Seth A. Herd

A unified framework for inhibitory control

Inhibiting unwanted thoughts, actions and emotions figures centrally in daily life, and the prefrontal cortex (PFC) is widely viewed as a source of this inhibitory control. We argue that the function of the PFC is best understood in terms of representing and actively maintaining abstract information, such as goals, which produces two types of inhibitory effects on other brain regions. Inhibition of some subcortical regions takes a directed global form, with prefrontal regions providing contextual information relevant to when to inhibit all processing in a region. Inhibition within neocortical (and some subcortical) regions takes an indirect competitive form, with prefrontal regions providing excitation of goal-relevant options. These distinctions are crucial for understanding the mechanisms of inhibition and how they can be impaired or improved.

Neural Mechanisms of Cognitive Control: An Integrative Model of Stroop Task Performance and fMRI Data

We address the connection between conceptual knowledge and cognitive control using a neural network model. This model extends a widely held theory of cognitive control [Cohen, J. D., Dunbar, K., & McClelland, J. L. On the control of automatic processes: A parallel distributed processing model of the Stroop effect. Psychological Review, 97, 332-361, 1990] so that it can explain new empirical findings. Leveraging other computational modeling work, we hypothesize that representations used for task control are recruited from preexisting representations for categories, such as the concept of color relevant to the Stroop task we model here. This hypothesis allows the model to account for otherwise puzzling fMRI results, such as increased activity in brain regions processing to-be-ignored information. In addition, biologically motivated changes in the models pattern of connectivity show how global competition can arise when inhibition is strictly local, as it seems to be in the cortex. We also discuss the potential for this theory to unify models of task control with other forms of attention.

Computational models of cognitive control

Cognitive control refers to the ability to perform task-relevant processing in the face of other distractions or other forms of interference, in the absence of strong environmental support. It depends on the integrity of the prefrontal cortex and associated biological structures (e.g., the basal ganglia). Computational models have played an influential role in developing our understanding of this system, and we review current developments in three major areas: dynamic gating of prefrontal representations, hierarchies in the prefrontal cortex, and reward, motivation, and goal-related processing in prefrontal cortex. Models in these and other areas are advancing the field further forward.

Recurrent Processing during Object Recognition.

How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of naturally occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain’s visual system and suggest ways in which object recognition can be understood in terms of interactions within and between processes over time.

From an executive network to executive control: A computational model of the n-back task

A paradigmatic test of executive control, the n-back task, is known to recruit a widely distributed parietal, frontal, and striatal “executive network,” and is thought to require an equally wide array of executive functions. The mapping of functions onto substrates in such a complex task presents a significant challenge to any theoretical framework for executive control. To address this challenge, we developed a biologically constrained model of the n-back task that emergently develops the ability to appropriately gate, bind, and maintain information in working memory in the course of learning to perform the task. Furthermore, the model is sensitive to proactive interference in ways that match findings from neuroimaging and shows a U-shaped performance curve after manipulation of prefrontal dopaminergic mechanisms similar to that observed in studies of genetic polymorphisms and pharmacological manipulations. Our model represents a formal computational link between anatomical, functional neuroimaging, genetic, behavioral, and theoretical levels of analysis in the study of executive control. In addition, the model specifies one way in which the pFC, BG, parietal, and sensory cortices may learn to cooperate and give rise to executive control.

Assembling old tricks for new tasks: A neural model of instructional learning and control

We can learn from the wisdom of others to maximize success. However, it is unclear how humans take advice to flexibly adapt behavior. On the basis of data from neuroanatomy, neurophysiology, and neuroimaging, a biologically plausible model is developed to illustrate the neural mechanisms of learning from instructions. The model consists of two complementary learning pathways. The slow-learning parietal pathway carries out simple or habitual stimulus–response (S-R) mappings, whereas the fast-learning hippocampal pathway implements novel S-R rules. Specifically, the hippocampus can rapidly encode arbitrary S-R associations, and stimulus-cued responses are later recalled into the basal ganglia-gated pFC to bias response selection in the premotor and motor cortices. The interactions between the two model learning pathways explain how instructions can override habits and how automaticity can be achieved through motor consolidation.

The Role of Competitive Inhibition and Top-Down Feedback in Binding during Object Recognition

How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain’s binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. Top-down feedback contributes to binding in a similar manner, but by reinforcing relevant features. Together, inhibition and top-down feedback contribute to a competitive environment that ensures only the most appropriate features are bound together. We demonstrate this overall proposal using a biologically realistic neural model of vision that processes features across a hierarchy of interconnected brain areas. Finally, we argue that temporal synchrony plays only a limited role in binding – it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features. Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes.

Serial visual search from a parallel model

We tested a parallel neural network model of visual search, and found that it located targets more quickly when allowed to take several fast guesses. We suggest that this serially iterated parallel search may be the mode used by the visual system, in accord with theories such as the Guided Search model. Furthermore, in our model the most efficient mode of processing varied with the type of search. If the nature of visual search varies with task demands, seemingly contradictory findings can be reconciled.

Strategic cognitive sequencing: a computational cognitive neuroscience approach

We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

Generalization of figure-ground segmentation from binocular to monocular vision in an embodied biological brain model

Monocular figure-ground segmentation is an important problem in the field of Artificial General Intelligence. A solution to this problem will unlock vast sets of training data, such as Google Images, in which salient objects of interest are situated against complex backgrounds. In order to gain traction on the figure-ground problem we enhanced the Leabra Vision (LVis) model, which is our state-of-the-art model of 3D invariant object recognition [8], such that it can continue to recognize objects against cluttered backgrounds that, while simple, are complex enough to substantially hurt object recognition performance. The principle of operation of the network is that it learns to use a low resolution view of the scene in which high spatial frequency information such as the background falls out of focus in order to predict which aspects of the high resolution scene are the figure. This filtered view then serves to enhance the figure in the input stages of LVis and substantially improves object recognition performance against cluttered backgrounds.