The Necessity of Rostrolateral Prefrontal Cortex for Higher-Level Sequential Behavior

Desrochers, Theresa M.; Chatham, Christopher H.; Badre, David

doi:10.1016/j.neuron.2015.08.026

Cited by 94 publications

(129 citation statements)

References 55 publications

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“…2b). Prior to disengagement, ramping activity is observed in both medial PFC–dACC76,101,102 and another region associated with exploration99, the rostrolateral PFC102.…”

Section: Properties Of the Frameworkmentioning

confidence: 99%

A distributed, hierarchical and recurrent framework for reward-based choice

2017

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Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organisation of timescales for information processing across the cortex. This account also suggests that certain correlates of value may be emergent rather than represented explicitly in the brain.

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“…2b). Prior to disengagement, ramping activity is observed in both medial PFC–dACC76,101,102 and another region associated with exploration99, the rostrolateral PFC102.…”

Section: Properties Of the Frameworkmentioning

confidence: 99%

A distributed, hierarchical and recurrent framework for reward-based choice

2017

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“…While ramping DA signals have not yet been demonstrated in humans, our laboratory recently demonstrated distinct timescales of activity in human ventral tegmental area (VTA), the primary source of forebrain DA, during novelty processing (Murty et al, 2016). Additionally, ramping activity dynamics have been explicitly modeled in rostrolateral prefrontal cortex as a function of task sequence position (Desrochers et al, 2015). Characterizing a human ramping DA response could potentially build on these approaches.…”

Section: Future Directionsmentioning

confidence: 99%

Reward Anticipation Dynamics during Cognitive Control and Episodic Encoding: Implications for Dopamine

Chiew

Stanek

Adcock

2016

Front. Hum. Neurosci.

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Dopamine (DA) modulatory activity critically supports motivated behavior. This modulation operates at multiple timescales, but the functional roles of these distinct dynamics on cognition are still being characterized. Reward processing has been robustly linked to DA activity; thus, examining behavioral effects of reward anticipation at different timing intervals, corresponding to different putative dopaminergic dynamics, may help in characterizing the functional role of these dynamics. Towards this end, we present two research studies investigating reward motivation effects on cognitive control and episodic memory, converging in their manipulation of rapid vs. multi-second reward anticipation (consistent with timing profiles of phasic vs. ramping DA, respectively) on performance. Under prolonged reward anticipation, both control and memory performances were enhanced, specifically when combined with other experimental factors: task-informative cues (control task) and reward uncertainty (memory task). Given observations of ramping DA under uncertainty (Fiorillo et al., 2003) and arguments that uncertainty may act as a control signal increasing environmental monitoring (Mushtaq et al., 2011), we suggest that task information and reward uncertainty can both serve as “need for control” signals that facilitate learning via enhanced monitoring, and that this activity may be supported by a ramping profile of dopaminergic activity. Observations of rapid (i.e., phasic) reward on control and memory performance can be interpreted in line with prior evidence, but review indicates that contributions of different dopaminergic timescales in these processes are not well-understood. Future experimental work to clarify these dynamics and characterize a cross-domain role for reward motivation and DA in goal-directed behavior is suggested.

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“…As this effect was so widespread, it is difficult to offer a precise interpretation, and different areas may increase for different reasons (Kalm and Norris 2017). For example, it is possible that increased activations in some regions reflect revision and reconfiguration of control representations that may increase in demand as larger portions of the task are complete (Farooqui et al 2012;Desrochers et al 2015Desrochers et al , 2016. These activity changes could also reflect gradual assembly of an episode representation (Dumontheil et al 2011) or accumulation of new information (Hasson et al 2008;Lerner et al 2011).…”

Section: Discussionmentioning

confidence: 99%

Hierarchical representation of multi-step tasks in multiple-demand and default mode networks

Wen

Duncan

Mitchell

2019

Preprint

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† Joint senior authors Number of pages: 37 Number of figures: 6 (Images of faces are covered to comply with bioRxiv requirements) Number of words for abstract: 230 Number of words for introduction: 649 Number of words for discussion: 1467The authors declare no competing financial interests. Abstract 27Task episodes consist of sequences of steps that are performed to achieve a goal. We used fMRI to 28 examine neural representation of task identity, component items, and sequential position, focusing on two 29 100 stew"), steps (e.g. third) and items associated with steps (e.g. "wash vegetables"). We used univariate 101 event-related and finite impulse response (FIR) analyses to characterize the temporal evolution of activity 102 across episodes, and representational similarity analysis (RSA) to investigate representations of task 103 structure and content. We hypothesized that different brain regions would be preferentially sensitive to 104 different levels of the temporal task hierarchy, and focused on the MD and DMN networks as a priori 105 regions of interest. 106 107 Task episode representation in cortical networks 6 Methods 108 Participants 109 42 participants (20 male, 22 female; ages 18-39, mean = 26.79, SD = 4.77) were included in the 110 experiment at the MRC Cognition and Brain Sciences Unit. An additional 19 participants were excluded 111(two were discovered to have cysts, one lost several slices due to poor bounding box positioning, ten were 112 excluded due to having no correct episodes for at least one combination of cued task × distractor task (see 113 later), and a further six were excluded due to excessive head motion > 5 mm). All participants were 114 neurologically healthy, right-handed, with normal or corrected-to-normal vision. Procedures were carried 115 out in accordance with ethical approval obtained from the Cambridge Psychology Research Ethics 116 Committee, and participants provided written, informed consent before the start of the experiment 117 118Stimuli and task procedures 119 The study consisted of a learning session outside the scanner and an execution session in the scanner. 120During the learning session, participants learned six everyday task sequences, each based in one of two 121 locations ("rooms"; three kitchen and three bathroom). Each task consisted of four ordered "steps". For 122 example, the task "make a stew" consisted of the steps "take food from fridge", "wash vegetables", "chop 123 vegetables", "cook on stove". Each step was associated with a unique image ("item"). The complete set 124 of stimuli is shown in Figure 1A. 125(e.g., "make a stew"). After a short delay, the first search array of four items appeared, and participants 132 were asked to select the item corresponding to the first step of that task (here, "take food from fridge"). 133 Participants selected this same target in three search arrays (total step duration = 9 s), then were given 134 a brief indicator that the step had been completed, and moved on to the next step (here "wash 135 vegetables"). ...

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The Necessity of Rostrolateral Prefrontal Cortex for Higher-Level Sequential Behavior

Cited by 94 publications

References 55 publications

A distributed, hierarchical and recurrent framework for reward-based choice

A distributed, hierarchical and recurrent framework for reward-based choice

Reward Anticipation Dynamics during Cognitive Control and Episodic Encoding: Implications for Dopamine

Hierarchical representation of multi-step tasks in multiple-demand and default mode networks

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