Habit strength is predicted by activity dynamics in goal-directed brain systems during training

Zwosta, Katharina; Ruge, Hannes; Goschke, Thomas; Wolfensteller, Uta

doi:10.1016/j.neuroimage.2017.09.062

Cited by 36 publications

(61 citation statements)

References 43 publications

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“…Indeed, the only other explanation would be that practice continued to change but somehow produced the exact same level of performance. Frequently therefore, in neuroscience and cognitive psychology research, the habit formation process is operationalised as the point at which the learning curve (measuring accuracy or completion time for a task) levels off, or approaches asymptote (Brasted & Wise, 2004; Daw et al ., 2005; Schwabe & Wolf, 2010; Brovelli et al ., 2011; de Wit et al ., 2018; Zwosta et al ., 2018).…”

Section: Evidence From Performance Asymptotesmentioning

confidence: 99%

“…Caudate activity was high during the upward sloping part of the learning curve but then began to fall just as performance approached asymptote (Figure 1, Panel B; see also Brasted & Wise, 2004; Brovelli et al ., 2011). Similarly, in an experiment using human subjects in which participants learned to categorise visual stimuli as either ‘natural’ or ‘artificial’ using a key press, improved performance was shown to be associated with increased functional connectivity between posterior putamen and premotor cortex, suggesting increasing strength of habit formation (Zwosta et al ., 2018).…”

Section: Evidence From Performance Asymptotesmentioning

confidence: 99%

“…It is important to note here that the correlation between activity in habit circuits and approaching performance asymptote is not evidence that the activity in these circuits causes such habits to emerge. Indeed, there is evidence to suggest that the ‘switch’ from goal‐directed to habitual behaviour is also a function of the remaining involvement (or not) in goal‐directed circuits, as much as increases in the activity of habit networks (Smith & Graybiel, 2013; Zwosta et al ., 2018). Equally, while the ‘habit’ and ‘goal‐directed’ circuits are necessary for the production of their associated behaviour, they are not entirely sufficient, relying both on each other and on a range of other brain regions (Steele & Penhune, 2010), as with any complex behaviour.…”

Section: Evidence From Performance Asymptotesmentioning

confidence: 99%

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Habit formation limits growth in teacher effectiveness: A review of converging evidence from neuroscience and social science

Hobbiss¹,

Sims²,

Allen

2020

Review of Education

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Teachers become rapidly more effective during the early years of their career but tend to improve increasingly slowly thereafter. This article reviews and synthesises converging evidence from neuroscience, psychology, economics and education suggesting that teachers’ rate of growth slows because their practice becomes habitual. First, we review evidence suggesting that teaching is highly conducive to habit formation and that teachers display characteristic features of habitual behaviour. Next, we review empirical findings that performance asymptotes, as seen in teachers’ learning curves, coincide with the reallocation of behaviour regulation to neural circuits governing habitual behaviour. Finally, original data is presented showing that teachers’ behaviour becomes automatic around the time that teacher effectiveness begins to level off. Collectively, this evidence implies that professional development should involve repeated practice in realistic settings in order to overwrite and upgrade existing habits.

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Section: Evidence From Performance Asymptotesmentioning

confidence: 99%

Section: Evidence From Performance Asymptotesmentioning

confidence: 99%

Section: Evidence From Performance Asymptotesmentioning

confidence: 99%

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Habit formation limits growth in teacher effectiveness: A review of converging evidence from neuroscience and social science

Hobbiss¹,

Sims²,

Allen

2020

Review of Education

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“…Less is known about the contribution of nonexecutive parietal brain regions to self-control. One possibility includes that parietal brain regions, such as the posterior inferior parietal lobe and precuneus, mediate self-control by the representation of future outcomes (Andrews-Hanna et al, 2010;Benoit & Schacter, 2015;Kruschwitz et al, 2018;Soutschek et al, 2016;Xu et al, 2016;Zwosta et al, 2015;Zwosta et al, 2018). This also could involve interactions with the dorsolateral PFC, for instance a vivid imagination of future consequences might increase the representation of long-term goals in the dorsolateral PFC.…”

Section: Introductionmentioning

confidence: 99%

Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis

Krönke

Mohr

Wolff

et al. 2021

Cogn Affect Behav Neurosci

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Despite its relevance for health and education, the neurocognitive mechanism of real-life self-control is largely unknown. While recent research revealed a prominent role of the ventromedial prefrontal cortex in the computation of an integrative value signal, the contribution and relevance of other brain regions for real-life self-control remains unclear. To investigate neural correlates of decisions in line with long-term consequences and to assess the potential of brain decoding methods for the individual prediction of real-life self-control, we combined functional magnetic resonance imaging during preference decision making with ecological momentary assessment of daily self-control in a large community sample (N = 266). Decisions in line with long-term consequences were associated with increased activity in bilateral angular gyrus and precuneus, regions involved in different forms of perspective taking, such as imagining one’s own future and the perspective of others. Applying multivariate pattern analysis to the same clusters revealed that individual patterns of activity predicted the probability of real-life self-control. Brain activations are discussed in relation to episodic future thinking and mentalizing as potential mechanisms mediating real-life self-control.

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“…The goal-directed system, which is subserved by frontostriatal regions (Valentin et al 2007; Tanaka et al 2008; de Wit et al 2009), controls voluntary instrumental behaviour by evaluating the potential consequences of actions. The habit system, which is subserved by corticostriatal circuits (Tricomi et al 2009; Brovelli et al 2011; de Wit et al 2012; Zwosta et al 2018), regulates automatic impulses in response to stimulus–response associations that have been formed over repeated experiences. Both systems are needed in everyday life, and optimal behavioural performance has been shown to require a balance between the joint regulation of these two systems (Balleine and O’Doherty 2010).…”

Section: Introductionmentioning

confidence: 99%

Impairments in reinforcement learning do not explain enhanced habit formation in cocaine use disorder

et al. 2019

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Rationale Drug addiction has been suggested to develop through drug-induced changes in learning and memory processes. Whilst the initiation of drug use is typically goal-directed and hedonically motivated, over time, drug-taking may develop into a stimulus-driven habit, characterised by persistent use of the drug irrespective of the consequences. Converging lines of evidence suggest that stimulant drugs facilitate the transition of goal-directed into habitual drug-taking, but their contribution to goal-directed learning is less clear. Computational modelling may provide an elegant means for elucidating changes during instrumental learning that may explain enhanced habit formation. Objectives We used formal reinforcement learning algorithms to deconstruct the process of appetitive instrumental learning and to explore potential associations between goal-directed and habitual actions in patients with cocaine use disorder (CUD). Methods We re-analysed appetitive instrumental learning data in 55 healthy control volunteers and 70 CUD patients by applying a reinforcement learning model within a hierarchical Bayesian framework. We used a regression model to determine the influence of learning parameters and variations in brain structure on subsequent habit formation. Results Poor instrumental learning performance in CUD patients was largely determined by difficulties with learning from feedback, as reflected by a significantly reduced learning rate. Subsequent formation of habitual response patterns was partly explained by group status and individual variation in reinforcement sensitivity. White matter integrity within goal-directed networks was only associated with performance parameters in controls but not in CUD patients. Conclusions Our data indicate that impairments in reinforcement learning are insufficient to account for enhanced habitual responding in CUD. Electronic supplementary material The online version of this article (10.1007/s00213-019-05330-z) contains supplementary material, which is available to authorized users.

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Habit strength is predicted by activity dynamics in goal-directed brain systems during training

Cited by 36 publications

References 43 publications

Habit formation limits growth in teacher effectiveness: A review of converging evidence from neuroscience and social science

Habit formation limits growth in teacher effectiveness: A review of converging evidence from neuroscience and social science

Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis

Impairments in reinforcement learning do not explain enhanced habit formation in cocaine use disorder

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