The brain produces mind by modeling

Shiffrin, Richard M.; Bassett, Danielle S.; Kriegeskorte, Nikolaus; Tenenbaum, Joshua B.

doi:10.1073/pnas.1912340117

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…The high interpretative potential of this model is grounded on the synthesis of present-day ideas of systemic and synergetic approaches (Bak, 1996;Haken, 2006;Haken 2012;Lukanin, 2008;Martináková-Rendeková, 2012;Penrose, 2016;Shiffrin et al, 2020;and others), whose main assumptions are put forward in our papers (Klymenyuk, 2010, pp. 75-97, 97-126;Klymenyuk, 2005, pp.…”

Section: Methodological Potential Of the Individual's Spiritual Being...mentioning

confidence: 98%

23

2021

Cognition, Communication, Discourse

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home Текст подано в авторській редакції. Статті пройшли внутрішнє та зовнішнє рецензування. Періодичність -2 випуски на рік Журнал "Kognitsia, kommunikatsia, diskurs" індексовано в міжнародних базах даних: Bielefeld Academic Search Engine (BASE), Directory of Open Access Journals (DOAJ),

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Section: Methodological Potential Of the Individual's Spiritual Being...mentioning

confidence: 98%

23

2021

Cognition, Communication, Discourse

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“…The goals of CCN involve looking at the lower-level mechanisms that give rise to neuroscientific findings and intelligent agents' behaviors (cf. Kietzmann et al, 2019;Shiffrin et al, 2020). If we allow flawed inference rules to govern CCN in a way that overlooks mechanism by ignoring the principle of multiple realizability (or if we permit the use of rules that are indeed formally fallacious), then we lead ourselves astray.…”

Section: Cognitiva (Meta)scientia Reduxmentioning

confidence: 99%

On Logical Inference over Brains, Behaviour, and Artificial Neural Networks

Guest

Martin

2023

Comput Brain Behav

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In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference.

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“…Another possibility might draw on Meltzoff's original active intermodal mapping (AIM) model, where an "equivalency detector" responds to the correspondence between, for example, the action observed on the face of an adult and the action performed by an infant [34]. A related cognitive framework assumes that social and asocial learning conditions produce distinct cognitive models (or simulations) against which performance during action execution is compared [35]. We might think of occipital and occipitotemporal regions implicated in imitation execution as areas which use the visual input of the demonstrator's hand actions to generate a model based on what is known about the task (e.g., the content of the demonstration).…”

Section: Grist and Millsmentioning

confidence: 99%

Are there dedicated neural mechanisms for imitation? A study of grist and mills

Renner,

Xie,

Subiaul

et al. 2023

PLoS ONE

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Are there brain regions that are specialized for the execution of imitative actions? We compared two hypotheses of imitation: the mirror neuron system (MNS) hypothesis predicts frontal and parietal engagement which is specific to imitation, while the Grist-Mills hypothesis predicts no difference in brain activation between imitative and matched non-imitative actions. Our delayed imitation fMRI paradigm included two tasks, one where correct performance was defined by a spatial rule and another where it was defined by an item-based rule. For each task, participants could learn a sequence from a video of a human hand performing the task, from a matched “Ghost” condition, or from text instructions. When participants executed actions after seeing the Hand demonstration (compared to Ghost and Text demonstrations), no activation differences occurred in frontal or parietal regions; rather, activation was localized primarily to occipital cortex. This adds to a growing body of evidence which indicates that imitation-specific responses during action execution do not occur in canonical mirror regions, contradicting the mirror neuron system hypothesis. However, activation differences did occur between action execution in the Hand and Ghost conditions outside MNS regions, which runs counter to the Grist-Mills hypothesis. We conclude that researchers should look beyond these hypotheses as well as classical MNS regions to describe the ways in which imitative actions are implemented by the brain.

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The brain produces mind by modeling

Cited by 8 publications

References 30 publications

23

23

On Logical Inference over Brains, Behaviour, and Artificial Neural Networks

Are there dedicated neural mechanisms for imitation? A study of grist and mills

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