Goals and Habits in the Brain
- Type:#article
- Year read:
- Subject: (in brackets, can also bracket keywords in text)
- Bibtex: @dolan2013
- Bibliography: Dolan, R. J., & Dayan, P. (2013). Goals and habits in the brain. Neuron, 80(2), 312–325. https://doi.org/10.1016/j.neuron.2013.09.007
Why and when I was reading this
I was doing a deep dive into the compulsivity-impulsivity construct and how it relates to OCD.
Key takeaways
- There’s a dichotomy between reflective versus reflexive decision making
- They propose that OCD patients have an over-reliance on Habits
- three
Paperpile notes
Page 1: Highlight annotation by Oskar Flygare on June 21st 2018, 11:34:33 am: An enduring and richly elaborated dichotomy in cognitive neuroscience is that of reflective versus reflexive decision making and choice.
Page 1: Highlight annotation by Oskar Flygare on June 21st 2018, 11:34:53 am: An important and pervasive idea in the psychology of decision making and choice is that there is more than one class of possible strategy for acting. A key division is between forms of reflective control, which depend on the more or less explicit consideration of possible prospective future courses of actions and consequent outcomes, and forms of reflexive control a term we use in the restricted sense to describe how retrospec- tive experience with good and bad outcomes sculpts present choice.
Page 3: Highlight annotation by Oskar Flygare on June 21st 2018, 11:36:20 am: By contrast, habitual instrumental behavior is supposed to have been stamped in by past reinforcement (Thorndike, 1911) and so is divorced from the current value of an associated outcome. Thus, key characteristics of habitual instrumental control include automaticity, computational effi- ciency, and inflexibility, while characteristics of goal-directed control include active deliberation, high computational cost, and an adaptive flexibility to changing environmental contin- gencies
Page 3: Highlight annotation by Oskar Flygare on June 21st 2018, 11:36:44 am: This double dissociation makes a strong case that prelimbic regions are crucial for goal-directed performance, while infralim- bic lesions prevent the emergence of habitual responding that overrides an initial dominance in goal-directed responding.
Page 10: Highlight annotation by Oskar Flygare on June 21st 2018, 11:38:10 am: here is currently great interest in using the sorts of ideas and tasks that we have discussed to provide a quantitative way of understand- ing the nature and underpinnings of abnormal decisions, choices, and evaluations. The suggestion that systems occupy something closer to a spectrum than a dichotomy makes this a potentially powerful way to parse deviance but also very challenging.
Page 10: Highlight annotation by Oskar Flygare on June 21st 2018, 11:38:26 am: One example is obsessive-compulsive disorder (OCD) (Gray- biel, 2008), where insensitivity to outcome devaluation and slips of action were used to test a hypothesis of dominance by a habitual system (Gillan et al., 2011). Patients with OCD (albeit potentially confounded by the effects of their neuromodulatory therapies) showed no deficit in using rewarding feedback to guide action but instead showed both lack of sensitivity to outcome devaluation and increased frequency in slips of action.
Page 10: Highlight annotation by Oskar Flygare on June 21st 2018, 11:38:49 am: Furthermore, evidence for abnormalities in components of a goal-directed system in OCD, particularly the caudate nucleus, aligns with a suggestion that key manifesta- tions of this condition reflect on overdominance of a habitual system
Page 11: Highlight annotation by Oskar Flygare on June 21st 2018, 11:39:22 am: We have provided an inevitably selective Review of the past, present, and future of model-based and model-free control in humans. The distinction is extremely long standing, has been an important source of ideas and experiments, has offered accounts of many brain regions critical to instrumental choice, and indeed has been a spur to computational modeling. How- ever, even though it is not yet evident how the computational challenges of model-based control are addressed, it is becoming clear that model-based and model-free predictions and controls are more richly intertwined than originally supposed and thereby offer flexible and adaptive solutions to the manifest problems of exploring and exploiting potentially dangerous but lucrative environments.