Shenhav, Botvinick, and Cohen tie together a number of observations and notions into a new theory of ACC function: allocation of control based on an evaluation of the expected value of control (EVC).
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Pinto et al, despite enough statistical power, fail to see any correlation between performance of a top-down attention task (search) and a bottom-up attention task (singleton capture). They argue that top-down and bottom-up attention systems operate independently.
They cite our work, which suggests that top-down vs bottom up attention signals originate from prefrontal vs parietal cortex:
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 » -
Dipoppa and Gutkin propose a model of working memory in which gamma-beta oscillations gates access, theta oscillations protects working memory from distractions, and alpha oscillations clears out old memories.
This is consistent with our observations that beta helps from ensembles for rules held in working memory while alpha clears out a dominant ensemble so that a weaker one can be used:
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 -
Zhang et al studied rule-based behavior by either having human subjects choose the rule themselves or by instructing them to the rule. They found context-dependent and context-independent (chosen vs instructed) rule representations in frontal and parietal cortex. This gives insight into the architecture of cognitive control.
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Peter Lakatos and Charlie Schroeder have conducted elegant work showing that the brain entrains its rhythms to attended sensory inputs. Here, Lakatos et al show that normal human subjects show increased rhythmic entrainment with increasing task demands, By contrast, schizophrenic patients are less able to match their brain rhythms to attended stimuli, even when the task is highly demanding.
Miller Lab work cited:
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 »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 »
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Your prefrontal cortex becomes less resistant to stress as you age. McEwen and Morrison tell you all about it.
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The 2013 Annual Review of Neuroscience is here. It includes a very nice review of the role of the prefrontal cortex in visual attention by Squire et al
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DiQuattro et al use dynamic causal modeling (DCM) of FMRI signals to show that the frontal eye fields (FEF) are more involved initiating shifts of attention than the temporoparietal junction (TPJ, another leading candidate). The FEF received sensory signals earlier than the TPJ and FEF to TPJ connectivity was modulated by appearance of a target.
Miller Lab work cited:
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 »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 »
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Miller Lab alumnus, Andreas Nieder, finds that abstract decisions divorced from motor plans are distributed across frontal areas, even those traditionally thought of as motor areas. In fact, they are more strongly encoded in the presupplementary motor area than the prefrontal cortex.
Merten and Nieder 2013Miller Lab work cited:
Freedman, D.J., Riesenhuber, M., Poggio, T., and Miller, E.K. (2001) Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291:312-316. View PDF »Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202. Designated a Current Classic by Thomson Scientific as among the most cited papers in Neuroscience and Behavior. View PDF »
Miller, E.K. (2000) The prefrontal cortex and cognitive control. Nature Reviews Neuroscience, 1:59-65.
Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Single neurons in the prefrontal cortex encode abstract rules. Nature, 411:953-956. View PDF »
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Totah et al find that increased synchrony between the prelimbic cortex and anterior cingulate cortex before stimulus onset predicted behavioral choices. Further, there was a switch from beta synchrony during attention to delta synchrony before the behavioral response. This shows, among other things, that the same neurons can participate in different functional networks by virtue of how that synchronize with other neurons.
This paper is one of several recent studies providing evidence that oscillatory synchrony allows multiplexing of neural function. Here’s an excerpt from Miller and Fusi (2013) that summarizes this point:
“It (oscillatory synchrony) could allow neurons to communicate different messages to different targets depending on whom they are synchronized with (and how, e.g., phase, frequency). For example, rat hippocampal CA1 neurons preferentially synchronize to the entorhinal or CA3 neurons at different gamma frequencies and theta phases (Colgin et al, 2009). Different frequency synchronization between human cortical areas supports recollection of spatial vs temporal information (Watrous et al., 2013). Different phases of cortical oscillations preferentially signal different pictures simultaneously held in short-term memory (Siegel et al., 2009). Monkey frontal and parietal cortices synchronize more strongly at lower vs higher frequency for top-down vs bottom-up attention, respectively (Buschman and Miller, 2007). Entraining the human frontal cortex at those frequencies produces the predicted top-down vs bottom-up effects on behavior (Chanes et al., 2013). Thus, activity from the same neurons has different functional outcomes depending on their rhythmic dynamics.”
Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF