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Associations between individual differences in approach motivation and effort-based task performance
Personality and Individual Differences xxx (xxxx) xxxx
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Associations between individual differences in approach motivation and effort-based task performance Thomas M. Olinoa, , Julia A.C. Casea, Mark V. Versellaa,b, Christina E. Cerraa,c, Breana G. Genaroa ⁎
a
Department of Psychology, Temple University, 1701 North 13th Street, Weiss Hall, Philadelphia, PA 19122, United States Rutgers University, Tillet Hall Room 539, 53 Avenue E, Piscataway, NJ 08854-8040, USA c University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104 b
ARTICLE INFO
ABSTRACT
Keywords: Approach motivation Reward function Behavioral paradigms Effort Value
Approach motivation is of interest to multiple domains of psychology and the principal method of assessing this construct is self-report. However, there are multiple behavioral measures available to assess approach motivation. More work is needed to examine the validity of these tasks. In this study, we examined the progressive ratio (PR) task and effort expenditure for rewards task (EEfRT). We examined how PR task parameters influenced performance; associations between self-report measures of approach motivation and behavioral performance; and associations between behavioral task performance. Overall, the results showed that PR task performance was impacted based on the magnitude of ratio increases. We found some associations between self-report and PR task and EEfRT performance of modest effect size. We also found associations between task performance, but only when EEfRT performance was based on high probability reward trials. Overall, this study provides preliminary convergent validity of these behavioral measures for assessing approach motivation.
Dimensions of approach motivation include reward anticipation, responding to reward, and engagement in reward pursuits. This broad construct is of interest to multiple areas of psychology (Carver & White, 1994; McCrae & Costa, 1987; Watson & Clark, 1997) and is associated with adaptive functioning (e.g., Coplan et al., 2013) and psychopathology (Fussner, Mancini & Luebbe, 2018; Kotov, Gamez, Schmidt & Watson, 2010; Olino, McMakin & Forbes, 2018; Zald & Treadway, 2017). Most of the work on approach motivation constructs has typically relied on self-report measures. However, there also have been behavioral measures used to assess the construct, with some of these measures being historic and trans-species (Richardson & Roberts, 1996) and others being more modern and specific to human performance (Chelonis, Gravelin & Paule, 2011; Treadway, Buckholtz, Schwartzman, Lambert & Zald, 2009). There have been multiple behavioral tasks proposed for use in the Positive Valence Systems (PVS; PVS Work Group, 2011) from the Research Domain Criteria (Sanislow et al., 2010). Unfortunately, there has been limited attention associations between self-report and behavioral indices of approach motivation. The PVS matrix (PVS Work Group, 2011) was initially proposed in 2010 and recently revised (National Advisory Mental Health Council Workgroup, 2018). This domain of function focuses on aspects of reward function and differentiates between reward responsiveness, ⁎
reward learning, and reward valuation. Reward responsiveness focuses on experiences and processes that unfold during the anticipation, receipt, and processing of rewards. Reward learning emphasizes the acquisition of information that leads to positive outcomes that involves changes in behavior that lead to rewards, or better than anticipated outcomes. Reward valuation involves the evaluation of the specific reward value and amount of effort required to receive the rewards. Within the RDoC matrix, two behavioral tasks were presented that assess reward valuation: the progressive ratio (PR; Richardson & Roberts, 1996) task and the effort expenditure reinforcement task (EEfRT; Treadway et al., 2009). Both of these tasks are based on behavioral economics (Salamone et al., 2017) to support motivated behavior. In one administration method for the PR task, participants are required to expend increasing effort to earn a reward, which is set at a fixed value across trials. Thus, participants evaluate whether the reward valuation justifies the effort expenditure to complete trials throughout the task. Based on this task design, at some point in the task, referred to as the break point, the effort required to earn the reward will exceed the value of the reward and participants should quit the task. This task was initially developed for use with animal models for which work exerted to earn drug or food rewards was examined (Hodos, 1961). However, there have been applications of PR tasks in humans examining
Corresponding author. E-mail address: [email protected] (T.M. Olino).
https://doi.org/10.1016/j.paid.2020.109903 Received 5 January 2020; Received in revised form 3 February 2020; Accepted 7 February 2020 0191-8869/ © 2020 Published by Elsevier Ltd.
Please cite this article as: Thomas M. Olino, et al., Personality and Individual Differences, https://doi.org/10.1016/j.paid.2020.109903
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motivation in similar contexts (e.g., alcohol, cocaine, tobacco, food; Audrain-McGovern, Wileyto, Ashare, Cuevas & Strasser, 2014; Haney, Foltin & Fischman, 1998; Sigmon, Tidey, Badger & Higgins, 2003; Stoops, Glaser, Fillmore & Rush, 2004; Willner, Field, Pitts & Reeve, 1998; Yang, Schnepp & Tucker, 2019). In addition to examining substance use and food rewards in humans, studies have relied on monetary rewards to examine associations with development and psychosis (Chelonis et al., 2011; Strauss et al., 2016; Wolf et al., 2014). Few studies, however, have examined associations between individual difference characteristics and PR performance. In one exception, Hershenberg et al. (2016) compared individuals with diagnoses of unipolar or bipolar depression to individuals without a history of mood disorders on PR task performance with monetary rewards. They found that depressed individuals performed significantly worse on the task (i.e., quit earlier, or had a lower break point) than controls and did not significantly differ from individuals with bipolar depression. Additionally, Hughes, Pleasants and Pickens (1985) examined PR task performance in individuals with melancholic depression who did and did not respond to treatment. Participants completed the PR task before, during, and after treatment. The authors reported that individuals who improved with treatment earned more money and made more responses to obtain monetary rewards than individuals who did not improve with treatment. However, these studies only indirectly address individual differences in approach motivation. Across human and animal models of task performance, multiple task parameters can influence performance on PR tasks. For example, continuing the task may be influenced by the magnitude of reward incentive, the pace of increases in effort, or the length of the task. However, previous work has relied on multiple different combinations of task parameters. Thus, it is challenging to find common patterns of results across studies due to these differences. There is a need for work systematically manipulating task parameters and evaluate their impact on performance (Der-Avakian & Pizzagalli, 2018). In contrast to the use of PR tasks in animals, the use of PR tasks in humans affords additional opportunities to understand subjective experiences of changes in reward and effort valuation throughout the task. Human participants can provide subjective ratings of their reward and effort values throughout the task. To our knowledge, previous studies have not examined broad individual differences in approach motivation traits and PR performance. In addition to the PR task, the EEfRT (Treadway et al., 2009) is an additional paradigm for assessing the balance of reward and effort valuation. This is a probabilistic reward task such that participants decide between completing an easy task for a small reward, or a harder task for a larger reward. Trials proceed in a quasi-randomized sequence and provide a full set of comparisons across multiple levels of reward and probability of reward. In this task, the easy and hard requirements are fixed across trials, but the probability of reward is manipulated. Although the task has been developed more recently, there have been multiple examinations of associations between EEfRT performance and psychopathology, including eating disorders (Mata et al., 2017), substance use (Wardle, Treadway & de Wit, 2012), sleep disorders (Lasselin et al., 2017), psychosis and schizophrenia (Barch, Treadway & Schoen, 2014; Fervaha et al., 2013; Gold et al., 2013; Treadway, Peterman, Zald & Park, 2015), and depression (Treadway, Bossaller, Shelton & Zald, 2012; Yang et al., 2016; Yang et al., 2014). Additionally, other studies have examined associations between EEfRT performance and reward traits, such as anhedonia, social anhedonia, and avolition (DeRosse, Barber, Fales & Malhotra, 2019; McCarthy, Treadway & Blanchard, 2015; Treadway et al., 2009). Treadway et al. (2009) found a significant inverse relationship between anhedonia and willingness to expend effort for rewards with small-moderate effects, where individuals with elevated trait and state anhedonia exhibited a reduced willingness to make choices requiring greater effort in exchange for greater reward. In contrast to this finding,
McCarthy et al. (2015) found that social anhedonia was associated with a greater likelihood to expend more effort when uncertainty for reward was greatest, again, with small-moderate effects. Finally, DeRosse et al. (2019) found moderate effect size associations between anticipatory pleasure and task persistence, but not initiation of effortful responding in an internet-based sample. Thus, there is consistent evidence for small-moderate associations between EEfRT performance and individual differences in self-reported approach constructs. This study addresses multiple gaps in the previous literature. First, we examined how parametric manipulations of reward and effort influence changes in performance on a PR task, which is critical for the field to advance (Der-Avakian & Pizzagalli, 2018). Specifically, we manipulated the (1) value of rewards that were earned when each trial was completed; (2) magnitude of the increase of ratios across trials; and (3) total task length. We hypothesized that lower reward values, larger magnitude of ratio increases, and longer task length would be associated with greater likelihood of quitting the PR task. Second, in our adapted PR task, we examined changes in subjective ratings of reward and effort evaluation across trials. We hypothesized that the value of rewards would decrease and the amount of effort on each trial would increase across trials. Moreover, we hypothesized that lower reward values, larger magnitude of ratio increases, and longer task length would be associated with more rapid declines in value and more rapid increases in effort ratings. Third, in addition to examining experimental manipulations of PR task performance, we also explored associations between self-reported approach motivation constructs and PR and EEfRT performance. Third, we explored associations between PR and EEfRT performance. 1. Methods Data for the present study came from 400 undergraduate students from a large northeastern university who earned course credit for their participation. The mean age of the sample was 20.53 years (SD = 1.6; range 18–28 years); 76.3% were female; and 59.3% were Caucasian, 16.6% were African American, 13.8% were Asian, and 10.3% were other races or biracial. Before study related procedures commenced, written informed consent was received by study staff. Participants completed two behavioral tasks, the order of which was counter balanced across participants, and a battery of self-report measures examining anhedonia, personality, and depressive symptomatology. In this report, we focus on the self-report measures of approach motivation. 2. Behavioral measures 2.1. Progressive ratio task In this study, participants completed a PR task that maintained the same absolute reward across trials, but required constant increases in the amount of effort (i.e., button presses) in order to receive the reward on trials. Participants were randomized across three experimental manipulations that yielded eight separate sets of task conditions. We manipulated the reward value for each trial (i.e., $0.10 vs. $0.30), the ratio increases (i.e., 20 vs. 50 presses), and the task length (i.e., the task lasting for a maximum of 10 vs. 20 min). For the 20 button press increase, the first trial required 20 presses, the second required 40, the third required 60, and so on. For the 50 button press increase, the first trial required 50 presses, the second required 100, the third required 150, and so on. After completing each ratio, participants responded to prompts about their subjective evaluation of the reward value and the effort required to earn the rewards on a 1 (lowest subjective reward value; greatest effort)−8 (highest subjective reward value; least effort) scale. For each of these items, participants were primed to understand that the high reward values and low effort ratings were at the beginning of the task. This was done to calibrate responses at the beginning of the 2
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task. After completing task trials, participants were also asked whether they wanted to continue in the task or whether they wished to quit. Participants could also quit the task during trials. For this task, we focused on four separate outcomes: whether participants quit; time until quitting; subjective experience of reward value; and subjective experience of effort. Across task conditions, participants completed an average of 11.88 trials (SD = 5.73; range: 1–29).
Table 1 Correlations between self-report approach motivation constructs.
Pleasure Sensitivity Social Interest Well-being
Assertiveness
Pleasure sensitivity
Social interest
.345 .39⁎⁎ .45⁎⁎
.15⁎⁎ .52⁎⁎
.35⁎⁎
⁎⁎
Note: *p < .05; **p < .01.
2.2. Effort-Expenditure for rewards task
pleasure (8 items). Respondents are asked about responses to specific pleasurable experiences that may have experienced in the past week and are asked to rate how true or false these statements are to their own experience on a 6-point Likert scale. Internal consistency for each scale in the current sample was good (α = 0.89). The average absolute correlation between the FCPS, RPAS, and TEPS scales in the current study was 0.50 (range = 0.38–0.60); given this moderate average association, these scales were standardized and averaged together to create a composite score. Social closeness was measured using the social closeness subscale of the MPQ (MPQ-SC) and the Chapman Revised Social Anhedonia Scale (RSAS; Chapman et al., 1976). The MPQ-SC consists of 12 self-report items, with higher scores on these items suggesting that individuals describe themselves as sociable, liking to be with people, taking pleasure in and valuing close personal ties, and turning to others for comfort and help. Internal consistency in this sample for the MPQ-SC was good (α = 0.84). The RSAS is a 40 item self-report scale that assesses deficits in the ability to experience pleasure from other people, such as talking or exchanging expressions of feelings. Respondents may mark each item as ``true'' or ``false'' as much as it pertains to them and their opinions. Internal consistency for this scale in the current sample was good (α = 0.87). The association between the social closeness subscale and RSAS was −0.73; given this strong association, these scales were scored in the same direction, standardized, and averaged together to create a composite social closeness score. Well-being was measured using the positive affect subscale of the Positive and Negative Affect Schedule (PANAS; Watson, Clark & Tellegen, 1988), the well-being subscale of the MPQ (MPQ-WB), items from the Center for Epidemiologic Studies-Depression scale (Radloff, 1977) focusing on positive affect, and activity level from the BFI. The PANAS consists of two 10-item mood subscales evaluating experiences of both positive and negative affect on that day on a 5-point Likert scale ranging from ``very slightly or not at all'' to ``extremely.'' The internal consistency in this sample for the positive affect subscale was excellent (α = 0.91). The MPQ-WB consists of 14 self-report items assessing an individual's disposition to experience positive emotions, with higher scores on these items indicating greater well-being, including having a cheerful or happy disposition, feeling good about the self, seeing a bright future ahead, and enjoying the things that they are doing. Internal consistency in this sample for the MPQ-WB was good (α = 0.86). Four items from the CES-D assessing positive affect were selected. These had good internal consistency (α = 0.80). The BFI assesses two dimensions of each of the Big Five. The activity level facet of extraversion includes two items (α = 0.77). The correlation between these scales in the current study was 0.56 (range: 0.44–0.66); given this moderate correlation, these scales were standardized and averaged together to create a composite well-being score.
(Treadway et al., 2012). On each trial, participants were presented with an option of choosing between a hard or easy trial that varied in the number of required button presses to earn rewards on each trial. Decisions of choosing easy trials provided participants an incentive of a $1.00 reward for success on the trial. Decisions of pursuing hard trials provided participants an incentive between $1.24 and $4.30 for each success on the trial. Participants were made aware of the probability of having these task earnings contributing towards their earnings, with high, moderate, and low magnitude rewards on each trial (high probability, 88%; medium probability, 50%; and low probability, 12%). Participants completed an average of 70.99 (SD = 11.53; range = 45–140) EEfRT trials. However, there were trials for which responses either were not provided or were provided beyond a 5 s response window. Across participants, there were on average 3.80 (SD = 9.09; range = 0–56) trials lost due to non-response or delayed response. Ultimately, participants provided an average of 67.19 (SD = 16.70; range = 1–140) valid trials on the task. We computed the proportion of valid trials for which the more difficult task option was selected for each probability level and for the entire task. Thus, this indexed willingness to work for rewards contingent on probability of rewards. 3. Self-Report measures Self-report measures broadly assessed approach motivation. Selected measures came from the full span of PVS self-report measures and also included related constructs (e.g., personality; interpersonal reward motivation). A previous study using data from an independent sample relied on the same battery of measures (Olino et al., 2018). We relied on results from this study to derive parsimonious set of constructs: assertiveness, pleasure sensitivity, social closeness, and wellbeing. Assertiveness was measured using the social potency scale from the Multidimensional Personality Questionnaire (MPQ-SP; Tellegen & Waller, 2008) and the assertiveness scale from the Big Five Inventory (BFI; John, Donahue & Kentle, 1991). The social potency scale includes 14 items (α = 0.76). The assertiveness scale includes five items (α = 0.78). These scales were moderately correlated (r = 0.62); thus, these scales were standardized and averaged to yield a single assertiveness score. Pleasure sensitivity was measured using the Fawcett-Clark Pleasure Scale (FCPS; Fawcett, Clark, Scheftner & Gibbons, 1983), the Chapman Revised Physical Anhedonia Scale (RPAS; Chapman, Chapman & Raulin, 1976), the Snaith-Hamilton Pleasure Scale (SHAPS; Snaith et al., 1995), and the Temporal Experience of Pleasure Scale (TEPS; Gard, Gard, Kring & John, 2006). The FCPS is a 36-item selfreport questionnaire asking participants to rate imagined hedonic reactions to hypothetical pleasurable situations. Responses are made on a 5-point Likert scale ranging from ``no pleasure at all'' to ``extreme and lasting pleasure.'' Internal consistency for this scale in the current sample was excellent (α = 0.94). The RPAS is a 61-item self-report scale asking participants to respond true or false to statements about their typical feelings about normally pleasurable stimuli and activities. Internal consistency for this scale in the current sample was good (α = 0.83). The TEPS is a measure composed of 18 self-reported items assessing both anticipatory pleasure (10 items) and consummatory
4. Data analytic strategy All analyses were conducted using Mplus 8.3 (Muthén & Muthén, 1998-2018) and facilitated using the MplusAutomation package (Hallquist & Wiley, 2018) in R (R Core Team, 2018). Analyses were conducted to address distinct questions. First, we examined how manipulated PR parameters influenced task performance. We examined main effects of reward value, the ratio increases, and the task length, as 3
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Table 2 PR Task parameters predicting likelihood of quitting in univariate and multivariate models.
Reward Magnitude Task Length Ratio Increase
Quitting Univariate OR (95% CI)
Multivariate OR (95% CI)
Time until quitting Univariate HR (95% CI)
Multivariate HR (95% CI)
.68 (0.45–1.01) 3.05 (2.01–4.61)⁎⁎⁎ 1.00 (0.67–1.49)
.66 (0.44–1.00) 3.07 (2.02–4.66)⁎⁎⁎ 1.02 (0.67–1.54)
.80 (0.62–1.02) 1.04 (0.79–1.37) 2.18 (1.64–2.90)⁎⁎⁎
.78 (0.61–1.01) .93 (0.71–1.23) 2.22 (1.67–2.95)⁎⁎⁎
Note: *p < .05. **p < .01. ***p < .001. OR = Odds ratios estimated via logistic regression models. HR = Hazard Ratios estimated via survival models.
Fig. 1. Rates of change in Effort (Top) and Value Ratings (Bottom) across the Progressive Ratio Task for Levels of Ratio Increases.
4
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Table 3 Rates of change in effort and value rating.
Reward Magnitude Task Length Ratio Increase
Effort rating Univariate Intercept b (SE) .28 (0.16) −0.10 (0.16) −0.18 (0.16)
Slope b (SE)
Multivariate Intercept b (SE)
.00 (0.03) .04 (0.03) −0.12 (0.03)⁎⁎⁎
.28 (0.16) −0.11 (0.16) −0.19 (0.16)
Slope b (SE)
Value rating Univariate Intercept b (SE)
Slope b (SE)
Multivariate Intercept b (SE)
Slope b (SE)
.00 (0.03) .04 (0.03) −0.12 (0.03)⁎⁎⁎
.39 (0.22) .33 (0.22) .10 (0.22)
.01 (0.03) −0.01 (0.03) −0.08 (0.03)*
.39 (0.21) .33 (0.22) .09 (0.22)
.01 (0.03) −0.01 (0.03) −0.08 (0.03)*
Note: *p < .05; **p < .01; ***p < .001.
faster pace of increases in presses required to earn the reward was associated with more rapid quitting on the task (Fig. 1). We estimated unconditional multilevel models to describe overall rate of change in value and effort ratings across the task, regardless of experimental manipulations. For value ratings, the intercept (γ00) was 5.29 (SE = 0.109; p < .001) and slope (γ01) was −0.21 (SE = 0.01; p < .001). There was significant variability in both the intercept and slope (variance components = 3.79; SE = 0.25, t = 15.25, p < .001 and variance components = 0.43; SE = 0.005, t = 8.91, p < .001, respectively). Next, we examined associations between task parameters and course of value and effort ratings across task trials. Table 3 displays results of multilevel models for the univariate and multivariate predictor models examining associations between task conditions and course of value and effort ratings. In univariate and multivariate models, the higher ratio increase was associated with more rapid reclines in value and effort ratings (Fig. 1). For each outcome, further multivariate models were estimated that included all two- and threeway interactions between task conditions. No interaction effects were significant for any of the outcomes.
Table 4 Self-report approach motivation and progressive ratio quitting & survival. Factor
Quitting OR (95% CI)
Survival HR (95% CI)
Assertiveness Pleasure Sensitivity Social Closeness Well-Being Sex
1.23 1.01 1.14 1.02 0.57
1.14 1.01 1.13 1.02 0.66
(0.98–1.55) (0.77–1.31) (0.64–2.03) (0.79–1.33) (0.35–0.94)
(1.00–1.31) (0.85–1.2) (0.80–1.59) (0.88–1.18) (0.47–0.92)
Note: *p < .05; **p < .01; ***p < .001. OR = Odds ratios estimated via logistic regression models. HR = Hazard Ratios estimated via survival models.
well as two- and three-way interactions between these factors. We examined associations with quitting the task using logistic regression; with time until quitting using Cox proportional hazards models; and with value and effort ratings using linear growth models. For the Cox proportional models, the unit of time was trial number. Similarly, multilevel models estimated the linear rate of change in value and effort ratings as a function of trial number. Second, we examined how selfreported approach motivation constructs were associated with behavioral task performance. For analyses of the PR task, we examined associations between self-reported approach motivation constructs and PR task performance when controlling for manipulated PR parameters. For analyses of the EEfRT, we examined bivariate correlations between self-reported approach motivation constructs and EEfRT performance. Finally, we examined associations between PR and EEfRT performance. We examined whether EEfRT performance was associated with PR performance after controlling for manipulated PR parameters.
7. Associations between behavioral task performance and selfreport of approach We examined associations between self-report measures of approach and quitting, time until quitting, and course of value and effort ratings on the PR task when controlling for main effects of task parameters. In these models (Table 4), we found no associations between quitting or time until quitting and self-report measures. We used multilevel models to examine associations between selfreport measures of approach motivation and changes in value and effort ratings (Table 5) when controlling for main effects of task parameters. Self-reported pleasure was associated with higher initial ratings of reward value. Self-reported affiliation was associated with lower initial ratings of effort. Univariate regression models examined associations between selfreport measures of approach motivation and proportion of hard trials played on the 12%, 50%, and 88% probability trials, and all EEfRT trials (Table 6). Assertiveness and well-being were associated with proportion of plays on 12% probability trials; pleasure was associated with proportion of plays on 88% probability trials; and pleasure was associated with proportion of plays across all task trials.
5. Results Table 1 displays the intercorrelations between the approach motivation constructs. Overall, these constructs were modestly correlated. 6. PR task parameters Table 2 displays results of logistic regression models predicting whether or not participants quit the PR task (left panel) and time until quitting (right panel). In both univariate and multivariate models, participants in the longer PR task (20 min) were more likely to quit (70.1%) than in the shorter PR task (10 min; 43.3%). In Cox pH models, only the ratio increase was associated with rate of task quitting. The
Table 5 Self-report approach motivation and course of progressive ratio task effort and value ratings. Effort rating Intercept Assertiveness Pleasure Sensitivity Social Closeness Well-Being Sex
b (SE) −0.04 (0.08) −0.10 (0.10) −0.47 (0.18)⁎⁎ −0.13 (0.09) 0.14 (0.20)
Value rating Intercept
Slope pr 0.02 0.05 0.11 0.07 0.04
b (SE) 0.00 (0.01) 0.02 (0.02) 0.00 (0.04) 0.02 (0.01) −0.02 (0.03)
pr 0.01 0.05 0.00 0.08 0.03
Note: *p < .05; **p < .01; ***p < .001. 5
b (SE) −0.05 (0.12) 0.31 (0.14)* −0.10 (0.28) 0.14 (0.13) −0.02 (0.26)
Slope pr 0.02 0.11 0.02 0.05 0.00
b (SE) −0.00 −0.03 −0.02 −0.02 −0.00
(0.02) (0.02) (0.03) (0.02) (0.03)
pr 0.01 0.09 0.03 0.05 0.00
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Table 6 Self-report approach motivation and EEfRT performance. Reward probability of trials Factor
12% b (SE)
pr
50% b (SE)
pr
88% b (SE)
pr
Overall b (SE)
pr
Assertiveness Pleasure Sensitivity Social Closeness Well-Being Sex
0.03 0.01 0.02 0.04 0.04
0.14 0.06 0.05 0.14 0.08
0.01 (0.01) 0.03 (0.01)* −0.03 (0.03) 0.01 (0.02) 0.10 (0.03)**
0.04 0.1 0.05 0.04 0.17
0.00 (0.01) 0.04 (0.02)* −0.05 (0.03) 0.02 (0.02) 0.08 (0.03)*
0.02 0.14 0.08 0.08 0.15
0.01 (0.01) 0.03 (0.01)* −0.02 (0.02) 0.02 (0.01) 0.07 (0.02)**
0.05 0.13 0.05 0.1 0.17
(0.01)* (0.01) (0.02) (0.01)⁎⁎ (0.03)
Note: *p < .05; **p < .01; ***p < .001.
In our examination of PR task performance, we found that longer task length was associated with overall likelihood of quitting the task. However, the rate of quitting, as likelihood of quitting per trial, differed according to the magnitude of the ratio increase. Moreover, the magnitude of the ratio increase was also associated with changes in the subjective ratings of reward value and effort throughout the task. Thus, across the set of manipulated parameters, the experience of more challenging trial tasks (i.e., 50 vs. 20 additional presses per trial) influenced performance. However, reward magnitude did not significantly influence performance. Moreover, we did not find evidence that there were computational tradeoffs between effort and value, as we did not find significant interactions between these task parameters. These findings begin to address the critical need for the literature to systematically examine parametric modulations of PR task performance in humans (Der-Avakian & Pizzagalli, 2018). We found modest evidence for associations between self-reports of approach motivation and task performance. We found that social interest/affiliation was associated with higher initial ratings of effort and pleasure sensitivity was associated with higher initial ratings of reward value on the PR Task. Pleasure sensitivity was associated with greater willingness to work for moderate, high probability, and all trials; and well-being and assertiveness were both associated with greater willingness to work for low probability trials. Thus, the sole consistent association appeared to be between pleasure sensitivity and engagement with engagement with the tasks to have stronger affinity towards monetary incentives. However, the significant inverse associations between social interest and PR task performance was not intuitive. Perhaps this association reflects a difference in evaluating associations with monetary versus social incentives. Consistent with previous work (DeRosse et al., 2019; McCarthy et al., 2015; Nguyen et al., 2019; Treadway et al., 2009; Zou et al., 2019), the magnitude of statistically significant associations was modest (rs ranged from 0.11–0.14). Finally, we found some associations between PR and EEfRT performance. We did not find associations between decisions to choose harder options with low or modest probabilities of earning reward on EEfRT and continuing on the PR task. However, decisions to choose harder options with higher probability of earning rewards (i.e., greater certainty) were associated with lower probabilities of quitting and longer times until quitting on the PR task. Thus, when both tasks have greater certainty, performance was more strongly associated. This study benefitted from a moderately large sample with careful control of multiple experimental manipulations of parameters in the PR
Table 7 EEfRT performance and progressive ratio quitting & survival. EEfRT performance
Quitting OR (95%CI)
Survival HR (95%CI)
Low Rew. Prob. (12%) Medium Rew. Prob. (50%) High Reward Prob. (88%) Across Task Trials
1.01 0.57 0.32 0.39
0.77 0.58 0.46 0.42
(0.28–3.62) (0.2–1.64) (0.12–0.89)* (0.1–1.52)
(0.36–1.64) (0.32–1.06) (0.27–0.78)⁎⁎ (0.2–0.91)*
Note: *p < .05; **p < .01; ***p < .001. OR = Odds ratios estimated via logistic regression models. HR = Hazard Ratios estimated via survival models.
8. Associations between PR task and EEfRT performance Univariate regression models examined associations between proportion of hard trials played on the 12%, 50%, and 88% probability trials, and across all EEfRT trials and PR task performance. All models controlled for PR task parameters. Decisions to engage in the harder task option in the high probability reward trials on the EEfRT were associated with less likelihood of quitting and longer time until quitting (Table 7). Similarly, overall decisions to engage in harder trials on the EEfRT were associated with longer time until quitting. Decisions to engage in harder EEfRT trials were not significantly associated with course of value and effort ratings (Table 8). 9. Discussion This study examined an adaptation of a PR task and examined how task parameters influenced task performance and experience of completing the task. We also examined associations between multiple selfreported approach motivation constructs and behavioral measure of approach motivation using this PR task and EEfRT, another probabilistic reward task that assess the balance of effort and reward valuation. Finally, we also examined associations between behavioral performance on the PR task and EEfRT. We found that the magnitude of ratio increase on the PR task was associated with more rapid quitting, decline of reward ratings, and increase of effort ratings. We found modest associations between self-report measures of approach motivation and PR and EEfRT performance. We found some convergence between PR and EEfRT performance, particularly when the likelihood of reward was more certain on the EEfRT. Thus, overall, there is equivocal support for the utility of the PR task in humans as a means of evaluating reward motivation. Table 8 EEfRT performance and course of progressive ratio task effort and value ratings.
EEfRT Performance Low Rew. Prob. (12%) Medium Rew. Prob. (50%) High Reward Prob. (88%) Across Task Trials
Effort rating Intercept
Slope
b (SE) 0.13 (0.43) 0.45 (0.38) 0.37 (0.36) 0.50 (0.48)
b (SE) −0.08 −0.06 −0.03 −0.08
Note: *p < .05; **p < .01; ***p < .001. 6
(0.08) (0.06) (0.06) (0.08)
Value rating Intercept
Slope
b (SE) 0.37 (0.67) 0.05 (0.53) 0.38 (0.47) 0.41 (0.67)
b (SE) 0.02 (0.09) 0.03 (0.06) −0.04 (0.06) −0.01 (0.08)
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task. This permitted evaluation of multiple factors that influenced performance, inclusive of task parameters and individual differences. We also included a second behavioral task that is purported to assess a similar reward seeking process. However, the study is not without limitations. First, in our testing of PR task parameters, we only tested two different levels of each parameter. Further testing of reward values may be worthwhile to better characterize levels of incentives that may increase perseverance in the task and be sensitive to individual differences. Second, although the sample was moderately large, it came from an undergraduate sample. Thus, the levels of self-report measures of approach motivation were largely within the normative range. Additional examinations of associations between self-report approach motivation and behavioral performance is needed with samples with lower levels of approach (e.g., samples enriched for depression or psychosis). Third, we focused only on main effect associations between individual differences in approach motivation and PR task performance. It is possible that individual differences in approach may have moderated relationships between task parameters and task performance. However, this would have led to many tests, with increased risk of false positive tests. Future work could examine hypothesis driven interactions to advance this work. Overall, this study found that increasing effort towards earning rewards on the PR task was associated with greater likelihood of reduced performance. We also found associations between individual differences in approach motivation and subjective experience of completing the PR task and EEfRT performance. Moreover, we found that PR task and EEfRT performance were associated for EEfRT trials that had greater certainty. Thus, there is some evidence for construct validity for these two behavioral paradigms to assess these domains of reward pursuit.
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Associations between individual differences in approach motivation and effort-based task performance Thomas M. Olinoa, , Julia A.C. Casea, Mark V. Versellaa,b, Christina E. Cerraa,c, Breana G. Genaroa ⁎
a
Department of Psychology, Temple University, 1701 North 13th Street, Weiss Hall, Philadelphia, PA 19122, United States Rutgers University, Tillet Hall Room 539, 53 Avenue E, Piscataway, NJ 08854-8040, USA c University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104 b
ARTICLE INFO
ABSTRACT
Keywords: Approach motivation Reward function Behavioral paradigms Effort Value
Approach motivation is of interest to multiple domains of psychology and the principal method of assessing this construct is self-report. However, there are multiple behavioral measures available to assess approach motivation. More work is needed to examine the validity of these tasks. In this study, we examined the progressive ratio (PR) task and effort expenditure for rewards task (EEfRT). We examined how PR task parameters influenced performance; associations between self-report measures of approach motivation and behavioral performance; and associations between behavioral task performance. Overall, the results showed that PR task performance was impacted based on the magnitude of ratio increases. We found some associations between self-report and PR task and EEfRT performance of modest effect size. We also found associations between task performance, but only when EEfRT performance was based on high probability reward trials. Overall, this study provides preliminary convergent validity of these behavioral measures for assessing approach motivation.
Dimensions of approach motivation include reward anticipation, responding to reward, and engagement in reward pursuits. This broad construct is of interest to multiple areas of psychology (Carver & White, 1994; McCrae & Costa, 1987; Watson & Clark, 1997) and is associated with adaptive functioning (e.g., Coplan et al., 2013) and psychopathology (Fussner, Mancini & Luebbe, 2018; Kotov, Gamez, Schmidt & Watson, 2010; Olino, McMakin & Forbes, 2018; Zald & Treadway, 2017). Most of the work on approach motivation constructs has typically relied on self-report measures. However, there also have been behavioral measures used to assess the construct, with some of these measures being historic and trans-species (Richardson & Roberts, 1996) and others being more modern and specific to human performance (Chelonis, Gravelin & Paule, 2011; Treadway, Buckholtz, Schwartzman, Lambert & Zald, 2009). There have been multiple behavioral tasks proposed for use in the Positive Valence Systems (PVS; PVS Work Group, 2011) from the Research Domain Criteria (Sanislow et al., 2010). Unfortunately, there has been limited attention associations between self-report and behavioral indices of approach motivation. The PVS matrix (PVS Work Group, 2011) was initially proposed in 2010 and recently revised (National Advisory Mental Health Council Workgroup, 2018). This domain of function focuses on aspects of reward function and differentiates between reward responsiveness, ⁎
reward learning, and reward valuation. Reward responsiveness focuses on experiences and processes that unfold during the anticipation, receipt, and processing of rewards. Reward learning emphasizes the acquisition of information that leads to positive outcomes that involves changes in behavior that lead to rewards, or better than anticipated outcomes. Reward valuation involves the evaluation of the specific reward value and amount of effort required to receive the rewards. Within the RDoC matrix, two behavioral tasks were presented that assess reward valuation: the progressive ratio (PR; Richardson & Roberts, 1996) task and the effort expenditure reinforcement task (EEfRT; Treadway et al., 2009). Both of these tasks are based on behavioral economics (Salamone et al., 2017) to support motivated behavior. In one administration method for the PR task, participants are required to expend increasing effort to earn a reward, which is set at a fixed value across trials. Thus, participants evaluate whether the reward valuation justifies the effort expenditure to complete trials throughout the task. Based on this task design, at some point in the task, referred to as the break point, the effort required to earn the reward will exceed the value of the reward and participants should quit the task. This task was initially developed for use with animal models for which work exerted to earn drug or food rewards was examined (Hodos, 1961). However, there have been applications of PR tasks in humans examining
Corresponding author. E-mail address: [email protected] (T.M. Olino).
https://doi.org/10.1016/j.paid.2020.109903 Received 5 January 2020; Received in revised form 3 February 2020; Accepted 7 February 2020 0191-8869/ © 2020 Published by Elsevier Ltd.
Please cite this article as: Thomas M. Olino, et al., Personality and Individual Differences, https://doi.org/10.1016/j.paid.2020.109903
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motivation in similar contexts (e.g., alcohol, cocaine, tobacco, food; Audrain-McGovern, Wileyto, Ashare, Cuevas & Strasser, 2014; Haney, Foltin & Fischman, 1998; Sigmon, Tidey, Badger & Higgins, 2003; Stoops, Glaser, Fillmore & Rush, 2004; Willner, Field, Pitts & Reeve, 1998; Yang, Schnepp & Tucker, 2019). In addition to examining substance use and food rewards in humans, studies have relied on monetary rewards to examine associations with development and psychosis (Chelonis et al., 2011; Strauss et al., 2016; Wolf et al., 2014). Few studies, however, have examined associations between individual difference characteristics and PR performance. In one exception, Hershenberg et al. (2016) compared individuals with diagnoses of unipolar or bipolar depression to individuals without a history of mood disorders on PR task performance with monetary rewards. They found that depressed individuals performed significantly worse on the task (i.e., quit earlier, or had a lower break point) than controls and did not significantly differ from individuals with bipolar depression. Additionally, Hughes, Pleasants and Pickens (1985) examined PR task performance in individuals with melancholic depression who did and did not respond to treatment. Participants completed the PR task before, during, and after treatment. The authors reported that individuals who improved with treatment earned more money and made more responses to obtain monetary rewards than individuals who did not improve with treatment. However, these studies only indirectly address individual differences in approach motivation. Across human and animal models of task performance, multiple task parameters can influence performance on PR tasks. For example, continuing the task may be influenced by the magnitude of reward incentive, the pace of increases in effort, or the length of the task. However, previous work has relied on multiple different combinations of task parameters. Thus, it is challenging to find common patterns of results across studies due to these differences. There is a need for work systematically manipulating task parameters and evaluate their impact on performance (Der-Avakian & Pizzagalli, 2018). In contrast to the use of PR tasks in animals, the use of PR tasks in humans affords additional opportunities to understand subjective experiences of changes in reward and effort valuation throughout the task. Human participants can provide subjective ratings of their reward and effort values throughout the task. To our knowledge, previous studies have not examined broad individual differences in approach motivation traits and PR performance. In addition to the PR task, the EEfRT (Treadway et al., 2009) is an additional paradigm for assessing the balance of reward and effort valuation. This is a probabilistic reward task such that participants decide between completing an easy task for a small reward, or a harder task for a larger reward. Trials proceed in a quasi-randomized sequence and provide a full set of comparisons across multiple levels of reward and probability of reward. In this task, the easy and hard requirements are fixed across trials, but the probability of reward is manipulated. Although the task has been developed more recently, there have been multiple examinations of associations between EEfRT performance and psychopathology, including eating disorders (Mata et al., 2017), substance use (Wardle, Treadway & de Wit, 2012), sleep disorders (Lasselin et al., 2017), psychosis and schizophrenia (Barch, Treadway & Schoen, 2014; Fervaha et al., 2013; Gold et al., 2013; Treadway, Peterman, Zald & Park, 2015), and depression (Treadway, Bossaller, Shelton & Zald, 2012; Yang et al., 2016; Yang et al., 2014). Additionally, other studies have examined associations between EEfRT performance and reward traits, such as anhedonia, social anhedonia, and avolition (DeRosse, Barber, Fales & Malhotra, 2019; McCarthy, Treadway & Blanchard, 2015; Treadway et al., 2009). Treadway et al. (2009) found a significant inverse relationship between anhedonia and willingness to expend effort for rewards with small-moderate effects, where individuals with elevated trait and state anhedonia exhibited a reduced willingness to make choices requiring greater effort in exchange for greater reward. In contrast to this finding,
McCarthy et al. (2015) found that social anhedonia was associated with a greater likelihood to expend more effort when uncertainty for reward was greatest, again, with small-moderate effects. Finally, DeRosse et al. (2019) found moderate effect size associations between anticipatory pleasure and task persistence, but not initiation of effortful responding in an internet-based sample. Thus, there is consistent evidence for small-moderate associations between EEfRT performance and individual differences in self-reported approach constructs. This study addresses multiple gaps in the previous literature. First, we examined how parametric manipulations of reward and effort influence changes in performance on a PR task, which is critical for the field to advance (Der-Avakian & Pizzagalli, 2018). Specifically, we manipulated the (1) value of rewards that were earned when each trial was completed; (2) magnitude of the increase of ratios across trials; and (3) total task length. We hypothesized that lower reward values, larger magnitude of ratio increases, and longer task length would be associated with greater likelihood of quitting the PR task. Second, in our adapted PR task, we examined changes in subjective ratings of reward and effort evaluation across trials. We hypothesized that the value of rewards would decrease and the amount of effort on each trial would increase across trials. Moreover, we hypothesized that lower reward values, larger magnitude of ratio increases, and longer task length would be associated with more rapid declines in value and more rapid increases in effort ratings. Third, in addition to examining experimental manipulations of PR task performance, we also explored associations between self-reported approach motivation constructs and PR and EEfRT performance. Third, we explored associations between PR and EEfRT performance. 1. Methods Data for the present study came from 400 undergraduate students from a large northeastern university who earned course credit for their participation. The mean age of the sample was 20.53 years (SD = 1.6; range 18–28 years); 76.3% were female; and 59.3% were Caucasian, 16.6% were African American, 13.8% were Asian, and 10.3% were other races or biracial. Before study related procedures commenced, written informed consent was received by study staff. Participants completed two behavioral tasks, the order of which was counter balanced across participants, and a battery of self-report measures examining anhedonia, personality, and depressive symptomatology. In this report, we focus on the self-report measures of approach motivation. 2. Behavioral measures 2.1. Progressive ratio task In this study, participants completed a PR task that maintained the same absolute reward across trials, but required constant increases in the amount of effort (i.e., button presses) in order to receive the reward on trials. Participants were randomized across three experimental manipulations that yielded eight separate sets of task conditions. We manipulated the reward value for each trial (i.e., $0.10 vs. $0.30), the ratio increases (i.e., 20 vs. 50 presses), and the task length (i.e., the task lasting for a maximum of 10 vs. 20 min). For the 20 button press increase, the first trial required 20 presses, the second required 40, the third required 60, and so on. For the 50 button press increase, the first trial required 50 presses, the second required 100, the third required 150, and so on. After completing each ratio, participants responded to prompts about their subjective evaluation of the reward value and the effort required to earn the rewards on a 1 (lowest subjective reward value; greatest effort)−8 (highest subjective reward value; least effort) scale. For each of these items, participants were primed to understand that the high reward values and low effort ratings were at the beginning of the task. This was done to calibrate responses at the beginning of the 2
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task. After completing task trials, participants were also asked whether they wanted to continue in the task or whether they wished to quit. Participants could also quit the task during trials. For this task, we focused on four separate outcomes: whether participants quit; time until quitting; subjective experience of reward value; and subjective experience of effort. Across task conditions, participants completed an average of 11.88 trials (SD = 5.73; range: 1–29).
Table 1 Correlations between self-report approach motivation constructs.
Pleasure Sensitivity Social Interest Well-being
Assertiveness
Pleasure sensitivity
Social interest
.345 .39⁎⁎ .45⁎⁎
.15⁎⁎ .52⁎⁎
.35⁎⁎
⁎⁎
Note: *p < .05; **p < .01.
2.2. Effort-Expenditure for rewards task
pleasure (8 items). Respondents are asked about responses to specific pleasurable experiences that may have experienced in the past week and are asked to rate how true or false these statements are to their own experience on a 6-point Likert scale. Internal consistency for each scale in the current sample was good (α = 0.89). The average absolute correlation between the FCPS, RPAS, and TEPS scales in the current study was 0.50 (range = 0.38–0.60); given this moderate average association, these scales were standardized and averaged together to create a composite score. Social closeness was measured using the social closeness subscale of the MPQ (MPQ-SC) and the Chapman Revised Social Anhedonia Scale (RSAS; Chapman et al., 1976). The MPQ-SC consists of 12 self-report items, with higher scores on these items suggesting that individuals describe themselves as sociable, liking to be with people, taking pleasure in and valuing close personal ties, and turning to others for comfort and help. Internal consistency in this sample for the MPQ-SC was good (α = 0.84). The RSAS is a 40 item self-report scale that assesses deficits in the ability to experience pleasure from other people, such as talking or exchanging expressions of feelings. Respondents may mark each item as ``true'' or ``false'' as much as it pertains to them and their opinions. Internal consistency for this scale in the current sample was good (α = 0.87). The association between the social closeness subscale and RSAS was −0.73; given this strong association, these scales were scored in the same direction, standardized, and averaged together to create a composite social closeness score. Well-being was measured using the positive affect subscale of the Positive and Negative Affect Schedule (PANAS; Watson, Clark & Tellegen, 1988), the well-being subscale of the MPQ (MPQ-WB), items from the Center for Epidemiologic Studies-Depression scale (Radloff, 1977) focusing on positive affect, and activity level from the BFI. The PANAS consists of two 10-item mood subscales evaluating experiences of both positive and negative affect on that day on a 5-point Likert scale ranging from ``very slightly or not at all'' to ``extremely.'' The internal consistency in this sample for the positive affect subscale was excellent (α = 0.91). The MPQ-WB consists of 14 self-report items assessing an individual's disposition to experience positive emotions, with higher scores on these items indicating greater well-being, including having a cheerful or happy disposition, feeling good about the self, seeing a bright future ahead, and enjoying the things that they are doing. Internal consistency in this sample for the MPQ-WB was good (α = 0.86). Four items from the CES-D assessing positive affect were selected. These had good internal consistency (α = 0.80). The BFI assesses two dimensions of each of the Big Five. The activity level facet of extraversion includes two items (α = 0.77). The correlation between these scales in the current study was 0.56 (range: 0.44–0.66); given this moderate correlation, these scales were standardized and averaged together to create a composite well-being score.
(Treadway et al., 2012). On each trial, participants were presented with an option of choosing between a hard or easy trial that varied in the number of required button presses to earn rewards on each trial. Decisions of choosing easy trials provided participants an incentive of a $1.00 reward for success on the trial. Decisions of pursuing hard trials provided participants an incentive between $1.24 and $4.30 for each success on the trial. Participants were made aware of the probability of having these task earnings contributing towards their earnings, with high, moderate, and low magnitude rewards on each trial (high probability, 88%; medium probability, 50%; and low probability, 12%). Participants completed an average of 70.99 (SD = 11.53; range = 45–140) EEfRT trials. However, there were trials for which responses either were not provided or were provided beyond a 5 s response window. Across participants, there were on average 3.80 (SD = 9.09; range = 0–56) trials lost due to non-response or delayed response. Ultimately, participants provided an average of 67.19 (SD = 16.70; range = 1–140) valid trials on the task. We computed the proportion of valid trials for which the more difficult task option was selected for each probability level and for the entire task. Thus, this indexed willingness to work for rewards contingent on probability of rewards. 3. Self-Report measures Self-report measures broadly assessed approach motivation. Selected measures came from the full span of PVS self-report measures and also included related constructs (e.g., personality; interpersonal reward motivation). A previous study using data from an independent sample relied on the same battery of measures (Olino et al., 2018). We relied on results from this study to derive parsimonious set of constructs: assertiveness, pleasure sensitivity, social closeness, and wellbeing. Assertiveness was measured using the social potency scale from the Multidimensional Personality Questionnaire (MPQ-SP; Tellegen & Waller, 2008) and the assertiveness scale from the Big Five Inventory (BFI; John, Donahue & Kentle, 1991). The social potency scale includes 14 items (α = 0.76). The assertiveness scale includes five items (α = 0.78). These scales were moderately correlated (r = 0.62); thus, these scales were standardized and averaged to yield a single assertiveness score. Pleasure sensitivity was measured using the Fawcett-Clark Pleasure Scale (FCPS; Fawcett, Clark, Scheftner & Gibbons, 1983), the Chapman Revised Physical Anhedonia Scale (RPAS; Chapman, Chapman & Raulin, 1976), the Snaith-Hamilton Pleasure Scale (SHAPS; Snaith et al., 1995), and the Temporal Experience of Pleasure Scale (TEPS; Gard, Gard, Kring & John, 2006). The FCPS is a 36-item selfreport questionnaire asking participants to rate imagined hedonic reactions to hypothetical pleasurable situations. Responses are made on a 5-point Likert scale ranging from ``no pleasure at all'' to ``extreme and lasting pleasure.'' Internal consistency for this scale in the current sample was excellent (α = 0.94). The RPAS is a 61-item self-report scale asking participants to respond true or false to statements about their typical feelings about normally pleasurable stimuli and activities. Internal consistency for this scale in the current sample was good (α = 0.83). The TEPS is a measure composed of 18 self-reported items assessing both anticipatory pleasure (10 items) and consummatory
4. Data analytic strategy All analyses were conducted using Mplus 8.3 (Muthén & Muthén, 1998-2018) and facilitated using the MplusAutomation package (Hallquist & Wiley, 2018) in R (R Core Team, 2018). Analyses were conducted to address distinct questions. First, we examined how manipulated PR parameters influenced task performance. We examined main effects of reward value, the ratio increases, and the task length, as 3
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Table 2 PR Task parameters predicting likelihood of quitting in univariate and multivariate models.
Reward Magnitude Task Length Ratio Increase
Quitting Univariate OR (95% CI)
Multivariate OR (95% CI)
Time until quitting Univariate HR (95% CI)
Multivariate HR (95% CI)
.68 (0.45–1.01) 3.05 (2.01–4.61)⁎⁎⁎ 1.00 (0.67–1.49)
.66 (0.44–1.00) 3.07 (2.02–4.66)⁎⁎⁎ 1.02 (0.67–1.54)
.80 (0.62–1.02) 1.04 (0.79–1.37) 2.18 (1.64–2.90)⁎⁎⁎
.78 (0.61–1.01) .93 (0.71–1.23) 2.22 (1.67–2.95)⁎⁎⁎
Note: *p < .05. **p < .01. ***p < .001. OR = Odds ratios estimated via logistic regression models. HR = Hazard Ratios estimated via survival models.
Fig. 1. Rates of change in Effort (Top) and Value Ratings (Bottom) across the Progressive Ratio Task for Levels of Ratio Increases.
4
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Table 3 Rates of change in effort and value rating.
Reward Magnitude Task Length Ratio Increase
Effort rating Univariate Intercept b (SE) .28 (0.16) −0.10 (0.16) −0.18 (0.16)
Slope b (SE)
Multivariate Intercept b (SE)
.00 (0.03) .04 (0.03) −0.12 (0.03)⁎⁎⁎
.28 (0.16) −0.11 (0.16) −0.19 (0.16)
Slope b (SE)
Value rating Univariate Intercept b (SE)
Slope b (SE)
Multivariate Intercept b (SE)
Slope b (SE)
.00 (0.03) .04 (0.03) −0.12 (0.03)⁎⁎⁎
.39 (0.22) .33 (0.22) .10 (0.22)
.01 (0.03) −0.01 (0.03) −0.08 (0.03)*
.39 (0.21) .33 (0.22) .09 (0.22)
.01 (0.03) −0.01 (0.03) −0.08 (0.03)*
Note: *p < .05; **p < .01; ***p < .001.
faster pace of increases in presses required to earn the reward was associated with more rapid quitting on the task (Fig. 1). We estimated unconditional multilevel models to describe overall rate of change in value and effort ratings across the task, regardless of experimental manipulations. For value ratings, the intercept (γ00) was 5.29 (SE = 0.109; p < .001) and slope (γ01) was −0.21 (SE = 0.01; p < .001). There was significant variability in both the intercept and slope (variance components = 3.79; SE = 0.25, t = 15.25, p < .001 and variance components = 0.43; SE = 0.005, t = 8.91, p < .001, respectively). Next, we examined associations between task parameters and course of value and effort ratings across task trials. Table 3 displays results of multilevel models for the univariate and multivariate predictor models examining associations between task conditions and course of value and effort ratings. In univariate and multivariate models, the higher ratio increase was associated with more rapid reclines in value and effort ratings (Fig. 1). For each outcome, further multivariate models were estimated that included all two- and threeway interactions between task conditions. No interaction effects were significant for any of the outcomes.
Table 4 Self-report approach motivation and progressive ratio quitting & survival. Factor
Quitting OR (95% CI)
Survival HR (95% CI)
Assertiveness Pleasure Sensitivity Social Closeness Well-Being Sex
1.23 1.01 1.14 1.02 0.57
1.14 1.01 1.13 1.02 0.66
(0.98–1.55) (0.77–1.31) (0.64–2.03) (0.79–1.33) (0.35–0.94)
(1.00–1.31) (0.85–1.2) (0.80–1.59) (0.88–1.18) (0.47–0.92)
Note: *p < .05; **p < .01; ***p < .001. OR = Odds ratios estimated via logistic regression models. HR = Hazard Ratios estimated via survival models.
well as two- and three-way interactions between these factors. We examined associations with quitting the task using logistic regression; with time until quitting using Cox proportional hazards models; and with value and effort ratings using linear growth models. For the Cox proportional models, the unit of time was trial number. Similarly, multilevel models estimated the linear rate of change in value and effort ratings as a function of trial number. Second, we examined how selfreported approach motivation constructs were associated with behavioral task performance. For analyses of the PR task, we examined associations between self-reported approach motivation constructs and PR task performance when controlling for manipulated PR parameters. For analyses of the EEfRT, we examined bivariate correlations between self-reported approach motivation constructs and EEfRT performance. Finally, we examined associations between PR and EEfRT performance. We examined whether EEfRT performance was associated with PR performance after controlling for manipulated PR parameters.
7. Associations between behavioral task performance and selfreport of approach We examined associations between self-report measures of approach and quitting, time until quitting, and course of value and effort ratings on the PR task when controlling for main effects of task parameters. In these models (Table 4), we found no associations between quitting or time until quitting and self-report measures. We used multilevel models to examine associations between selfreport measures of approach motivation and changes in value and effort ratings (Table 5) when controlling for main effects of task parameters. Self-reported pleasure was associated with higher initial ratings of reward value. Self-reported affiliation was associated with lower initial ratings of effort. Univariate regression models examined associations between selfreport measures of approach motivation and proportion of hard trials played on the 12%, 50%, and 88% probability trials, and all EEfRT trials (Table 6). Assertiveness and well-being were associated with proportion of plays on 12% probability trials; pleasure was associated with proportion of plays on 88% probability trials; and pleasure was associated with proportion of plays across all task trials.
5. Results Table 1 displays the intercorrelations between the approach motivation constructs. Overall, these constructs were modestly correlated. 6. PR task parameters Table 2 displays results of logistic regression models predicting whether or not participants quit the PR task (left panel) and time until quitting (right panel). In both univariate and multivariate models, participants in the longer PR task (20 min) were more likely to quit (70.1%) than in the shorter PR task (10 min; 43.3%). In Cox pH models, only the ratio increase was associated with rate of task quitting. The
Table 5 Self-report approach motivation and course of progressive ratio task effort and value ratings. Effort rating Intercept Assertiveness Pleasure Sensitivity Social Closeness Well-Being Sex
b (SE) −0.04 (0.08) −0.10 (0.10) −0.47 (0.18)⁎⁎ −0.13 (0.09) 0.14 (0.20)
Value rating Intercept
Slope pr 0.02 0.05 0.11 0.07 0.04
b (SE) 0.00 (0.01) 0.02 (0.02) 0.00 (0.04) 0.02 (0.01) −0.02 (0.03)
pr 0.01 0.05 0.00 0.08 0.03
Note: *p < .05; **p < .01; ***p < .001. 5
b (SE) −0.05 (0.12) 0.31 (0.14)* −0.10 (0.28) 0.14 (0.13) −0.02 (0.26)
Slope pr 0.02 0.11 0.02 0.05 0.00
b (SE) −0.00 −0.03 −0.02 −0.02 −0.00
(0.02) (0.02) (0.03) (0.02) (0.03)
pr 0.01 0.09 0.03 0.05 0.00
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Table 6 Self-report approach motivation and EEfRT performance. Reward probability of trials Factor
12% b (SE)
pr
50% b (SE)
pr
88% b (SE)
pr
Overall b (SE)
pr
Assertiveness Pleasure Sensitivity Social Closeness Well-Being Sex
0.03 0.01 0.02 0.04 0.04
0.14 0.06 0.05 0.14 0.08
0.01 (0.01) 0.03 (0.01)* −0.03 (0.03) 0.01 (0.02) 0.10 (0.03)**
0.04 0.1 0.05 0.04 0.17
0.00 (0.01) 0.04 (0.02)* −0.05 (0.03) 0.02 (0.02) 0.08 (0.03)*
0.02 0.14 0.08 0.08 0.15
0.01 (0.01) 0.03 (0.01)* −0.02 (0.02) 0.02 (0.01) 0.07 (0.02)**
0.05 0.13 0.05 0.1 0.17
(0.01)* (0.01) (0.02) (0.01)⁎⁎ (0.03)
Note: *p < .05; **p < .01; ***p < .001.
In our examination of PR task performance, we found that longer task length was associated with overall likelihood of quitting the task. However, the rate of quitting, as likelihood of quitting per trial, differed according to the magnitude of the ratio increase. Moreover, the magnitude of the ratio increase was also associated with changes in the subjective ratings of reward value and effort throughout the task. Thus, across the set of manipulated parameters, the experience of more challenging trial tasks (i.e., 50 vs. 20 additional presses per trial) influenced performance. However, reward magnitude did not significantly influence performance. Moreover, we did not find evidence that there were computational tradeoffs between effort and value, as we did not find significant interactions between these task parameters. These findings begin to address the critical need for the literature to systematically examine parametric modulations of PR task performance in humans (Der-Avakian & Pizzagalli, 2018). We found modest evidence for associations between self-reports of approach motivation and task performance. We found that social interest/affiliation was associated with higher initial ratings of effort and pleasure sensitivity was associated with higher initial ratings of reward value on the PR Task. Pleasure sensitivity was associated with greater willingness to work for moderate, high probability, and all trials; and well-being and assertiveness were both associated with greater willingness to work for low probability trials. Thus, the sole consistent association appeared to be between pleasure sensitivity and engagement with engagement with the tasks to have stronger affinity towards monetary incentives. However, the significant inverse associations between social interest and PR task performance was not intuitive. Perhaps this association reflects a difference in evaluating associations with monetary versus social incentives. Consistent with previous work (DeRosse et al., 2019; McCarthy et al., 2015; Nguyen et al., 2019; Treadway et al., 2009; Zou et al., 2019), the magnitude of statistically significant associations was modest (rs ranged from 0.11–0.14). Finally, we found some associations between PR and EEfRT performance. We did not find associations between decisions to choose harder options with low or modest probabilities of earning reward on EEfRT and continuing on the PR task. However, decisions to choose harder options with higher probability of earning rewards (i.e., greater certainty) were associated with lower probabilities of quitting and longer times until quitting on the PR task. Thus, when both tasks have greater certainty, performance was more strongly associated. This study benefitted from a moderately large sample with careful control of multiple experimental manipulations of parameters in the PR
Table 7 EEfRT performance and progressive ratio quitting & survival. EEfRT performance
Quitting OR (95%CI)
Survival HR (95%CI)
Low Rew. Prob. (12%) Medium Rew. Prob. (50%) High Reward Prob. (88%) Across Task Trials
1.01 0.57 0.32 0.39
0.77 0.58 0.46 0.42
(0.28–3.62) (0.2–1.64) (0.12–0.89)* (0.1–1.52)
(0.36–1.64) (0.32–1.06) (0.27–0.78)⁎⁎ (0.2–0.91)*
Note: *p < .05; **p < .01; ***p < .001. OR = Odds ratios estimated via logistic regression models. HR = Hazard Ratios estimated via survival models.
8. Associations between PR task and EEfRT performance Univariate regression models examined associations between proportion of hard trials played on the 12%, 50%, and 88% probability trials, and across all EEfRT trials and PR task performance. All models controlled for PR task parameters. Decisions to engage in the harder task option in the high probability reward trials on the EEfRT were associated with less likelihood of quitting and longer time until quitting (Table 7). Similarly, overall decisions to engage in harder trials on the EEfRT were associated with longer time until quitting. Decisions to engage in harder EEfRT trials were not significantly associated with course of value and effort ratings (Table 8). 9. Discussion This study examined an adaptation of a PR task and examined how task parameters influenced task performance and experience of completing the task. We also examined associations between multiple selfreported approach motivation constructs and behavioral measure of approach motivation using this PR task and EEfRT, another probabilistic reward task that assess the balance of effort and reward valuation. Finally, we also examined associations between behavioral performance on the PR task and EEfRT. We found that the magnitude of ratio increase on the PR task was associated with more rapid quitting, decline of reward ratings, and increase of effort ratings. We found modest associations between self-report measures of approach motivation and PR and EEfRT performance. We found some convergence between PR and EEfRT performance, particularly when the likelihood of reward was more certain on the EEfRT. Thus, overall, there is equivocal support for the utility of the PR task in humans as a means of evaluating reward motivation. Table 8 EEfRT performance and course of progressive ratio task effort and value ratings.
EEfRT Performance Low Rew. Prob. (12%) Medium Rew. Prob. (50%) High Reward Prob. (88%) Across Task Trials
Effort rating Intercept
Slope
b (SE) 0.13 (0.43) 0.45 (0.38) 0.37 (0.36) 0.50 (0.48)
b (SE) −0.08 −0.06 −0.03 −0.08
Note: *p < .05; **p < .01; ***p < .001. 6
(0.08) (0.06) (0.06) (0.08)
Value rating Intercept
Slope
b (SE) 0.37 (0.67) 0.05 (0.53) 0.38 (0.47) 0.41 (0.67)
b (SE) 0.02 (0.09) 0.03 (0.06) −0.04 (0.06) −0.01 (0.08)
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task. This permitted evaluation of multiple factors that influenced performance, inclusive of task parameters and individual differences. We also included a second behavioral task that is purported to assess a similar reward seeking process. However, the study is not without limitations. First, in our testing of PR task parameters, we only tested two different levels of each parameter. Further testing of reward values may be worthwhile to better characterize levels of incentives that may increase perseverance in the task and be sensitive to individual differences. Second, although the sample was moderately large, it came from an undergraduate sample. Thus, the levels of self-report measures of approach motivation were largely within the normative range. Additional examinations of associations between self-report approach motivation and behavioral performance is needed with samples with lower levels of approach (e.g., samples enriched for depression or psychosis). Third, we focused only on main effect associations between individual differences in approach motivation and PR task performance. It is possible that individual differences in approach may have moderated relationships between task parameters and task performance. However, this would have led to many tests, with increased risk of false positive tests. Future work could examine hypothesis driven interactions to advance this work. Overall, this study found that increasing effort towards earning rewards on the PR task was associated with greater likelihood of reduced performance. We also found associations between individual differences in approach motivation and subjective experience of completing the PR task and EEfRT performance. Moreover, we found that PR task and EEfRT performance were associated for EEfRT trials that had greater certainty. Thus, there is some evidence for construct validity for these two behavioral paradigms to assess these domains of reward pursuit.
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