Cognitive Neuroscience最新文献

In the present study, we investigated whether differences in spatial working memory (SWM) abilities - assessed through the Corsi block task (CBT) - impact the processes of mental rotation (MR) engaged during a classic letter rotation task. Based on the median split of their scores in the CBT, participants were divided into a higher and a lower SWM group. Behavioral and electrophysiological data were recorded while participants completed the MR task and were compared across groups. Higher error rates were observed in individuals with lower than higher SWM scores, while no RT differences emerged. Systematic group differences were observed before and during the MR process of canonical letters. A delayed onset of the event-related potential (ERP) rotation-related negativity (RRN), a reliable psychophysiological marker for MR processes, was observed in the lower SWM group for all rotation angles, suggesting that a longer time is needed to generate a mental representation of familiar stimuli in individuals with lower SWM scores. Furthermore, a delayed RRN offset indicating the end of the MR process and longer RRN durations suggesting longer MR processes were found for letters with larger rotation angles (i.e. 120°, 150°) in individuals with lower SWM scores on canonical character trials. These observed group differences provided evidence for the debated issue of the interaction between SWM and MR, suggesting that SWM plays a role in both the initial phase to generate the mental representation of familiar objects and during the MR process, especially for larger angles.

A hallmark of human intelligence is the ability to infer abstract rules from limited experience and apply these rules to unfamiliar situations. This capacity is widely studied in the visual domain using the Raven's Progressive Matrices. Recent advances in deep learning have led to multiple artificial neural network models matching or even surpassing human performance. However, while humans can identify and express the rule underlying these tasks with little to no exposure, contemporary neural networks often rely on massive pattern-based training and cannot express or extrapolate the rule inferred from the task. Furthermore, most Raven's Progressive Matrices or Raven-like tasks used to train neural networks consist only of symbolic challenges, whereas humans can flexibly solve both symbolic and perceptual challenges. In this work, we present an algorithmic approach to rule detection and application using feature detection, affine transformation estimation and search. We applied our model to a simplified Raven's Progressives Matrices task, previously designed for behavioral testing and neuroimaging in humans. The model exhibited one-shot inference and achieved near human-level performance in the symbolic reasoning condition of the simplified task. Furthermore, the model can express the relationships discovered and generate multi-step predictions in accordance with the underlying rule. Finally, the model can handle perceptual challenges containing continuous patterns. We discuss our results and their relevance to studying abstract reasoning in humans, as well as their implications for improving intelligent machines.

Many procedures to correct for multiple comparisons in functional magnetic resonance imaging (fMRI) analysis require a minimum cluster-extent threshold; however, sample size (N) is often not modeled. In this study, a series of simulations was conducted where N was varied to determine whether this parameter affected cluster threshold. The primary hypothesis was that modeling N in the simulations would reduce cluster thresholds. A secondary hypothesis was that this cluster size reduction was due to between-subject variability, which was tested by eliminating the corresponding standard error term. Acquisition volume parameters were fixed, while key parameters were varied to reflect reasonable ranges: N (10, 20, or 30), corrected p-value (.05, .01, or .001), individual-voxel p-value (.01, .005, or .001), FWHM (3, 5, or 7 mm), and voxel resolution (2 or 3 mm). Each simulation consisted of 100 iterations repeated 100 times, with a total of 4,860,000 iterations and 66,420,000 simulated subjects. There was a significant effect of condition with clusters approximately 18% smaller with versus without N modeled and a significant increase in cluster thresholds for larger sample sizes. Bayesian analysis provided very strong support for the secondary hypothesis. These simulation results were replicated in a real fMRI data set. The present findings indicate that sample size should be incorporated into all methods to provide the most accurate thresholds possible and reduce type II error. A broader range of topics is discussed including balancing type I and type II error, and the assumption that non-task fMRI activity reflects null data is questioned.

The provocative review by Richie et al. (this issue) provides a platform for reflection on developing new experimental designs and data analysis methods. Here I offer support for their ideas, and add some additional considerations related to: (1) environmental image statistics, (2) multisensory experimentation, (3) embracing non-linearities in brain-body function and tackling data with non-linear analysis approaches; (4) perturbing mature cortical networks with Focused Ultrasound (FUS) or Transcranial Magnetic Stimulation (TMS) guided by functional magnetic resonance imaging (fMRI) activation; and (5) considering spatial scales and aberrant scaffolding in human development.

Decades of work demonstrate that the ventral temporal cortex (VTC) comprises category selective regions. Ritchie et al. urge a shift in perspective: new research should be grounded in behavioral relevance, not category selectivity. Here, we outline how leveraging, not shifting away from category selectivity, expands our understanding of brain function, complex cognition, and development. Further, while we agree that naturalistic paradigms will accelerate progress in this field, given category selectivity is central to VTC's information processing, we suggest future work to examine information transfer from VTC initial object recognition computation to other cortices for facilitating complex human behavior.

Ritchie and colleagues propose that the functional organization of higher visual cortex is best understood through the lens of behavioral relevance, advocating for a shift away from theories that center around category selectivity. Building on this, I suggest the statistical structure of visual inputs acts as an additional critical constraint on visual cortex, and that a complete understanding of visual system organization must account for input statistics and how they interact with behavioral relevance. I discuss this using cortical food selectivity as a case study, and additionally describe how deep neural networks can provide new avenues for testing these theories.

Ritchie et al. (this issue) argue that a deeper understanding of occipitotemporal cortex (OTC) requires shifting emphasis from category selectivity to behavioral relevance. They suggest that focusing on categories such as faces, bodies, or scenes is too narrow and overlooks how OTC supports flexible, goal-directed behavior. We agree that linking neural representations to behavior is essential but caution against treating category selectivity and behavioral relevance as opposing views. Category selectivity provides valuable insight into how cortical representations are organized to support behavior, and recent advances in computational modeling, particularly with deep neural networks, offer a powerful framework for probing this relationship.

When using novel tools with low semantic content, the left inferior-frontal-gyrus (IFG) plays a role. We argue that this activation is not purely specific to novel tool use but rather represents part of a cross-domain cognitive network supporting sequential planning processes. The IFG does not only manage information flow between distributed areas but functionally contributes by maintaining focus on the intended target state and supporting the processing, monitoring, and adjustment of steps needed to achieve that goal. These cognitive functions are particularly important when compensation for reduced tool-related semantic knowledge is needed during the usage of novel tools and technologies.

Ritchie et al. argue that the traditional framework of category selectivity has limited value for understanding the organization of the ventral visual stream and propose shifting focus toward behavioral relevance and examining vision under naturalistic task conditions. While I agree with many of their points, I expand on their discussion of category selectivity, as well as the drivers of ventral stream organization, from a nonhuman primate perspective.

The category selectivity model has shaped our understanding of the organization of object-related information in the occipitotemporal visual cortex (OTC). Ritchie et al. propose that OTC represents objects depending on the properties that are behaviorally relevant in a specific task/context, rather than by encoding the invariant visual properties to determine category membership. We consider this proposal in the context of recent developments that have extended the function of vision (and OTC) beyond object recognition, to include a representation of how objects relate to each other, a key piece of information for planning and acting toward behavioral goals.