Phenomenal consciousness: its scope and limits In the history of life, consciousness of sensory states with ‘phenomenal properties’—the basis of ‘sentience’—is, arguably, a late evolutionary development, which occurred long after conscious access to a ‘global mental workspace’ had become widely established as a strategy for cognitive information processing. In this article, I focus on phenomenal consciousness. I propose a step-by-step sequence by which the mental representation of sensory stimulation could have acquired phenomenal content through small changes in the brain. Also—addressing the question of evolutionary function—I point to the crucial psychological benefits to an animal of having a ‘phenomenally conscious self’. A thread running through the article is the phenomenon of ‘blindsight’, which I take to be a model for the non-phenomenal cognition that characterizes the majority of insentient animal species.
Meditation induces shifts in neural oscillations, brain complexity, and critical dynamics: novel insights from MEG While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We examined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open-monitoring technique. By combining source-space magnetoencephalography with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity, and criticality in meditation. In addition to power spectral density, we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel–Ziv complexity, 1/f slope, Higuchi fractal dimension, and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, alongside widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence likely due to the correction of the power spectrum by the 1/f slope, which could reduce potential confounds from broadband 1/f activity. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open-monitoring meditation practices.
Humans Select Subgoals That Balance Immediate and Future Cognitive Costs During Physical Assembly From building a new piece of furniture to replacing a lightbulb, people must often figure out how to assemble an object from its parts. Although these physical assembly problems take on many different forms, they also pose common challenges. Chief among these is the question of how to break a complex problem down into subproblems that are easier to solve. What principles determine why some strategies for decomposing a problem are favored over others? Here, we investigate the decisions that people make when considering different visual subgoals in the context of attempting to build a series of virtual block towers. We hypothesized that people favor subgoals achieving a balance between how much progress the subgoals would help achieve toward the final goal and how effortful they would be to solve. We tested this hypothesis by defining several computational models of planning and subgoal selection, then evaluating how well these models predicted human planning and subgoal selection behavior on the same problems. Our results suggest that participants rapidly differentiated the computational costs of otherwise similarly ambitious subgoals, and used these judgments to drive subgoal selection. Moreover, our findings are consistent with the possibility that participants were not only sensitive to the immediate computational costs associated with solving the very next subgoal, but also future costs that might be incurred when attempting the rest of the problem. Taken together, these results contribute to our understanding of how humans make efficient use of cognitive resources to solve complex, grounded planning problems.
Executive Resources Shape the Impact of Language Predictability Across the Adult Lifespan Humans routinely anticipate upcoming language, but whether such predictions come at a cognitive cost remains debated. In this study, we demonstrate the resource-dependent nature of predictive mechanisms in language comprehension across the lifespan: Experimentally limiting executive resources through a concurrent task reduces the effect of language predictability on reading time. Participants (N=175, replication N=96) read short articles presented word-by-word while completing a secondary font colour n-back task, thus varying cognitive demand. Language predictability was indexed by word surprisal as derived from a pre-trained large language model (GPT-2). Across two independent samples, our findings reveal that language predictions are not cost-free: They draw on executive control resources, and this dependency becomes more pronounced with age (18–85 years). These results help resolve the debate over cognitive demands in language comprehension and highlight prediction as a dynamic, resource-dependent process across the lifespan.
The role of attention in multi attribute decision making Real-life decisions typically involve multiple options, each with multiple attributes affecting value. In such complex situations, sequential shifts of attention to specific options and attributes are thought to guide the decision process. Using a task that allowed us to monitor attention during such multi-attribute decisions, we recorded decision-related signals in pre-supplementary motor area neurons from two male macaques. Attention influences activity in these neurons through two mechanisms. First, attention enhances the activity of neurons representing the currently sampled option, independent of its value, without fully suppressing the representation of other options. Second, attention up-regulates the gain of information integration towards the evolving value estimate for the attended option. In contrast, we found no evidence for a third suggested mechanism, in which only the attended option is represented. Instead, attention influences the ongoing parallel information accumulation and competition process by modulating the strength of the value information that drives this circuit.
Selective Attention Shapes Neural Representations of Complex Auditory Scenes: The Roles of Object Identity and Scene Composition Everyday auditory scenes contain overlapping sound objects, requiring attention to isolate relevant objects from irrelevant background objects. This study examined how selective attention shapes neural representations of complex sound scenes in the auditory cortex (AC). Using functional magnetic resonance imaging, we recorded brain activity from participants (12 males, 8 females) as they attended to a designated object in scenes comprising three overlapping sounds. Scenes were constructed in two manners: one where each object belonged to a different category (speech, animal, instrument) and another where all objects were from the same category. Attending to speech enhanced activations in lateral AC subfields, while attention to animal and instrument sounds preferentially modulated medial AC subfields, supporting models where attention modulates feature-selective neural gain in AC. Remarkably, however, spatial pattern analysis revealed that the attended object dominated the AC activation patterns of the entire scene in a manner depending on both object type and scene composition: When scene objects belonged to different categories, attention effects were dominated by category-level processing. In contrast, when all scene objects shared the same category, dominance shifted to exemplar-level processing in fields processing acoustic features. Thus, attention seems to dynamically prioritize the features offering maximal contrast within a given context, emphasizing object-specific patterns in feature-similar scenes and category-level patterns in feature-diverse scenes. Our results support models where top-down signals not only modulate gain but also affect scene decomposition and analysis—influencing stream segregation and gating of higher-level processing in a contextual manner, adapting to specific auditory environments.
Fluctuations in Sequential Many-Alternative Decisions Reveal Strategies Beyond Immediate Reward Maximisation Humans are strategic animals. We constantly make prospective choices, allocating limited resources in situations of uncertain, future outcomes. The management of our finite monthly budget, financial investments, or the allocation of time to the different questions in an exam are just a few examples. In these scenarios, both decision-making and resource allocation tend to fluctuate over time even under invariable set of constraints. However, it is unclear whether these fluctuations affect performance and whether they underlie additional objectives beyond pure reward maximisation. We address these questions using the breadth-depth dilemma, a novel ecological protocol where participants engage in sequential multiple-choice scenarios characterised by limited capacity. We designed two experimental environments. In one environment, optimal performance, formalised with an ideal allocator model, is associated with homogeneous resource allocation across consecutive choices. In contrast, the other environment entails that fluctuating resource allocation leads to greater expected rewards. Our study evaluates participants’ adherence to these scenarios and measures fluctuations as deviation from homogeneous allocations. The results revealed that participants’ behaviour fluctuates more than optimal, but critically, behavioural fluctuations adapt to the available capacity and the environmental context. Moreover, our findings unveil pronounced sequential strategies, such as save-for-later and reward history-dependent choice, further implying that these strategies contribute to decision variability. An extension of the optimal allocator model demonstrates that the characteristic excess fluctuations facilitate better-informed future choices (information gain), reduce uncertainty (risk avoidance), and generate diverse potential strategies (entropy seeking). Although having a modest impact on performance, these strategies may reflect advantageous behaviours in the long run under ever changing real-world environments.
Exploiting correlations across trials and behavioral sessions to improve neural decoding Traditional neural decoders link neural activity to behavior within single trials of a session, overlooking correlations across trials and sessions. However, animals show similar neural patterns when performing the same task, and their behaviors are influenced by prior experiences. To capture these dependencies, we introduce two complementary models: a multi-session reduced-rank regression model that shares behaviorally relevant neural structure across sessions and a multi-session state-space model that captures behavioral structure across trials and sessions. On 433 sessions spanning 270 brain regions in the International Brain Laboratory (IBL) mouse Neuropixels dataset, our decoders outperform traditional approaches on four behaviors, with results generalizing across datasets, species, and tasks. Unlike deep learning methods, our models are efficient and interpretable, providing low-dimensional neural representations, task-related single-neuron contributions, and brain-wide timescales of neural activation.