Subjects

Thirty-two healthy non-smokers completed the study (15 female; 13 African American, 17 Caucasian, 2 Asian). Participants were 22–54 years of age (mean ± stdev: 33.6 ± 10.5) with 12–22 years of education (15.6 ± 2.5). They were recruited from the local community through internet advertising, flyers and referrals, and gave written informed consent for a protocol approved by the University of Maryland Baltimore Institutional Review Board. Because a proof of principle that nicotine can facilitate human associative learning depends on studying organisms devoid of neuroadaptive changes by chronic nicotine exposure, all participants were current non-smokers, had never been dependent smokers, and had no exposure to tobacco products or vaping within the last year. Only three participants had ever smoked any tobacco in their lifetime (see Supplementary Materials for details).

Procedures

During an initial visit, participants were screened for eligibility and trained on the associative learning tasks to be performed in the later test sessions. Training of the Stop Signal task consisted of two 100-trial blocks in which no stimuli were systematically paired with stop or go trials. Training of the Conditional association learning (CAL) task consisted of 20 trials of the Unique and Crossed task each. The experimenter was careful to ensure that all participants understood the rules governing stimulus matching in the Crossed task (see below).

Two test sessions followed, scheduled with two to six intermediate days. In one session, participants received a transdermal nicotine patch (7 mg/24 h; Nicoderm CQ®, GlaxoSmithKline), in the other a placebo patch. The sequence of patch conditions was counterbalanced between participants. Participants and investigators were blind with regard to patch condition, although some participants may have guessed the test sequence based on subjective nicotine effects (see Supplementary Materials for details on the blinding procedures and subjective side effects of nicotine).

Each test session took approximately 7 h (more details in the Supplement). Study patch application was followed by a drug-absorption period, during which participants were permitted to read or use the internet. 5 h after patch administration, at which time nicotine blood concentrations were expected to have plateaued, testing began. The Stop Signal task was always performed first and the CAL task second.

Equipment

Tasks were performed on a desktop PC and presented on a 19-inch 5:4 IPS LCD monitor with a screen resolution of 1280 × 1024 and a 60 Hz refresh rate. Responses were recorded using a standard keyboard and mouse. The Stop Signal task was implemented in Matlab using the Psychophysics Toolbox extensions. Stop signals were presented via Sony MDRZX100 headphones. The CAL task was implemented in E-Prime 2.0.

Task paradigms

Stop signal task

The paradigm was based on Lenartowicz et al. [42] and Verbruggen and Logan [43] (Fig. 1). A series of face stimuli (50% male, 50% female), selected from a collection compiled by Neal Cohen at the University of Illinois [44], was presented against a black background. Each face was presented for 1000 ms, preceded by a 500 ms white fixation cross, and followed by a variable intertrial interval of 500–4000 ms (1000 ms average; following a continuous exponential distribution) during which the screen remained blank. Participants made gender judgments about the faces by pressing either the < (male) or > (female) key of the keyboard with their index and middle finger as quickly as possible.

Fig. 1 Screen displays in the Stop signal task. The stimuli are shown enlarged relative to the size of the screen for illustrative purposes. Three trials are shown: two go trials and one stop trial. The table lists all possible stimulus types as described in the Methods Full size image

On 24% of trials, a 900 Hz tone indicated that the response should be withheld (stop trials). The tone was presented for 500 ms with a variable stop signal delay (SSD) relative to the onset of the face stimulus. A staircase procedure was aimed at achieving 50% probability of stopping successfully [45]: SSD increased by 50 ms after a successful stop and decreased by 50 ms after a stop failure, thus making stopping more difficult or easier on the next stop trial (note that the stop signal was always presented after face stimulus onset). To minimize SSD predictability, two staircases with different starting-SSDs (250 and 300 ms) were initialized and presented interleaved.

The task consisted of ten 100-trial blocks. For each staircase, the average SSD over the last 10 trials of a block was used as the starting SSD for the next block. The first six blocks made up the training phase, followed by a brief break while a new script was initialized. The next two blocks still followed the contingencies of the training phase, but in the last two blocks (transfer phase), contingencies changed (see below). The stimulus set consisted of 50 faces, each presented twice per block.

Unbeknownst to participants, 6 of the 50 faces (12 trials per block) were always paired with stop trials in the training phase, but only with go trials in the transfer phase (“stop–go” stimuli). The main prediction was that, over time, the association with stop trials would facilitate the stop response to these stimuli, but cause interference, reflected by slower go reaction times (goRTs), when they were subsequently paired with go trials. Another 12 faces were paired with only go trials in both phases (“go–go” stimuli); this association was expected to facilitate the go response, as reflected by faster goRTs. Another 8 faces were randomly paired with stop trials or go trials (25:75% chance) during training, but only with go trials in the transfer phase (“go/stop–go” stimuli). These stimuli enabled the comparison of goRTs in the transfer phase between stimuli that had previously been paired with only stop trials or only go trials on the one hand, and stimuli that had not been systematically paired with either on the other hand.

Sixteen faces were randomly paired with stop or go trials (25:75% chance) in both phases (“go/stop–go/stop” stimuli), and eight faces were paired with go trials in the training phase but with stop trials in the transfer phase (“go–stop” stimuli), to create a 24% stop ratio overall. The table incorporated in Fig. 1 provides an overview of all stimulus types, which were presented pseudorandomly with the constraint that no stimulus repeated on consecutive trials. Importantly, each stimulus type that involved stop-trials, i.e., (1) stimuli associated with stop trials only, and (2) stimuli associated with either stop or go trials, had their own two staircases with the same two starting values in each phase, thus allowing us to test the effects of the systematic pairing with stop trials on SSD.

We predicted that over the course of the training phase the average SSD for “stop–go” stimuli (paired with stop trials in the training phase) would increase relative to “go/stop–go” stimuli (not systematically paired with stop or go trials in the training phase), reflecting facilitation of the stop response to stop-associated stimuli. We hypothesized that this increase would be greater in the presence of nicotine, reflecting enhanced formation of associations between the paired stimuli and the stop signal or the stop response (stimulus–stimulus and stimulus–response associations cannot be distinguished in this paradigm).

For the transfer phase, we predicted that goRTs would be slowed for “stop–go” relative to “go/stop–go” stimuli, reflecting interference due to the prior association with only stop trials. This slowing was expected to be greater in the presence of nicotine, again reflecting stronger associations between the “stop–go” face stimuli and the stop signal/stop response. Conversely, we expected greater speeding of goRTs for “go–go” stimuli relative to “go/stop–go” stimuli with nicotine.

Two task versions, each with a unique set of 50 face stimuli, enabled repeat-testing over the nicotine and the placebo session. Task versions were counterbalanced across drug conditions. Total task duration was ~50 min.

The conditional associative learning (CAL) task

The present task version was based on Gold et al. [46] (Fig. 2). Participants were presented with one out of five possible test stimuli in the top row, and five response stimuli in the bottom row of the screen display. They were asked to select, by mouse-click, the one response stimulus that “goes with” the test stimulus. The display was shown until response. All stimuli were shapes, white against a black background. Subjects initially guessed the correct response shape and, over time, learned the associations from feedback. Feedback was presented for 2 s after each response (“correct” written in green, or “incorrect” in red). An incorrect response was always followed by another presentation of the same display, until the correct response shape was selected. All incorrect choices within a trial were added and counted toward the total number of incorrect choices analyzed. The stimulus mappings never changed.

Fig. 2 Task displays in the conditional associative learning task. The stimuli are shown to scale. The table lists possible stimulus–response mappings in the Unique task and Crossed task. Although the table utilizes the same letter placeholder, the two subtasks employed different shape stimuli from each other Full size image

There were two subtasks, tested in counterbalanced order, employing different shape stimuli from each other (see table incorporated in Fig. 2):

(1) In the Unique task, there was a set of five test stimuli and a set of five response stimuli, consisting of all different shapes, i.e., there was no overlap between test and response stimuli. Each stimulus was part of one pair, creating five unique pairs.

(2) In the Crossed task, the test and response stimuli consisted of two identical sets of five shapes, and each test shape was associated with one of the four response shapes not identical to itself. Importantly, when shape A was the test stimulus, it may require the selection of response shape C, but when shape C was the test stimulus, it required the selection of a response stimulus other than shape A (shape E in the example given in Fig. 2). Thus, forming a simple association between A and C would be insufficient for choosing the correct response shape; shape C may be associated with either shape A or shape E depending on whether it was drawn from the test set or from the response set. Participants were fully aware of these rules.

We predicted that, in the presence of nicotine, participants would make relatively more “context reversal errors” in the Crossed task. Context reversal errors are incorrect choices of response stimuli that were associated with the current test stimulus when the context was reversed, i.e., when they were themselves the test stimuli. An example from the table in Fig. 2 would be the choice of shape A as the response stimulus when shape C is the test stimulus, because shape C “goes with” shape A when shape A is the test stimulus. More context reversal errors would demonstrate stronger incidental formation of stimulus–stimulus associations while failing to flexibly account for the context of the pairing.

Each subtask consisted of 40 trials. To verify learning with time on task, the total number of incorrect choices of any kind was quantified in each of four 10-trial blocks. Repeat-testing across the nicotine and placebo sessions was enabled by creating two CAL task versions, each with a unique set of 15 shape stimuli (10 shapes for each Unique task version and 5 shapes for each Crossed task version). Total task duration was ~25 min.

Data analysis

Stop-signal task

Training phase

SSDs were averaged across blocks 1–3 and across blocks 4–6 of the training phase for each stimulus type. SSDs averaged over “go/stop–go” and “go/stop–go/stop” stimuli (not systematically paired with stop or go trials in the training phase; 144 trials) were subtracted from SSDs averaged over “stop–go” stimuli (paired with stop trials; 36 trials). These difference values were analyzed by 3-factor ANOVA for repeated measures with Drug (nicotine, placebo) and Block (1–3, 4–6) as within-subject factors and Drug sequence (nicotine tested before placebo, placebo tested before nicotine) as between-subjects factor, followed by paired t-tests where appropriate.

Transfer phase

goRTs of trials in which a correct male/female response was made were averaged over “stop–go” stimuli (previously paired with stop trials; 24 trials), “go–go” stimuli (previously paired with go trials; 48 trials), and “go/stop–go” stimuli (not previously paired with stop or go trials in a systematic manner; 32 trials). Average goRTs in “go/stop–go” trials were then subtracted from goRTs in “stop–go” trials and from goRTs in “go–go” trials. These difference values were compared between the nicotine and placebo session by 2-factor ANOVA with Drug as within-subject factor and Drug sequence as between-subjects factor.

CAL task

To verify learning with time on task, 4-factor ANOVA was performed on the total number of incorrect choices, including within-subject factors Drug (nicotine, placebo), Subtask (unique, crossed), and Block (1, 2, 3, 4), and between-subjects factor Drug sequence. To test the main hypothesis, a 3-factor ANOVA was performed on the percentage of context reversal errors out of all incorrect choices made in the Crossed task. This ANOVA included within-subject factors Drug and Block, and between-subjects factor Drug sequence.