Earl Miller is quoted on NBCnews.com. In brief: Don’t multitask and if you do don’t drive.
NBCnews.com: The ‘smart life’: How connected cars, clothes and homes could fry your brain
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 »
VanRullen previews a recent paper by Kastner and colleagues and discusses evidence that covert attention involves rhythmic sampling of the environment.
Cannon et al review the contributions of brain oscillations to neural computations and relate them to a variety of cognitive functions.
Newman et al used the drug scopolamine to disrupt theta-gamma coupling in the medial entorhinal cortex of rats. Gamma power at the peak of theta was reduced and shifted to subsequent phases. Scopolamine also seemed to reduce the rats’ familiarity with the testing enclosure. The data support the hypothesis that memory encoding and retrieval occur at different theta phases.
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
Sabine Kastner and crew show that when humans are cued to direct their attention to part of an object, uncued locations that are part of the same object are sampled periodically at about 8 Hz. Different, uncued, objects are also sampled at 4 Hz. This adds to a growing body of evidence that attention, and cognition in general, is rhythmic not continuous.
For reviews on this topic see:
- 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. “Brain Rhythms for Cognition and Consciousness”. Neurosciences and the Human Person: New Perspectives on Human Activities A. Battro, S. Dehaene and W. Singer (eds), Pontifical Academy of Sciences, Scripta Varia 121, Vatican City, 2013. View PDF
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.
Roux and Uhlhaas propose an interesting and provocative theory: Different frequencies of neural oscillations carry different information in working memory. Gamma oscillations maintain information in working memory. Alpha suppresses irrelevant information. Theta orders information. Gamma is thought to be coupled to both alpha and theta.
This is consistent with our observations for phase-coding of different working memories in gamma (Siegel et al., 2009) and alpha suppressing a dominant, but current irrelevant, neural ensemble (Buschman et al., 2012).
Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron. 76: 838-846. 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 »
Visual attention increases synchrony of neural activity in visual cortex. Fries and colleagues showed that synchronization differs for putative excitatory (broad-spiking) and inhibitory (narrow-spiking) neurons. The inhibitory neurons synchronize in the gamma band twice as strongly as excitatory neurons but the excitatory neurons synchronize to an earlier phase than inhibitory neurons. Further, attention increases gamma synchrony for the most active neurons but decreases synchrony for the least active neurons. These results show that attention-related neural synchrony is not uniform but instead an orchestration between different neuron types showing different types of synchrony. This lends further support for the role of neural synchrony in attention.
Frontiers for Young Minds is edited by kids 8 to 18. Best quote: ““Woe betide the contributor who falls under my editorial pen,” wrote 14-year-old Caleb from Canada.
Corbetta and colleagues studied attention by recording from patients undergoing surgery for epilepsy. They found evidence for frequency-based attention mechanisms, in particular phase modulation at lower frequencies. Different types of attentional operations (holding vs shifting attention) were associated with synchrony at different frequencies.
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.
Miguel Remondes and Matt Wilson show that theta synchrony between the hippocampus and anterior cingulate during a task in which rats chose one of four trajectories. Theta coherence at 8 Hz lowers slightly (by 1 Hz) when rats enter the decision phase of the task and hippocampal processing of trajectory information precedes that of the anterior cingulate. Thus, lowering of theta synchrony may coordinate the integration of hippocampal information by neurons in the anterior cingulate.
Brown University researchers show how increases in sigma and delta brain oscillations during sleep correlate with learning new visual and motor skills. The sigma oscillations in particular were traced to the occipital representation of the visual quadrant where the learning took place.
The all-star team of Buzaki, Logothetis, and Singer discuss how the hierarchy of brain oscillations is remarkably consistent despite huge differences in brain size across different animals. This may allow for multiple time-scale communication and may be a key factor in time management in the brain. They discuss the implications for mental disease and therapy.
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.
Michale Fee and crew review and synthesize three major models of basal ganglia output (disinhibition, rebound, and entrainment).
The title says it all. Theta oscillations in humans increased with prediction error and predicted the subject’s learning rates.
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.
Bradley Voytek walks us through a brain surgery (with video)
Bob Desimone (Director of the McGovern Institute for Brain Research at MIT) demonstrates auto-synchronization of metronomes
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.