Inhibiting unwanted thoughts, actions and emotions figures centrally in daily life, and the prefrontal cortex is widely viewed as a source of this inhibitory control. We argue that the function of the prefrontal cortex 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 critical for understanding the mechanisms of inhibition and how they can be impaired or improved.

† Randall C. O'Reilly and Dean Wyatte have contributed equally to this work.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. Keywords: object recognition, computational model, recurrent processing, feedback, winners-take-all mechanism INTRODUCTIONOne of the most salient features of the mammalian neocortex is the structure of its connectivity, which provides for many forms of recurrent processing, where neurons mutually influence each other through direct, bidirectional interactions. There are extensive bidirectional excitatory and inhibitory connections within individual cortical areas, and almost invariably, every area that receives afferent synapses from another area, also sends back efferent synapses in return (Felleman and Van Essen, 1991;Scannell et al., 1995;Sporns and Zwi, 2004;Sporns et al., 2007). We describe an explicit computational model (LVis -Leabra Vision) of the function of this recurrent architecture in the context of visual object recognition, demonstrating a synergy between the learning and processing benefits of recurrent connectivity.Recurrent processing, for example, has been suggested to be critical for solving certain visual tasks such as figure-ground segmentation (Hupe et al., 1998;Roelfsema et al., 2002;Lamme and Roelfsema, 2000), which requires integration of information from outside the classical receptive field. We demonstrate how recurrent excitatory processing could provide a similar function in visual occlusion, which requires the organization of image fragments that span multiple receptive fields into a logical whole Gestalt and involves the filling-in of missing visual information Lerner et al., 2002;Rauschenberger et al., 2006;Weigelt et al., 2...

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 switching abilities are separable from other executive function (EF) abilities (such as response inhibition). We explore hypotheses regarding neural mechanisms underlying the “Shifting-Specific” and “Common EF” components of EF proposed in the Unity/Diversity model (Miyake & Friedman, 2012) and similar components in related theoretical frameworks. We do so by adapting a well-developed neural network model of working memory (Prefrontal cortex, Basal ganglia Working Memory or PBWM; Hazy, Frank, & O’Reilly, 2007) to task switching and the Stroop task, and comparing its behavior on those tasks under a variety of individual difference manipulations. Results are consistent with the hypotheses that variation specific to task switching (i.e., Shifting-Specific) may be related to uncontrolled, automatic persistence of goal representations, whereas variation general to multiple EFs (i.e., Common EF) may be related to the strength of PFC representations and their effect on processing in the remainder of the cognitive system. Moreover, increasing signal to noise ratio in PFC, theoretically tied to levels of tonic dopamine and a genetic polymorphism in the COMT gene, reduced Stroop interference but increased switch costs. This stability-flexibility tradeoff provides an explanation for why these two EF components sometimes show opposing correlations with other variables such as attention problems and self-restraint.

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 model's 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.