Alexander and Brown show how frontal lobe function can be explained by a hierarchical stack of a computational motif based on predictive coding.
Alexander, W. H., & Brown, J. W. (2018). Frontal cortex function as derived from hierarchical predictive coding. Scientific reports, 8(1), 3843.
Dopamine alters the neural oscillations associated with executive functions but leave sensory-related evoked potential unchanged.
Ott, T., Westendorff, S., & Nieder, A. (2018). Dopamine Receptors Influence Internally Generated Oscillations during Rule Processing in Primate Prefrontal Cortex. Journal of cognitive neuroscience, (Early Access), 1-15.
Martínez-Vázquez and Gail show different channels of influence in different frequency bands between frontal and parietal cortex.
Martínez-Vázquez, P., & Gail, A. (2018). Directed Interaction Between Monkey Premotor and Posterior Parietal Cortex During Motor-Goal Retrieval from Working Memory. Cerebral Cortex.
A new addition to the proposed circuitry for top-down control.
White, M. G., Panicker, M., Mu, C., Carter, A. M., Roberts, B. M., Dharmasri, P. A., & Mathur, B. N. (2018). Anterior Cingulate Cortex Input to the Claustrum Is Required for Top-Down Action Control. Cell reports, 22(1), 84-95.
A review by Mike Halassa and Sabine Kastner about our emerging understanding of the role of the thalamus in cognitive control.
Halassa, M. M., & Kastner, S. (2017). Thalamic functions in distributed cognitive control. Nature Neuroscience, 20(12), 1669.
The Dean of Prefrontal Cortex, Joaquin Fuster, breaks down prefrontal function along three lines: Executive attention, working memory, and decision-making.
Fuster, J. M. (2017). Prefrontal Executive Functions Predict and Preadapt. In Executive Functions in Health and Disease(pp. 3-19).
Randoph Helfrich and Robert Knight review evidence that the infrastructure of cognitive control is rhythmic. The general idea is that the prefrontal cortex controls large-scale oscillatory dynamics in the cortex and subcortex. But there is much more. Do yourself a favor: Read it.
Helfrich, R. F., & Knight, R. T. (2016). Oscillatory Dynamics of Prefrontal Cognitive Control. Trends in Cognitive Sciences.
Woolgar et al show preferential engagement of human frontoparietal networks with an increase in the complexity of task rules. Plus, the frontoparietal cortex adjusts representations to make rules that are more behavioral confusable easier to discriminate.
Goal-direction and top-down control
Timothy J. Buschman and Earl K. Miller
We review the neural mechanisms that support top-down control of behavior. We suggest that goal-directed behavior utilizes two systems that work in concert. A basal ganglia-centered system quickly learns simple, fixed goal-directed behaviors while a prefrontal cortex-centered system gradually learns more complex (abstract or long-term) goal-directed behaviors. Interactions between these two systems allows top-down control mechanisms to learn how to direct behavior towards a goal but also how to guide behavior when faced with a novel situation.
Ibos and Freedman show that area LIP is more than just space and spatial attention. They trained monkeys to make decisions based on conjunctions of motion and color. LIP neurons integrated color and motion when it was task-relevant.
Botvinick and Cohen provide a very nice overview of where computational modeling of executive control has been and where it is going.
This review examines evidence for a neurobiological explanation of executive functions of working memory. We suggest that executive control stems from information about task rules acquired by mixed selective, adaptive coding, multifunction neurons in the prefrontal cortex. Their output dynamically links the cortical-wide networks needed to complete the task. The linking may occur via synchronizing of neural rhythms, which may explain why we have a limited capacity for simultaneous thought.
Eiselt and Nieder trained monkeys to make greater/less than judgments to line lengths and dot numerosities. They compared neural activity in the prefrontal cortex (PFC), anterior cingulate (AC), and premotor cortex (PMC). The greatest proportion of greater/less than rule neurons were found in the PFC. Further, only the PFC had neurons that were “generalists”; they signaled the greater/less than rules for both judgments. Neurons in other areas were specialized for one judgment or the other.
This is consistent with our work showing that a large proportion of PFC neurons are multifunction, mixed selectivity neurons. They may be key in providing the computational power for complex, flexible behavior. For further reading see:
Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. (2013) “The importance of mixed selectivity in complex cognitive tasks”. Nature, 497, 585-590, doi:10.1038/nature12160. View PDF
Cromer, J.A., Roy, J.E., and Miller, E.K. (2010) Representation of multiple, independent categories in the primate prefrontal cortex. Neuron, 66: 796-807. View PDF »
Ardid and Wang propose a model for task switching in which a weak rule signal provides a small bias that is dramatically amplified by reverberating dynamics in neural circuits. This leads to complete reconfiguration of sensory to motor mapping. It seems to explain many observations in the extant literature. Rules signals are often weak (but ubiquitous in frontal cortex), yet somehow manage to gain control over behavior
Rouhinen et al provide evidence for the role of neural oscillations in the limitations of cognitive capacity. Subjects tracked multiple objects. Strength of oscillations were different preceding detected vs undetected objects. Suppression of low-frequency oscillations (<20 Hz) and strengthening of high-frequency oscillations (>20 Hz) in the frontoparietal cortex was correlated with attentional load. Load-dependent strengthening of 20-90 Hz oscillations was predictive of individual capacity. This supports hypotheses that oscillations play major role in attention and are responsible for the limited bandwidth of cognition.
Further reading on attention, capacity, and oscillations:
- Buschman,T.J. and Miller, E.K. (2010) Shifting the Spotlight of Attention: Evidence for Discrete Computations in Cognition. Frontiers in Human Neuroscience. 4(194): 1-9. View PDF »
- Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
- Buschman, T.J. and Miller, E.K. (2009) Serial, covert, shifts of attention during visual search are reflected by the frontal eye fields and correlated with population oscillations. Neuron, 63: 386-396. View PDF »
- Buschman, T.J. and Miller, E.K. (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 315: 1860-1862 The Scientist’s “Hot Paper” for October 2009. View PDF »
- Siegel, M., Warden, M.R., and Miller, E.K. (2009) Phase-dependent neuronal coding of objects in short-term memory. Proceedings of the National Academy of Sciences, 106: 21341-21346. View PDF »
Nee and Jonides argue that short-term memory (STM) is not monolithic, but instead involves multiple processes with different characteristics. There are frontal selection mechanisms (normally associated with attention), medial temporal binding mechanisms (associated with long-term memory) and synaptic plasticity. As a result, STM involves a single representation that can be focused on, a set of active representations that focused can be switched to, and passive long-term memory representations with residual traces that can be easily activated. The authors show how this model can explain many discrepancies across studies.
Everybody agrees that we can only hold a few things in mind simultaneously. However, there is disagreement about why. One theory is that limited cognitive resources are flexible and spread among the items held in mind; the more items, the “thinner” the information about each. Another theory is more of a fixed limit model: Resources are allocated in a discrete fashion and there is a fixed number of items that can be held in mind. Ester et al provide evidence for the latter, fixed, model. Subjects monitored a number of locations and then asked details about one of the locations. The subject’s performance and neural data was best described by a fixed limit model.
Our work with Stefano Fusi’s Lab makes The Wall Street Journal.
Peters et al used functional imaging in humans to examine the effects of the contents of working memory on extrastriate visual cortex. Subjects performed a visual search task. The target item in working memory enhanced processing of a matching visual input whereas other “accessory” items held in working memory suppressed extrastriate activity. These dual effects may help focus on relevant tasks while avoiding distractions.
Miller Lab alumnus Jon Wallis and crew studied two different types of cost-benefit decisions (delay vs effort). They found that different neurons in the dorsolateral prefrontal cortex, orbitofrontal, and anterior cingulate encoded the different types of decisions. Thus, rather than have neurons encode decisions on an abstract level, frontal cortex neurons encode stimuli based on their exact consequences.
Blogger John Borghi lists the most highly cited papers in neuroscience and has kind words for Miller and Cohen (2001). Thanks, John!
Behavior can be fast and automatic, but inflexible (model-free) vs slower, more deliberate, and flexible (model-based) Ray Dolan and crew show that disruption of the prefrontal cortex by transcranial magnetic stimulation (TMS) pushes humans towards more inflexible, model-free, behavior.
More evidence for domain-general processing in higher-level cortex. Federenko et al tested human subjects with seven tasks with different cognitive demands. FMRI revealed overlapping activation zones in the frontal and parietal cortex. This is consistent with neurophysiological studies showing that many neurons in these areas are multifunctional. Rigotti et al recently demonstrated that these multifunctional “mixed selectivity” neurons provide the computational power needed for high-level cognition.
For further reading:
Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. “The importance of mixed selectivity in complex cognitive tasks”. Nature, 497, 585-590, 2013 doi:10.1038/nature12160. View PDF
Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF
Everybody knows that we can only hold a limited number of things in mind simultaneously. Is this capacity limit due to a limited number of “slots” in working memory or due a limited resource pool that is divided among the items held in mind? We found evidence for both (Buschman et al, 2011). Now, Roggeman et al use computational modeling to provide further evidence for a hybrid model for capacity limits of working memory.
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
Bea Luna and colleagues used graph theory to examine the development of functional hubs in the human brain. The hub architecture develops earlier, but connections between the hubs and “spokes” continue to develop and change into adulthood.