Seth A. Herd

Learn More
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(More)
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(More)
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(More)
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(More)
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(More)
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(More)
This chapter provides a synthetic review of a long-term effort to produce an internally consistent theory of the neural basis of human cognition, the Leabra cognitive architecture, which explains a great deal of brain and behavioral data. In a highly influential commentary, Allen Newell (1973) first issued a call for a more comprehensive, principled(More)
We use a biologically grounded neural network model to investigate the brain mechanisms underlying individual differences specific to the selection and instantiation of representations that exert cognitive control in task switching. Existing computational models of task switching do not focus on individual differences and so cannot explain why task(More)
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(More)
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(More)