B6

Individual differences in effort discounting and adjustments in volitional control

Project aims

A central question of the control dilemma framework of the present CRC is which variables determine the settings of meta-control parameters in order to optimize performance. While, e.g., learning and environmental features are important factors, individual differences might also play a role in balance between complementary control modes in the service of volitional control of behavior. In project B6, we focus on individual differences in effort investment and take a trait-perspective based on recent findings that personality traits characterized by habitually higher effort investment such as Need for Cognition, and Self-Control, respectively, are related to less effort discounting (Westbrook et al., 2013), and less demand avoidance (Kool et al., 2013), respectively. Building on our past research on Need for Cognition and using a broad methodological spectrum ranging from questionnaire-based research over experimental psychology, electroencephalography and eye-tracking to computational modeling, project B6 sets out to answer the following questions   

• (How) are Need for Cognition and Self-Control related to each other and can be integrated into a higher-order trait Cognitive Effort Investment? (Study 1)
• Is behavioral Demand Avoidance a trait, and are the trait variances of Cognitive Effort Investment and Demand Avoidance related to each other as the above-mentioned studies suggest? (Study 2).
• Are individual differences in self-reported and/or behavioral effort investment mirrored in objective measures of effort investment such as pupil size or midfrontal theta power? (Study 3)
• Do individual differences in self-reported and/or behavioral effort investment predict control adjustments in typical cognitive tasks? (Study 4).
• (How) can we model effort-related and/or uncertainty-based control adjustments? (Study 5)

Recent findings

In Study 1 (Kührt et al., 2020), we were able to establish a hierarchical factor model of self-reported Cognitive Effort Investment that integrates Need for Cognition and Trait Intellect as measures of Cognitive Motivation on the one hand and Self-Control and Effortful Control as measures of Effortful Self-Control. In Study 2 (Strobel et al., 2020)—apart from replicating the integrative factor model (see Fig. 1, left part printed in red) with good fit, CFI = 1, RMSEA = .03, 95% CI [.00, .07], SRMR = .03—we used Latent State Trait Modeling to examine whether self-reported Cognitive Effort Investment and behavioral Demand Avoidance as assessed using two versions of the Demand Selection Task actually exhibit considerable portions of trait variance and whether they correlate. Based on the available evidence, we hypothesized that individuals with higher levels of Cognitive Effort Investment would show less Demand Avoidance. To control for possible influences of cognitive ability, we also included a factor Cognitive Functioning composed of the Trail Making Test versions A and B. In a sample of N = 217 participants who were assessed at two time points with an interval of five weeks, we found that 54% of the variance in the measures of self-reported Cognitive Effort Investment and 59% of the variance in the behavioral measures of Demand Avoidance were due to stable individual differences. However, with a standardized covariance of -.06, the two trait components were not related to each other (see Fig. 1).

If we repeated the analysis with a recently proposed new Demand Avoidance measure (Juvina et al., 2018), the standardized covariance was -.09, but still insignificant, . If we restricted our analyses to measures of Self-Control only, the trait covariance was even lower, standardized estimates for original and new Demand Avoidance measure -.01 and -.05. Thus, Study 2 shows that both self-report and behavioral measures of effort investment exhibit a considerable amount of trait variance, but does not lend support for earlier findings of a relation of self-reported Self-Control and behavioral Demand Avoidance.

Data from Studies 3 and 4 are currently being analyzed. 

In Study 5 (Ott et al., 2020), we addressed the question of how humans balance heuristics and effortful forward planning when deciding between goals during goal-directed action sequences.To decide which goal to pursue at what point in time, one has to evaluate the consequences of one’s actions over multiple future time steps. However, if the goal is temporally distant, detailed forward planning can be prohibitively costly. One way to select actions at minimal computational costs is to use heuristics. It is an open question how humans mix heuristics with forward planning to balance computational costs with goal reaching performance. To test a hypothesis about dynamic mixing of heuristics with forward planning, we used a novel stochastic sequential decision making with mini-blocks of 15 trials. Model-based analyses showed that subjects used heuristic preferences, when the goal was temporally distant, and switched to forward planning, when the goal was close. The current study exemplifies an exciting avenue for future research on the temporal dynamics of cognitive processes underlying goal-directed action and how humans employ useful heuristics to balance the computational costs incurred by forward planning.     

References

_{Juvina, I., Nador, J., Larue, O., Green, R., Harel, A., & Minnery, B. (2018). Measuring individual differences in cognitive effort avoidance. In T.T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 1886-1891). Austin, TX: Cognitive Science Society.
Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010). Decision making and the avoidance of cognitive demand. Journal of Experimental Psychology: General, 139(4), 665-682.
Kool, W., McGuire, J. T., Wang, G. J., & Botvinick, M. M. (2013). Neural and behavioral evidence for an intrinsic cost of self-control. PLoS One, 8(8), e72626.
Kührt, C., Pannasch, S., Kiebel, S. J. & Strobel, A. (in press). Dispositional individual differences in cognitive effort investment: Establishing the core construct. BMC Psychology.
Ott, F., Markovic, D., Strobel, A., & Kiebel, S. J. (2020). Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit. PLoS Computational Biology, 16(2), e1007685.
Strobel, A.*, Wieder, G., Paulus, P. C., Ott, F., Pannasch, S., Kiebel, S. J., & Kührt, C.* (2020). Dispositional cognitive effort investment and behavioral demand avoidance: Are they related? PLoS ONE, 15(10): e0239817.
Westbrook, A., Kester, D., & Braver, T. S. (2013). What is the subjective cost of cognitive effort? Load, trait, and aging effects revealed by economic preference. PLoS One, 8(7), e68210.}

Project Members

Principal Investigators

Prof. Dr. rer. nat. Alexander Strobel
Phone:+49 (0)351 463-37000
E-Mail:

Prof. Dr. Stefan Kiebel
Phone:+49 (0)351 463-43145
E-Mail:

Prof. Dr. Sebastian Pannasch
Phone:+49 (0)351 463-34306
E-Mail:

Staff

Dipl.-Psych. Corinna Kührt
Doctoral researcher
Phone:+49 (0)351 463-36995
E-Mail:

Florian Ott, M.Sc.
Doctoral researcher
Phone: +49 (0)351 463-43148
Email: 

Publications

• Cuevas Rivera, D., Ott, F., Marković, D., Strobel, A., Kiebel, S. J. (2018). Context-dependent risk aversion: a model-based approach. Frontiers in Psychology, 9, 2053. doi:10.3389/fpsyg.2018.02053
• Cuevas Rivera, D., Strobel, A., Goschke, T., Kiebel, S. J. (2020). Modeling dynamic allocation of effort in a sequential task using discounting models. Frontiers in Neuroscience, 14, 242. doi:10.3389/fnins.2020.00242
• Grass, J., Krieger, F., Paulus, P., Greiff, S., Strobel, A. & Strobel, A. (2019). Thinking in action: Need for Cognition predicts Self-Control together with Action Orientation. PLoS One, 14(8), e0220282. doi:10.1371/journal.pone.0220282 
• Kührt, C., Pannasch, S., Kiebel, S. J. & Strobel, A. (in press). Dispositional individual differences in cognitive effort investment: Establishing the core construct. BMC Psychology.
• Ott, F., Markovic, D., Strobel, A., & Kiebel, S. J. (2020). Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit. PLoS Computational Biology, 16(2), e1007685. doi:10.1371/journal.pcbi.1007685
• Strobel, A., Anacker, K., & Strobel, A. (2017). Cognitive engagement mediates the relationship between positive life events and positive emotionality. Frontiers in Psychology, 8, 1861. doi: 10.3389/fpsyg.2017.01861
• Strobel, A., Strobel, A., Enge, S. Fleischhauer, M., Reif, A., Lesch, K.-P., & Anacker, A. (2018). Intellectual investment, dopaminergic gene variation, and life events: A critical examination. Personality Neuroscience, 1, e3. doi:10.1017/pen.2018.2
• Strobel, A.*, Wieder, G., Paulus, P. C., Ott, F., Pannasch, S., Kiebel, S. J., & Kührt, C.* (2020). Dispositional cognitive effort investment and behavioral demand avoidance: Are they related? PLoS ONE, 15(10): e0239817.  doi:10.1371/journal.pone.0239817