Abstract
Cognitive flexibility helps us to navigate through our ever-changing environment and has often been examined by reversal learning paradigms. Performance in reversal learning can be modeled using computational modeling which allows for the specification of biologically plausible models to infer psychological mechanisms. Although such models are increasingly used in cognitive neuroscience, developmental approaches are still scarce. Additionally, though most reversal learning paradigms have a comparable design regarding timing and feedback contingencies, the type of feedback differs substantially between studies. The present study used hierarchical Gaussian filter modeling to investigate cognitive flexibility in reversal learning in children and adolescents and the effect of various feedback types. The results demonstrate that children make more overall errors and regressive errors (when a previously learned response rule is chosen instead of the new correct response after the initial shift to the new correct target), but less perseverative errors (when a previously learned response set continues to be used despite a reversal) adolescents. Analyses of the extracted model parameters of the winning model revealed that children seem to use new and conflicting information less readily than adolescents to update their stimulus-reward associations. Furthermore, more subclinical rigidity in everyday life (parent-ratings) is related to less explorative choice behavior during the probabilistic reversal learning task. Taken together, this study provides first-time data on the development of the underlying processes of cognitive flexibility using computational modeling.
Highlights
In an ever-changing environment, it is essential to shift strategies and adapt response patterns based on received feedback
Reversal learning has been found to be impaired in various neurological and psychiatric conditions including obsessive compulsive disorder (OCD; Verfaillie et al, 2016; Hauser et al, 2017; Tezcan et al, 2017), Huntington’s Disease (Nickchen et al, 2016), schizophrenia (Culbreth et al, 2016; Reddy et al, 2016), Parkinson’s disease (Buelow et al, 2015), Attention-Deficit/Hyperactivity disorder (ADHD; Hauser et al, 2015a), and autism spectrum disorder (ASD; Lionello-DeNolf et al, 2010; D’Cruz et al, 2013; Costescu et al, 2014; D’Cruz et al, 2016). Despite these numerous studies on cognitive flexibility, it remains difficult to draw exact conclusions about the development of the underlying learning processes mainly due to three crucial factors: (1) In the existing studies participants varied in age from young childhood to adulthood with only one study systematically comparing learning processes at various ages during development (Crawley et al, 2019), it is known that cognitive flexibility changes over the course of development (e.g., Crone and van der Molen, 2004; YurgelunTodd, 2007; Van Der Schaaf et al, 2011; Ionescu, 2012; Luking et al, 2014)
Using Bayesian Model Selection (BMS) for groups (Stephan et al, 2009; Rigoux et al, 2014), we found that the hierarchical Gaussian filter (HGF) model with meta-volatility parameter θ being fixed performed better compared to the HGF including an estimation of the metavolatility parameter θ and the anti-correlated RW model across all subjects as well as for both age groups separately, the probability that the HGF including a fixed meta-volatility parameter θ performs better than the other two models included in the comparison is 95%
Summary
In an ever-changing environment, it is essential to shift strategies and adapt response patterns based on received feedback. Reversal learning has been found to be impaired in various neurological and psychiatric conditions including obsessive compulsive disorder (OCD; Verfaillie et al, 2016; Hauser et al, 2017; Tezcan et al, 2017), Huntington’s Disease (Nickchen et al, 2016), schizophrenia (Culbreth et al, 2016; Reddy et al, 2016), Parkinson’s disease (Buelow et al, 2015), Attention-Deficit/Hyperactivity disorder (ADHD; Hauser et al, 2015a), and autism spectrum disorder (ASD; Lionello-DeNolf et al, 2010; D’Cruz et al, 2013; Costescu et al, 2014; D’Cruz et al, 2016) Despite these numerous studies on cognitive flexibility, it remains difficult to draw exact conclusions about the development of the underlying learning processes mainly due to three crucial factors: (1) In the existing studies participants varied in age from young childhood to adulthood with only one study systematically comparing learning processes at various ages during development (Crawley et al, 2019), it is known that cognitive flexibility changes over the course of development (e.g., Crone and van der Molen, 2004; YurgelunTodd, 2007; Van Der Schaaf et al, 2011; Ionescu, 2012; Luking et al, 2014). We will shortly outline these three aspects before proposing how to overcome these limitations in the current study
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