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Preferential encoding of behaviorally relevant predictions revealed by EEG.

Stokes MG, Myers NE, Turnbull J, Nobre AC - Front Hum Neurosci (2014)

Bottom Line: In this electroencephalogram (EEG) study, we test how task relevance influences the way predictions are learned from the statistics of visual input, and exploited for behavior.The behavioral results confirmed that participants learned and exploited task-relevant predictions even when not explicitly defined.These results show that task relevance critically influences how the brain extracts predictive structure from the environment, and exploits these regularities for optimized behavior.

View Article: PubMed Central - PubMed

Affiliation: Department of Experimental Psychology, University of Oxford Oxford, UK ; Oxford Centre for Human Brain Activity, University of Oxford Oxford, UK.

ABSTRACT
Statistical regularities in the environment guide perceptual processing; however, some predictions are bound to be more important than others. In this electroencephalogram (EEG) study, we test how task relevance influences the way predictions are learned from the statistics of visual input, and exploited for behavior. We developed a novel task in which participants are simply instructed to respond to a designated target stimulus embedded in a serial stream of non-target stimuli. Presentation probabilities were manipulated such that a designated target cue stimulus predicted the target onset with 70% validity. We also included a corresponding control contingency: a pre-designated control cue predicted a specific non-target stimulus with 70% validity. Participants were not informed about these contingencies. This design allowed us to examine the neural response to task-relevant predictive (cue) and predicted stimuli (target), relative to task-irrelevant predictive (control cue) and predicted stimuli (control non-target). The behavioral results confirmed that participants learned and exploited task-relevant predictions even when not explicitly defined. The EEG results further showed that target-relevant predictions are coded more strongly than statistically equivalent regularities between non-target stimuli. There was a robust modulation of the response for predicted targets associated with learning, enhancing the response to cued stimuli just after 200 ms post-stimulus in central and posterior electrodes, but no corresponding effects for predicted non-target stimuli. These effects of target prediction were preceded by a sustained frontal negativity following presentation of the predictive cue stimulus. These results show that task relevance critically influences how the brain extracts predictive structure from the environment, and exploits these regularities for optimized behavior.

No MeSH data available.


Related in: MedlinePlus

Event-related potentials to cued vs. uncued targets and cued vs. uncued control non-targets. (A) Target-related ERPs were modulated by target cues during learning. Plots show the mean potential difference between cued and uncued target stimuli, separately for each block, for three ROIs: frontal sensors (top panel, see inset for sensor locations), central (middle panel), and posterior (bottom panel). (B) Topography of learning effect. The topography shows the mean slope derived from the linear regression of task block onto potential difference, from three separate time-windows post target onset. (C) and (D) show the same as (A) and (B), but for the control non-target stimulus. (E) The mean regression slope across the eight task blocks (fit separately at each time point) is shown for the same frontal, central, and posterior ROIs shown in (A) and (C), for targets (blue lines) and non-targets (red lines). Shading indicates the standard error of the means (SEM). Horizontal bars indicate significant regression slopes in the target learning condition compared to chance (in black; central: p = 0.053, cluster-corrected, dashed line, posterior: p = 0.0130, cluster-corrected, solid line), and directly compared to the control non-target condition (in gray; central: p = 0.026; posterior: p = 0.045, cluster corrected).
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Figure 2: Event-related potentials to cued vs. uncued targets and cued vs. uncued control non-targets. (A) Target-related ERPs were modulated by target cues during learning. Plots show the mean potential difference between cued and uncued target stimuli, separately for each block, for three ROIs: frontal sensors (top panel, see inset for sensor locations), central (middle panel), and posterior (bottom panel). (B) Topography of learning effect. The topography shows the mean slope derived from the linear regression of task block onto potential difference, from three separate time-windows post target onset. (C) and (D) show the same as (A) and (B), but for the control non-target stimulus. (E) The mean regression slope across the eight task blocks (fit separately at each time point) is shown for the same frontal, central, and posterior ROIs shown in (A) and (C), for targets (blue lines) and non-targets (red lines). Shading indicates the standard error of the means (SEM). Horizontal bars indicate significant regression slopes in the target learning condition compared to chance (in black; central: p = 0.053, cluster-corrected, dashed line, posterior: p = 0.0130, cluster-corrected, solid line), and directly compared to the control non-target condition (in gray; central: p = 0.026; posterior: p = 0.045, cluster corrected).

Mentions: First, we examined the EEG activity triggered by cued target stimuli relative to uncued targets (Figure 2). For illustration, difference voltages are plotted over the course of the experimental session in Figures 2A,C, but the statistical inference is drawn from the regression analysis in Figure 2E. Visual inspection of the difference plot (Figures 2A,B) reveals a cue-related positivity that was most evident in the central cluster of electrodes, emerging increasingly early in the trial toward the end of the experimental session. In contrast to the effects of learning task-relevant predictions, there was no evidence for a similar effect for the control non-target stimuli (Figures 2C,D).


Preferential encoding of behaviorally relevant predictions revealed by EEG.

Stokes MG, Myers NE, Turnbull J, Nobre AC - Front Hum Neurosci (2014)

Event-related potentials to cued vs. uncued targets and cued vs. uncued control non-targets. (A) Target-related ERPs were modulated by target cues during learning. Plots show the mean potential difference between cued and uncued target stimuli, separately for each block, for three ROIs: frontal sensors (top panel, see inset for sensor locations), central (middle panel), and posterior (bottom panel). (B) Topography of learning effect. The topography shows the mean slope derived from the linear regression of task block onto potential difference, from three separate time-windows post target onset. (C) and (D) show the same as (A) and (B), but for the control non-target stimulus. (E) The mean regression slope across the eight task blocks (fit separately at each time point) is shown for the same frontal, central, and posterior ROIs shown in (A) and (C), for targets (blue lines) and non-targets (red lines). Shading indicates the standard error of the means (SEM). Horizontal bars indicate significant regression slopes in the target learning condition compared to chance (in black; central: p = 0.053, cluster-corrected, dashed line, posterior: p = 0.0130, cluster-corrected, solid line), and directly compared to the control non-target condition (in gray; central: p = 0.026; posterior: p = 0.045, cluster corrected).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4151094&req=5

Figure 2: Event-related potentials to cued vs. uncued targets and cued vs. uncued control non-targets. (A) Target-related ERPs were modulated by target cues during learning. Plots show the mean potential difference between cued and uncued target stimuli, separately for each block, for three ROIs: frontal sensors (top panel, see inset for sensor locations), central (middle panel), and posterior (bottom panel). (B) Topography of learning effect. The topography shows the mean slope derived from the linear regression of task block onto potential difference, from three separate time-windows post target onset. (C) and (D) show the same as (A) and (B), but for the control non-target stimulus. (E) The mean regression slope across the eight task blocks (fit separately at each time point) is shown for the same frontal, central, and posterior ROIs shown in (A) and (C), for targets (blue lines) and non-targets (red lines). Shading indicates the standard error of the means (SEM). Horizontal bars indicate significant regression slopes in the target learning condition compared to chance (in black; central: p = 0.053, cluster-corrected, dashed line, posterior: p = 0.0130, cluster-corrected, solid line), and directly compared to the control non-target condition (in gray; central: p = 0.026; posterior: p = 0.045, cluster corrected).
Mentions: First, we examined the EEG activity triggered by cued target stimuli relative to uncued targets (Figure 2). For illustration, difference voltages are plotted over the course of the experimental session in Figures 2A,C, but the statistical inference is drawn from the regression analysis in Figure 2E. Visual inspection of the difference plot (Figures 2A,B) reveals a cue-related positivity that was most evident in the central cluster of electrodes, emerging increasingly early in the trial toward the end of the experimental session. In contrast to the effects of learning task-relevant predictions, there was no evidence for a similar effect for the control non-target stimuli (Figures 2C,D).

Bottom Line: In this electroencephalogram (EEG) study, we test how task relevance influences the way predictions are learned from the statistics of visual input, and exploited for behavior.The behavioral results confirmed that participants learned and exploited task-relevant predictions even when not explicitly defined.These results show that task relevance critically influences how the brain extracts predictive structure from the environment, and exploits these regularities for optimized behavior.

View Article: PubMed Central - PubMed

Affiliation: Department of Experimental Psychology, University of Oxford Oxford, UK ; Oxford Centre for Human Brain Activity, University of Oxford Oxford, UK.

ABSTRACT
Statistical regularities in the environment guide perceptual processing; however, some predictions are bound to be more important than others. In this electroencephalogram (EEG) study, we test how task relevance influences the way predictions are learned from the statistics of visual input, and exploited for behavior. We developed a novel task in which participants are simply instructed to respond to a designated target stimulus embedded in a serial stream of non-target stimuli. Presentation probabilities were manipulated such that a designated target cue stimulus predicted the target onset with 70% validity. We also included a corresponding control contingency: a pre-designated control cue predicted a specific non-target stimulus with 70% validity. Participants were not informed about these contingencies. This design allowed us to examine the neural response to task-relevant predictive (cue) and predicted stimuli (target), relative to task-irrelevant predictive (control cue) and predicted stimuli (control non-target). The behavioral results confirmed that participants learned and exploited task-relevant predictions even when not explicitly defined. The EEG results further showed that target-relevant predictions are coded more strongly than statistically equivalent regularities between non-target stimuli. There was a robust modulation of the response for predicted targets associated with learning, enhancing the response to cued stimuli just after 200 ms post-stimulus in central and posterior electrodes, but no corresponding effects for predicted non-target stimuli. These effects of target prediction were preceded by a sustained frontal negativity following presentation of the predictive cue stimulus. These results show that task relevance critically influences how the brain extracts predictive structure from the environment, and exploits these regularities for optimized behavior.

No MeSH data available.


Related in: MedlinePlus