A role for proactive control in rapid instructed task learning

Cole, Michael W.; Patrick, Lauren; Meiran, Nachshon; Braver, Todd S.

doi:10.1016/j.actpsy.2017.06.004

Cited by 20 publications

(48 citation statements)

References 45 publications

(75 reference statements)

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“…Behavioral results support the role of proactive control (Braver, 2012) on instructed action (Luria, 1966;Duncan et al, 2008; e.g., Liefooghe et al, 2012; see also Cole et al, 2018). Recently, neuroimaging studies have revealed a link between novel instruction preparation and the frontoparietal (FP) network (e.g., Cole et al, 2010;Hartstra et al, 2011;Palenciano et al, 2019).…”

Section: Introductionsupporting

confidence: 53%

Representational Organization of Novel Task Sets during Proactive Encoding

et al. 2019

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Recent multivariate analyses of brain data have boosted our understanding of the organizational principles that shape neural coding. However, most of this progress has focused on perceptual visual regions (Connolly et al., 2012), whereas far less is known about the organization of more abstract, action-oriented representations. In this study, we focused on humans' remarkable ability to turn novel instructions into actions. While previous research shows that instruction encoding is tightly linked to proactive activations in frontoparietal brain regions, little is known about the structure that orchestrates such anticipatory representation. We collected fMRI data while participants (both males and females) followed novel complex verbal rules that varied across control-related variables (integrating within/across stimuli dimensions, response complexity, target category) and reward expectations. Using representational similarity analysis (Kriegeskorte et al., 2008), we explored where in the brain these variables explained the organization of novel task encoding, and whether motivation modulated these representational spaces. Instruction representations in the lateral PFC were structured by the three control-related variables, whereas intraparietal sulcus encoded response complexity and the fusiform gyrus and precuneus organized its activity according to the relevant stimulus category. Reward exerted a general effect, increasing the representational similarity among different instructions, which was robustly correlated with behavioral improvements. Overall, our results highlight the flexibility of proactive task encoding, governed by distinct representational organizations in specific brain regions. They also stress the variability of motivation-control interactions, which appear to be highly dependent on task attributes, such as complexity or novelty.

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Section: Introductionsupporting

confidence: 53%

Representational Organization of Novel Task Sets during Proactive Encoding

et al. 2019

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“…This would also be consistent with our observation that participants were actually faster to encode new task instructions in mostly new contexts, without the cost of reduced task performance on the inducer task (and even improved performance in the second half of the experiment). In contrast, Cole, Patrick, Meiran, and Braver (2018) have shown that participants usually show reduced task performance when they take less time to encode task instructions.…”

Section: Discussionmentioning

confidence: 90%

Learning when to learn: Context-specific instruction encoding

Braem

Xu²,

Liefooghe³

et al. 2020

Preprint

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Humans are very efficient at learning new behaviors through verbal instructions, bypassing the need for trial-and-error learning. However, we encounter a plethora of instructions in daily life, which raises the question how people can quickly determine when to rely on certain instructions, and how strongly. Recent theories emphasize that abstract functions of cognition can be (up)regulated by contextual features in our environment with which they are associated. Here, we tested whether the act of encoding a new task instruction can be enhanced in contexts where one frequently learns new task instructions. Specifically, participants had to encode and execute several new task instructions, but were more likely to encounter a new (relative to old) task instruction when the instruction was presented above or below the center of the screen (Experiment 1) or in a blue or green font (Experiment 2). The results show that retaining new task instructions interfered more with secondary task performance (which is suggestive of stronger task representations), when these mappings were instructed in contexts where one is more used to seeing new task instructions. Similarly, the results showed that participants were also faster to encode new instructions in these contexts. Finally, a post-hoc analysis indicated that, over time, participants were also faster to execute new instructions in contexts where they are more used to execute new instructions. Together, these findings suggest that the encoding (and execution) of new task instructions can bind to a specific context, providing a (self-)regulating mechanism for cognitive control processes like instruction encoding.

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“…Listeners show an advantage on the type of post-test sentences they heard in training, which is a predictable outcome based on previous understandings of how participants in studies adapt to tasks. When a participant practices a particular type of task, they are better at that task than a novel task (Cole, Patrick, Meiran, & Braver, 2018 Alternatively, we hypothesized that high predictability training sentences would provide less generalizable adaptation because training with sentences that require more effort helps listeners improve, so the lower predictability conditions provide participants with better tools to understand the listener (Winn, 2016). In this case, listeners who heard high predictability sentences during training might ignore pronunciations to some extent and rely heavily on sentence context to transcribe.…”

Section: Discussionmentioning

confidence: 99%

The role of semantic predictability in adaptation to non-native-accented speech

Walker

Baese‐Berk

Bent

2019

The Journal of the Acoustical Society of America

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Nonnative-accented speech is more difficult for native listeners to understand than native-accented speech. However, listeners can improve their abilities to understand nonnative-accented speech through exposure and training. The goal of this project was to explore whether exposing native listeners to different sentence types affects listeners' adaptation to nonnative-accented speech. Listeners were trained on high predictability sentences (e.g., "The color of a lemon is yellow"), low predictability sentences (e.g., "Mom said that it is yellow"), or semantically anomalous sentences (e.g., "The green week did the page"). Previous research has demonstrated that semantic predictability impacts speech perception, but its influence on adaptation to nonnativeaccented speech is unclear. This experiment indicated that there is no generalizable advantage to training on high predictability versus low predictability versus semantically anomalous sentence types. ii I would like to thank Melissa Baese-Berk and Tessa Bent for helping me to formulate and design this thesis project. I would especially like to thank Melissa Baese-Berk for her expertise and constant support throughout this long process. I would also like to express my gratitude for Zachary Jaggers and the R scripts he wrote to help organize and clean my data. Furthermore, I want to convey my appreciation for the members of the Speech Perception and Production Lab, who provided valuable feedback at every point in this process. I also would like to extend my thanks to my thesis committee, Melissa Baese-Berk, Timothy Williams, and Misaki Kato for agreeing to read and provide feedback on this document. Finally, special thanks to the

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A role for proactive control in rapid instructed task learning

Cited by 20 publications

References 45 publications

Representational Organization of Novel Task Sets during Proactive Encoding

Representational Organization of Novel Task Sets during Proactive Encoding

Learning when to learn: Context-specific instruction encoding

The role of semantic predictability in adaptation to non-native-accented speech

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