Highlights
- •Trait cognitive fatigue does not impact valuation of performance feedback in MS.
- •Low confidence in cognitive performance motivates feedback-seeking behavior in MS.
- •Learning from error-related feedback particularly aids cognitive performance in MS.
- •Findings have direct implications for cognitive rehabilitation efficacy in MS.
Abstract
Background
Performance feedback is vital to rehabilitation interventions that treat cognitive
impairments from multiple sclerosis (MS). Optimal treatment relies on participants’
motivation to learn from feedback throughout these interventions. Cognitive fatigue,
a prevalent symptom of MS, is associated with aberrant reward processing, which necessitates
research into how fatigue affects perceived reward value of feedback in these individuals.
The current study investigated how trait fatigue influences subjective valuation of
feedback and subsequent feedback-seeking behavior in people with MS.
Methods
33 MS and 32 neurotypical (NT) participants completed a willingness-to-pay associative
memory paradigm that assessed feedback valuation via trial-by-trial decisions to either
purchase or forego feedback in service of maximizing a performance-contingent monetary
reward. Participant ratings of trait fatigue were also collected. Generalized logistic
mixed modeling was used to analyze factors that influenced trial-wise feedback purchase
decisions and task performance.
Results
Despite reporting greater trait fatigue, MS participants purchased comparable amounts
of feedback as NT participants. Like NT participants, MS participants were more likely
to purchase feedback when they were less confident about response accuracy. MS participants
also performed comparably to NT participants, who both particularly benefited from
purchase decisions that yielded negative feedback (i.e., indicating a response error).
Conclusions
Trait cognitive fatigue may not impact performance feedback valuation in people with
MS. Nonetheless, confidence in performance may drive their feedback-seeking behavior
and may serve as a target for improving learning throughout cognitive rehabilitation
and maximizing treatment success.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Multiple Sclerosis and Related DisordersAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Random effects structure for confirmatory hypothesis testing: keep it maximal.J. Mem. Lang. 2013; 68: 255-278https://doi.org/10.1016/j.jml.2012.11.001
- Altered functional connectivity during performance feedback processing in multiple sclerosis.NeuroImage Clin. 2022; 37103287https://doi.org/10.1016/j.nicl.2022.103287
- Fatigue and basal ganglia.J. Neurol. Sci. 2000; 179: 34-42https://doi.org/10.1016/S0022-510X(00)00411-1
- Fatigue-related diurnal variations of cognitive performance in multiple sclerosis and stroke patients.J. Neurol. Sci. 2010; 295: 75-81https://doi.org/10.1016/j.jns.2010.04.018
- Efficacy of computer-based cognitive training in neuropsychological performance of patients with multiple sclerosis: a systematic review and meta-analysis.Mult. Scler. Relat. Disord. 2018; 20: 58-66https://doi.org/10.1016/j.msard.2017.12.017
- Neural correlates of cognitive fatigue in multiple sclerosis using functional MRI.J. Neurol. Sci. 2008; 270: 28-39https://doi.org/10.1016/j.jns.2008.01.018
- Subjective confidence predicts information seeking in decision making.Psychol. Sci. 2018; 29: 761-778https://doi.org/10.1177/0956797617744771
- A postdecisional neural marker of confidence predicts information-seeking in decision-making.J. Neurosci. 2019; 39: 3309-3319https://doi.org/10.1523/JNEUROSCI.2620-18.2019
- Basal ganglia engagement during feedback processing after a substantial delay.Cogn. Affect. Behav. Neurosci. 2013; 13: 725-736https://doi.org/10.3758/s13415-013-0182-6
- Neural correlates of cognitive fatigue: cortico-striatal circuitry and effort–reward imbalance.J. Int. Neuropsychol. Soc. 2013; 19: 849-853https://doi.org/10.1017/S1355617713000684
- Fronto-striatal network activation leads to less fatigue in multiple sclerosis.Mult. Scler. J. 2017; 24: 1174-1182https://doi.org/10.1177/1352458517717087
- Multiple sclerosis - a review.Eur. J. Neurol. 2019; 26: 27-40https://doi.org/10.1111/ene.13819
- Thalamo-striato-cortical determinants to fatigue in multiple sclerosis.Brain Behav. 2013; 3: 715-728https://doi.org/10.1002/brb3.181
- Distinguishing between fatigue and fatigability in multiple sclerosis.Neurorehabil. Neural Repair. 2021; 35: 960-973https://doi.org/10.1177/15459683211046257
- Measuring the functional impact of fatigue: initial validation of the fatigue impact scale.Clin. Infect. Dis. 1994; 18: S79-S83https://doi.org/10.1093/clinids/18.Supplement_1.S79
- Examination of cognitive fatigue in multiple sclerosis using functional magnetic resonance imaging and diffusion tensor imaging.PLoS One. 2013; 8: 1-10https://doi.org/10.1371/journal.pone.0078811
- A theory-driven system for the specification of rehabilitation treatments.Arch. Phys. Med. Rehabil. 2019; 100: 172-180https://doi.org/10.1016/j.apmr.2018.09.109
- Fatigue, depression, and pain in multiple sclerosis: how neuroinflammation translates into dysfunctional reward processing and anhedonic symptoms.Mult. Scler. J. 2020; : 1-8https://doi.org/10.1177/1352458520972279
- The effort paradox: effort is both costly and valued.Trends Cogn. Sci. 2018; 22 (Regul. Ed.): 337-349https://doi.org/10.1016/j.tics.2018.01.007
- Distinct cognitive impairments in different disease courses of multiple sclerosis - a systematic review and meta-analysis.Neurosci. Biobehav. Rev. 2017; 83: 568-578https://doi.org/10.1016/j.neubiorev.2017.09.005
- The neural correlates of subjective value duringintertemporal choice.Nat. Neurosci. 2007; 10: 1625-1633https://doi.org/10.1038/nn2007
- The value of being wrong: intermittent feedback delivery alters the striatal response to negative feedback.J. Cogn. Neurosci. 2015; 28: 261-274https://doi.org/10.1162/jocn_a_00892
- Defining the clinical course of multiple sclerosis: the 2013 revisions.Neurology. 2014; 83: 278-286https://doi.org/10.1212/WNL0000000000000560
- Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis.J. Neurol. Neurosurg. Psychiatry. 2019; 90: 642-651https://doi.org/10.1136/jnnp-2018-320050
- Fatigue in multiple sclerosis patients with different clinical phenotypes: a clinical and magnetic resonance imaging study.Eur. J. Neurol. 2020; 27: 2549-2560https://doi.org/10.1111/ene.14471
- Subjective fatigue is not associated with cognitive impairment in multiple sclerosis: cross-sectional and longitudinal analysis.Mult. Scler. 2009; 15: 998-1005https://doi.org/10.1177/1352458509106213
- Reward responsiveness and fatigue in multiple sclerosis.Mult. Scler. J. 2012; 19: 233-240https://doi.org/10.1177/1352458512451509
- PsychoPy2: experiments in behavior made easy.Behav. Res. Methods. 2019; 51: 195-203https://doi.org/10.3758/s13428-018-01193-y
- Neural representations of subjective reward value.BehaviouralBrainResearch. 2010; 213: 135-141https://doi.org/10.1016/j.bbr.2010.04.031
- Event-related potentials and cognitive performance in multiple sclerosis patients with fatigue.Neurolog. Sci. 2016; 37: 1545-1556https://doi.org/10.1007/s10072-016-2622-x
RStudio Team. https://posit.co/ (accessed 8 November 2022).
- Subjective cognitive fatigue in multiple sclerosis depends on task length.Front. Neurol. 2014; 5: 1-7https://doi.org/10.3389/fneur.2014.00214
- Neural correlates of effort-dependent and effort-independent cognitive fatigue components in patients with multiple sclerosis.Mult. Scler. J. 2019; 25: 256-266https://doi.org/10.1177/1352458517743090
- Performance feedback drives caudate activation in a phonological learning task.J. Cogn. Neurosci. 2006; 18: 1029-1043https://doi.org/10.1162/jocn.2006.18.6.1029
- Feedback signals in the caudate reflect goal achievement on a declarative memory task.Neuroimage. 2008; 41: 1157-1167https://doi.org/10.1016/j.neuroimage.2008.02.066
- The importance of voluntary behavior in rehabilitation treatment and outcomes.Arch. Phys. Med. Rehabil. 2019; 100: 156-163https://doi.org/10.1016/j.apmr.2018.09.111
Article info
Publication history
Published online: May 12, 2023
Accepted:
May 7,
2023
Received in revised form:
April 20,
2023
Received:
January 10,
2023
Identification
Copyright
© 2023 Elsevier B.V. All rights reserved.
