A Network for Computing Value Equilibrium in the Human Medial Prefrontal Cortex

Juechems, Keno; Balaguer, Jan; Castañón, Santiago Herce; Ruz, Marı́a; O’Reilly, Jill X.; Summerfield, Christopher

doi:10.1016/j.neuron.2018.12.029

Cited by 38 publications

(39 citation statements)

References 54 publications

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“…[33], showing that pursuit strategies depend on environmental characteristics, subjective preferences and changes in context when getting closer to the goal. In line with our findings, a recent study [34] showed that decisions whether to redress the imbalance between two assets or to focus on a distinct asset during sequential goal pursuit were best fit by a dynamic programming model with a limited time horizon of 7.5 trials (20 trials would be the optimum). In future research, the pursuit of multiple goals in sequential decision making tasks may also be a basis for addressing questions about cognitive control during goal-reaching, e.g.…”

Section: Discussionsupporting

confidence: 90%

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

et al. 2020

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Selecting goals and successfully pursuing them in an uncertain and dynamic environment is an important aspect of human behaviour. In order to decide which goal to pursue at what point in time, one has to evaluate the consequences of one's actions over future time steps by forward planning. However, when the goal is still temporally distant, detailed forward planning can be prohibitively costly. One way to select actions at minimal computational costs is to use heuristics. It is an open question how humans mix heuristics with forward planning to balance computational costs with goal reaching performance. To test a hypothesis about dynamic mixing of heuristics with forward planning, we used a novel stochastic sequential two-goal task. Comparing participants' decisions with an optimal full planning agent, we found that at the early stages of goal-reaching sequences, in which both goals are temporally distant and planning complexity is high, on average 42% (SD = 19%) of participants' choices deviated from the agent's optimal choices. Only towards the end of the sequence, participant's behaviour converged to near optimal performance. Subsequent model-based analyses showed that participants used heuristic preferences when the goal was temporally distant and switched to forward planning when the goal was close.

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Section: Discussionsupporting

confidence: 90%

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

et al. 2020

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“…5). In the more complex situations generated by human neuroimaging paradigms, we propose that value predictions, based on RPE signals generated through stimuli-outcome and response-outcome pairings, are further influenced by external contextual factors (Behrens et al, 2007;Fiorillo, Tobler & Schultz, 2007;Hariri et al, 2006;Porcelli & Delgado, 2009;Preuschoff & Bossaerts, 2007;Rangel, 2008;Seaman et al, 2018), and/or by subjective internal factors (Hetherington, Pirie & Nabb, 2002;Juechems et al, 2019;Lopez-Persem, Domenech & Pessiglione, 2016;Singer, Critchley & Preuschoff, 2009). Figure 5.…”

Section: Discussionmentioning

confidence: 99%

“…Internal states, memories of past experiences, and subjective preferences also influence subjective value and choice decisions (Engelmann & Tamir, 2009;Koeneke et al, 2008;Lopez-Persem, Domenech & Pessiglione, 2016). Accordingly, neuroimaging reward processing studies have not only demonstrated RPE-related striatal and mid-brain responsivity (Cooper et al, 2009;Garrison, Erdeniz & Done, 2013;Knutson et al, 2008), but also implicate a wider network of brain activity linked with processing these various peripheral influences (Acikalin, Gorgolewski & Poldrack, 2017;Behrens et al, 2007;Glascher et al, 2010;Juechems et al, 2019;Seaman et al, 2018;Sugrue, Corrado & Newsome, 2004). As such, neurocognitive models of reward learning should endeavor to more fully characterize these additional contextual influences.…”

Section: Introductionmentioning

confidence: 99%

Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms

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Riedel

Bottenhorn

et al. 2019

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Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we utilized emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants.We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution which represented dissociable patterns of convergent brain activity across reward processing tasks.Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

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“…To survive, animals must maximize the minimum-take care of the least satisfied among their basic needs. In this issue of Neuron, a study by Juechems et al (2019) illustrates that this core principle might shape the way medial prefrontal regions of the human brain drive and valuate sequential choices between different types of reward.…”

mentioning

confidence: 99%

“…Yet he did not explain how the human brain can make such a choice. A recent study by Juechems and colleagues provides Neuron Previews some key insights into the neural machinery that drives the simultaneous pursuit of concurrent goals (Juechems et al, 2019).…”

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confidence: 99%