Introduction
We thank Ionescu and Gros for their thought-provoking commentary, which continues an important discussion on the conceptual foundations of Flexibility. The term Flexibility has long suffered from fragmentation across domains—cognitive, behavioral, affective, and psychological—leading to inconsistent operationalizations and reduced theoretical clarity. Cognitive Flexibility, in particular, is often treated either as the core construct or as entirely distinct from broader forms such as behavioral regulation or experiential openness in therapeutic models like ACT. In our model, we propose Mental Flexibility as a unifying, overarching construct that integrates these diverse expressions. Defined as an emergent capacity for adaptive variability, Mental Flexibility accounts for both changes in behavior and the maintenance of self-coherence across shifting contexts. Due to Flexibility's strong connection to the concept of variability, which involves shifting or changing, our framework also explores its interplay with affect dynamics, highlighting how shifts in cognition and behavior correspond to emotional fluctuations. The critiques addressed here pertain to conceptual clarity, measurement issues, and our theoretical positioning within the variability-stability-flexibility continuum.
Response to critique on mechanism, ability, and the conceptual framing of mental flexibility
argue that conceptualizing Mental Flexibility as a property is incompatible with its association with mechanisms, skills, or abilities. They maintain that these are distinct categories and suggest that our use of Mental Flexibility lacks originality. However, we contend that these levels—property, mechanism, skill—are not mutually exclusive but represent interconnected layers of abstraction, like in intelligence or consciousness (; ).
As an emergent property, Mental Flexibility arises from the coordinated activity of sub-processes and not reducible to their components (), yet they require mechanistic elucidation (). Mechanisms such as shifting, inhibition, updating, and control (; ) provide the operational basis for Flexibility, just as encoding and retrieval support memory. These mechanisms are embedded within broader systems like cognitive control and episodic memory (). Skills and abilities describe how such mechanisms manifest behaviorally, evolving from effortful to automatized processes (; ; ). Hence, Flexibility manifests as both ability and trait (). Performance tasks assess momentary flexibility, based on time response of switching cost (; ; ), while self-reports capture dispositional tendencies (; ; ; ; ). Low correlations between methods are common across constructs—such as Intelligence, Empathy, Creativity—where different behavioral measures tap into the same underlying trait but remain weakly correlated (; ; , ; ). Finally, in our conceptualization, Mental Flexibility is not a synonym for Cognitive Flexibility as in ; it is a superordinate construct uniting cognitive, affective, behavioral, and psychological domains, re-anchored within psychological science. Throughout our conceptual work, we used the terms Flexibility and Mental Flexibility interchangeably, as both refer to an emergent property or meta-function involved in cognitive and affective shifting or switching processes. The addition of the adjective Mental was a deliberate choice to prevent confusion with uses of Flexibility in other scientific domains—such as material science, chemistry, physics (), logistic organization of workplace () and motor functional ability (). Rather than being redundant, this specification serves a clarifying and boundary-setting function within psychological science. It aims to identify overarching features that may unify the various domain-specific definitions of Flexibility (e.g., cognitive, affective, behavioral, and psychological flexibility). These definitions remain distinct and autonomous, but the goal is to facilitate integration by highlighting their shared, higher-order characteristics-connections that, until now, have rarely been conceptually aligned.
Methodological considerations and the use of Markov chains
criticize the omission of several classical tasks—such as the Dimensional Change Card Sort (DCCS) (), Navon task (; ), Plus-Minus (), and Brixton test ()—from our framework, and our reliance on self-report measures. Our article, however, did not aim to review all flexibility tools, but to present a novel model integrating cognitive, affective, and behavioral dimensions within affect dynamics. We included representative direct and indirect measures to illustrate a key point: tools labeled as Flexibility measures often show weak intercorrelations (), underscoring the need for a more integrated model. The excluded tasks are valid but context-specific: DCCS is optimized for preschoolers; Navon targets low-level perceptual shifts, not executive flexibility. Plus–Minus and Brixton assess rule-shifting mechanisms already covered by other tools in our model. Even recent reviews () mention only the DCCS from Ionescu's list.
Regarding Discrete Time and Space Markov chains, we framed them as a heuristic for modeling affective transitions (; ), not as a validated tool. Inspired by socioemotional flexibility models (), we propose that affective variability may reflect Mental Flexibility. Although our initial work utilizes self-report data, the model is extendable to performance-based tasks and warrants further empirical refinement.
Dynamic models and the interplay of variability, stability, and flexibility
critique our conceptualization of Mental Flexibility as an emergent property characterized by adaptive variability, favoring instead a developmental continuum from variability to stability and then flexibility (). However, empirical evidence suggests this progression is neither linear nor universally adaptive. In , children move from mixed responding to perseveration—not directly to flexibility—highlighting that variability is not random, and perseveration does not represent functional stability. Similarly, Event-Related Potentials (ERPs) studies show that infants exhibit systematic responses to phonological prediction errors (), supporting predictive coding models (), where behavior is guided by internal models that minimize prediction error (). This finding aligns with predictive coding theories, which propose that behavioral variability is structured and guided by internal models continuously updated to minimize prediction error. Supporting this, liken children's exploration to simulated annealing algorithms, reflecting strategic, inference-driven behavior. similarly show that children's openness to noncanonical object use stems from Cognitive Flexibility, not random variability. These findings challenge Ionescu's developmental continuum by showing that early variability is structured, purposeful, and indicative of an already flexible and exploratory cognitive system.
Nonetheless, the proposal by Ionescu and Gros fits within a broader discussion regarding the relationship between stability and flexibility. Traditionally, many scholars have embraced a stability–flexibility trade-off model, wherein cognitive stability (i.e., task focus, resistance to distraction) and flexibility (i.e., task-switch readiness) are conceptualized as opposing ends of a single continuum. According to this model, enhancing stability necessarily reduces flexibility, and viceversa, due to a shared control parameter—often described as an “updating threshold” within working memory (; ; ).
Our conceptualization draws from this flourishing debate, shifting the focus toward the trade-off between stability and variability—an approach that aligns with statistical frameworks commonly used in dynamic analysis (; ). We conceptualize Mental Flexibility as a meta-property that dynamically balances variability and stability in response to internal goals and contextual demands. Flexibility, in this sense, is not mere reactivity but involves intentional modulation—choosing stability when consistency is required and shifting when adaptation is needed. This adaptive variability reflects a goal-directed form of switching, rather than random change, and is closely tied to personal meaning and self-regulation. The capacity for variability is typically assessed through switching costs in neuropsychological tasks, and through self-report measures that emphasize the exploration of alternatives, such as shifts in perspective and behavior. Conversely, the capacity for stability, often neglected in literature, is reflected in self-report instruments like the Cognitive Flexibility Inventory (CFI) (), the Cognitive Control and Flexibility Questionnaire () (CCFQ – Control subscale), the Coping Flexibility Scale (Co-Flex—Reflection component) (), and the Multidimensional Psychological Flexibility Inventory (MPSI—Acceptance and Values subscales) (). Stability also emerges in performance-based tasks through the analysis of switching costs, which quantify the cognitive effort required to maintain vs. shift responses across trials.
question our use of “adaptability”, suggesting it conflates behavioral adjustment with a loss of authenticity. However, our references to were intended to highlight that adaptation may sometimes reflect reactive rather than agentic strategies—such as “avoidance crafting,” which is driven by threat appraisals and may lead to disengagement. Similarly, our use of aimed to emphasize the role of emotional complexity in promoting authentic, context-sensitive adaptation: “The ability to experience and distinguish multiple emotions can help inform more nuanced and flexible responses.”
Discussion
Our model thus distinguishes between superficial adaptation and deep, emotionally integrated Flexibility. It treats Flexibility not as a midpoint between extremes but as a meta-function coordinating transitions between stability and variability across domains-cognitive, affective, behavioral, and psychological.
Mental Flexibility offers a clear, integrative visualization of Mental Flexibility as the dynamic regulation between stability and variability over time. The U-shaped curve could represent Flexibility as optimal when both behavioral change and duration remain within a balanced range. At the two extremes, Flexibility collapses: inertia arises when the system remains overly stable for too long (minimal change), while instability reflects prolonged, uncontrolled variability (excessive change).
The central zone—labeled the normal spectrum of Flexibility—illustrates where adaptive functioning occurs. Here, the individual can oscillate between stable and variable behaviors depending on internal goals and contextual demands. The central vertical marker further highlights that Flexibility is not about avoiding change or pursuing it blindly, but about selectively and meaningfully regulating one's position along this continuum.
Our reconceptualization supports a multidimensional view of Flexibility and opens practical avenues for profiling patterns—rigid, disorganized, or adaptively flexible—to guide interventions such as Acceptance and Commitment Therapy (ACT) or emotion regulation training. It invites future research to develop assessments that bridge momentary performance and long-term dispositional flexibility across domains.
Statements
Author contributions
FB: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. AC: Conceptualization, Supervision, Writing – review & editing. PC: Methodology, Supervision, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. The research was funded by PON R&I 2014-2020 (FSE REACT-EU) and Grants PRIN 2022 PNRR P2022PXAZW funded by European Union NextGenerationEU.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Generative AI statement
The author(s) declare that no Gen AI was used in the creation of this manuscript.
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References
1
AckermanP. L. (1992). Predicting individual differences in complex skill acquisition: dynamics of ability determinants. J. Appl. Psychol.77, 598–614. 10.1037/0021-9010.77.5.598
2
AnzianoM.MouthonM.ThoenyH.SperberC.SpiererL. (2023). Mental flexibility depends on a largely distributed white matter network: causal evidence from connectome-based lesion-symptom mapping. Cortex. 165, 38–5610.1016/j.cortex.2023.04.007
3
BlakeyE.VisserI.CarrollD. J. (2016). Different executive functions support different kinds of cognitive flexibility: evidence from 2-, 3-, and 4-year-olds. Child Dev.87, 513–526. 10.1111/cdev.12468
4
BondF. W.HayesS. C.BaerR. A.CarpenterK. M.GuenoleN.OrcuttH. K.et al. (2011). Preliminary psychometric properties of the acceptance and action questionnaire–II: a revised measure of psychological inflexibility and experiential avoidance. Behav. Ther.42, 676–688. 10.1016/j.beth.2011.03.007
5
BorghesiF.SimonciniG.CremascoliR.BianchiL.MendolicchioL.CappelliS.et al. (2025). A stochastic model for affect dynamics: methodological insights from heart rate variability in an illustrative case of Anorexia Nervosa. Front. Psychiatry16:1502217. 10.3389/fpsyt.2025.1502217
6
BrunsB.HerrmannF.PolyakovaM.GrünewaldM.RieseJ. (2020). A systematic approach to define flexibility in chemical engineering. J. Adv. Manuf. Process. 2. 10.1002/amp2.10063
7
ChenL.TangK. (2022). Adapting to frequent changes: the roles of job crafting and personal needs. J. Appl. Behav. Sci.58, 417–441. 10.1177/00218863211026093
8
CostaA. C. F.FariaL. (2020). Implicit theories of emotional intelligence, ability and trait-emotional intelligence and academic achievement. Psihologijske Teme29, 43–61. 10.31820/pt.29.1.3
9
de SchottenM. T.ForkelS. J. (2022). The emergent properties of the connected brain. Science378, 505–510. 10.1126/science.abq2591
10
DecetyJ. (2011). The neuroevolution of empathy. Ann. N. Y. Acad. Sci.1231, 35–45. 10.1111/j.1749-6632.2011.06027.x
11
DefeyterM. A.GermanT. P. (2003). Acquiring an understanding of design: evidence from children's insight problem solving. Cognition89, 133–155. 10.1016/S0010-0277(03)00098-2
12
Del GiudiceM.CrespiB. J. (2018). Basic functional trade-offs in cognition: An integrative framework. Cognition179, 56–70. 10.1016/j.cognition.2018.06.008
13
DemanetJ.LiefoogheB.VerbruggenF. (2011). Valence, arousal, and cognitive control: a voluntary task-switching study. Front. Psychol. 2:336. 10.3389/fpsyg.2011.00336
14
DennisJ. P.Vander WalJ. S. (2010). The cognitive flexibility inventory: instrument development and estimates of reliability and validity. Cognit. Ther. Res.34, 241–253. 10.1007/s10608-009-9276-4
15
DreisbachG.MusslickS.BraemS. (2024). Flexibility and stability can be both dependent and independent. Nat. Rev. Psychol.3, 636–636. 10.1038/s44159-024-00348-3
16
EgnerT. (2023). Principles of cognitive control over task focus and task switching. Nat. Rev. Psychol.2, 702–714. 10.1038/s44159-023-00234-4
17
FangS.DingD. (2022). Which outcome variables are associated with psychological inflexibility/flexibility for chronic pain patients A three level meta-analysis. Front. Psychol.13:1069748. 10.3389/fpsyg.2022.1069748
18
GabrysR. L.TabriN.AnismanH.MathesonK. (2018). Cognitive control and flexibility in the context of stress and depressive symptoms: the cognitive control and flexibility questionnaire. Front. Psychol. 9:2219. 10.3389/fpsyg.2018.02219
19
GamezD. (2020). The relationships between intelligence and consciousness in natural and artificial systems. J. Artif. Intellig. Consciousn.7, 51–62. 10.1142/S2705078520300017
20
GeddertR.EgnerT. (2022). no need to choose: independent regulation of cognitive stability and flexibility challenges the stability-flexibility trade-off. J. Exp. Psychol.: General151, 3009–3027. 10.1037/xge0001241
21
GopnikA.O'GradyS.LucasC. G.GriffithsT. L.WenteA.BridgersS.et al. (2017). Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood. Proc. Natl. Acad. Sci. USA.114, 7892–7899. 10.1073/pnas.1700811114
22
GoschkeT.BolteA. (2014). Emotional modulation of control dilemmas: The role of positive affect, reward, and dopamine in cognitive stability and flexibility. Neuropsychologia62, 403–423. 10.1016/j.neuropsychologia.2014.07.015
23
GrolM.De RaedtR. (2018). The effect of positive mood on flexible processing of affective information. Emotion18, 819–833. 10.1037/emo0000355
24
HamakerE. L.CeulemansE.GrasmanR. P. P. P.TuerlinckxF. (2015). Modeling affect dynamics: state of the art and future challenges. Emot. Rev.7, 316–322. 10.1177/1754073915590619
25
HambrickD. Z.BurgoyneA. P.OswaldF. L. (2018). “Domain-general models of expertise: the role of cognitive ability,” in The Oxford Handbook of Expertise, 56–84.
26
HohlK.DolcosS. (2024). Measuring cognitive flexibility: a brief review of neuropsychological, self-report, and neuroscientific approaches. Front. Hum. Neurosci.18:1331960. 10.3389/fnhum.2024.1331960
27
HollensteinT. (2015). This time, its real: affective flexibility, time scales, feedback loops, and the regulation of emotion. Emot. Rev.7, 308–315. 10.1177/1754073915590621
28
HommelB.ColzatoL. S. (2017). The social transmission of metacontrol policies: Mechanisms underlying the interpersonal transfer of persistence and flexibility. Neurosci. Biobehav. Rev.81, 43–58. 10.1016/j.neubiorev.2017.01.009
29
HowellB. C.HamiltonD. A. (2021). Trait affective differences influence behavioral flexibility in virtual spatial and non-spatial discrimination tasks. Pers. Individ. Dif.170:110424. 10.1016/j.paid.2020.110424
30
HowlettC. A.WewegeM. A.BerrymanC.OldachA.JenningsE.MooreE.et al. (2021). Same room - different windows A systematic review and meta-analysis of the relationship between self-report and neuropsychological tests of cognitive flexibility in healthy adults. Clin. Psychol. Rev.88:102061. 10.1016/j.cpr.2021.102061
31
HowlettC. A.WewegeM. A.BerrymanC.OldachA.JenningsE.MooreE.et al. (2022). Back to the drawing board—the relationship between self-report and neuropsychological tests of cognitive flexibility in clinical cohorts: a systematic review and meta-analysis. Neuropsychology36, 347–372. 10.1037/neu0000796
32
IllariP. M. K.WilliamsonJ. (2012). What is a mechanism Thinking about mechanisms across the sciences. Eur. J. Philosophy Sci.2, 119–135. 10.1007/s13194-011-0038-2
33
IonescuT. (2017). The variability-stability-flexibility pattern: a possible key to understanding the flexibility of the human mind. Rev. General Psychol.21, 123–131. 10.1037/gpr0000110
34
IonescuT.GrosH. (2025). Commentary: outlining a novel psychometric model of mental flexibility and affect dynamics. Front. Psychol.16:1463888. 10.3389/fpsyg.2025.1463888
35
KanaiR.FujisawaI. (2024). Toward a universal theory of consciousness. Neurosci. Consciousn.2024:niae022. 10.1093/nc/niae022
36
KandlerC.RiemannR.AngleitnerA.SpinathF. M.BorkenauP.PenkeL. (2016). The nature of creativity: the roles of genetic factors, personality traits, cognitive abilities, and environmental sources. J. Pers. Soc. Psychol.111, 230–249. 10.1037/pspp0000087
37
KimchiR. (1992). Primacy of wholistic processing and global/local paradigm: a critical review. Psychol. Bull.112, 24–38. 10.1037/0033-2909.112.1.24
38
MachamerP.DardenL.CraverC. F. (2000). Thinking about mechanisms. Philos. Sci.67, 1–25. 10.1086/392759
39
MandersJ. H. M.CaniëlsM. C. J.GhijsenP. W. Th. (2017). Supply chain flexibility. Int. J. Logist. Manag.28, 964–1026. 10.1108/IJLM-07-2016-0176
40
MartinM. M.RubinR. B. (1995). A new measure of cognitive flexibility. Psychol. Rep.76, 623–626. 10.2466/pr0.1995.76.2.623
41
MillerE. K.BrincatS. L.RoyJ. E. (2024). Cognition is an emergent property. Curr. Opin. Behav. Sci.57:101388. 10.1016/j.cobeha.2024.101388
42
MillidgeB.SethA. K.BuckleyC. L. (2021). Predictive coding: a theoretical and experimental review. arXiv [preprint] arXiv.2107.12979. 10.48550/arXiv.2107.12979
43
MiyakeA.FriedmanN. P.EmersonM. J.WitzkiA. H.HowerterA.WagerT. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cogn. Psychol.41, 49–10010.1006/cogp.1999.0734
44
NavonD. (2003). What does a compound letter tell the psychologist's mindActa Psychol.114, 273–309. 10.1016/j.actpsy.2003.06.002
45
O'TooleM. S.RennaM. E.ElkjærE.MikkelsenM. B.MenninD. S. (2020). A systematic review and meta-analysis of the association between complexity of emotion experience and behavioral adaptation. Emot. Rev.12, 23–38. 10.1177/1754073919876019
46
RoggeR. D.LinY.-Y. (2024). “Multidimensional psychological flexibility inventory (MPFI),” in Handbook of Assessment in Mindfulness Research, 1–18.
47
RolffsJ. L.RoggeR. D.WilsonK. G. (2018). “Multidimensional psychological flexibility inventory,” in PsycTESTS Dataset.
48
ShefflerP.RodriguezT. M.CheungC. S.WuR. (2022). Cognitive and metacognitive, motivational, and resource considerations for learning new skills across the lifespan. Wiley Interdiscipl. Rev.: Cognit. Sci.13:e1585. 10.1002/wcs.1585
49
SpitoniG. F.BevacquaS.CeriniC.CiurliP.PiccardiL.GuarigliaP.et al. (2018). Normative data for the hayling and brixton tests in an italian population. Arch. Clini. Neuropsychol.33, 466–476. 10.1093/arclin/acx072
50
StathokostasL.LittleR. M. D.VandervoortA. A.PatersonD. H. (2012). Flexibility training and functional ability in older adults: a systematic review. J. Aging Res.2012, 1–30. 10.1155/2012/306818
51
van de CruysS.EversK.van der HallenR.van EylenL.BoetsB.de-WitL.et al. (2014). Precise minds in uncertain worlds: predictive coding in autism. Psychol. Rev.121, 649–675. 10.1037/a0037665
52
VriezekolkJ. E.van LankveldW. G. J. M.EijsboutsA. M. M.van HelmondT.GeenenR.van den EndeC. H. M. (2012). The coping flexibility questionnaire: Development and initial validation in patients with chronic rheumatic diseases. Rheumatol. Int.32, 2383–2391. 10.1007/s00296-011-1975-y
53
YlinenS.BosselerA.JunttilaK.HuotilainenM. (2017). Predictive coding accelerates word recognition and learning in the early stages of language development. Dev. Sci.20:12472. 10.1111/desc.12472
54
ZelazoP. D. (2006). The Dimensional Change Card Sort (DCCS): A method of assessing executive function in children. Nat. Protoc.1, 297–301. 10.1038/nprot.2006.46
55
ZhangL. M.AidmanE.BurnsB.KleitmanS. (2020). Integrating self-report and performance-based assessment of adaptability in a university context. J. Res. Pers.88:103988. 10.1016/j.jrp.2020.103988
Summary
Keywords
Mental Flexibility, Cognitive Flexibility, affect, stability, variability, psychometrics
Citation
Borghesi F, Chirico A and Cipresso P (2025) Response: Commentary: Outlining a novel psychometric model of mental flexibility and affect dynamics. Front. Psychol. 16:1631344. doi: 10.3389/fpsyg.2025.1631344
Received
19 May 2025
Accepted
27 July 2025
Published
01 October 2025
Volume
16 - 2025
Edited by
Begoña Espejo, University of Valencia, Spain
Reviewed by
Maura Crepaldi, University of Bergamo, Italy
Updates
Copyright
© 2025 Borghesi, Chirico and Cipresso.
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*Correspondence: Francesca Borghesi francesca.borghesi@unito.it
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.