The Best Laid Plans: Computational Principles of Anterior Cingulate Cortex

Holroyd, Clay B.; Verguts, Tom

doi:10.1016/j.tics.2021.01.008

Cited by 78 publications

(93 citation statements)

References 120 publications

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“…It is likely that when making real-world decisions, people reduce effort by remembering solutions that might be useful in the future (Dasgupta & Gershman, 2021), not only for choices among options, but also for higher order decisions about how to decide (Griffiths et al, 2019) and what information to pay attention to (Callaway, Rangel, et al, 2021;Jang et al, 2021). These solutions across different levels of hierarchy (e.g., choice parameters, strategies), could then serve as defaults and be iteratively refined to support goal-directed behaviors (J. W. Brown & Alexander, 2017;Holroyd & Verguts, 2021;Holroyd & Yeung, 2012;Lieder et al, 2018). In addition to better understanding the strategies people use to simplify decision-making, future work should also seek to better explain how people learn when and how to use these different strategies.…”

Section: Decisions and Control Over Future Research Directionsmentioning

confidence: 99%

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

Frömer

Shenhav

2022

Neuroscience & Biobehavioral Reviews

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While often seeming to investigate rather different problems, research into value-based decision making and cognitive control have historically offered parallel insights into how people select thoughts and actions. While the former studies how people weigh costs and benefits to make a decision, the latter studies how they adjust information processing to achieve their goals.Recent work has highlighted ways in which decision-making research can inform our understanding of cognitive control. Here, we provide the complementary perspective: how cognitive control research has informed understanding of decision-making. We highlight three particular areas of research where this critical interchange has occurred: (1) how different types of goals shape the evaluation of choice options, (2) how people use control to adjust how they make their decisions, and (3) how people monitor decisions to inform adjustments to control at multiple levels and timescales. We show how adopting this alternate viewpoint offers new insight into the determinants of both decisions and control; provides alternative interpretations for common neuroeconomic findings; and generates fruitful directions for future research.

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Section: Decisions and Control Over Future Research Directionsmentioning

confidence: 99%

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

Frömer

Shenhav

2022

Neuroscience & Biobehavioral Reviews

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“…In more standard machine learning terms, this could be thought as moving from more "model-based" to more "model-free" control, or "amortized inference" with respect to policy selection (S. Gershman & Goodman, 2014;Mannella et al, 2013). However, when these more unconscious forms of inference fail, associated prediction errors will ascend until they can receive more elaborative handling by conscious systems, whose degree of activity may be modulated by overall surprisal (Holroyd & Verguts, 2021;Safron, 2020b;Sales et al, 2019;Shea & Frith, 2019;J. Shine, 2019).…”

Section: Kinds Of Minds: Bringing Forth Worlds Of Experiencementioning

confidence: 99%

Integrated World Modeling Theory (IWMT) Expanded: Implications for Theories of Consciousness and Artificial Intelligence

Safron¹

2021

Preprint

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Integrated World Modeling Theory (IWMT) is a synthetic theory of consciousness that uses the Free Energy Principle and Active Inference (FEP-AI) framework to combine insights from Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT’s integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Particular emphasis is placed on the hippocampal/entorhinal system, which may provide a source of high-level reasoning via predictive contrasting and generalized navigation, so affording multiple kinds of conscious access. Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the “Bayesian blur problem”, or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. Finally, I go on to describe parallels between FEP-AI and theories of universal intelligence, including with respect to implications for the future of artificially intelligent systems. Particular emphasis is given to recurrent computation and its relationships with feedforward processing, including potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.

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“…These "defaults" can then be iteratively refined across different levels of hierarchy, e.g. choice parameters, or strategies, to support goal-directed behaviors (J. W. Brown & Alexander, 2017;Holroyd & Verguts, 2021;Holroyd & Yeung, 2012;F. Lieder et al, 2018).…”

Section: Decisions and Control Over Future Research Directionsmentioning

confidence: 99%

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

Frömer¹,

Shenhav²

2021

Preprint

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Research into value-based decision making has made tremendous progress in identifying behavioral and neural correlates of choice value. However, these correlates have been primarily viewed through a field-specific lens, focusing on how they contribute to the evaluation and selection between options to arrive at a choice. Here, we reveal blind-spots resulting from this limited perspective, and how they can be filled in through taking the perspective of cognitive control. We highlight three particular insights that this perspective offers: (1) a view towards the goal-relevance of one’s options and their features; (2) a view of decision-making correlates as a proxy for monitoring to determine control adjustments; (3) a view of those correlates as a proxy for monitoring that extends temporally and hierarchically beyond the immediate choice task. We show how adopting these complementary perspectives offers new insight into the determinants of both decisions and control; alternative interpretations for common findings in the neuroeconomic literature; and fruitful directions for future research.

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The Best Laid Plans: Computational Principles of Anterior Cingulate Cortex

Cited by 78 publications

References 120 publications

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

Integrated World Modeling Theory (IWMT) Expanded: Implications for Theories of Consciousness and Artificial Intelligence

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

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