General Psychology Article Critique VII

BRIEF REPORT

Motivation Drives Conflict Adaptation

David Dignath University of Freiburg

Robert Wirth University of Würzburg

Jan Kühnhausen and Caterina Gawrilow University of Tübingen

Wilfried Kunde University of Würzburg

Andrea Kiesel University of Freiburg

The notion that impaired performance in the past increases motivational vigor in the future is central to many modern theories. The conflict monitoring model predicts that conflict between incompatible responses reduces susceptibility to upcoming conflict. Recently, this conflict-adaptation effect has been reframed in terms of motivational control: Conflict in the past elicits negative affect, which can be reduced by increasing control, thereby alleviating future conflict and associated negative affect. While evidence supports that conflict adaptation serves a motivational purpose (e.g., affect regulation), it remains unclear whether conflict adaptation is actually triggered by motivational mechanisms. The present research tested this hypothesis of a motivational conflict-adaptation effect. We recorded contin- uous finger movements toward target stimuli and away from distractor stimuli. Motivational conflict was instigated by assigning reward and penalty to stimuli that functioned either as targets or distractors. To index motivational vigor and increased precision due to motivation, we measured initiation times and spatial deviation from the shortest path to the target. Both a reanalysis of published data and data from a new replication study established a motivational conflict-adaptation effect. Together, the results extend a motivational control framework, showing that motivational dynamics (i.e., motivational conflicts) can be the driving force of control.

Keywords: conflict adaptation, motivation, conflict monitoring, continuous movement

When facing difficulties, people have the remarkable ability to persist and overcome obstacles. But how do we know when to put more effort in a task? In theory, it has been argued that the difficulty of an action motivates additional effort investment (Ach, 1935; Brehm & Self, 1989; Gendolla & Richter, 2010; Trope & Fishbach, 2000). For instance, the conflict monitoring model (Bot-

vinick, Braver, Barch, Carter, & Cohen, 2001) proposed that recent difficulty, as indexed by conflict between incompatible responses, is used as a teaching signal to upregulate control. Behaviorally, the so-called conflict-adaptation effect provides an index for this in- crease in control—when a conflicting stimulus was encountered in the previous trial, conflict has less detrimental consequences in the following trial (Gratton, Coles, & Donchin, 1992). Further support for a motivational account of the conflict-adaptation effect comes from studies showing that conflict triggers negative affect (Dreisbach & Fischer, 2012; Fritz & Dreisbach, 2013) and in- creases avoidance motivation (Dignath & Eder, 2015; Dignath, Kiesel, & Eder, 2015), as well as from studies suggesting that adaptation to conflict is modulated by affect and rewards (Braem, Verguts, Roggeman, & Notebaert, 2012; van Steenbergen, Band, & Hommel, 2009; but see Dignath, Janczyk, & Eder, 2017).

While there is good reason to assume that conflict adaptation serves a motivational purpose (Dreisbach & Fischer, 2015; In- zlicht, Bartholow, & Hirsh, 2015), the role of motivation as a trigger for conflict adaptation remains unclear. Here we tested the possibility that conflict adaptation is driven by motivational con- flict. To induce conflicting motivations, we presented stimuli that

This article was published Online First April 11, 2019. David Dignath, Department of Psychology, University of Freiburg; Robert

Wirth, Department of Psychology, University of Würzburg; Jan Kühnhausen and Caterina Gawrilow, Department of Psychology, University of Tübingen; Wilfried Kunde, Department of Psychology, University of Würzburg; Andrea Kiesel, Department of Psychology, University of Freiburg.

This research was supported by a grant within the Priority Program, SPP 1772 from the German Research Foundation (Deutsche Forschungsge- meinschaft, DFG), Grant DI 2126/1-1 and DI 2126/1-2 to DD.

Supplementary information containing additional analysis, data, and analysis scripts can be retrieved from https://osf.io/tdegq/.

Correspondence concerning this article should be addressed to David Dignath, Department of Psychology, University of Freiburg, Engelberger- strasse 41, 79085 Freiburg, Germany. E-mail: [email protected]

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

Motivation Science © 2019 American Psychological Association 2020, Vol. 6, No. 1, 84 – 89 2333-8113/20/$12.00 http://dx.doi.org/10.1037/mot0000136

84

were associated with reward or penalty. To disentangle cognitive conflict from motivational conflict, we ruled out any differences between targets and distractors in terms of overlearned behavior (e.g., Stroop), automatic activation of responses (e.g., Simon task), or spatial proximity (e.g., flanker task) and selectively manipulated the value association of target and distractor. Following previous work, we hypothesized that value energizes corresponding actions, causing conflict if the distractor is motivational, but not the target, and causing no conflict if the target is motivational, but not the distractor (Dignath, Pfister, Eder, Kiesel, & Kunde, 2014; Wirth, Dignath, Pfister, Kunde, & Eder, 2016).

To provide direct evidence for the assumption that motivational conflict drives conflict adaptation, we measured continuous re- cordings of finger movements toward targets and away from distractors. Because recent research has shown that motivation not only increases speed but also reduces noise of motor signals (Manohar et al., 2015), we considered both the speed of action initiation (as an indicator of vigor; Niv, Daw, Joel, & Dayan, 2007) and the spatial variability of movement execution (as an indicator of precision; Summerside, Shadmehr, & Ahmed, 2018).

Experiment

The present research used a finger-tracking paradigm to mea- sure continuous movements. Participants moved toward a target while a distractor was presented at the opposite location. Target and distractor were defined at the beginning of each trial by a spatial cue. To manipulate motivational valence, points were as- signed according to the color of the target stimulus (the color payoff regime from hereon): In trials with a gain-colored target, participants received 5 points; in trials with a loss-colored target, they lost 5 points; and in trials with a neutral-colored target, they received zero points. Importantly, this payoff was independent of the actual performance. Therefore, participants could not avoid a penalty in trials with a loss target, for example, by selecting a neutral distractor. Conversely, since points were only assigned to target stimuli, participants could not gain extra points, for example, by selecting a gain distractor. As a consequence, any influence of gain/loss distractors reflects value learning of color-incentive as- sociations during target trials. Independently from the color payoff regime, additional points were assigned according to the perfor- mance in a trial: Errors (wrong responses and omissions) were punished with �1 point and correct responses were rewarded with �1 point.

In the motivational-target condition, a gain- or loss-colored target was presented along with a neutral distractor; in the motivational-distractor condition, a gain- or loss-colored distractor was presented along with a neutral target; and in the neutral-

condition, a neutral-colored target was presented along with a neutral-colored distractor. In the present context, the term motiva- tional target/distractor refers to both reward and penalty, because research has shown that not only rewards but also penalties have an energizing effect on behavior and speed up responses toward punishment (Eder, Dignath, Erle, & Wiemer, 2017; Neiss, 1988; for additional analysis of the present data that considers the va- lence of targets/distractors, see Supplement 3; Table 1 provides an overview of the online supplement).

Our hypothesis of a motivational conflict-adaptation effect pre- dicts that motivational conflict in the previous trial mitigates the impact of motivational conflict in the current trial (see Figure 1, lower panel for an illustration). We measured initiation times (ITs) of movements as an indicator of motivational vigor. To index precision, we measured variability of movement trajectories in each trial (see Figure 1, upper panel for an illustration of the trial sequence and the dependent variables [DVs]). To test this hypoth- esis, we first reanalyzed a data set previously reported in a differ- ent context (Wirth et al., 2016, Experiment 3) and then replicated this finding in a new sample. Both analyses returned similar results, and for brevity, we present here only confirmatory tests of the replication study (reanalysis of the published data can be found in Supplement 1).

Method The present experiment is a direct replication of Experiment 3 of

Wirth et al. (2016). For completeness, an analysis analog to the one reported in Experiment 3 in the original paper can be found in Supplement 4.

Participants

Sixty-five participants were recruited (mean age � 21.3 years, SD � 3.8, seven male, six left-handed) and received course credit. Given an effect size of d � .4 in the reanalysis of our published data, this sample size allowed us to replicate conflict-adaptation effects with a power of 88% (alpha level of .05, two-sided). All participants gave informed consent, were naïve to the purpose of the experiment, and were debriefed after the session. Data of one participant were replaced by a new participant due to technical difficulties during testing.

Apparatus and Stimuli

All materials and procedure were identical to Wirth et al. (2016), Experiment 3. The experiment was run on an iPad (move- ments sampled at 100 Hz). The starting position (Ø 1 cm) was located at the bottom center of the screen; target and distractor

Table 1 Index of Supplementary Materials (Available at https://osf.io/tdegq/)

Section Pages

Supplement 1. Reanalysis of conflict-adaptation effects in Experiment 3 of Wirth, Dignath, Pfister, Kunde, and Eder (2016) 1–4

Supplement 2. Analysis of movement time and mean area under the curve of the current data set 5–6 Supplement 3. Modulation by previous and current valence in the reanalysis of Wirth et al.

(2016), Experiment 3 and the current data set 7–9 Supplement 4. Replication of the results of Wirth et al. (2016) in the present data set 9–10

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

85MOTIVATION DRIVES CONFLICT ADAPTATION

(Ø 2 cm) were presented in the upper left and right corners of the display in blue, yellow (color-value mapping counterbalanced across participants), or gray (neutral). Arrow cues prompted move- ments to the target (presented at the top of the screen). Each combination of motivational target/neutral distractor and neutral target/motivational distractor was presented 10 times per block. Additionally, label trials (randomly intermixed four times per block) were used to strengthen the color-value association during the experiment (not included in the analyses). In these trials, gain and loss colors were presented simultaneously, and a plus or minus sign indicated whether participants had to reach the gain or the loss stimulus.

Procedure

Participants touched the starting area with the index finger of the dominant hand to start a trial. Simultaneously, the two circles in

the upper half of the screen appeared, and after a dwell time of 200/300/400 ms (chosen randomly for each trial), an arrow indi- cated the current target. Participants were then to execute a smooth and continuous movement to the target as quickly as possible. Instructions emphasized accumulating as many points as possible (participant’s score was presented at the top). Points were calcu- lated based on the color payoff and the performance-contingent payoff regime independently (see above for details). Overall, par- ticipants completed 10 blocks of 58 trials each.

Results

Preprocessing

We focused on two complementary DVs to assess motivational drive and analyzed the average time to initiate movements (ITmean) and the average variability of the area under the curve (AUCSD)

Figure 1. Upper panel: Illustration of the trial sequence and depend variables. Participants started a trial by touching the start area with their index finger. After a variable dwell interval, jittered between 200 and 400 ms, the movement cue indicated the target. The initiation times reflected the time from cue onset until the finger left the start area and provides an index of how fast the movement was started. The standard deviation of the area under the curve reflected the spatial deviation between the actual trajectory and a straight line from start to end point and provides an index of variability of the movement. Lower panel: Illustration of the hypothesized motivational conflict adaptation effect: Motivational conflict (i.e., moving toward a neutral target in the presence of a motivational distractor [reward or penalty]) in the previous trial reduces the impact of motivational conflict in the current trial. See the online article for the color version of this figure.

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

86 DIGNATH ET AL.

between the actual trajectory and a straight line from start to end point. For completeness, similar analysis for movement time and mean AUC can be found in Supplement 2. Movements to the left were mirrored at the vertical midline for all analyses. AUC was computed from the time-normalized coordinate data of each trial by using custom MATLAB scripts (The Mathworks, Inc.) as the signed area relative to a straight line from start to end point of the movement (positive values indicating attraction toward the distrac- tor, negative values indicating attraction toward the edge of the display).

Data Selection and Analyses

Participants gained 546 points on average (SD � 25.7). For the following analyses, we removed error trials and response omis- sions (2.9%). Trials were discarded as outliers if any of the DVs deviated more than 2.5 SDs from the respective cell mean (5.3%).

Overall Conflict Adaptation

In the present study, we were interested in whether motivational conflict triggers conflict adaptation. To investigate this question, we first computed the conflict-adaptation effect for ITmean and AUCSD. The difference between conflict (C) versus nonconflict trials (I) after previously nonconflict (c) trials and conflict versus nonconflict trials after previously conflict (i) trials provides an index of conflict adaptation � (cI – cC) – (iI – iC). Both measures produced significant conflict-adaptation effects, ITmean, t(64) � 2.32, p � .024, d � 0.29 and AUCSD, t(64) � 4.32, p � .001, d � 0.53, indicating that previous motivational conflict reduced the impact of motivational conflict in the current trial. As shown in Figure 2 (i.e., the difference between the green and red lines depending on previous trial type), conflict in the previous trial increased vigor and decreased variability in the next trial to coun- teract further conflict.

Reduced Distractor Susceptibility Versus Increased Target Processing

Next, we investigated whether motivational conflict adaptation was driven by enhanced target focus or increased shielding against distractors. Therefore, we included trials following the neutral baseline condition (see Figure 2, right side of each plot) and analyzed overall performance with the factors previous trial type (motivational target vs. motivational distractor vs. baseline) and current trial type (motivational target vs. motivational distractor). Statistically, the interaction between both factors indicates a pos- sible difference in processing (target facilitation vs. distractor shielding) following conflict.

For ITmean, the main effect previous trial, F(2, 63) � 4.75, p � .012, �p2 � .13, showed slower response initiation after motiva- tional distractors (691 ms) than after motivational targets (686 ms) or baseline (686 ms), ts � 2.49, ps � .015, ds � 0.31, and no significant difference between the latter conditions, t � 1. The main effect of current trial, F(1, 64) � 15.44, p � .001, �p2 � .19, indicated faster response initiation for motivational targets (684 ms) than for motivational distractors (692 ms). The interaction was not significant, F(2, 63) � 2.94, p � .060. Analysis of AUCSD showed a main effect of previous trial, F(2, 63) � 6.14, p � .004, �p

2 � .16, with larger variability after motivational targets (18,226 px2) than after motivational distractors (16,682 px2) or baseline (17,052 px2), ts � 2.44, ps � .017, ds � 0.30, and no difference between the latter conditions, t � 1. Further, there was a main effect of the current trial, F(1, 64) � 83.47, p � .001, �p2 � .56, with higher spatial variability for motivational distractors (20,248 px2) than for motivational targets (14,393 px2). The significant interaction, F(2, 63) � 9.88, p � .001, �p2 � .24, indicated smaller differences between current motivational targets and motivational distractors after a motivational distractor (� � 4,118 px2) than after a motivational target (� � 7,451 px2) or baseline trials (� � 5,996 px2), ts � 2.07, ps � .043, ds � 0.26, and no difference

Figure 2. Mean initiation times (ITs; left) and standard deviation of areas under the curve (AUCs; right) for current motivational targets (red lines) and current motivational distractors (green lines), separate for trials after motivational targets, after motivational distractors, and after neutral baseline trials. See the online article for the color version of this figure.

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

87MOTIVATION DRIVES CONFLICT ADAPTATION

between the latter conditions, t(64) � 1.37, p � .176, d � 0.17. In other words, spatial variability of movements in motivational conflict trials was reduced following conflict, indicating better shielding against distraction, while target processing was not fa- cilitated by previous conflict.

Discussion

The present study tested whether previous motivational conflict causes adaptation to conflict in the current trial. Results from a reanalysis of a previous data set and a new replication provided converging evidence for motivational conflict-adaptation effect— prior motivational conflict alleviates future conflict. This is in line with recent research on decision making (Heitmann & Deutsch, 2019), showing faster decisions following motivational conflict compared to control trials. Moreover, it provides further support for theoretical accounts that aim to explain why and how people overcome obstacles during goal pursuit (e.g., Botvinick et al., 2001; Inzlicht et al., 2015; see also Shenhav, Botvinick, & Cohen, 2013). The present results extend this framework, showing that control mechanisms not only serve motivational functions but that motivational conflict is a driving force of control in itself.

Closer inspection of the results (see Figure 2) suggests that motivational conflict adaptation during movement execution was caused by an increased shielding against distraction. In contrast, conflict-adaptation effects in movement initiation seemed to be more likely due to increased target processing following conflict, although it should be noted that the critical comparison against baseline failed to reach significance in the confirmatory study (p � .060). Why did the mechanisms underlying conflict adaptation differ between DVs? One plausible explanation is that action initiation and execution reflect distinct processes underlying con- trol (Song & Nakayama, 2009). For instance, for conflict- adaptation effects in the Stroop task, it has been suggested that action initiation describes a reactive change in control, while action execution reflects monitoring of conflict (Erb & Marco- vitch, 2018; Erb, Moher, Sobel, & Song, 2016). Furthermore, neurophysiological work found that distinct neuronal population code separately for action initiation and execution (Gaidica, Hurst, Cyr, & Leventhal, 2018). However, it is unclear how such a sharp distinction translates into continuous processing models, suggest- ing a constant flow of information from beginning until the end of an action (Miller, 1988; Pfister, Janczyk, Wirth, Dignath, & Kunde, 2014; Spivey, Grosjean, & Knoblich, 2005). Clearly, more research is needed to determine when and how control is instigated during motivational conflicts.

In previous work (and also in this study; see Supplement 4), an asymmetric influence of the current motivational distractor on movement execution was observed. More specifically, movement execution showed an increased pull toward gain distractors, while targets and distractors associated with losses had no effect on movement execution. Why did this valence asymmetry in the current trial not influence conflict-adaptation effects? In theory, the control signal is indexed by competing response activation. Possibly, conflict monitoring is confined to the response prepara- tion stage and later conflict during response execution does not feed into the control signal. This speculation fits with research on dorsal stream processing during movement execution, which is

often assumed to run encapsulated from other ongoing processing (e.g., Milner, 2017; but see Erb et al., 2016).

Summary

This experiment (and a reanalysis of a previous data set) tested a key prediction of a motivational view of conflict adaptation, suggesting that previous motivational conflict reduced susceptibil- ity to current motivational conflict. Using a simple measure of motivational vigor and precision, we provided evidence that sup- ports this prediction by showing that previous motivational con- flict drives control adaptation.

References

Ach, N. (1935). Analyse des Willens [Analysis of the will]. In E. Abder- halden (Ed.), Handbuch der biologischen Arbeitsmethoden. Berlin, Ger- many: Urban & Schwarzenberg.

Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Re- view, 108, 624 – 652. http://dx.doi.org/10.1037/0033-295X.108.3.624

Braem, S., Verguts, T., Roggeman, C., & Notebaert, W. (2012). Reward modulates adaptations to conflict. Cognition, 125, 324 –332. http://dx .doi.org/10.1016/j.cognition.2012.07.015

Brehm, J. W., & Self, E. A. (1989). The intensity of motivation. Annual Review of Psychology, 40, 109 –131. http://dx.doi.org/10.1146/annurev .ps.40.020189.000545

Dignath, D., & Eder, A. B. (2015). Stimulus conflict triggers behavioral avoidance. Cognitive, Affective & Behavioral Neuroscience, 15, 822– 836. http://dx.doi.org/10.3758/s13415-015-0355-6

Dignath, D., Janczyk, M., & Eder, A. B. (2017). Phasic valence and arousal do not influence post-conflict adjustments in the Simon task. Acta Psychologica, 174, 31–39. http://dx.doi.org/10.1016/j.actpsy.2017.01 .004

Dignath, D., Kiesel, A., & Eder, A. B. (2015). Flexible conflict manage- ment: Conflict avoidance and conflict adjustment in reactive cognitive control. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41, 975–988. http://dx.doi.org/10.1037/xlm0000089

Dignath, D., Pfister, R., Eder, A. B., Kiesel, A., & Kunde, W. (2014). Something in the way she moves—Movement trajectories reveal dy- namics of self-control. Psychonomic Bulletin & Review, 21, 809 – 816. http://dx.doi.org/10.3758/s13423-013-0517-x

Dreisbach, G., & Fischer, R. (2012). Conflicts as aversive signals. Brain and Cognition, 78, 94 –98. http://dx.doi.org/10.1016/j.bandc.2011.12 .003

Dreisbach, G., & Fischer, R. (2015). Conflicts as aversive signals for control adaptation. Current Directions in Psychological Science, 24, 255–260. http://dx.doi.org/10.1177/0963721415569569

Eder, A. B., Dignath, D., Erle, T. M., & Wiemer, J. (2017). Shocking action: Facilitative effects of punishing electric shocks on action control. Journal of Experimental Psychology: General, 146, 1204 –1215. http:// dx.doi.org/10.1037/xge0000332

Erb, C. D., & Marcovitch, S. (2018). Deconstructing the Gratton effect: Targeting dissociable trial sequence effects in children, pre-adolescents, and adults. Cognition, 179, 150 –162. http://dx.doi.org/10.1016/j .cognition.2018.06.007

Erb, C. D., Moher, J., Sobel, D. M., & Song, J.-H. (2016). Reach tracking reveals dissociable processes underlying cognitive control. Cognition, 152, 114 –126. http://dx.doi.org/10.1016/j.cognition.2016.03.015

Fritz, J., & Dreisbach, G. (2013). Conflicts as aversive signals: Conflict priming increases negative judgments for neutral stimuli. Cognitive, Affective, & Behavioral Neuroscience, 13, 311–317.

Gaidica, M., Hurst, A., Cyr, C., & Leventhal, D. K. (2018). Distinct populations of motor thalamic neurons encode action initiation, action

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

88 DIGNATH ET AL.

selection, and movement vigor. Journal of Neuroscience, 38, 6563– 6573. http://dx.doi.org/10.1523/JNEUROSCI.0463-18.2018

Gendolla, G. H., & Richter, M. (2010). Effort mobilization when the self is involved: Some lessons from the cardiovascular system. Review of General Psychology, 14, 212–226. http://dx.doi.org/10.1037/a0019742

Gratton, G., Coles, M. G., & Donchin, E. (1992). Optimizing the use of information: Strategic control of activation of responses. Journal of Experimental Psychology: General, 121, 480 –506. http://dx.doi.org/10 .1037/0096-3445.121.4.480

Heitmann, C., & Deutsch, R. (2019). Post-conflict speeding: Evidence of sequential effects in motivational conflicts. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45, 452– 469. http://dx .doi.org/10.1037/xlm0000585

Inzlicht, M., Bartholow, B. D., & Hirsh, J. B. (2015). Emotional founda- tions of cognitive control. Trends in Cognitive Sciences, 19, 126 –132. http://dx.doi.org/10.1016/j.tics.2015.01.004

Manohar, S. G., Chong, T. T.-J., Apps, M. A., Batla, A., Stamelou, M., Jarman, P. R., . . . Husain, M. (2015). Reward pays the cost of noise reduction in motor and cognitive control. Current Biology, 25, 1707– 1716. http://dx.doi.org/10.1016/j.cub.2015.05.038

Miller, J. (1988). Discrete and continuous models of human information processing: Theoretical distinctions and empirical results. Acta Psycho- logica, 67, 191–257. http://dx.doi.org/10.1016/0001-6918(88)90013-3

Milner, A. D. (2017). How do the two visual streams interact with each other? Experimental Brain Research, 235, 1297–1308. http://dx.doi.org/ 10.1007/s00221-017-4917-4

Neiss, R. (1988). Reconceptualizing arousal: Psychobiological states in motor performance. Psychological Bulletin, 103, 345–366. http://dx.doi .org/10.1037/0033-2909.103.3.345

Niv, Y., Daw, N. D., Joel, D., & Dayan, P. (2007). Tonic dopamine: Opportunity costs and the control of response vigor. Psychopharmacol- ogy, 191, 507–520. http://dx.doi.org/10.1007/s00213-006-0502-4

Pfister, R., Janczyk, M., Wirth, R., Dignath, D., & Kunde, W. (2014). Thinking with portals: Revisiting kinematic cues to intention. Cognition, 133, 464 – 473. http://dx.doi.org/10.1016/j.cognition.2014.07.012

Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013). The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron, 79, 217–240. http://dx.doi.org/10.1016/j.neuron.2013.07.007

Song, J.-H., & Nakayama, K. (2009). Hidden cognitive states revealed in choice reaching tasks. Trends in Cognitive Sciences, 13, 360 –366. http://dx.doi.org/10.1016/j.tics.2009.04.009

Spivey, M. J., Grosjean, M., & Knoblich, G. (2005). Continuous attraction toward phonological competitors. Proceedings of the National Academy of Sciences of the United States of America, 102, 10393–10398. http:// dx.doi.org/10.1073/pnas.0503903102

Summerside, E. M., Shadmehr, R., & Ahmed, A. A. (2018). Vigor of reaching movements: Reward discounts the cost of effort. Journal of Neurophysiology, 119, 2347–2357. http://dx.doi.org/10.1152/jn.00872 .2017

Trope, Y., & Fishbach, A. (2000). Counteractive self-control in overcom- ing temptation. Journal of Personality and Social Psychology, 79, 493– 506. http://dx.doi.org/10.1037/0022-3514.79.4.493

van Steenbergen, H., Band, G. P., & Hommel, B. (2009). Reward coun- teracts conflict adaptation. Evidence for a role of affect in executive control. Psychological Science, 20, 1473–1477. http://dx.doi.org/10 .1111/j.1467-9280.2009.02470.x

Wirth, R., Dignath, D., Pfister, R., Kunde, W., & Eder, A. B. (2016). Attracted by rewards: Disentangling the motivational influence of re- warding and punishing targets and distractors. Motivation Science, 2, 143–156. http://dx.doi.org/10.1037/mot0000037

Received October 25, 2018 Revision received February 5, 2019

Accepted March 6, 2019 �

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

ti on

or on

e of

it s

al li

ed pu

bl is

he rs

. T

hi s

ar ti

cl e

is in

te nd

ed so

le ly

fo r

th e

pe rs

on al

us e

of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

89MOTIVATION DRIVES CONFLICT ADAPTATION