Both attention and prediction are necessary for adaptive neuronal tuning in sensory processing

I would assume your doctor will need to know how to apply this for your protocols  on sensation recovery. It's way over my head.
http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00152/full?

Yi-Fang Hsu^1,2*, Jarmo A. Hämäläinen³ and Florian Waszak^1,2

• ¹Université Paris Descartes, Sorbonne Paris Cité, Paris, France
• ²CNRS, Laboratoire Psychologie de la Perception, UMR 8242, Paris, France
• ³Department of Psychology, University of Jyväskylä, Jyväskylä, Finland

The brain as a proactive system processes sensory information under the top-down influence of attention and prediction. However, the relation between attention and prediction remains undetermined given the conflation of these two mechanisms in the literature. To evaluate whether attention and prediction are dependent of each other, and if so, how these two top-down mechanisms may interact in sensory processing, we orthogonally manipulated attention and prediction in a target detection task. Participants were instructed to pay attention to one of two interleaved stimulus streams of predictable/unpredictable tone frequency. We found that attention and prediction interacted on the amplitude of the N1 ERP component. The N1 amplitude in the attended/predictable condition was larger than that in any of the other conditions. Dipole source localization analysis showed that the effect came from the activation in bilateral auditory areas. No significant effect was found in the P2 time window. Our results suggest that attention and prediction are dependent of each other. While attention might determine the overall cortical responsiveness to stimuli when prediction is involved, prediction might provide an anchor for the modulation of the synaptic input strengths which needs to be operated on the basis of attention.

Introduction

Recent theories of sensory processing consider the brain as a proactive system which adapts quickly to the environment. Neurons in the sensory cortices can undergo short-term, task-dependent, and context-specific changes in receptive field properties when attention and prediction are involved (Fritz et al., 2003, 2007, 2008). Such adaptive plasticity driven by attention and prediction can be the underlying mechanism for the optimization of perception.

Attention is suggested to have a global effect on perception at an early stage of sensory processing. Electroencephalography (EEG) studies revealed the neuronal consequences of attention on event-related potentials (ERPs), particularly the enhancement of the N1 (Hillyard et al., 1973; Alcaini et al., 1994; Lange et al., 2003, 2006; Lange and Röder, 2006). This may result from changes in the selectivity of neurons in the sensory cortex (Chawla et al., 1999; Kastner et al., 1999; Ahveninen et al., 2006). Specifically, research showing that the auditory N1 response is modulated by task demands and notched-noise masking suggests that the spectrotemporal receptive fields of neurons are tuned according to attentional manipulations (Kauramäki et al., 2007), as attention excites neurons responsive to attended features and inhibits neurons responsive to unattended features (Fritz et al., 2003, 2007, 2008; Jääskeläinen et al., 2007). Neurocomputational studies demonstrated that attention may function via optimizing the synaptic gain to represent the precision of sensory information during hierarchical inference (Feldman and Friston, 2010).

Prediction, or the statistical regularity in the environment, is also suggested to modulate the early stage of sensory processing, albeit its effect on ERPs manifests as a suppression of the N1 (Lange, 2013). Prediction-related N1 suppression was demonstrated when participants had foreknowledge of the upcoming stimuli (Schafer and Marcus, 1973; Schafer et al., 1981; Lange, 2009; SanMiguel et al., 2013; Timm et al., 2013). The predictive coding model postulates that the prediction effect indexes the difference in neurocomputational demand for predictable/unpredictable information (Friston, 2005; Egner et al., 2010). Specifically, it indexes how much of the sensory input cannot be accounted for by the internal model. Moreover, prediction was reported to alter the contrast gain of sensory evidence accumulation (Melloni et al., 2011; Rohenkohl et al., 2012). Neurophysiologically, this is reflected in sharper sensory representations where the reduction of neuronal activity is smaller in neurons responsive to predictable features than in neurons responsive to unpredictable features (Kok et al., 2012a).

Despite their ERP effects being opposite, the relation of attention and prediction remains undetermined. This might be due to the conflation of these two mechanisms in the literature, where attention and prediction were often treated as the same concept (Corbetta and Shulman, 2002). However, attention and prediction can rely on orthogonal sources of information (Summerfield and Egner, 2013). While attention operates on the basis of motivational relevance, prediction operates on the basis of prior likelihood (Summerfield and Egner, 2009). It is possible that attention and prediction are two independent mechanisms which may have antagonistic (Summerfield and Egner, 2009) or additive (Timm et al., 2013) effects on neuronal signals for sensory processing. Alternatively, attention and prediction may be dependent of each other. One possibility is that one of the two mechanisms is necessary for the other to take effect, but not the other way round (Kok et al., 2012b). Another possibility is that such dependency is bidirectional, with both attention and prediction being necessary to modulate sensory processing.

To examine the relation between these two top-down mechanisms, we orthogonally manipulated attention and prediction in a target detection task. Participants were instructed to pay attention to one of two interleaved stimulus streams of predictable/unpredictable tone frequency. Using EEG, we quantified N1 and P2 as dependent variables given that the former is involved in auditory perception and the latter is suggested to reflect the comparison between the sensory input and the internal model (Evans and Federmeier, 2007; Costa-Faidella et al., 2011). The design allowed us to evaluate whether attention and prediction are dependent of each other, and if so, how these two top-down mechanisms may interact on sensory processing.

Figures and more at the link.