Multivoxel patterns for perceptual confidence are associated with false color detection

Knotts, Jeffrey D.; Cortese, Aurelio; Taschereau-Dumouchel, Vincent; Kawato, Mitsuo; Lau, Hakwan

doi:10.1101/735084

Cited by 5 publications

(5 citation statements)

References 46 publications

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“…Participants progressively improved in their ability to induce neural patterns that were similar to the target concept (i.e., dogs). This observation is noteworthy because previous DecNef studies that used a similar amount of training sessions as here (Amano et al, 2016; Cortese et al, 2016; Knotts et al, 2019; Koizumi et al, 2016; Taschereau-Dumouchel et al, 2018), did not reliably observe significant learning effects, despite observing DecNef effects on behavioral performance. Here we used an improved decoder to provide a better calibrated and more fine-grained feedback to participants (Margolles et al, 2023).…”

Section: Discussionsupporting

confidence: 55%

“…Because of this, we elected to include a control group of participants that did not undergo DecNef in order to test that the behavioral pattern of search performance in the DecNef group was indeed specific to DecNef training. Note that a similar approach was used in prior associative DecNef studies (Amano, Shibata, Kawato, Sasaki, & Watanabe, 2016; Knotts, Cortese, Tascherau-Dumouchel, Kawato, & Lau, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, this approach would contribute to reducing potential sources of variability in participants’ behavioral responses. This approach has been utilized in previous associative DecNef studies (Amano et al, 2016; Knotts et al, 2019). Due to these considerations and the known responsiveness of the bilateral fusiform gyrus ROI to living entities (Connolly et al, 2012; Downing, Chan, Peelen, Dodds, & Kanwisher, 2006; Sha et al, 2015), indicating that targeting dogs representations might be more effective in inducing neural changes, we chose to assign dogs as the target category for all participants.…”

Section: Methodsmentioning

confidence: 99%

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Unconscious manipulation of conceptual representations with decoded neurofeedback impacts search behaviour

Margolles¹,

Elosegi²,

Mei³

et al. 2023

Preprint

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The necessity of conscious awareness in human learning has been a long-standing topic in psychology and neuroscience. Previous research on non-conscious associative learning is limited by the low signal-to-noise ratio of the subliminal stimulus, and the evidence remains controversial, including failures to replicate. Here, functional MRI decoded neurofeedback (DecNef) allowed participants to generate brain activity patterns consistent with a perceptual state without awareness of the represented content. We aimed to instill perceptual meaning into a meaningless hiragana by conditioning it through reinforcing neural patterns associated with perceptual content (i.e. living), thereby bypassing the conscious mapping between symbol and the perceptual referent. Despite being unaware of the goal of DecNef training, participants learned to induce the target representation in fusiform cortex. Then, the behavioural significance of DecNef training was evaluated in a visual search task. DecNef and control participants searched for living or non-living targets that were pre-cued by the targeted hiragana during DecNef or by a novel hiragana. The targeted hiragana did not prime search for the associated target but, strikingly, participants were impaired at searching for the perceptual content targeted by DecNef. Hence, conscious awareness may function to support higher-order associative learning, while lower-level forms of re-learning, modification, or plasticity in established neural representations can occur unconsciously, with behavioural consequences outside the original training context. The work also offers an account of DecNef effects in terms of neural representational drift.

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Section: Discussionsupporting

confidence: 55%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Unconscious manipulation of conceptual representations with decoded neurofeedback impacts search behaviour

Margolles¹,

Elosegi²,

Mei³

et al. 2023

Preprint

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“…Our ultimate, and possibly long-term scientific goal is to cause phenomenal consciousness by decoded neurofeedback without sensory stimuli or motor tasks. Knotts et al (2019) obtained intriguing results toward this direction, finding that reinforcing mental representations of high confidence and a stimulus feature (e.g., the color red) was associated with a higher chance of making false alarms. Yet, this study also showed that the goal of generating phenomenal consciousness is probably too difficult without explicit computational models like CRMN.…”

Section: Discussionmentioning

confidence: 99%

From internal models toward metacognitive AI

Kawato¹,

Cortese²

2021

Biol Cybern

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In several papers published in Biological Cybernetics in the 1980s and 1990s, Kawato and colleagues proposed computational models explaining how internal models are acquired in the cerebellum. These models were later supported by neurophysiological experiments using monkeys and neuroimaging experiments involving humans. These early studies influenced neuroscience from basic, sensory-motor control to higher cognitive functions. One of the most perplexing enigmas related to internal models is to understand the neural mechanisms that enable animals to learn large-dimensional problems with so few trials. Consciousness and metacognition—the ability to monitor one’s own thoughts, may be part of the solution to this enigma. Based on literature reviews of the past 20 years, here we propose a computational neuroscience model of metacognition. The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. In the prefrontal cortex, a distributed executive network called the “cognitive reality monitoring network” (CRMN) orchestrates conscious involvement of generative-inverse model pairs in perception and action. Based on mismatches between computations by generative and inverse models, as well as reward prediction errors, CRMN computes a “responsibility signal” that gates selection and learning of pairs in perception, action, and reinforcement learning. A high responsibility signal is given to the pairs that best capture the external world, that are competent in movements (small mismatch), and that are capable of reinforcement learning (small reward-prediction error). CRMN selects pairs with higher responsibility signals as objects of metacognition, and consciousness is determined by the entropy of responsibility signals across all pairs. This model could lead to new-generation AI, which exhibits metacognition, consciousness, dimension reduction, selection of modules and corresponding representations, and learning from small samples. It may also lead to the development of a new scientific paradigm that enables the causal study of consciousness by combining CRMN and decoded neurofeedback.

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“…, 2008b ) or using multiple brain decoders. This later possibility was notably explored in a previous experiment ( Knotts et al. , 2019 ).…”

Section: Future Applicationsmentioning

confidence: 94%

Conducting decoded neurofeedback studies

Taschereau-Dumouchel

Cortese²,

Lau

et al. 2020

Social Cognitive and Affective Neuroscience

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Abstract Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of machine learning approaches, it is now possible to target unconscious occurrences of specific multivoxel representations. In this tools of the trade paper, we discuss how to implement these interventions in rigorous double-blind placebo-controlled experiments. We aim to provide a step-by-step guide to address some of the most common methodological and analytical considerations. We also discuss tools that can be used to facilitate the implementation of new experiments. We hope that this will encourage more researchers to try out this powerful new intervention method.

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Multivoxel patterns for perceptual confidence are associated with false color detection

Cited by 5 publications

References 46 publications

Unconscious manipulation of conceptual representations with decoded neurofeedback impacts search behaviour

Unconscious manipulation of conceptual representations with decoded neurofeedback impacts search behaviour

From internal models toward metacognitive AI

Conducting decoded neurofeedback studies

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