Instructions to suppress semantic memory enhances or has no effect on P300 in a concealed information test (CIT)

    Instructions to Suppress Semantic Memory Enhances or has no effect on P300 in a Concealed Information Test (CIT) J. Peter Rosenfeld, Anne Ward, Jesse Drapekin, Elena Labkovsky, Samuel Tullman PII: DOI: Reference:

S0167-8760(17)30003-X doi:10.1016/j.ijpsycho.2017.01.001 INTPSY 11222

To appear in:

International Journal of Psychophysiology

Received date: Revised date: Accepted date:

17 June 2016 11 December 2016 3 January 2017

Please cite this article as: Peter Rosenfeld, J., Ward, Anne, Drapekin, Jesse, Labkovsky, Elena, Tullman, Samuel, Instructions to Suppress Semantic Memory Enhances or has no effect on P300 in a Concealed Information Test (CIT), International Journal of Psychophysiology (2017), doi:10.1016/j.ijpsycho.2017.01.001

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RUNNING HEAD: P300 SUPPRESSION FAILS WITH SEMANTIC

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MEMORIES.

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Instructions to Suppress Semantic Memory Enhances or has no effect

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on P300 in a Concealed Information Test (CIT).

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Samuel Tullman.

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J. Peter Rosenfeld, Anne Ward, Jesse Drapekin, Elena Labkovsky,

Department of Psychology Northwestern University

Evanston, Il., USA, 60201

Corresponding author: J. Peter Rosenfeld [email protected]

12/12/2016

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Abstract:

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The present study investigated the extent to which people can

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suppress semantic memory as indexed with the P300 ERP and the autobiographical implicit association test (aIAT). In EXP 1, participants

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(22) were run in a counterbalanced repeated measures study in both

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simply knowledgeable (SK) and knowledgeable with suppression (SP)

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conditions. A P300-based, concealed information test (“Complex Trial Protocol”; CTP) with a 50/50 Target/Nontarget (T/NT) ratio was given

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both with and without instructions to suppress semantic memories. The results showed increased P300s to probe name stimuli, reduced (but still high positive) aIAT d-scores, and increased simple reaction times to all stimuli used in ERP tests in the SP condition. EXP 2 was similar, but with SP and SK in two separate groups, and a 20/80 T/NT ratio. Again, ERP and aIAT results failed to show a suppression effect for semantic memory. The behavioral data suggest some task demand effects under suppression instructions, and that EXP 1 was more demanding than EXP 2.

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General Introduction:

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Key Words: memory suppression, memory detection, autobiographical memory, P300, complex trial protocol, autobiographical implicit association test, neuroscience and law.

Voluntary suppression of memory has attracted much recent

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interest (e.g., Anderson & Hanslmayr, 2014; Gronau, Elber, Satran, Breska & Ben-Shakhar, G. , 2015). Evidence for suppression is largely

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based on demonstrations of suppression of response term memories in paired associate learning (Anderson& Green, 2001). Further support has come from recent reports of ERP correlates of such suppression: Bergstrom, Anderson, Buda, Simons, and Richardson, Klavehn, A. (2013) and Hu, X., Bergström, Z. M., Bodenhausen, G. V., & Rosenfeld, J. P. (2015) both reported that the P300 sign of recognition (Fabiani, Gratton, & Coles, 2000, but also related to familiarity; Rugg & Curran, 2007) could be voluntarily suppressed. Such data are of interest to memory researchers, as well as to applied researchers in concealed information detection, since suppression could pose a serious

ACCEPTED MANUSCRIPT countermeasure threat. In this paper, in two experiments, we: 1) challenge the support for suppression provided by ERP data when

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probing for semantic rather than episodic memory. This is the main goal of the present paper. We also 2) investigate how task demand in

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the Complex Trial Protocol (CTP) may appear to produce suppression-

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like results. We do this because the previous studies (Bergstrom et al.,

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2013; Hu et al., 2015) did not consider the possibility that demand can produce effects that resemble effects of suppression. We also review

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reservations with respect to the Bergstom et al. and Hu et al. studies, since this present paper is part of set of studies from this lab

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(including Ward & Rosenfeld, in press, and Hu et al., 2015) designed to challenge the claim that voluntary suppression of memory may pose a

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serious countermeasure threat to P300-based detection of concealed information (as implied by both earlier studies). Our critique of the earlier papers (including the one from our lab, i.e., Hu et al., 2015) is strictly methodological and theoretical; we are not concerned here with experimentally challenging the earlier efforts. Our actual experimental concern here, as noted above, is examining possible suppression effects on semantic memory and investigating demand properties of the suppression protocol. The Bergstrom et al. (2013) study used a variant of the Concealed Information Test (CIT) protocol (Lykken, 1959) in which

ACCEPTED MANUSCRIPT three kinds of stimuli are presented in a random order to subjects: The 1) rarely presented probes are the to-be-remembered items--

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often from a crime scene-- such as a stolen necklace. The frequently presented 2) Irrelevant stimuli are other similarly valuable items (a

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watch, a bracelet, a ring, etc.) which are in the same category

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(jewelry) as the probe, but are not recognized by the thief as the

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stolen item. The probe is recognized, and thus evokes a P300 in only the knowledgeable (guilty) subject. To unknowledgeable (innocent)

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suspects, the probe is also irrelevant and doesn’t evoke P300. The

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third stimulus, the 3) target, is another irrelevant item to which all

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subjects are instructed to respond with a unique (“target”) button press, indicating truly that the subject recognizes the target. This item helps enforce attention, since the subject must be alert for all stimuli as he/she awaits the specific, response-demanding target. A different “non-target” button is pressed (signifying nonrecognition) for both probe and irrelevants. (This is an honest response for innocents, but a lie for guilty subjects who do recognize the probe, as they signal non-recognition.) Although its task relevance helps generate P300 for the rare target, the rarely presented probe is also remembered automatically by the knowledgeable subject as the

ACCEPTED MANUSCRIPT crime-related item, so it too evokes P300 (despite the denial via the non-recognition button press). This 3-stimulus protocol (3SP,

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Rosenfeld, 2011) is effective but has been reported to be vulnerable to countermeasures (Rosenfeld, Soskins, Bosh, & Ryan, 2004; Mertens

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& Allen, 2008). This limitation led Rosenfeld, Labkovsky, Winograd,

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Lui, Vandenboom, and Chedid (2008) to develop the more

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countermeasure-resistant Complex Trial Protocol (CTP) in which probe or irrelevant presentation is separated by about one second from

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target or non-target presentation.

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Bergstrom et al. (2013) used the 3SP, whereas Hu et al., (2015) used the CTP in their P300 suppression studies. The former had three experimental conditions: 1) a guilty cooperative condition in which subjects were encouraged to rehearse the key response knowledge when cue words were presented (analogous to a blocked Think condition in the Think-No Think Paradigm; Anderson & Green, 2001); 2) a guilty uncooperative condition in which subjects were encouraged to resist the tendency of key knowledge to come to mind when cue words occurred (analogous to a no-think condition in the Think-No Think paradigm); 3) an innocent condition in which subjects were

ACCEPTED MANUSCRIPT tested on items from a scene in which they weren’t present. The key evidence of suppression offered was that the P300s of condition 2

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were smaller than those of condition 1.

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However this study did not make it possible to gauge the degree

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of suppression, if indeed any existed, because the uncooperative condition involved no rehearsal and the cooperative group did.

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Ideally, there should have also been a simply knowledgeable control

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group with neither suppression nor rehearsal. Suppression then could have been unambiguously demonstrated by comparing P300s from a

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non-cooperative condition with those of the simply knowledgeable

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group. Since it is possible in the Bergstrom et al. study that the

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noncooperative P300 was not different than the P300 of a simply

knowledgeable condition, it can be argued that P300 evidence of suppression was not demonstrated; the cooperative-non cooperative difference could have simply represented rehearsal versus no rehearsal effects. The Hu et al. (2015) study extended Bergstrom et al. by indeed including a simply knowledgeable (SK) group and comparing its P300s with those of a suppression (SP) group resembling the uncooperative

ACCEPTED MANUSCRIPT group of Bergstrom et al. (2013). This comparison showed a reduction in the P300 of the SP group compared to the SK group. Hu et al. (2015)

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also presented supportive evidence based on the autobiographical Implicit Association Test (aIAT; Agosta & Sartori, 2013). Moreover, Hu

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et al. (2015) used a more realistic mock crime scenario to expose the

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critical details that were to become probes in the CIT. However the Hu

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et al. P300 data were based on measuring P300 from the pre-stimulus baseline to the P300 peak (b-p method). Although for many

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theoretical purposes, this method is preferred, in detection of concealed information, much evidence shows that the peak-to-peak

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(p-p) measurement of P300 (see methods) is at least 25% more accurate in discriminating knowledgeable and un-knowledgeable

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subjects,1 as explained by Soskins et al, (2001. See also Meijer,

Smulders, Johnston, Merckelbach, 2007). In fact, and very important, Hu et al (2015) saw no suppression with this superior p-p P300 index of recognition.

ACCEPTED MANUSCRIPT Furthermore, in the CTP version used by Hu et al. (2015), the subject presses one simple stimulus acknowledgement button for either

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probe or irrelevant in the first part of the trial, and either a target or non-target button in the second part of the trial. These latter stimuli

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are number strings, (where 11111 is the target and any other 5-digit

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string is a non-target.) Although many previous studies (e.g.,

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Meixner & Rosenfeld, 2011, 2014) used low target-to-nontarget ratios (e.g., 10 to 90), Hu et al used a 50-50 ratio. Although in the CTP,

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targets and non-targets are presented in random order, the 50-50 ratio means that a subject must be switching target and non-target

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buttons on average about half the trials. Such switching may impose a greater task demand than that found in CTP protocols with lower

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T/NT ratios. (In the present paper, we will also compare effects of 2080 versus 50-50 T/NT ratios.) This demand effect of the 50-50 ratio is likely more so in CITs in which the detected items represent incidentally learned, episodic memory items, as in the Hu et al. (2015) mock crime scenario, and which are not as well detected as more rehearsed, self-referring, semantic memory items (Rosenfeld, Shue, & Singer, 2007).

ACCEPTED MANUSCRIPT Another difference between the well-known studies of suppression from Anderson and colleagues (e.g., Anderson & Hanslmayr, 2014)

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and that of Hu et al. (2015) is that in the former, the stimulus words presented are cues to suppress/forget associated response words,

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whereas in the latter, the cue words on the screen are integral parts

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of the crime scenes to be suppressed. These should be more difficult

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to forget (suppress) with the explicit reminder on the screen. Thus P300 suppression data presented by Hu et al. (2015) could also be

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explained by task demand (distraction) associated with 1) executing suppression instructions, and 2) frequent response

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switching associated with a 50/50 T/NT ratio. (Unfortunately, in the Hu et al. study, simple reaction time and error rate data from the ERP

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task, possibly bearing on the demand hypothesis, were not available.) Thus in the present study, in EXP 1, we replicated all manipulations of Hu et al., (2015), including the aIAT test, except we utilized the well-rehearsed semantic information category of subjects’ names for probe items, and sought evidence for suppression. Moreover, unlike Hu et al.(2015), we also use a repeated measures design in which the same subjects experience both suppression and no suppression conditions. There is good reason to expect episodic and semantic memory to be differentially affected by suppression manipulations (see Kopelman, 1994 and Semkovska & McLoughlin, 2013). We also

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recorded simple reaction times during the ERP task to help assess

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demand effects. Our expectation was that even with the demanding

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50/50 target-nontarget ratio from Hu et al., subjects’ names would

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yet be so familiar, that such semantic stimuli, even under a “NoThink”-like condition, would in fact elicit larger than normal P300s—

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analogous to the situation in which one is told to not think about a

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pink elephant or white bear (as in Wegner, Schneider, Carter, S. & White, 1987). Similarly, and for related reasons, we did not expect

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(2015).

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evidence of suppression in the aIAT test, in contrast with Hu et al.,

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In a second experiment (EXP 2) we exactly repeated EXP 1 with two exceptions: 1) We use a 20-80 T/NT ratio, 2) We compare suppression versus non-suppression between two separate groups, as in Hu et al., (2015).

The present report is not intended to be directly comparable with Hu et al. (2015) and Bergstrom et al. (2013). It cannot be first because these papers dealt with episodic information rather than semantic information which we do presently address. (In fact, we did present a suppression study with episodic information in Ward & Rosenfeld, in

ACCEPTED MANUSCRIPT press). The present studies are part of a series of studies in our lab in

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which we

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are exploring the suppression effect under various parameter settings

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including semantic versus episodic, T/NT ratio, etc.

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There are two clear and important field applications for detection of semantic (e.g., autobiographical) information, one rather topical: 1) In

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anti-terror applications, investigators typically want to uncover

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names—of fellow terror cell members, superiors, organization names,

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cities to be attacked, (as in Meixner & Rosenfeld, 2011, 2014) and so forth. Indeed terrorists use false passports and claim to have names

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which are false. This point has been emphasized by Verschuere & Kleinberg (2016) and Barber (2015). There are surely P300 CIT applications here that probe for semantic memory. In these situations, the incentive for countermeasures such as suppression is high, and thus the importance of an investigation of the extent to which suppression-like countermeasures affect semantic memory. 2) In cases of malingered memory—which are common (Sweet, 1999) -head injury malingerers sometimes pretend to not remember names, phone numbers, birth dates, etc., so as to receive large, undeserved financial settlements. P300 has been heavily investigated by many

ACCEPTED MANUSCRIPT labs so as to investigate the ability of the P300 CIT to detect concealed semantic information in numerous studies, of which the following

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represent just a fraction of the literature: Ellwanger, Rosenfeld,

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Sweet, & Bhatt, (1996), Rosenfeld, Sweet, Chuang, Ellwanger, & Song,

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(1996), Rosenfeld, Reinhart, Bhatt, Ellwanger, Gora, Sekera, & Sweet, (1998), Rosenfeld, Ellwanger, Nolan, Wu, Bermann, & Sweet, (1999),

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Sweet, (1999), van Hooff, Sargeant, Foster, & Schmand, (2009), Van Hooff, and Golden, (2002), Van Hooff, Brunia, and Allen, (1996).

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Apart from forensic applications for concealed semantic information detection, there is the wider area of memory suppression

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research to consider. This area, (e.g., Anderson, & Hanslmayr, 2014).) would certainly find it worthwhile and important to know that perhaps episodic information may be directly suppressed but not semantic. Theories or proposed mechanisms of suppression depend on information about such possible dissociations between effects on semantic vs. episodic memory.

ACCEPTED MANUSCRIPT EXPERIMENT 1 (EXP 1) METHODS

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Participants and procedures: We recruited 24 subjects, aiming to run

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at least 20. This was based on a power analysis as in Hu et al. (2015).

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Two subjects failed to appear. Twenty-two participants were run in

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both conditions, creating two order counterbalance groups, 1 and 2,

with the P300 CTP, plus two administrations of the aIAT task. All data

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from one participant were lost. All 21 remaining participants (9 male)

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experienced one of two orders shown in Table 1a, which were

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balanced for whether or not the SK vs. SP condition was experienced first before the ERP task. The aIAT task had no such counterbalance, possibly producing an order confound leading to reduced d-scores in SP condition due to repetition as opposed to suppression, since both groups 1 and 2 do the aIAT in the SK before the SP condition. In fact, there is evidence of adaptation of d-scores in simple test-retest conditions (Agosta & Sartori, in preparation) that we did not know about prior to the present study. However, in contrast, our own lab published data to the effect that there was a non-significant reduction in d-scores in test-retest conditions (Hu & Rosenfeld, 2012; Hu,

ACCEPTED MANUSCRIPT Rosenfeld, & Bodenhausen, 2012). Despite this possible order confound, we will here present aIAT d-score results anyway. This is

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because only a large decrease in aIAT d-scores from non-suppression to suppression conditions may be uninterpretable, since one could

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not know if such an effect were due to suppression instructions, to

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repetition (practice) or a combination of the two. However, if there

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obtains a non-meaningful decrease such that the d-score in suppression is large and positive (e.g., around +1.0, as happened

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here), this would mean that the subject is well detected as

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knowledgeable, and that the suppression attempt does not help him escape detection (i.e., the suppression countermeasure fails; Agosta & Sartori, 2013).

There were 21 sets of ERP data. Due to malfunctions (undiagnosed computer mouse failure) only 18 subjects yielded aIAT data, and 14 provided Reaction Time (RT) data for all four ERP stimuli. We note that most (14/21) participants, ERP data were validated for correct performance on the T/NT task. In these 14 subjects, mean error rate was.058 in SK and .070 in SP conditions. (In EXP 2, these rates were .019 and .034, respectively). Thus in that clear majority of

ACCEPTED MANUSCRIPT subjects for whom we had T/NT data, the correct response rates were well over 90%. So we assumed that all subjects (i.e., with and without

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T/NT data) were cooperative and we used all subjects’ ERP data. It should also be noted that there were surprise quizes at unpredictable

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times during the ERP presentation phase of the trial (see next page),

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and any participant with > 1 incorrect response regarding the just

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presented probe or irrelevant was eliminated. There were none.

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We expected that the Group (order) factor would not significantly affect other dependent variables, so that ANOVAs could

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be done on combined groups, despite possible order effects. To verify

the lacking Group effect, we first did mixed ANOVAs with the between-group factor of Group, and the within-group factors of Condition (SK vs. SP) and stimulus type (probe vs. irrelevant or target vs. non-target), using all dependent measures analyzed later over collapsed groups. The results in Table 2 showed no significant effects of Group in all cases. In the last case (All RTs), we will retain Group as a factor in the ANOVAs on RTs (below) since the effect of group, though ns at p = .111, is the closest to significant in the table for

ACCEPTED MANUSCRIPT behavioral data. Other suggestive but ns effects in Table 2 are discussed below as limitations of this EXP 1 study.

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Complex Trial Protocol (See Fig. 1.)

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The present study employed the CTP version of the P300-CIT,

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which is more countermeasure-resistant than other P300- CITs (Rosenfeld et al., 2013). On each trial, participants were presented

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with one of the following items for 300 msec: a probe (their given

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name) or one of six irrelevant stimuli (other names). Each stimulus was repeated 50 times. Participants were told to respond by pressing

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one stimulus acknowledgement button (on a left hand mouse) as soon

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as they saw this stimulus (whether it was a probe or irrelevant).

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Following a randomly designated inter-stimulus interval lasting from

1400-1700 ms, a target (1111) or non-target stimulus (a randomly selected string of numbers, 22222 to 55555) was presented for 300 ms. Participants pressed one button for the target “11111”, and pressed another button for any other number string (non-targets) on a right hand mouse. The target and non-target occurred at an equal probability following probe and irrelevant stimuli. The next trial began

ACCEPTED MANUSCRIPT 2400 ms following the offset of the target/non-target stimulus. The CIT assumes that for guilty participants, the probe will elicit a larger

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P300 than an irrelevant stimulus because subjects should recognize this self-referring item. In this study, only knowledgeable subjects,

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tested on their and other names, were run in SK and SP conditions.

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When P300s to the probe are larger than P300s to irrelevant stimuli,

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one can infer probe recognition.

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RT-based aIAT:

At various times during the run (Table 1a), all participants twice

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finished a seven-block aIAT (Agosta & Sartori, 2013), once in SK and

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once in SP conditions. Blocks are run in which general truth

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statements and false statements are paired to the same response button with either the subject’s or another’s name, producing congruent or incongruent blocks. Congruent responses are faster and produce fewer errors than incongruent ones, and D-scores are

functions of incongruent minus congruent reaction times. (Details in supplementary materials; SM). Suppression Instructions:

ACCEPTED MANUSCRIPT These instructions are patterned exactly based on Hu et al. (2015), except names rather than mock crime items were to be suppressed.

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See SM, where it is noted that in both this study (EXP 1) and in EXP 2, suppression instructions also offered a reward to motivate SP

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reward was offered to SK subjects.

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participants to execute the suppression instructions, whereas no such

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This introduces the possibility of a confounding of suppression and incentive effects, since Meijer, Selle, Elber, & Ben‐Shakhar, (2014)

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point out that they and many (though not all) others have observed that motivation and incentive typically increase the CIT effect in

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Autonomic Nervous System (ANS) measures. Regarding P300 CITs, they note that “The bulk of CIT studies based on P300 did not use

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motivational instructions.” This is true, as indeed most of those studies are from this lab where we informally have never noticed such effects of motivation on P300 in several papers. This impression was formally confirmed in Ellwanger et al., 1996: A truth-telling group (n=14), instructed only to do their best on P300 (p-p) tests (involving semantic, as well as incidentally acquired, episodic memory) was compared to a motivated/incentivized “dishonest” group (n=25)

ACCEPTED MANUSCRIPT offered $10 to “beat the test.” There were no significant P300 differences, and indeed the sensitivity of the truth tellers was .74, vs.

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.725 for the incentivized dishonest group. This study was based on the older “3-stimulus protocol” (Rosenfeld, 2011). Recently, we showed

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the same result (Rosenfeld, Davydova, Labkovsky, & Ward, 2016)

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using the newer complex trial protocol (CTP; Rosenfeld et al., 2008).

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We thus continue to assume that motivation/incentive does not affect P300 in the CIT context, so here motivated only the suppressor groups

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in order to get them—but not the control (simply knowledgeable/guilty) groups to work as hard as possible to suppress

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Data Acquisition:

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the probe memories.

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P300, b-p and p-p, from Fz, Cz, and Pz, was recorded, filtered, artifacted, and averaged as in most of our previous papers (e.g., Rosenfeld, Ward, Frigo, Drapekin, & Labkovsky, 2015). See SM for details.

Group statistical analyses: ANOVA methods and t-tests were used for group analyses. Effect sizes reported are partial eta squares (ep2), whose values can be benchmarked against Cohen’s (1969, pp. 278–280) criteria of small

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(.01), medium (.06) and large (.14) effects, according to .Richardson

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(2011). In most cases, and in all cases of marginally (less than) significant effects, Bayes factors (JZS BFs, with scaled r = .707, as in

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Rouder, Speckman, Sun, Morey, & Iverson, 2009; as obtained from

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http://pcl.missouri.edu/bayesfactor) are also reported. These

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likelihood ratios of alternative to null hypothesis will be stated as favoring the null (no difference) hypothesis or the alternative

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hypothesis, and the associated numbers will be odds ratios favoring either null or alternative hypothesis. When these ratios are close to

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1.0, they cannot be interpreted as favoring either the alternative or

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null hypotheses, as one is about as likely as the other. Within individual analysis: Bootstrapped amplitude difference method: To determine whether or not the P300 evoked by one stimulus is greater than that evoked by another within an individual, the bootstrap method (Efron, 1979) was used on the Pz site where P300 is typically largest. The bootstrap yields 2 critical dependent variables, 1) BSITERS, which is the number of bootstrapped probe and irrelevant averages over multiple iterations (see SM) in which the probe P300 is larger than the irrelevant P300; and 2) BSMEAN, which is the average difference between multiple averaged bootstrapped probe and

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irrelevant trials for one run in one subject (see SM for analytic

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details).

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Target and Non-Target Error Rates

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BEHAVIORAL RESULTS (See Tables 3a and b for both experiments):

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Error rates increase under conditions of greater task demand. Thus we analyzed in both this and the next experiment the effects of

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suppression on target and non-target response errors. We could not analyze errors during probe and irrelevant parts of the trial since the

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same response was required for both probes and irrelevants. See Table 3a, left side for EXP 1. A paired t-test on SK vs. SP target error rates yielded t (13) = 1.34, p = .204 (SK = 6.3%; SP = 8.7%). The JZS BF favored the null at 1.76 and ep2 = .12, a medium effect size. For non-target error rates, t (13) = .118, p = .908. (SK = 5.3%; SP = 5.2%). The JZS BF favored the null at 3.68 and ep2= 0, a negligible effect. For target and non-target error rates combined, t (13) = 1.103, p =.29 (SK = 5.8%; SP = 7.0%). The JZS BF favored the null at 2.22, and ep2= .083, a medium effect. Error rate evidence for demand was thus

ACCEPTED MANUSCRIPT lacking, although SP overall had a numerically, if not statistically, a

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higher error rate. We suggest that these null results were because in

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this study, the 50/50 T/NT ratio had both SK and SP participants at a

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near peak level of demand, obscuring further observation of the

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putative SP demand effect.

Within only the subjects in the SK condition (Table 3a, left side), a

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paired samples t-test on target vs. non- target error rates yielded t

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(13) =.664, p =.518. (target = 6.3%; non-target = 5.3%). The JZS BF

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favored the null at 3.06, and ep2 = .05, a small effect. Within only the subjects in the SP condition, however, t (13) = 3.68, p < .003. (Target = 8.7%; Non-Target = 5.2 %). The JZS BF favored the alternative at 16.5, with a very large ep2 of .5. This may reflect, in the SP condition, demand from both SP instructions, and the 50/50 T/NT ratio.

Simple Reaction Time (RT) to probe and irrelevant items, ERP task. Regarding simple RT effects (Table 3a, left side) in the ERP task, only the specifically suppression-trained probe RTs would be expected to

ACCEPTED MANUSCRIPT change in SP conditions due to suppression. This did not happen as probe RTs for SK vs. SP did not differ; t (14) = 1.444, p= .172; JZS BF

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supported the null hypothesis at 1.61.

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Task demand effects, however, which could also be generated by suppression instructions, would be expected to increase all simple

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reaction times to all stimuli; probe, irrelevant, target, and non-target

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in the ERP task, as subjects may perseverate on suppression

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instructions from part 1 of the ERP trial to part 2 (see Fig.1 and Table 1a). Therefore, we averaged all four stimulus types in the SK condition to be compared with a comparable average in the SP condition. These data are in Fig. 2. A mixed, 2-way (Group 1 vs Group 2 x Condition) ANOVA on averaged RTs to all four stimuli comparing SK and SP RTs was done; (Note these are order countermeasure groups as in Table 1a). The main effect of counterbalance group was an ns trend; F[1,12] = 2.964, p = .111. The JZS BF favored neither the alternative hypothesis (Group 1 < Group 2) at 1.12 nor the null; ep2 was .198. Importantly, the main effect of condition (SK vs. SP) was significant; F(

ACCEPTED MANUSCRIPT 1,12) = 4.72, p = .05. Ep2 was .244, and the JZS BF favored the alternative hypothesis (SK = 422.45 msec < SP = 455.05 msec) at 1.60.

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The interaction was not significant; F(1,12) = 2.572, p = .135. This had a medium ep2 at .13, the JZS BF favored neither the null nor the

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alternative at 1.29. Thus, there is evidence here that RTs to all stimuli

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are increased in the SP condition; These data are consistent with a

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during ERP tasks.

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task demand rather than suppression hypothesis of simple RT effects

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Autobiographical Implicit Association Test (aIAT) D-score data: The mean D-scores (regarding name recognition, Fig, 3a, left) before and after the suppression task were +1.156 and + 0.946, respectively; thus D-scores in both conditions were large and positive. A one way, repeated measures ANOVA yielded F (1,17) = 7.818, p < .02. This corresponded to an ep2 of .31. The JZF BF = 4.42 favored the alternative. Yet, the Block 3 vs. 5 error rates in SK vs. SP did not differ, F (1,17) = .001, p = .971, JZS BF favored the null at 4.11. This was related to the crossover interaction of Block (3 vs. 5) x condition (SK vs. SP). F (1,17) = 6.88, p < .02. The Block difference was greater in SK

ACCEPTED MANUSCRIPT than in SP. However In neither Block 3 nor in Block 5 were there significant SK vs SP differences (p’s =.079 and .141, respectively).

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Thus, with the SP treatment, there may have been some loss of

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associative strength of the connection between truth and the

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subject’s recognition of his/her name. Yet, despite the decrease, SK to SP, this latter value was +.946, which hardly indicates impaired (i.e.,

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suppressed) recognition: Agosta and Sartori (2013) noted that “D-IAT

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values greater than +.6 are always classified correctly.” This means

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that subjects in the SP condition were well detected/classified, and thus not successful at suppression. Moreover, the SK-SP reduction seen here is much weaker than that reported in Hu et al., 2015, where the D-score for SK was .47 versus .13 for SP; p = .009. These lower scores could be attributed to memory differences between episodic vs. semantic items. The SK-SP D-score reduction seen here may also be attributed to test-retest adaptation, as noted above, but importantly, this putative effect likewise did not obviate discrimination of knowledgeable and not knowledgeable subjects.

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ERP RESULTS:

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Probe vs. Irrelevant P300s, SK vs. SP:

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Fig. 3 shows the superimposed grand averaged probe and irrelevant

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responses for SK and SP conditions. Unlike the data of Hu et al. (2015), probe P300s (b-p or p-p) seem increased in SP. Irrelevant P300s

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appear unchanged in Fig. 3. P300 latency for both SK and SP groups is

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close to 500 msec, post stimulus onset. Computed values of b-p and pp values are seen in Figs. 4 and 5, respectively. These are similar, and both show an apparent crossover interaction. (Importantly, Fig. 4 is precisely the opposite of the crossover interaction reported by Hu et al., 2015, for b-p data.) Separate, repeated measures, 2 (condition, SK vs SP) by 2 (stimulus type, probe vs. irrelevant) ANOVAs were done on b-p and pp amplitudes. For b-p P300, the main effect of condition did not reach significance; F (1,20) = 1.693, p > .2, possibly due to a crossover interaction. The ep2 was medium at .078, but the JZS BF favored the

ACCEPTED MANUSCRIPT null hypothesis at 2.1. The main effect of stimulus type, as expected, was large; F(1,20) = 76.31, p < .0001. Ep2 was large at .792, and the JZS

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BF = 467,668 clearly favored the alternative. The crossover interaction was significant; F(1,20) = 18.17, p < .0001. Ep2 was large at .476, and the JZS BF

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(84.4) favored the alternative hypothesis. Fig. 4 suggests that the source of

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the interaction is the larger probe P300 in SP than SK, and a small reverse

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relation for irrelevant items. Post hoc t-tests confirmed this with t (20) = 2.95, p < .009 for probes, but t = 1.21, p = .25 for irrelevants. This

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interaction means that probe-minus- irrelevant P300—the key index of the

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“CIT effect”-

- differed between groups, being larger in SP than in SK, just the opposite of the b-p P300 result emphasized by Hu et al. (2015). Results were similar for p-p P300s in Fig. 5. For p-p P300, the main effect of condition did not reach significance; F (1,20) = .524, p > .4. This ns result probably also related to the crossover interaction. Ep2= .026; the JZS BF favored the null hypothesis at 3.47. The main effect of stimulus type was large; F(1,20) = 115.293, p < .0001. The ep2 was

ACCEPTED MANUSCRIPT .852, and the JZS BF =11,399,676 decisively favored the alternative. The interaction was significant; F(1,20) = 11.71, p = .0031. The ep2 was

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large at .369, and the JZS BF (15.31) favored the alternative. Fig. 5 suggests that, as with b-p P300, the source of the interaction is the

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larger p-p probe P300 in SP than SK, and a small reverse relation for

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irrelevant items (again, opposite to results in Hu et al, 2015). Post hoc

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t-tests marginally confirmed this with t (20) =

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1.81, p < .086 for probes, but t (20) = .999, p > .33 for irrelevants. There is thus no support for the notion that the suppression

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manipulation reduced the P300 probe recognition response, or

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probeirrelevant differences. Rather, the data show that the

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suppression manipulation mostly increased P300, and/or the probeirrelevant difference.

Intra-individual Diagnostic data based on bootstrapping: Since diagnosis of knowledgeable vs. not knowledgeable depends on probe versus irrelevant p-p P300 results, what is next described is based only on probe-irrelevant results within individuals; (b-p P300 and target/non-target data are not considered).

ACCEPTED MANUSCRIPT The average bootstrapped mean p-p probe-irrelevant differences (BSMEAN) for SK and SP conditions were respectively 5.4 and 8.2

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microvolts, comparable (as expected) with the actual sample differences seen in Fig. 5. A one-way ANOVA on these difference data

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yielded F (1,20) = 11.27, p = .003. The ep2 was .36 and the JZF BF

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favored the alternative at 13.46.

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The mean numbers of probe > irrelevant iterations out of 100

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(BSITERS) in the SK vs. SP conditions were 89.43 and 96.76 respectively, corresponding to 76% and 95% correct hit decisions

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based on a knowledgeable criterion of at least 90 out of 100 probe >

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irrelevant iterations in the bootstrap. A one way ANOVA on these

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iteration scores yielded a non-significant trend favoring the SP condition: F (1,20) = 3.074, p < .096. The ep2 was .13, and the JZS BF favored neither the null nor the alternative at 1.2. There is no P300 evidence here for suppression.

DISCUSSION OF EXP 1: There are some limitations to the generality of the present findings: Most important, the repeated measures nature of the present design

ACCEPTED MANUSCRIPT required order counterbalance, and as can be seen in Table 1a, and already discussed, the counterbalance regarding the aIAT

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administration was imperfect. We used two order groups designed to counterbalance for whether or not the SK vs. SP condition was

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experienced first before the ERP task. To check on a perhaps naïve

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expectation that order group would not significantly interact with our

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main manipulations of SK vs. SP, and of stimulus type, we performed ANOVAs on variables possibly interacting with Group. The results in

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Table 2 show that while all F of interaction terms were ns at p > .05, some were suggestive at [.05 < p < .15]. In main analyses, orders were

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combined, so as to reduce any real group interaction effects, however it is acknowledged that with df varying from 7,7 (simple RT) to 9,9

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(aIAT) to 11,10 (P300) in the ANOVAs , there are reservations about Type II errors, particularly with RT-based measures, and less so with ERP data, which were our main concern. In particular, the present results clearly suggest that the P300 ERP (measured b-p or p-p) in response to a suppressed probe item based on a semantic memory is not reduced by attempted voluntary suppression. Given that the P300 represents recognition of a rarely presented and meaningful item (such as the subject’s name here), it follows that the suppression manipulation did not weaken recognition. Indeed the probe-irrelevant P300 amplitude differences

ACCEPTED MANUSCRIPT here were larger in the SP than the SK condition, suggesting superior recognition for the self-referring semantic information that

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participants were vainly trying to suppress. These results were supported by the BSMEAN data: These mean bootstrapped p-p P300

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probe-irrelevant differences were also greater in SP than in SK. Indeed

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the proportion of bootstrapped iterations with probe > irrelevant

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(BSITERS, the direct measure used for individual diagnosis in P300 CITs) was also greater in SP (96/100) than in SK (89/100); this

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difference was suggestive but not quite significant at p < 0.1, however, as both proportions were near the 100/100 ceiling.

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The present lack of support for suppression of semantic memory is not surprising, given the well-known experience that it is virtually

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impossible to not think of salient item -- a white bear-- on command; (Wegner et al., 1987; Wenzlaff & Wegner, 2000). Indeed, the extreme salience of such self-referring items here easily accounts for the larger Probe P300s in SP than in SK here, which was not the case in the Hu et al. (2015) paper dealing with suppression of episodic information. This is particularly so given that the critical probe item itself is actually on the screen as one tries to suppress its recognition. In contrast, in the

ACCEPTED MANUSCRIPT related Bergstrom et al. (2013) paper noted earlier, cues for the tobesuppressed information were on the screen during testing, but not

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the to-be-suppressed information itself, so suppression should have been easier, although the evidence for suppression presented by

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Bergstrom et al. was (as noted above) not unambiguous. In contrast,

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Hu et al. (2015) presented to-be-suppressed items as stimuli (as in the

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present paper), but presented ERP suppression evidence only in the bp (not p-p) P300s. B-p P300 can also be reduced by increased task

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demand (Donchin et al., 1986) which is probably generated by attempting to suppress. Hu et al. (2015) presented no RT or error data

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in P300 tasks bearing on demand effects. In the present study, we did, and these RT data suggest increased task demand for SP, as RTs to all

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stimuli in the ERP task were greater in the SP condition. Target and non-target error rate data, however, mainly did not reflect task demand differences, SK vs. SP. However. Table 3a (left column, row 5, EXP 1 data) does show more target errors than non-target errors, possibly reflecting combined demands of the SP condition and the 50/50 T/NT ratio. The data do not however support suppression since the specifically to-be-suppressed information—the name—did not have a different RT from SK to SP.

ACCEPTED MANUSCRIPT

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The above discussion does not imply that the many demonstrations

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of suppression by Anderson and colleagues (e.g.,

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Anderson & Hanslmayr, 2014) are challenged by the present data.

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These studies differ in many ways from the present effort: Most important, the studies of Anderson et al. (as with Bergstrom et al.,

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2013) involve suppression of paired associate memories when signaled by the cue stimulus for the associated memory, not the

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specific memory item, itself, as discussed above. Second, the total

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numbers of memory items in Anderson’s studies are much greater

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than here, which would facilitate suppression given Anderson’s large memory loads. Third, the present effort (in contrast to studies of Anderson and colleagues) involved the Complex Trial Protocol (Rosenfeld et al., 2008, 2013) in which there is a secondary targetnontarget discrimination task on each trial. As discussed above, the 50-50 T/NT ratio used here can be a source of demand on resource allocation leading to reduced P300s (Donchin et al., 1986). This may have been the source of b-p P300 reduction that Hu et al., (2015) reported. Thus in EXP 2, we explored the effect of suppression

ACCEPTED MANUSCRIPT instructions on semantic (again, name) information in a P300 CIT that

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uses a presumably less demanding 20-80 T/NT ratio.

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The present aIAT results did show a small but significant reduction in

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D-scores between SK and SP. Yet it is difficult to argue that these results support a suppression effect, in that the scores from both

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conditions were strongly positive (close to about +1.0, which would

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be considered by Agosta & Sartori,2013, as indicative of highly reliable

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classification, although with somewhat less in the suppression condition). This suggests some-- but incomplete-- resistance by the

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aIAT to the suppression manipulation. It also suggests that suppression as a countermeasure is not very effective since the high values of D-scores in the SP group indicate good detection. EXPERIMENT (EXP) 2: Introduction and Methods: EXP 2 was designed to replicate EXP 1 in all particulars but two: 1) The T/NT ratio was 20/80 rather than 50/50 as in EXP1. 2) As in Hu et al., (2015), the design was between subjects, with one randomly assigned SK group (n=29) that received general but no suppression instructions, and one randomly assigned SP group (n=23) which was

ACCEPTED MANUSCRIPT given explicit memory suppression instructions; (See SM). After providing consent, all Ps received the appropriate instructions (See

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SM, Table 1b). All Ps (SK and SP) then completed the P300-based CIT,

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followed by an aIAT (Sartori et al., 2008; Agosta & Sartori, 2013). This

study was approved by Northwestern’s Institutional Review Board. All

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Ps had normal or corrected to normal vision and reported no history

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of neurological or psychological abnormalities.

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BEHAVIORAL RESULTS (See Tables 3a and b):

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Target and Non-Target Error Rates A between groups t-test on SK vs. SP target error rates yielded t (50) = 1.7, p = .095. (SK = 5.2%; SP = 8.8%; Table 3a, right side). The JZS BF favored neither null nor alternative at 1.10. Ep2 = .055, a small effect size. However, in contrast to EXP 1, for non-target error rates, t (50) = 2.18, p < .035. (SK = 1.1%; SP = 2.1%). The JZS BF favored the alternative at 1.88. The effect size was medium at .067. For target and non-target error rates combined, t (50) = 2.33, p < .025. (SK =

ACCEPTED MANUSCRIPT 1.9%; SP = 3.4%). The JZS BF favored the alternative at 2.48. Ep2 = 0.10, a medium effect. Error rate evidence for demand was present, and SP overall

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had a numerically higher error rate (greater demand).

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Within the SK group, a paired samples t-test on target vs. nontarget

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yielded t (28) = 3.73, p <.002. (target = 5.2%; non-target = 1.1%). The JZS BF clearly favored the alternative at 38.1. Ep2 = 0.33,

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i.e., quite large. Within the SP group, t (22) = 3.83, p < .002. (Target =

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8.8%; Non-Target = 2.1%). The JZS BF clearly favored the alternative at

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38.4. Ep2 = 0.40, quite large. Many more error rate-related demand effects were in evidence here than were seen in EXP 1. (These effects are directly compared between experiments below). Simple Reaction Time (RT) in ERP Task (Table 3a, right side): There were no SK vs SP effects on simple RT during the ERP task. For probes, SK = 432.9, SP = 429.6; t (50) = .132, p = .89, JZS BF favored the null at 3.55. For all (probe, irrelevant, target, non-target) stimuli combined (as in EXP 1), t (50) = .113. p = .91, the JZS BF favored the null at 3.56. a-IAT data (Table 3a, right side):

ACCEPTED MANUSCRIPT Comparing d-scores between SK (1.22) and SP (1.37) revealed no effect, t(32) = 1.24, p = .226, JZS BF = 1.68 (favoring null). However,

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the Block 3 (3.79) and Block 5 (12.5) error rates did differ, t (32) = 2.57, p < .02, and the JZS BF favored the alternative at 3.71. [Of lesser

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ERP Data:

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of block (3v5) and Condition (SP v SK)].

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interest, Block 5 errors > Block 3 errors, and there was no interaction

No evidence of a suppression effect (SK vs. SP) is seen in ERP data.

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(Fig. 6 shows ERP waves, Fig. 7 shows computed p-p P300 amplitudes.) For b-p probe, voltages are about 18 microvolts for both SP and SK, t (50) = .181, p = .86, the JZS BF favors the null at 3.53, Ep2 = .0007. For b-p probe-irrelevant differences, (representing the “CIT effect”) both differences are about 7 microvolts, t (50) = .022, p = .983, and the JZS BF favored null at 3.574, Ep2 = .000009. For p-p probe voltages, although SK = 20.7 and SP = 23.0 microvolts, suggesting a possible “white bear” effect as in EXP 1 (see Fig. 7), t (50) = 1.11, p = .28, and the JZS BF favors the null at 2.156 for SK vs. SP. Ep2 = .024, a

ACCEPTED MANUSCRIPT small effect. For the key p-p probe-irrelevant difference, t(50) = .202, p = .84, and the JZS BF favors the null at 3.51, Ep2 = .00082 Although

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Fig. 7 suggests no interaction, we did a confirmatory 2 (probe vs. irrelevant) x 2 (SK vs. SP) ANOVA for the interaction term

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(representing Probe-minus- Irrelevant P300 differences between SK

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and SP, the “CIT effect”) which was F (1, 50) = .578, p = .45. The JZS BF

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was 2.82 in favor of the null and Ep2 =.011.

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We confirmed the above p-p results on amplitude in uV, by examining bootstrapped mean probe-irrelevant differences. For SK

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(9.62 uV) vs SP (10.9 uV), t (50)=.781, p = .439 , Ep2 = .01, JZS BF = 2.78

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in favor of the null. Also, as expected for number of bootstrap

iterations, for SK (94.2) vs SP (97.6), t (50) = 1.484, p = .144, Ep2 = .042, JZS BF = 1.455 in favor of the null. Hit rates for the SP and SK groups were 96% and 86%, respectively, based on a knowledgeable criterion of at least 90 out of 100 probe>irrelevant iterations in the bootstrap (p = .368, Fisher Exact Test). COMPARISONS OF EXP1 AND EXP 2 BEHAVIORAL DATA. (See Table

ACCEPTED MANUSCRIPT 3b.) We note that in these direct comparisons, we look within the SK

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blocks of trials only, which are represented in Table 1a as the first two

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tasks for counterbalance Group 1, but Tasks 1 and 6 for

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counterbalance Group 2. This is because the SK block in EXP 1 (a repeated measures study) is always presented first, whereas the SP

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block occurred at different times in the counterbalance orders. In EXP

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2 (a between groups study) only one of two groups has the SP block. Thus SP blocks are problematic to compare between EXP 1 and 2.

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Weighted (Unbiased) Target/Non-target error rates:

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We note in these comparisons, we did not simply compare

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numbers of errors, as we could within each experiment. That is because with lower target/non-target ratios (50/50) there is more

opportunity to make errors with targets, as there are more of them. Thus for direct comparison of EXP 1 vs. EXP 2, for each subject, the number of target errors was divided by the total number of target trials, and likewise for non-target trials, to obtain the error rates, unbiased by error opportunity.

ACCEPTED MANUSCRIPT For targets, there was no significant error rate difference between EXP 1 and EXP 2 (= 1.22); t (41) =.458, p =.649, the JZS BF

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favors null at 2.91. However, for non-targets: t (41) = 3.54, p = .001, JZS BF = 30.4 favoring alternative, and the error rate was greater in

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EXP 1. For combined targets and non-targets, t (41) was 2.917, p =

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.006, with the JZS BF favoring alternative at 7.62, and again, the error

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rate was greater in EXP 1. This supports the expected overall greater

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demand associated with the 50/50 target/non-target ratio. a-IAT data:

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Within the SK groups of both studies, there was no significant

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dscore difference between experiments; (EXP 1 = 1.16 vs. EXP 2 =

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1.22), t (36) = .619, p = .54, JZS BF favored null at 2.73. Neither was there a significant difference in Block 3 and 5 error rates between experiments; t (36) = .396, p = .695, JZS BF = 2.98 favoring the null. Reaction Time Data:

One usually expects demanding tasks (EXP 1) to have higher RTs than less demanding tasks (EXP 2), Thus we made various comparisons of RT in the two experiments, but again, only within the SK condition

ACCEPTED MANUSCRIPT where we have a pure paradigm comparison, without an interaction with suppression instructions or design (see above) which could add a

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confound: For probe RTs, RT (msec) in EXP 1 was 352.2 vs 432.9 in EXP 2. T (41) = 2.48, p <.02, with JZS BF favoring the

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alternative at 2.30 and ep2 = .129, a high medium effect size. This is

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superficially opposite to the effect predicted by a simple demand

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hypothesis. For irrelevant RTs, RT in EXP 1 was 358.1 vs 400.6 in EXP 2. T (41) = 1.27, p = .212, with JZS BF favoring the null at 1.89 and ep2 =

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.038, a small effect size. This too (though ns) is superficially opposite

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to the effect predicted by a simple demand hypothesis. For target RTs,

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RT in EXP 1 was 490.3 vs 551.0 in EXP 2. T (41) = 1.96, p = .057, with

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JZS BF favoring the alternative at 1.33 and ep2 = .086, a medium effect size. This too is superficially opposite to the effect predicted by a simple demand hypothesis. For non-target RTs, RT in EXP 1 was 489.1 vs 492.2 in EXP 2. T ( 41) = .089, p =.93, with JZS BF favoring the null at 3.16 and ep2 = .0002, a negligible effect size. EXP 2 has higher RTs for probe and target stimuli, perhaps because subjects in EXP 2 execute (T

ACCEPTED MANUSCRIPT or NT) response switches much less often than those in EXP 1, who are

COMPARISONS OF EXP 1 AND EXP 2 ERP data:

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thus more ready to switch, and thus have shorter RTs.

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Our expectation that EXP 1 would be more demanding than EXP 2

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predicts that P300s will be generally smaller in the former

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experiment (with the 50/50 T/NT ratio), than in the latter (with the 80/20 T/NT ratio). Thus we made various direct comparisons of P300

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in the two experiments, but only within the SK condition where we have a pure paradigm comparison, without a possible confound:

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Within the SK condition, a comparison of b-p probe P300s yielded t (48) =3.63, p = .001 (favoring EXP 2), with the JZS BF favoring

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the alternative at 42.52, and ep2 = .215, a large effect. (The EXP 1 mean b-p value was 12.2 uV vs 18.21 uV for EXP 2). Within the SK condition, a comparison of b-p probe-minus-irrelevant P300 differences yielded t (48) = 2.41, p = .02 with the JZS BF favoring the alternative at 2.86, and ep2 = .108, a medium effect. (The EXP 1 mean b-p difference value was 3.71 uV vs 6.92 uV for EXP 2). Results were similar for the p-p P300s. Within the SK condition, a comparison of p-p probe P300s yielded t (48) = 3.85, p < .001, with the

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JZS BF favoring the alternative at 75.16, and ep2 = .236, a large effect.

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(The EXP 1 mean p-p value was 13.8 uV vs 20.7 uV for EXP 2). Within

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the SK condition, a comparison of p-p probe-minus-irrelevant P300

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differences yielded t (48) = 4.17, p = < .001 with the JZS BF favoring the alternative at 178.5, and ep2 =.27, a large effect. (The EXP 1 mean

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p-p difference value was 5.76 uV vs 11.3 uV for EXP 2).

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Confirming these sample p-p P300 comparisons, a comparison of the bootstrapped probe-minus-irrelevant differences between EXP1

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(5.43 u V) and EXP2 (9.56 uV), SK only, t (48) = 3.0, p < .005, ep2 = .16, a large effect, with JZS BF favoring the alternative at 9.49. However,

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bootstrapped numbers of iterations where probe exceeded irrelevant did not differ between EXP 1 (mean = 89.43 ) and EXP 2 (mean = 94.17), as expected, since both are near a ceiling of 100, t (48) = 1.288, p = .204, ep2 = .01, with JZS BF= 1.78 in favor of the null. In summary, the ERP data do not show suppression effects in either b-p or p-p P300 in either experiment. Indeed they show a significant reverse (white bear) effect in EXP 1 and an ns trend for this in EXP 2. Moreover, the P300s (b-p and p-p) of EXP 2 were much larger (and significantly so) than those of EXP 1, which is as expected since

ACCEPTED MANUSCRIPT

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the demand of response switching is much greater in EXP 1 which has

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a 50/50 T/NT ratio.

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GENERAL DISCUSSION:

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We note that even though the SP instructions are given just before

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the SP ERP task, which immediately precedes the aIAT task in both counterbalance groups of EXP 1 and in the SP group of EXP 2, in the

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into the IAT task in SP.

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following discussion we assume that demand effects may carry over

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There were notable differences between P300 results of the two experiments. Most notably, there was the “white bear” effect during SP for EXP 1 but not significantly so for EXP 2 (although p-p P300 in SP was numerically larger than in SK). This ns effect was at first surprising; we assumed that EXP 2 with the 20/80 T/NT ratio would be less demanding than the 50-50 ratio of EXP 1, such that P300s would be larger in EXP 2, with reduced distracting demand (Donchin et al., 1986). Indeed, we did find that the P300s of EXP 1 were overall much smaller (significantly so) than those of EXP 2. Yet the “white bear” effect of EXP 1 did not reach significance in EXP 2. We suggest this

ACCEPTED MANUSCRIPT obtained because the much larger P300s in EXP 2 were near ceiling in

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the SK group such that the SP instruction effect could not add

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observably more amplitude. This, of course is a hypothesis in need of

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confirmation.

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In the behavioral data collected in both experiments, there was much evidence that task demand, related to both the T/NT ratio and

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the SP instructional burden, was exerting an influence on behavioral

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variables as well as on ERPs. As shown in Table 3a, it is first seen that

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in EXP 2 (and not in EXP 1), T/NT errors differed for NT and combined T & NT between SK and SP, with the latter showing higher numbers of errors. (This effect was an ns trend for T stimuli (p = .095). Likewise, within both SK and SP conditions, there are more target errors. This appears due to the fact that although in the 50/50 T/NT ratio of EXP 1, participants are under a maximal response switching requirement such that additional effects of the SP condition are (as they were with P300s) difficult to observe (although numerically, there were ns trends in EXP 1 also showing larger error numbers for SP than SK). In contrast, in the 20/80 ratio of EXP 2, subjects are more relaxed due to less response switching demand such that the SP vs. SK difference in

ACCEPTED MANUSCRIPT error rates is observable. The demanding need to switch responses is thus seen in both SK and SP in EXP 2 but only in SP in EXP 1.

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There were mostly no simple RT (SK vs. SP) differences in the

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ERP task in both experiments, although in EXP 1 all stimuli averaged together had a larger RT in SP than in SK, which reflects greater

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demand in the SP task. This may be a conjoint effect of SP instructions

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and the 50/50 T/NT ratio.

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It is problematic to compare SK vs SP aIAT effects between

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experiments in Table 3a, since the SP condition in EXP 1 was improperly counterbalanced as opposed to the between groups design of EXP 2. Within EXP 1, the d-score reduction in SP may be a consequence of task demand in SP, as noted above, where it was also suggested that suppression per se was not practically effective in SP as the d-scores in SP were near +1.0. In EXP 2, regarding error rates in the congruent (Block 3) and Incongruent (Block 5) blocks of the aIAT, there was a main effect such that SP had more errors than SK in both Blocks 3 and 5. This could simply be due to suppression-produced distraction. However, although the relation of SP > SK was also true for both blocks of EXP 1, there was an interaction such that the Block

ACCEPTED MANUSCRIPT 3 v 5 difference in SP was less than in SK. Given the counterbalance and order confound issues (cited above) in EXP 1 aIAT data, it is

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difficult to account for this interaction.

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In summary, these experiments did not reveal neural correlates

(reduced probe-minus-irrelevant P300) of the suppression

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manipulation, neither in b-p nor p-p P300s. This contrasts with the findings of a b-p P300 suppression correlate reported by Hu et al.

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(2015). However Hu et al.(2015) did not find a neural sign of suppression in p-p P300, this latter index being the more accurate and

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frequently used detector of concealed information (Soskins et al., 2001, Meijer et al., 2007). Of course, Hu et al. (2015) aimed to suppress episodic (mock crime) memory, whereas the present studies aimed to suppress semantic memories. However, Rosenfeld, Ward, Drapekin, and Labkovsky (2015) failed to find an effect of suppression using episodic stimuli identical to those in Hu et al., (2015), but with a 20/80 T/NT ratio. Given the demand effects reported here as a result of two sources of demand—SP instructions and 50/50 T/NT ratio-- we suggest that the b-p P300 SP effects reported by Hu et al. (2015) may have been due to task demand. (This hypothesis, however, still needs

ACCEPTED MANUSCRIPT direct demonstration via exact replication of Hu’s manipulations, supported by such critical demand indicators as RTs that were absent

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remain to be unambiguously demonstrated.

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in Hu et al., 2015).Thus, suppression correlates in p-p P300 amplitude

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However, it is also plausible to predict the opposite about task-

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switching effects: increased task demands lead to reduced ability to

suppress retrieval and an enhanced P300. Studies of retrieval-induced

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forgetting have shown that memory inhibition is less efficient (as indicated

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by reduced levels of retrieval-induced forgetting) when task demands are

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increased by a secondary task that demand executive attention (Roman, Soriano, Gomez-Ariza & Bajo, 2009; Ortega, Gomez-Ariza, Roman & Bajo, 2012). The point is that task switching could lead both to reduced attention to the cue (resulting in reduced recognition and reduced P300 amplitude) and/or to reduced ability to suppress retrieval due to increased task demands (resulting in more recognition and enhanced P300 amplitude).

Taken together, these results continue to be supportive of the potential field application of the P300-based, Complex Trial Protocol. This protocol was developed to resist the familiar countermeasure of

ACCEPTED MANUSCRIPT secretly responding to irrelevant items, thus converting them to covert targets (see Rosenfeld, 2011). It now appears that this CTP,

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when probing for semantic stimuli, is also resistant to suppressiontype countermeasures (and Rosenfeld et al., 2015,

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supports the same claim for episodic stimuli also). A future study

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should be tried in which subjects are trained to simultaneously use

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both suppression-type, as well as irrelevant-to-target conversion

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countermeasures in the same run. The CTP showed greater resistance

here than the aIAT to suppression countermeasures, but that may not hold if challenged by the two different countermeasure types used in combination.

Another caveat to be noted here concerns the generalizability of the current failures to suppress semantic information, based on participants’ names, to other semantic stimuli. Tacikowski and Nowicka (2010) showed that one’s own name –a highly overlearned item of information--evokes a larger P300 and shorter RT than other known (famous) names. Indeed the attention grabbing nature of one’s

ACCEPTED MANUSCRIPT own name as a stimulus may provide strong resistance to suppression. Thus P300 suppression of other semantic stimuli may be possible. We

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would guess not, since with the appropriate T/NT ratio, episodic information is also not suppressed (Ward & Rosenfeld, in press).

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However the “white bear” effect may not replicate with other

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semantic information. This is an empirical question. We were

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interested in name stimuli in this present paper since such stimuli are critical in identity verification, a most topical type of searched for,

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concealed information, as pointed out in the introduction.

Finally, although of course these studies were done mainly since they apply to the area of detecting concealed information, as in the CIT field, they plainly have applications to other areas. From a theoretical perspective, we have already implied that workers in the area of memory

and its fragility who do studies of suppression (e.g., Anderson & Green, 2001) should take note of the presently documented failure to reproduce the suppression phenomenon, and the probable reasons for its failure. First of all, it may be of interest to see if suppression may still be shown in

ACCEPTED MANUSCRIPT a situation in which the to-be-suppressed stimulus—rather than its paired associate—is present on the screen during the recall test. (There is already

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some evidence of suppression-induced forgetting in such tests. Waldhauser, Lindgren & Johansson ,2012, observed prolonged response

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times for No-Think items compared with baseline items in a recognition

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test. )

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It would additionally be of interest to explore whether or not suppression still obtains with suppression instructions for the stimulus

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paired associate for semantic, as opposed to episodic infomation information. The answers to these questions may cast further light on

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the mechanisms of suppression and forgetting.

There is also clinical interest in the suppression phenomenon, especially with respect to Obsessive Compulsive Disorder, which is characterized, obviously, with the intrusion of unwanted thoughts. Clearly it would be highly desirable to have available a technique –such as suppression—to defeat such intrusion; (Najmi,. Riemann, & Wegner, 2009; Tolin, Abramowitz, Hamlin, Foa, & Synodi, 2002). Indeed, the Tolin et al., group suggested that suppression operations have a paradoxical “white bear” effect in patients with OCD.

Footnote:

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1. Soskins et al (2001) showed that the second negative peak to which p-p P300 is referenced is a strong correlate of P300’s recovery slope, suggesting both components of the p-p P300 are part of the P300 process.

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Table 1a: TASK ORDER COUNTERBALANCE GROUPS, EXP 1: Group 1

1. aIAT (SK)

Group 2 1. aIAT (SK)

2. ERP task (SK)

2. Suppress training

3. suppress training

3. ERP task (SP)

4. ERP task (SP)

4. aIAT (SP)

5. aIAT (SP)

5. Stop suppressing instruction

6. ERP task (SK)

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SP GROUP SP instructions

2. ERP task

ERP task

3. aIAT task

aIAT task

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1. SK Instructions

Table 2: Effects of task order Group (Grp), 1 vs. 2. Anova

DV

t

N1,N2

p

Alt - Null & JZS BF

PvI x Grp x Cnd

ppP300

1.53 11,10 .140

Null 1.14

PvI x Grp x Cnd

ppBSMN

1.65 11,10 .116

Null 1.01

PvI x Grp x Cnd

ppBSITR

1.62 11,10 .122

Null 1.04

PvI x Grp x Cnd

bpP300

0.92 11,10 .367

Null 1.70

PvI x Grp x Cnd

bpBSMN

0.40 11,10 .693

Null 2.40

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0.72 11,10 .479

Null 2.12

TvNT x Grp x Cnd

ppP300

0.89 11,10 .271

Null 1.46

TvNT x Grp x Cnd

bpP300

0.58 11,10 .567

Null 2.27

Grp x Cnd

D-score

0.96 9,9

Grp x Cnd x Cong

RTs in aIAT 0.0

Grp x Cnd

All RTs

Null 1.77

.998

Null 4.11

.111

Null 1.10

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1.72 7,7

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PvI x Grp x Cnd

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NOTES: PvI is stimulus type variable, probe vs. irrelevant; TvNT likewise for target vs. non-target; Grp is task order group, 1 vs 2; Cnd means condition, SK vs. SP. DV = dependent variable. BSMN is BSMEAN from text; BSITR is BSITERS from text . D-score is from the aIAT, whose RTs in aIAT means RTs to congruent and incongruent (Cong) stimuli. All RTs = averaged simple reaction times to all stimuli (P, I, T, NT) in ERP tasks. The last column gives the JZS Bayes Factor (defined in Methods) which states whether the results support the experimental alternative hypothesis (Alt.) versus the Null hypothesis (Null). The square root of F values from ANOVAs were the t-values used to compute Bayes Factors.

Table 3a: Behavioral results, both experiments, separately.

EXP 1, T/NT= 50-50

EXP 2, T/NT =20-80

Dependent variable: T/NT Errors COMPARISON: SK v SP Stimulus T

Null

p=.204

BF = 1.76

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p = .095 BF= 1.1

NT

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p=.908

BF = 3.68

Alt (SP>SK) p < .035 BF= 1.88

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Null

p=.29

BF= 2.22

Alt (SP>SK) p < .025 BF= 2.48

COMPARISON: T v NT Null

p=.518 BF=3.06

Alt (T > NT) p < .002 BF= 38.1

Within SP

Alt (T > NT)

p < .003 BF= 16.5

Alt (T > NT) p < .002 BF= 38.4

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COMPARISON: SK v SP

Probe

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p=.17 BF= 1.61

Dependent variable: D-score in aIAT

Null p=.895 BF=3.55 Null p=.911 BF= 3.56

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All Stimuli Alt (SP >SK ) p = .05 BF= 1.77

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Dependent variable: Simple RT in ERP task

Alt (SK>SP) p <.02 BF= 4.45

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COMPARISON: SK v SP Null

p=.226 BF=1.68

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Dependent variable: Blk 3,5 errors in aIAT p=.97

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Alt (SP > SK) p <.02 BF=3.71

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Note: Alt, Null are Bayes Factors (BF) favoring alternative or null hypothesis, respectively. T/NT refers to the target/non-target ratio. Probability = p. SK & SP are the simply knowledgeable or suppression groups, respectively. T&NT are combined targets and nontargets. EXP means experiment (1 vs. 2). Blk = block. These apply also to Table 3b.

Table 3b: Comparison of EXP 1 and EXP 2 Dependent variable: Weighted T/NT Error Rate COMPARISON: EXP 1 vs EXP 2 Stimulus T

Null

p =.649 BF = 2.91

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Alt (EXP 1 > EXP2) p =.001 BF = 30.4

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Alt (EXP 1 > EXP2) p =.006 BF = 7.62

Dependent variable: D-score in aIAT

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Within SK Null, p=.54 BF = 2.73

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Dependent variable: Blk 3,5 errors in aIAT

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Within SK Null p = .695, BF = 2.98

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Dependent variable: Simple RT in ERP task

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Null

p = .212

BF = 1.89

Alt (EXP 2 > EXP 1) p = .057

BF = 1.33

Null

BF = 3.16

p = .93

Note: Abbreviations explained in note for Table 3a.

Figure Legends: Fig 1.The present Complex Trial Protocol with a name stimulus (Stimulus 1) and a Target (11111) shown for Stimulus 2. Stimulus 1 is

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Fig. 2. Averaged simple reaction times (RT) over all stimuli (probe, Iall, target and non-target) as a function of task order group (GRP: 1 vs. 2) and condition (SK vs. SP).

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Fig. 3. EXP 1: Superimposed grand average Pz probe (black) and irrelevant (red) waveforms from simply knowledgeable (SK) and suppression (SP) conditions. Vertical dotted lines represent onset (at 100 msec into sweep) and offset (300 msec later at 400 msec).

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Fig. 4. EXP 1: Computed grand average b-p P300 values for probes and irrelevants as a function of condition (SK, SP) and stimulus type.

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Fig. 5. EXP 1: Computed grand average p-p P300 values for probes and irrelevants as a function of condition (SK, SP) and stimulus type.

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Fig. 6 EXP 2: Superimposed grand average Pz probe (black) and irrelevant (red) waveforms from simply knowledgeable (SK) and suppression (SP) conditions. Vertical dotted lines represent onset (at 200 msec into sweep) and offset (300 msec later at 500 msec). Fig. 7 EXP 2: Computed grand average p-p P300 values for probes and irrelevants as a function of condition (SK, SP) and stimulus type.

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Highlights Jesseann 10-3-2016 >Two Experiments investigated effects of Suppression Manipulations on semantic memory. > P300 amplitude and D-score on the aIAT were studied with P300-based complex trial protocol (ctp). > Support for Suppression was not obtained. Task demand effects were seen. > We conclude the ctp is resistant to suppression countermeasures.