information to isolate the time duce the observed waveform. And as Figure 1.3 90 39 course of each component. illustrates, factors such as latency variability can 91 40 When evaluating these diff erent approaches, it is strongly infl uence peak amplitude. Moreover, it 92 41 important to remember that, just as every researcher seems simplistic to assume that a process that 93 42 has his or her own individual limitations, each tech- extends over hundreds of milliseconds can be quan- 94 43 nique used to isolate ERP components is limited in tifi ed by the value of a single time point. In addi- 95 44 its own special way. No technique — despite what its tion, when the values are measured at multiple 96 45 proponents may shout loudly from the research electrode sites, it makes no sense to use the peak at 97 46 pulpit— is without its shortcomings, fl aws, and lim- each electrode site to measure a single component: 98 47 itations. Successfully using any of the techniques at Th e peak will occur at a diff erent time at each elec- 99 48 our disposal requires that we know and understand trode site, but a given component necessarily has 100 49 the limitations of the method. Before using source the same time course at each electrode site (because 101 50 localization, ICA, or even simple diff erence waves, of the instantaneous transmission of voltage). Peak 102 51 one must be careful to fully understand how the measures have other shortcomings as well (summa- 103 52 technique works and when it might fail. rized in Luck, 2005 , chap. 6), and there is a clear 104

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1 trend away from peak measures among sophisti- MMN from the deviant-minus-standard diff erence 29 2 cated ERP researchers. wave. 30 3 How, then, can one better quantify the magni- By measuring amplitude or latency from a diff er- 31 4 tude and timing of an ERP component? Th e fi rst ence wave, the contributions of the overlapping 32 5 step is usually to isolate the component by comput- peaks are reduced or eliminated. Of course, this will 33 6 ing some kind of diff erence wave that subtracts away work well only if the other components are equiva- 34 7 most of the other components. As an example, con- lent across the two waveforms that are used for the 35 8 sider the MMN data shown in Figure 1.6 . In this subtraction so that they are eliminated in the diff er- 36 9 experiment, subjects were presented with a fre- ence wave. One could use a peak amplitude measure 37 10 quently occurring standard pitch or a rare deviant to quantify the amplitude of a component in the dif- 38 11 pitch every 1000 ms (see Chapter 6, this volume, ference, and this would certainly be an impro vement 39 12 for details). When the deviant pitch was suffi ciently over measuring peak amplitude from the two origi- 40 13 diff erent from the standard pitch, the ERP wave- nal waveforms used in the subtraction. However, 41 14 form was more negative for the deviant pitch than there is still no particular reason to choose this one 42 15 for the standard pitch from approximately 100 to point as a refl ection of the magnitude of the underly- 43 16 200 ms poststimulus. If we attempted to quantify ing process. If one is interested in the overall magni- 44 17 the magnitude of this eff ect by measuring the ampli- tude of a brain response, it is usually more reasonable 45 18 tude of the most negative peak between 100 and to measure the area under the curve or the mean 46 19 200 ms, we would face two serious problems. First, voltage over the duration of the component (these 47 20 because the overall waveform contains a P2 peak are nearly equivalent: mean is simply area divided by 48 21 during this interval, there is no negative peak to be duration). An important exception arises, however, 49 22 measured in many of the waveforms shown in when one is trying to measure the amplitude of a 50 23 Figure 1.6 (especially in the waveforms elicited by component that varies in latency across conditions 51 24 the standards). Second, even if we could fi nd a nega- or across groups; in this case, it may be necessary to 52 25 tive peak, the voltage at this peak would refl ect a com- use a method that fi nds the peak and then measures 53 26 bination of this P2 wave, the MMN, and any other the amplitude at (or around) this peak. 54 27 components that were active during this period.5 Peak latency is also a poor measure in most cases, 55 28 Th us, it is better to quantify the magnitude of the because the latency of the peak is not usually a par- 56 ticularly interesting time point. Quantifying the 57 latency of an ERP component by fi nding the peak is 58 Fz Difference analogous to quantifying RT by fi nding the mode 59 Deviant (Hz) of the RT distribution for each subject. Instead, it 60 − is sometimes possible to quantify the midpoint of a 61 5 mV 1004 + component by fi nding the time point that divides 62 1008 the area under the curve into two equal portions. Th is 63 is called the 50 % area latency measure, and it is closely 64 1016 related to median RT (see Luck, 2005 , chap. 6). 65 MMN In addition, theories of cognitive processes often 66 1032 make predictions about the onset or duration of a 67 process rather than the midpoint. Kiesel and col- 68 leagues ( 2008 ) provide an excellent comparison of 69 200 400 ms 200 400 ms the diff erent methods that can be used for the onset 70 S S of a component, and these methods can be easily 71 extended to measure the off set and duration of a 72 Standard (1000 Hz) component. 73 Deviant

Fig. 1.6. Example of the use of diff erence waves in the context Assessing the Time Course of Processing 74 of MMN. Th e left side shows the waveform elicited by a 1000 Th e temporal resolution of ERPs makes them an 75 Hz standard tone that occurred on 80% of trials, overlaid with excellent tool for determining the time course of a 76 deviant stimuli that diff ered in pitch from the standard by 77 varying amounts and occurred on 20% of trials. Th e right side neural or psychological process. Th e simplest way to shows the deviant-minus-standard diff erence waves. Note that do this is to measure the latency of a given peak in 78 this is the same as in Figure 6.1 in Chapter 6, this volume. two diff erent conditions or two diff erent groups and 79

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1 use this as a measure of the amount of time required in scalp ERP recordings, the brain might make a dis- 54 2 for this process to occur in the two conditions or tinction between two stimuli long before the fi rst 55 3 two groups. However, this approach is not usually point at which the scalp-recorded signals diff er. 56 4 very powerful, because it does not isolate a specifi c Th erefore, one can use ERPs to say that a particular 57 5 component and because it uses the peak as a mea- eff ect has occurred by a particular time point, but 58 6 sure of timing. A more powerful approach is to one cannot use ERPs to conclude that an eff ect did 59 7 compare the waveforms from two conditions or not begin until a particular time. In our emotional 60 8 from two groups of subjects to ascertain the point in content example, one could conclude that the brain 61 9 time at which the waveforms begin to diverge. For has begun to process information related to emo- 62 10 example, ERPs have been used in the emotion lit- tional information by the point at which the wave- 63 11 erature to determine when, after the onset of a stim- forms diverge. However, one could not say that 64 12 ulus, processing diff ers between emotion-evoking emotional processing did not begin until that time 65 13 and neutral stimuli (see Chapter 16, this volume). point, because the eff ect could have begun earlier in 66 14 Th ere are advantages and limitations to using ERPs brain areas that did not give rise to a scalp-recorded 67 15 in this manner, and we will explore both of these ERP. Generally speaking, this technique is valuable 68 16 through some examples below. in providing evidence that an eff ect happens early in 69 17 Let’s consider the emotion example mentioned the processing stream, but it cannot be used to prove 70 18 above, in which we wish to know by what point in that an eff ect does not happen until late in the pro- 71 19 time processing related to the emotional content of cessing stream. 72 20 a stimulus has begun. In other words, by what point Th e limitations in the conclusions that can be 73 21 in time has the brain distinguished between emo- drawn about timing from ERPs may seem debil- 74 22 tional and nonemotional stimuli? We can answer itating to the technique, but using ERPs in this 75 23 this question by comparing the ERP waveforms manner has answered many important questions 76 24 elicited by neutral stimuli (e.g., a picture of a land- about cognitive and neural processing. For example, 77 25 scape) and emotion-eliciting stimuli (e.g., a picture ERPs were able to end a long-standing debate in the 78 26 of a mutilation). We can use the time point at which attention literature about whether attention oper- 79 27 the waveforms begin to diverge as a measure of ates at an early stage or a late stage of processing (for 80 28 when the brain has distinguished between the neu- reviews, see Hillyard et al., 1998 ; Luck et al., 2000 ). 81 29 tral and emotional stimuli. Th at is, the waveforms It is diffi cult to determine from behavioral studies 82 30 between an emotional and a nonemotional condi- whether the eff ects of attention on response speed 83 31 tion cannot diverge until the brain has begun to and accuracy arose from changes in perceptual pro- 84 32 distinguish the emotional content of the stimulus cessing or changes in a postperceptual stage of 85 33 (provided that all other factors, including physical processing. However, because ERPs provide a con- 86 34 stimulus factors, are matched between the condi- tinuous measure of processing between the stim ulus 87 35 tions). Th e advantage of this approach is that, and the response, they can indicate whether the 88 36 although specifi c ERP components may diff er bet- attention eff ects begin early or late in the processing 89 37 ween the conditions, the conclusions about timing stream. Th at is, the locus of selection can be assessed 90 38 do not rely on isolating a specifi c ERP component. directly by asking whether the ERP waveforms for 91 39 Th at is, the presence of a diff erence between condi- attended and ignored stimuli diverge early in time 92 40 tions at a given time indicates that the brain has dis- (e.g., within the fi rst 100 ms after stimulus onset) or 93 41 tinguished between the two conditions by this time, late in time (e.g., more than a few hundred millisec- 94 42 regardless of which component was responsible for onds after stimulus onset). Research using this 95 43 this diff erence. Th is approach is one case of what are approach has shown that — at least under some con- 96 44 called component-independent experimental designs ditions — attention infl uences sensory processing 97 45 (see Luck, 2005 , chap. 2). within the fi rst 50 ms after stimulus onset for audi- 98 46 Because this method does not require isolating a tory stimuli and within the fi rst 100 ms after stimu- 99 47 specifi c component or linking a component with a lus onset for visual stimuli (see Chapter 11, this 100 48 specifi c process, it generally requires fewer assump- volume). Th ese ERP results provided key evidence 101 49 tions than using ERPs in a component-dependent in favor of early selection models of attention, help- 102 50 manner. However, there are some limitations to this ing to answer a fundamental question that could not 103 51 approach. For example, it is important to note that be easily addressed using behavioral techniques. 104 52 this method provides an upper bound on the timing Th is time-based approach is often combined 105 53 of an eff ect. Because many processes may be invisible with the process-specifi c approach described in the 106

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1 previous section, in which the eff ects are linked with under other conditions attention only infl uences 53 2 specifi c components. For example, researchers have postperceptual processes that follow word identifi ca- 54 3 argued that the early ERP attention eff ects consist tion (see the reviews by Luck & Hillyard, 1999 ; Luck 55 4 of modulations of specifi c sensory-evoked ERP & Vecera, 2002 ). Under these latter conditions, atten- 56 5 components (see, e.g., Di Russo et al., 2003 ; tion has no impact on the diff erence in N400 ampli- 57 6 Woldorff et al., 1993 ). Th is has been diffi cult to tude for words that match versus mismatch the 58 7 establish with complete certainty because of the current semantic context. 59 8 many diffi culties associated with trying to identify As a second example, consider the P3b compo- 60 9 specifi c components, as discussed earlier in the nent, which every ERP researcher knows is larger 61 10 chapter. However, the converging evidence approach for infrequent target stimuli than for frequently 62 11 described earlier in this chapter has been used to occurring standard stimuli. However, an important 63 12 provide substantial support for the hypothesis that implication of this probability dependence often 64 13 attention infl uences specifi c ERP components. Even goes unnoticed. Specifi cally, the onset of the diff er- 65 14 more important, the simple fact that the waveforms ence in P3b amplitude between rare and frequent 66 15 for attended and unattended stimuli diverge at an stimuli cannot occur until the brain has at least 67 16 early time provides very strong evidence that atten- begun to determine whether the eliciting stimulus 68 17 tion can infl uence perceptual processing. belongs to the rare category or the frequent category. 69 Th is implication was spelled out very clearly by 70 18 measuring processes that occur prior Kutas and colleagues (1977 ), who framed it in terms 71 19 to a component of the then-popular idea that the P3b component 72 20 A related approach uses an ERP component to was elicited by surprising stimuli: “before a stimulus 73 21 assess the processes that must have occurred prior to can surprise it must be identifi ed. As P300 com- 74 22 the ERP component. Th e advantage of this approach monly appears as a discriminative response to spe- 75 23 is that it does not require that we fi rst determine a cifi c stimuli within a series, its elicitation must 76 24 solid link between an ERP component and a spe- be preceded by an adequate evaluation of the stimu- 77 25 cifi c process (i.e., we do not need to solve the for- lus at some level of processing” (p. 792–793). Th is 78 26 ward inference problem). Instead, we can use simple idea is commonly described by saying that the 79 27 assumptions about the processes that must have latency of the P3 wave refl ects stimulus evaluation 80 28 occurred prior to the ERP component to draw time, but this is a somewhat vague description. It is 81 29 inferences about these processes. much more precise— and powerful— to say that the 82 30 As an example, consider the N400 component, onset of the diff erence between the waveforms elic- 83 31 which countless studies have shown is larger for words ited by the rare and frequent stimuli refl ects a time 84 32 that mismatch the current semantic context than for at which the brain has begun to determine whether 85 33 words that match this context (reviewed by Kutas, the stimulus belongs to the rare or the frequent cat- 86 34 1997 ). For example, the word nurse will elicit a larger egory. Th at is, the waveforms between these two 87 35 N400 if it is preceded by an unrelated word such as conditions could not diff er until the brain has deter- 88 36 cup than if it is preceded by a related word such as mined whether the stimulus belongs to the rare or 89 37 doctor . Substantial controversy surrounds the ques- the frequent category, indicating that by that point 90 38 tion of exactly what process the N400 component the brain has begun to categorize the stimuli. 91 39 represents (see Chapter 15, this volume). However, it We have applied this more precise framing of P3b 92 40 is safe to assume that this diff erence in N400 between latency to understanding why behavioral RTs are 93 41 words that match and mismatch a semantic context slowed in patients with schizophrenia (Luck et al., 94 42 could not occur unless the words were perceived. 2009 ). Each stimulus in this experiment was 95 43 Th us, if we see that a given word elicits a larger N400 a digit or a letter, with one category rare ( p = .2) and 96 44 when the preceding word was related than if it was the other frequent ( p = .8). Subjects were asked 97 45 unrelated, then we can be certain that the words were press a button with one hand for digits and a button 98 46 perceived. Th is logic has been used to show that, with the other hand for letters, and patient RTs were 99 47 under certain conditions, attention does not infl u- approximately 60 ms slower than control RTs. 100 48 ence sensory processing and that words are fully per- As shown in Figure 1.7 , the voltage in the P3 latency 101 49 ceived even when unattended (Luck et al., 1996 ; range was larger for control subjects than for patients, 102 50 Vogel et al., 1998 , 2005 ). Th at is, although attention for both the rare and frequent stimulus categories, 103 51 infl uences sensory processing under some conditions, but the latency of the P3 peak was similar across 104 52 modulating the early sensory-evoked components, groups. However, given that many diff erent processes 105

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to the categorization of the stimuli, but was instead 25 caused by postcategorization slowing. Th is conclu- 26 +5 mV Frequent sion was further supported by a reduction in the 27 amplitude and a slowing of the latency of the lateral- 28 −200 200 400 600 800 1000 −5 mV ized readiness potential in the patients compared to 29 the controls. 30 It is important to note that, in both of these 31 examples, conclusions were drawn about the pro- 32 +5 mV cesses that logically must have preceded the compo- 33 Rare nent being measured rather than the process the 34 −200 200 400 600 800 1000 component directly refl ected. Th at is, the N400 was 35 −5 mV used to assess the perceptual processes that must 36 occur before the brain can distinguish between 37 semantically related and unrelated words, and the 38 Rare minus +5 mV Frequent P3b was used to assess the perceptual and categori- 39 zation processes that must occur before the brain 40 − 200 200 400 600 800 1000 can determine whether a stimulus belongs to a rare 41 −5 mV Controls or a frequent category. An important advantage of 42 43 Patients this approach is that we do not need to know with certainty what process produces a given ERP com- 44 Fig. 1.7. Grand average ERPs recorded at the Pz electrode site ponent. Instead, we can make very straightforward 45 from schizophrenia patients and control subjects (from the assumptions about what processes must occur for a 46 study of Luck et al., 2009 ). Th e patient and control waveforms are overlaid for the frequent stimuli, the standard stimuli, and component to diff er across conditions. In many 47 the rare-minus-frequent diff erence wave. Triangles show mean cases, it does not actually matter which component 48 P3 latency for each group, quantifi ed as peak latency for the diff ers across conditions; the mere presence of a dif- 49 % rare and frequent stimuli and 50 area latency (the point that ference indicates that the brain has made a specifi c 50 divides the area under the curve into two equal portions) for discrimination by a given point in time. Th us, this 51 the diff erence wave. is another example of a component-independent 52 approach. Th is does not mean that components are 53 1 presumably overlap during the P3 time range, it is irrelevant in the design of the experiment. Instead, 54 2 diffi cult to draw fi rm conclusions on the basis of the it means that the conclusions do not depend on 55 3 time of the peak voltage in this time range. More whether a specifi c component has been identifi ed in 56 4 precise conclusions can be drawn by examining the the results. 57 5 rare-minus-frequent diff erence waves in each group 6 (Figure 1.7 , bottom). Th ese diff erence waves refl ect Uncovering and Subdividing 58 7 the diff erential processing of the rare and frequent Mental Processes 59 8 stimulus categories, and any nonzero voltages in Event-related potentials have also been useful in 60 9 these diff erence waves must be a consequence of a identifying new, previously unknown mental pro- 61 10 preceding process that determined the category to cesses and subdividing known processes into multi- 62 11 which a stimulus belonged. Th e only diff erence ple separate subprocesses. From behavioral measures, 63 12 between patients and controls in these diff erence it is diffi cult to ascertain how many mental processes 64 13 waves was a reduction in amplitude in the time range intervene between the occurrence of a stimulus and 65 14 of the N2 wave in patients. Th e diff erence waves the execution of a behavior. However, ERPs provide 66 15 were nearly identical across groups in the P3 time a continuous measure of processing before, during, 67 16 range, and the midpoint of the defl ection in this and after the execution of the behavior. Th erefore, it 68 17 wave (the time that divided the area under the curve is possible with ERPs to identify processes that were 69 18 into equal halves) was nearly identical across groups. previously unknown. 70 19 Th us, no delay was observed in the brain’s diff eren- For example, error-related negativity (ERN; see 71 20 tial responding to rare versus frequent stimuli in the Chapter 10, this volume) occurs after the execution 72 21 patients compared to the controls, despite a 60 ms of a response and therefore refl ects a process that 73 22 slowing of the behavioral response in patients. Th is behavioral measures cannot directly measure. 74 23 suggests that the slowing of behavioral responses was Although previous studies had pointed to the exis- 75 24 not caused by a slowing of the processes that lead up tence of processes related to detecting and correcting 76

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1 errors (e.g., Laming, 1979 ; Rabbitt, 1966 ), no one Behavioral methods used with infants almost 53 2 had hypothesized a process with the timing of the always take advantage of the fact that infants tend to 54 3 ERN. Th e ERN helped to focus research on the pro- orient toward some types of stimuli (e.g., complex, 55 4 cesses occurring within 100 ms of an error response, dynamic, or novel stimuli) more than other types 56 5 which has led not only to numerous studies of pro- of stimuli (Brennan et al., 1966 ). And if they exhibit 57 6 cesses related to error detection, but also to a large greater looking times toward one category of stimuli 58 7 literature on response-confl ict monitoring. than another, then this is evidence that they were 59 8 Similarly, ERPs can be used to determine whether able to distinguish between these categories (Spelke, 60 9 a given behavioral eff ect is the result of a change in a 1985 ). Th e categories can be simple sensory catego- 61 10 single process or of multiple separable subprocesses. ries (e.g., the presence versus absence of a fi ne pat- 62 11 Almost every experimental manipulation that pro- tern) or complex conceptual categories (e.g., animal 63 12 duces a behavioral eff ect leads to diff erences between versus nonanimal). However, it is always possible 64 13 conditions in multiple ERP components, and this that infants are able to make a particular discrimina- 65 14 naturally leads to the idea that the behavioral eff ect tion even if they fail to exhibit any behavioral ori- 66 15 refl ects changes in more than one process. Consider, enting on the basis of this discrimination. Moreover, 67 16 for example, manipulations of attention. It is parsi- these techniques are diffi cult to use prior to about 68 17 monious to assume that any experiment in which 4 months of age owing to poor motor control. 69 18 behavioral responses are faster or more accurate for Event-related potentials can be useful in these situa- 70 19 attended stimuli than for unattended stimuli refl ects tions to determine whether the brain has made a 71 20 the operation of a single mechanism of attention, given discrimination. 72 21 and most behavior-inspired theories of attention For many years, ERPs have been used in this way 73 22 have taken a monolithic view of attention. However, to determine whether newborn infants might be 74 23 ERP studies have demonstrated that diff erent suff ering from hearing loss. Specifi cally, a rapid 75 24 manipulations of attention infl uence diff erent ERP sequence of clicks is presented, and the amplitude 76 25 components, demonstrating that diff erent mecha- and latency of the early brainstem evoked responses 77 26 nisms of attention operate to produce the observed are used to determine whether the sensory response 78 27 behavioral eff ects under diff erent conditions (see is abnormal (Stapells, 1989 ). Th e auditory MMN 79 28 Chapter 11, this volume). Th ese ERP studies have component has also been widely used to assess the 80 29 inspired behavioral studies demonstrating that the ability of infants to make more complex perceptual 81 30 details of the behavioral attention eff ects are indeed discriminations, such as distinctions between pho- 82 31 best explained by the existence of multiple mecha- nemes (see Chapter 6, this volume). Other compo- 83 32 nisms of attention (see, e.g., Vogel et al., 2005 ). nents have been used to assess higher-level aspects 84 33 Th us, the ability to monitor multiple processes with of visual processing in infancy, such as face percep- 85 34 ERPs makes it possible to provide empirical evi- tion, and even higher-level cognitive discrimina- 86 35 dence against simplistic explanations of behavior tions (see Chapter 17, this volume). It is generally 87 36 that invoke a single mechanism. easier to assess lower-level sensory processes than 88 higher-level cognitive processes with ERPs, because 89 37 Covert Monitoring the sensory processes can typically be assessed with- 90 38 A fi nal ERP approach involves using ERPs as a out any kind of task. Higher-level processes are 91 39 means of “covertly monitoring” processing in situa- typically task-dependent, and it is diffi cult to teach 92 40 tions in which behavioral output is uninformative, infants a task that will elicit these processes reliably. 93 41 inapplicable, or unavailable. Th ere are three gen- One can sometimes take advantage of spontaneous 94 42 eral situations in which this approach is applied: diff erences in processing between, for example, rare 95 43 (1) assessing processing in individuals who cannot and frequent stimulus categories, but these sponta- 96 44 or will not make a behavioral response (e.g., infants, neous diff erences may habituate before enough 97 45 coma patients); (2) assessing processing under con- trials have been acquired to obtain reliable averaged 98 46 ditions in which requiring a behavioral response ERP waveforms. 99 47 might invalidate the task (e.g., monitoring the pro- Event-related potentials can also be used in indi- 100 48 cessing of unattended stimuli); and (3) assessing viduals who are unable to make behavioral responses 101 49 processes that might not be evident in behavior due to a medical condition. In amyotrophic lateral 102 50 (e.g., the processing of subliminal stimuli). In this sclerosis, for example, ERPs have been used to create 103 51 section, we will provide examples of all three of brain–computer interfaces that allow patients to 104 52 these situations. communicate with their families and caregivers 105

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1 (Silvoni et al., 2009 ). Another recent example comes but it does impair the processes needed to generate 54 2 from coma research, where ERPs have been used an N400 diff erence between words that match 55 3 to predict which patients are likely to recover versus mismatch a semantic context (Reiss & 56 4 (Fischer et al., 2004 ). Th ere are also cases in which Hoff man, 2006 ). Th is pattern of results indicates 57 5 an individual might refuse to make a valid behav- that this variety of masking operates after early per- 58 6 ioral response, such as a suspect in a in crime, and ceptual processing but prior to semantic analysis. 59 7 ERPs have been used to assess whether people have Similarly, stimuli that are associated with a given 60 8 knowledge of an event that they are not admitting response will activate the preparation of that 61 9 (e.g., Farwell & Donchin, 1991 ). response, as indexed by the LRP, even if the subject 62 10 Another type of covert monitoring approach is is unaware of the stimulus and does not actually 63 11 used when the requirement to make a behavioral execute the response (Dehaene et al., 1998 ). 64 12 response might interfere with the processing of 13 a task. Th e most obvious example of this arises in Conclusions 65 14 attention research, in which ERPs have been widely Th e ERP technique provides a unique and highly 66 15 used to compare the processing of attended and informative perspective on brain processing, but 67 16 unattended stimuli (see Chapter 11, this volume). like all techniques it suff ers from challenges, diffi - 68 17 Requiring a behavioral response for an unattended culties, and limitations. Th e goal of this chapter was 69 18 stimulus presumably creates an incentive to attend to chronicle both the positive and negative sides of 70 19 to the stimulus, which is problematic for the study ERPs, exploring issues that are often unaddressed in 71 20 of attention. However, because ERPs can be recorded the literature while providing a detailed set of strate- 72 21 just as easily for unattended stimuli as for attended gies that allow the technique to be optimally 73 22 stimuli, they can be used to assess the processing employed. We hope that these recommendations 74 23 of stimuli for which there is absolutely no incentive allow the reader to understand and avoid the down 75 24 to attend. sides of ERP research while also adopting our view 76 25 Th is approach has also been used extensively in that the positives of ERP research outweigh the 77 26 language research (see Chapter 15, this volume). negatives. 78 27 In studies of sentence comprehension, it is diffi cult 28 to assess the processing of each individual word by 79 29 means of behavioral measures, because this would Acknowledgment We thank Bill Gehring and Greg Hajcak for helpful 80 30 require interrupting the sentence for a response. editorial comments and suggestions. 81 31 Eye movement measures have often been used for 32 this purpose in studies of reading, because the eye 33 movements are a naturally occurring part of the Notes 82 34 reading process. However, the eye movements are 1 Th e negative peaks of the waveform are sometimes referred to 83 as troughs; however, there is nothing special about whether 84 35 still discrete events that occur some time after the the activity is positive or negative in polarity. Th erefore, we 85 36 eyes have landed on a given word, and they are will refer to both the positive and negative defl ections in the 86 37 applicable primarily in the context of written lan- waveform as peaks. 87 38 guage comprehension rather than spoken language 2 Th e necessity for summation across large groups of neurons 88 39 comprehension. to observe a scalp ERP has implications that are often 89 neglected by researchers. First, the magnitude of an ERP will 90 40 Th e third variety of covert monitoring involves depend on both the size of the individual postsynaptic poten- 91 41 asking questions about processes that might not be tials and the number of neurons that are active. Second, 92 42 evident in behavior. Th at is, the brain may engage in many neurons that are simultaneously active within a given 93 43 a given process and reach a specifi c result without cortical region may actually be doing very diff erent things, 94 44 that result reaching awareness or triggering a behav- and an ERP component may therefore refl ect a mixture 95 of diff erent neural responses. Bill Gehring suggested to us 96 45 ioral response. Th e most obvious case of this arises that recording the ERP waveform is analogous to measuring 97 46 in research on perception without awareness. By the number of cars crossing the San Francisco Bay Bridge 98 47 using ERPs, it is possible to determine how much at a given time of day: Th ese cars may have nothing in 99 48 information has been extracted from a stimulus that common except that many of their drivers are heading home 100 49 fails to reach awareness. For example, research has for dinner. Th us, ERPs may be useful for answering broad 101 questions about neural activity (analogous to asking when 102 50 shown that a specifi c type of masking (object substi- most people end their workday in San Francisco) and not as 103 51 tution masking) does not eliminate the orienting useful for answering narrow questions (analogous to asking 104 52 of attention to a target stimulus, as indexed by where individual cars are going or what their occupants are 105 53 the N2pc component (Woodman & Luck, 2003a ), doing). 106

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1 3 Analogous eff ects can be seen for neural fi ring rates; for exam- Farwell , L. A. , & Donchin , E. ( 1991 ). Th e truth will out: 63 2 ple, the duration of a change in the fi ring rate of a typical Interrogative polygraphy (“lie detection”) with event-related 64 3 neuron in visual cortex following a brief stimulus is typically brain potentials . Psychophysiology , 28 , 531 – 547 . 65 4 at least 100 ms. Th is is presumably a result of PSPs that last at Fischer , C. , Luaute , J. , Adeleine , P. , & Morlet , D. ( 2004 ). 66 5 least 100 ms. Predictive value of sensory and cognitive evoked potentials 67 6 4 Th e claim that brain processes involve individual brain areas for awakening from coma . Neurology , 63 , 669 – 673 . 68 7 requires us to be a bit more specifi c about what we mean by Galambos , R. , & Sheatz , G. C. ( 1962 ). An electroencephalo- 69 8 the term process, because much brain activity involves the graphic study of classical conditioning. American Journal of 70 9 interaction of multiple brain areas. We are using the term Physiology , 203 , 173 – 184 . 71 10 process to mean an elementary computation that might plau- Gehring , W. J. , Gratton , G. , Coles , M. , & Donchin , E. ( 1992 ). 72 11 sibly occur within a single brain area (e.g., spatial fi ltering Probability eff ects on stimulus evaluation and response pro- 73 12 based on lateral inhibition within an area) rather than a mul- cesses . Journal of Experimental Psychology: Human Perception 74 13 tistep computation that likely involves the coordinated oper- and Performance , 18 , 198 – 216 . 75 14 ation of multiple brain areas (e.g., retrieval of an item from Girelli , M. , & Luck , S. J. ( 1997 ). Are the same attentional mech- 76 15 memory). anisms used to detect visual search targets defi ned by color, 77 16 5 It should be noted that these two problems are not this orientation, and motion? Journal of Cognitive Neuroscience , 9 , 78 17 extreme in all cases. For example, if one measures the ampli- 238 – 253 . 79 18 tude or latency of the P3 peak when this component is much Hickey , C. , Di Lollo , V. , & McDonald , J. J. ( 2009 ). 80 19 larger than all of the other components, then these measures Electrophysiological indices of target and distractor process- 81 20 will not be greatly distorted by the overlapping components. ing in visual search . Journal of Cognitive Neuroscience , 21 , 82 21 However, other shortcomings of peak measures still apply 760 – 775 . 83 22 in this situation, and small diff erences between groups or Hillyard , S. A. , Vogel , E. K. , & Luck , S. J. ( 1998 ). Sensory gain 84 23 conditions could easily refl ect diff erences in the overlapping control (amplifi cation) as a mechanism of selective attention: 85 24 components. Electrophysiological and neuroimaging evidence. Philoso- 86 phical Transactions of the Royal Society: Biological Sciences , 87 25 References 353 , 1257 – 1270 . 88 26 Bates , A. T. , Kiehl , K. 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1 Luck , S. J. , Girelli , M. , McDermott , M. T. , & Ford , M. A. Rabbitt , P. M. ( 1966 ). Errors and error correction in choice- 49 2 (1997 ). Bridging the gap between monkey neurophysiology response tasks. Journal of Experimental Psychology , 71 , 50 3 and human perception: An ambiguity resolution theory of 264 – 272 . 51 4 visual selective attention . Cognitive Psychology , 33 , 64 – 87 . Reiss , J. E. , & Hoff man , J. E. ( 2006 ). Object substitution mask- 52 5 Luck , S. J. , & Hillyard , S. A. ( 1994 ). Spatial fi ltering during ing interferes with semantic processing: Evidence from event- 53 6 visual search: Evidence from human electrophysiology. related potentials . Psychological Science , 17 , 1015 – 1020 . 54 7 Journal of Experimental Psychology: Human Perception and Shulman , R. G. ( 1996 ). Interview with Robert G. Shulman . 55 8 Performance , 20 , 1000 – 1014 . Journal of Cognitive Neuroscience , 8 , 474 – 480 . 56 9 Luck , S. J. , & Hillyard , S. A . ( 1999 ). 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Journal of Cognitive Neuroscience , 17 , 79 32 Mazaheri , A. , & Jensen , O. ( 2008 ). Asymmetric amplitude mod- 1907 – 1922 . 80 33 ulations of brain oscillations generate slow evoked responses . Woldorff , M. G. , Gallen , C. C. , Hampson , S. A. , Hillyard , S. A. , 81 34 Journal of Neuroscience , 28 , 7781 – 7787 . Pantev , C. , Sobel , D. , & Bloom , F. E. ( 1993 ). Modulation of 82 35 Miller , J. , Patterson , T. , & Ulrich , R. ( 1998 ). Jackknife-based early sensory processing in human auditory cortex during 83 36 method for measuring LRP onset latency diff erences . Psycho- auditory selective attention. Proceedings of the National 84 37 physiology , 35 , 99 – 115 . Academy of Sciences USA , 90 , 8722 – 8726 . 85 38 Miller , J. , Ulrich , R. , & Schwarz , W. ( 2009 ). Why jackknifi ng Woodman , G. F. , & Luck , S. J. ( 1999 ). Electrophysiological 86 39 yields good latency estimates. 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