Journal of Cognitive Neuroscience最新文献

Nonhuman primate studies traditionally use two or three animals. We previously used standard statistics to argue for using either one animal, for an inference about that sample, or five or more animals, for a useful inference about the population. A recently proposed framework argued for testing three animals and accepting the outcome found in the majority as the outcome that is most representative for the population. The proposal tests this framework under various assumptions about the true probability of the representative outcome in the population, that is, its typicality. On this basis, it argues that the framework is valid across a wide range of typicalities. Here, we show (1) that the error rate of the framework depends strongly on the typicality of the representative outcome; (2) that an acceptable error rate requires this typicality to be very high (87% for a single type of outlier), which actually renders empirical testing beyond a single animal obsolete; and (3) that moving from one to three animals decreases error rates mainly for typicality values of 70%-90% and much less for both lower and higher values. Furthermore, we use conjunction analysis to demonstrate that two out of three animals with a given outcome only allow to infer a lower bound to typicality of 9%, which is of limited value. Thus, the use of two or three animals does not allow a useful inference about the population, and if this option is nevertheless chosen, the inferred lower bound of typicality should be reported.

Human navigation heavily relies on visual information. Although many previous studies have investigated how navigational information is inferred from visual features of scenes, little is understood about the impact of navigational experience on visual scene representation. In this study, we examined how navigational experience influences both the behavioral and neural responses to a visual scene. During training, participants navigated in the virtual reality (VR) environments, which we manipulated navigational experience while holding the visual properties of scenes constant. Half of the environments allowed free navigation (navigable), while the other half featured an "invisible wall" preventing the participants to continue forward even though the scene was visually navigable (nonnavigable). During testing, participants viewed scene images from the VR environment while completing either a behavioral perceptual identification task (Experiment 1) or an fMRI scan (Experiment 2). Behaviorally, we found that participants judged a scene pair to be significantly more visually different if their prior navigational experience varied, even after accounting for visual similarities between the scene pairs. Neurally, multivoxel pattern of the parahippocampal place area distinguished visual scenes based on prior navigational experience alone. These results suggest that the human visual scene cortex represents information about navigability obtained through prior experience, beyond those computable from the visual properties of the scene. Taken together, these results suggest that scene representation is modulated by prior navigational experience to help us construct a functionally meaningful visual environment.

Whenever a perceived event violates expectations compared with an expected event, the cortical response to this event tends to be augmented. The increase in cortical responses signals a mismatch between expectation and observation. Mismatch patterns of neural activity have been repeatedly observed in adults, but their emergence and evolution in infancy are not well understood, since most prediction-inducing paradigms require learning the association or rule underpinning the expectation, thus conflating the violation response with the ability to learn. To address this shortcoming, this article reports a neuroimaging study with 2- to 6-month-olds that measured neural responses to the colocation (expected or matched) or dislocation (deviant or mismatch) of sounds and visual events. The results indicated that even in early infancy, the brain is sensitive to violations of expectation: Stimuli that deviated from expectation elicited stronger neural responses in these infants' sensory cortices than expected stimuli.

This study investigates intelligence-related differences in the adjustment of brain activity and connectivity to varying cognitive demands, testing for a moderation of an association between intelligence and neural efficiency by task difficulty. In 72 young adults (34 female, 38 male), fMRI brain activity changes during a decision-making task with five levels of difficulty were related to intelligence scores from a nonverbal matrix reasoning test. In frontoparietal, subcortical, and cerebellar regions activated during task processing, we observed smaller increases in brain activity in more intelligent participants-independent of task difficulty. However, in two regions of the default mode network, dorsomedial prefrontal cortex and left angular gyrus, more intelligent participants showed greater decreases in activity with increasing task difficulty. Furthermore, with increasing difficulty, more intelligent participants showed greater increases in functional connectivity of dorsomedial prefrontal cortex and angular gyrus. These findings suggest a more dynamic adjustment of neural processing to varying cognitive demands in more intelligent individuals. Particularly when it comes to more difficult tasks, more intelligent people seem to be better able to down-regulate activity in the brain's default mode network. Due to the relatively small sample size, these findings must be considered preliminary. While their interpretation should therefore be treated with caution, they suggest conceptually new avenues for replication in larger samples. As far as the observed processes reflect the suppression of task-unrelated neural processing and a better focus on the task at hand, they can potentially explain the general performance advantage of more intelligent individuals across various cognitive tasks.

Statistical learning (SL) is a powerful mechanism that supports the ability to extract regularities from environmental input. Yet, its neural underpinnings are not well understood. Previous EEG studies of SL have found that the brain tracks regularities by synchronizing its activity with the presented stimuli-a phenomenon known as neural entrainment. However, EEG lacks the spatial resolution to unveil the specific brain regions where this process takes place. In our study, 18 patients with drug-resistant epilepsy who were implanted with intracranial electrodes for presurgical investigation listened to a continuous speech stream containing embedded trisyllabic words. Neural entrainment was measured at the syllable and word frequencies, with the latter providing an online index of learning. SL was further assessed through both explicit and implicit behavioral measures. Behaviorally, we found evidence of learning at the group level in both tasks. At the neural level, our analyses revealed three temporal tuning profiles: 25% of contacts showed entrainment at the syllable frequency, 11% of contacts showed entrainment at both the word and syllable frequencies, and 4% showed entrainment only to the word frequency. Word entrainment, indicating sensitivity to word structures, was most commonly found in auditory and language-related regions, including insula, middle temporal gyrus, superior temporal gyrus, and supramarginal gyrus. In contrast, evidence for neural entrainment in the hippocampus was weak. Overall, these results support the idea that speech-based SL is largely supported by modality-specific brain regions.

Our minds frequently drift from the task at hand to other mental content, a process commonly referred to as mind-wandering. Task focus typically leads to high-quality encoding of task events, whereas mind-wandering tends to result in low-quality encoding. This study conducted a meta-analysis of fMRI studies comparing high-quality and low-quality encoding to explore the neural correlates of mind-wandering. Key findings show that activation during mind-wandering is closely associated with four specific subnetworks: Default Mode Network-A, Frontoparietal Network-B and -C, and Ventral Attention Network-B. In contrast, deactivation primarily occurs within Dorsal Attention Network-A, Frontoparietal Network-A, and Default Mode Network-B and -C. These findings offer empirical support for several prominent theoretical accounts of mind-wandering, including those emphasizing internal cognition, perceptual decoupling, executive control (both failure and engagement), and reduced filtering. These results highlight the importance of a fine-grained, network-based approach to understanding the complex neural dynamics of mind-wandering.

We mentally represent all kinds of objects across a variety of tasks and source modalities (i.e., mental objects). Recent work has proposed that mental objects are represented by content-free, reassignable pointers (or indexicals, or tokens) in our moment-to-moment processing. Are all mental objects represented by the same set of pointers? If not, where should we draw the lines between different kinds of pointers? In this Perspective, we propose a novel research program aiming at unraveling the neural taxonomy of mental objects by testing how the neural markers for pointers generalize across different paradigms, task goals, source modalities, and more.

Humans complete different types of sequences as a part of everyday life. These sequences can be divided into two important categories: those that are abstract, in which the steps unfold according to a rule at super-second to minute time scale, and those that are motor, defined solely by individual movements and their order that unfold at the subsecond to second timescale. For example, the sequence of making spaghetti consists of abstract tasks (preparing the sauce and cooking the noodles) and nested motor actions (stir pasta water). Previous work shows neural activity increases (ramps) in the rostrolateral prefrontal cortex (RLPFC) during abstract sequence execution. During motor sequence production, activity occurs in regions of PFC. However, it remains unknown if ramping is a signature of motor sequence production as well or solely an attribute of abstract sequence monitoring and execution. We tested the hypothesis that significant ramping activity occurs during motor sequence production in the RLPFC. Contrary to our hypothesis, we did not observe significant ramping activity in the RLPFC during motor sequence production, but we found significant ramping activity in bilateral inferior parietal cortex, in regions distinct from those observed during an abstract sequence task. Our results suggest different prefrontal-parietal circuitry may underlie abstract versus motor sequence execution.