Specific Contributions of Ventromedial, Anterior Cingulate, and Lateral Prefrontal Cortex for Attentional Selection and Stimulus Valuation

Functional clusters of neurons in the monkey prefrontal and anterior cingulate cortex are involved in guiding attention to the most valuable objects in a scene

The Current Limitations of Archaeomagnetic Testing Pertaining to the Authentication of Displaced and Unprovenanced Ceramics: An Examination for Archaeologists

The current state of archaeomagnetic testing, as applied to orphaned ceramic materials, has been assessed in this dissertation. More specifically, the combination of archaeointensity measurement and magnetic susceptibility testing has been examined as a prospective authentication method. Intended to elevate awareness and comprehension for archaeologists and museum professionals unfamiliar with archaeomagnetic testing, this study has been created in the hope that expanding accessibility will also expand the scope of its application. Interdisciplinary cooperation and research, fostered through increased access to excavation materials and data, will be integral to improving testing methods. In order to demonstrate the immense effort required to improve the ease and reliability of testing methods and the integrity of data models, the difficulties restricting the use of archaeomagnetic testing as a means of authentication have also been discussed in extent

CASCB Annual Report 2019/2020

CASCB Annual Reports are a review of accomplishments of the Centre for the Advanced Study of Collective Behaviour in research, engagement, infrastructure, membership, grants, and awards.publishe

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks

Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex

Microcircuits are composed of multiple cell classes that likely serve unique circuit operations. But how cell classes map onto circuit functions is largely unknown, particularly for primate prefrontal cortex during actual goal-directed behavior. One difficulty in this quest is to reliably distinguish cell classes in extracellular recordings of action potentials. Here we surmount this issue and report that spike shape and neural firing variability provide reliable markers to segregate seven functional classes of prefrontal cells in macaques engaged in an attention task. We delineate an unbiased clustering protocol that identifies four broad spiking (BS) putative pyramidal cell classes and three narrow spiking (NS) putative inhibitory cell classes dissociated by how sparse, bursty, or regular they fire. We speculate that these functional classes map onto canonical circuit functions. First, two BS classes show sparse, bursty firing, and phase synchronize their spiking to 3-7 Hz (theta) and 12-20 Hz (beta) frequency bands of the local field potential (LFP). These properties make cells flexibly responsive to network activation at varying frequencies. Second, one NS and two BS cell classes show regular firing and higher rate with only marginal synchronization preference. These properties are akin to setting tonically the excitation and inhibition balance. Finally, two NS classes fired irregularly and synchronized to either theta or beta LFP fluctuations, tuning them potentially to frequency-specific subnetworks. These results suggest that a limited set of functional cell classes emerges in macaque prefrontal cortex (PFC) during attentional engagement to not only represent information, but to subserve basic circuit operations