arXiv subjects

Liad Mudrik

Publications and source records attributed to Liad Mudrik.

4 recordsLinked to original sources

arXiv2026

Neural correlates of perceptual consciousness from within: a narrative review of human intracranial research

Despite many years of research, the quest to identify neural correlates of perceptual consciousness (NCC) remains unresolved. One major obstacle lies in methodological limitations: most studies rely on non-invasive neural measures with limited spatial or temporal resolution making it difficult to disentangle proper NCCs from concurrent cognitive processes. Additionally, the relatively low sensitivity of non-invasive neural measures limits the interpretation of null findings in studies targeting proper NCCs. In this review, we discuss how human intracranial recordings can advance the search for NCCs, by offering high spatiotemporal resolution, improved signal sensitivity, and broad cortical and subcortical coverage. We review studies that have examined NCCs at the level of single neurons and populations of neurons, and evaluate their implications on the debates between cognitive and sensory theories of consciousness. Finally, we highlight the limits of current intracranial human recordings and propose future directions based on emerging technologies and novel experimental paradigms.

q-bio.NC↗

arXiv2026

Indications of Belief-Guided Agency and Meta-Cognitive Monitoring in Large Language Models

Rapid advancements in large language models (LLMs) have sparked the question whether these models possess some form of consciousness. To tackle this challenge, Butlin et al. (2023) introduced a list of indicators for consciousness in artificial systems based on neuroscientific theories. In this work, we evaluate a key indicator from this list, called HOT-3, which tests for agency guided by a general belief-formation and action selection system that updates beliefs based on meta-cognitive monitoring. We view beliefs as representations in the model's latent space that emerge in response to a given input, and introduce a metric to quantify their dominance during generation. Analyzing the dynamics between competing beliefs across models and tasks reveals three key findings: (1) external manipulations systematically modulate internal belief formation, (2) belief formation causally drives the model's action selection, and (3) models can monitor and report their own belief states. Together, these results provide empirical support for the existence of belief-guided agency and meta-cognitive monitoring in LLMs. More broadly, our work lays methodological groundwork for investigating the emergence of agency, beliefs, and meta-cognition in LLMs.

cs.CL↗

arXiv2025

Time to adjust: Improving replicability in experimental psychology by adjustment for evident selective inference

The field of psychological sciences has been grappling with the replicability crisis. Various issues have been identified as potential sources of this problem. We bring to light a potential source that has largely been overlooked and demonstrate its significant contribution to the problem: the practice of multiple comparisons. We analyzed 88 papers from the Reproducibility Project in Psychology and found that multiple results are commonly reported in a single paper, ranging from 4 to 730 (M=77.7), without multiple comparison adjustments. We retroactively applied such an adjustment using a hierarchical FDR controlling procedure (TreeBH; Bogomolov et al., 2021). 21 of 88 results were deemed insignificant after adjustment. Twenty of these 21 results indeed failed to replicate, constituting over a third of the non-replicable findings, while maintaining 97% power. We propose that this should become a common practice as an essential means to increase replicability in experimental psychology.

stat.ME↗

arXiv2023

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.

cs.AI↗