Prefrontal Cortex Codes Representations of Target Identity and Feature Uncertainty.

Many objects in the real world have features that vary over time, creating uncertainty in how they will look in the future. This uncertainty makes statistical knowledge about the likelihood of features critical to attention demanding processes such as visual search. However, little is known about how the uncertainty of visual features is integrated into predictions about search targets in the brain. In the current study, we test the idea that regions prefrontal cortex code statistical knowledge about search targets before the onset of search. Across 20 human participants (13 female; 7 male), we observe target identity in the multivariate pattern and uncertainty in the overall activation of dorsolateral prefrontal cortex (DLPFC) and inferior frontal junction (IFJ) in advance of the search display. This indicates that the target identity (mean) and uncertainty (variance) of the target distribution are coded independently within the same regions. Furthermore, once the search display appears the univariate IFJ signal scaled with the distance of the actual target from the expected mean, but more so when expected variability was low. These results inform neural theories of attention by showing how the prefrontal cortex represents both the identity and expected variability of features in service of top-down attentional control.SIGNIFICANCE STATEMENT Theories of attention and working memory posit that when we engage in complex cognitive tasks our performance is determined by how precisely we remember task-relevant information. However, in the real world the properties of objects change over time, creating uncertainty about many aspects of the task. There is currently a gap in our understanding of how neural systems represent this uncertainty and combine it with target identity information in anticipation of attention demanding cognitive tasks. In this study, we show that the prefrontal cortex represents identity and uncertainty as unique codes before task onset. These results advance theories of attention by showing that the prefrontal cortex codes both target identity and uncertainty to implement top-down attentional control.

Neural mechanisms of credit assignment for delayed outcomes during contingent learning.

Adaptive behavior in complex environments critically relies on the ability to appropriately link specific choices or actions to their outcomes. However, the neural mechanisms that support the ability to credit only those past choices believed to have caused the observed outcomes remain unclear. Here, we leverage multivariate pattern analyses of functional magnetic resonance imaging (fMRI) data and an adaptive learning task to shed light on the underlying neural mechanisms of such specific credit assignment. We find that the lateral orbitofrontal cortex (lOFC) and hippocampus (HC) code for the causal choice identity when credit needs to be assigned for choices that are separated from outcomes by a long delay, even when this delayed transition is punctuated by interim decisions. Further, we show when interim decisions must be made, learning is additionally supported by lateral frontopolar cortex (FPl). Our results indicate that FPl holds previous causal choices in a "pending" state until a relevant outcome is observed, and the fidelity of these representations predicts the fidelity of subsequent causal choice representations in lOFC and HC during credit assignment. Together, these results highlight the importance of the timely reinstatement of specific causes in lOFC and HC in learning choice-outcome relationships when delays and choices intervene, a critical component of real-world learning and decision making.

Attentional priority is determined by predicted feature distributions.

Visual attention is often characterized as being guided by precise memories for target objects. However, real-world search targets have dynamic features that vary over time, meaning that observers must predict how the target could look based on how features are expected to change. Despite its importance, little is known about how target feature predictions influence feature-based attention, or how these predictions are represented in the target template. In Experiment 1 (N = 60 university students), we show observers readily track the statistics of target features over time and adapt attentional priority to predictions about the distribution of target features. In Experiments 2a and 2b (N = 480 university students), we show that these predictions are encoded into the target template as a distribution of likelihoods over possible target features, which are independent of memory precision for the cued item. These results provide a novel demonstration of how observers represent predicted feature distributions when target features are uncertain and show that these predictions are used to set attentional priority during visual search. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Neural mechanisms of credit assignment for inferred relationships in a structured world.

Animals abstract compact representations of a task's structure, which supports accelerated learning and flexible behavior. Whether and how such abstracted representations may be used to assign credit for inferred, but unobserved, relationships in structured environments are unknown. We develop a hierarchical reversal-learning task and Bayesian learning model to assess the computational and neural mechanisms underlying how humans infer specific choice-outcome associations via structured knowledge. We find that the medial prefrontal cortex (mPFC) efficiently represents hierarchically related choice-outcome associations governed by the same latent cause, using a generalized code to assign credit for both experienced and inferred outcomes. Furthermore, the mPFC and lateral orbitofrontal cortex track the current "position" within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance of both tracking the current position in an abstracted task space and efficient, generalizable representations in the prefrontal cortex for supporting flexible learning and inference in structured environments.

Memory precision for salient distractors decreases with learned suppression.

Attention operates as a cognitive gate that selects sensory information for entry into memory and awareness (Driver, 2001, British Journal of Psychology, 92, 53-78). Under many circumstances, the selected information is task-relevant and important to remember, but sometimes perceptually salient nontarget objects will capture attention and enter into awareness despite their irrelevance (Adams & Gaspelin, 2020, Attention, Perception, & Psychophysics, 82[4], 1586-1598). Recent studies have shown that repeated exposures with salient distractor will diminish their ability to capture attention, but the relationship between suppression and later cognitive processes such as memory and awareness remains unclear. If learned attentional suppression (indicated by reduced capture costs) occurs at the sensory level and prevents readout to other cognitive processes, one would expect memory and awareness to dimmish commensurate with improved suppression. Here, we test this hypothesis by measuring memory precision and awareness of salient nontargets over repeated exposures as capture costs decreased. Our results show that stronger learned suppression is accompanied by reductions in memory precision and confidence in having seen a color singleton at all, suggesting that such suppression operates at the sensory level to prevent further processing of the distractor object.

The orbital frontal cortex, task structure, and inference.

The orbital frontal cortex (OFC) has long been linked to goal-directed, flexible behaviors. Recent evidence suggests the OFC plays key roles in representing the abstracted structure of task spaces, and using this representation for flexible inferences during both learning and choice. Here, we review convergent evidence from studies in animal models and humans in support of this view. We begin by considering early accounts of OFC function, then discuss how more recent evidence supports theories that have re-cast OFC's function as representing the structure of a task or environment for flexible inference. Finally, we turn to neural recording studies that provide insights into the underlying representations and computations the OFC may implement in coordination with other brain areas. (PsycInfo Database Record (c) 2021 APA, all rights reserved).