Christian Ruff pays tribute to the late, great, Jon Driver by reviewing neural mechanisms of top-down control of attention and memory.
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Eldar et al show that neural gain influences learning style. Subjects learned associations between pictures and reward. The association could be based on different stimulus dimensions and different people had different predispositions for one dimension or the other. Eldar et al assessed neural gain by pupil dilation (which is correlated with locus coeruleus norepinephrine activity) and found that the higher the gain, the more likely subjects were to follow their predispositions. The increase in gain was thought to boost the asymmetry of strength between different functional networks which are responsible for the predisposition in learning style.
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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 » -
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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Nicole Rust and crew show how the perirhinal cortex can take signals from the inferior temporal cortex and sort out visual targets from distractors.
Pagan et al (2013) -
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
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Soltani et al (2013) explored the role of D1 and D2 dopamine receptors in saccade target selection. They find evidence that D1 receptors modulate the strength of inputs to the frontal eye fields and recurrent connectivity whereas D2 may modulate the output of the FEF. This may be because D1 seems to reduce LTP and LTD, which is consistent with observations that D1 receptors contribute to associative learning (Puig and Miller, 2012). Like Puig and Miller (2012), they also found that D1 blockade increases response perseveration.
Further reading:
Puig, M.V. and Miller, E.K. (2012) The role of prefrontal dopamine D1 receptors in the neural mechanisms of associative learning. Neuron. 74: 874-886. View PDF » -
Attentional blink is decreased attention to a second stimulus if it quickly follows (200-500 ms) another stimulus. Maloney et al find that neural information in area LIP tracks attentional blink.