Edited by:
Reviewed by:
*Correspondence:
This article was submitted to the journal Frontiers in Human Neuroscience.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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.
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 (
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 (
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 (
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 (
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 (
Sixteen healthy volunteers (average age 28; six males; all right-handed) with no history of neurological, psychiatric, or hearing impairments as indicated by self-report participated in the experiment. Participants gave written informed consent and were paid for participation. Ethical approval was granted by the Comité de Protection des Personnes (CPP) Ile de France II. The experiment conforms with The Code of Ethics of the World Medical Association (Declaration of Helsinki).
Sinusoidal tones with a loudness of 80 phons (i.e., 80 dB for tones of 1000 Hz) were generated using Matlab. The duration of each tone was 50 ms (including 5 ms rise/fall times). The frequency of each tone was within the range of 261.626–493.883 Hz and 2093.000–3951.070 Hz, matching the absolute frequency of two sets of seven natural keys on a modern piano (low frequency set: C4 D4 E4 F4 G4 A4 B4; high frequency set: C7 D7 E7 F7 G7 A7 B7). Within each frequency set, the predictable/unpredictable stimulus streams were created. The predictable stimulus stream consisted of 400 pairs of tones. Each pair of tones was arranged in ascending order with the second tone being two natural keys higher than the first tone (e.g., C4-E4; G7-B7). The unpredictable stimulus stream consisted of 400 pairs of tones. Each pair of tones was arranged in random order with the second tone being any tone except the repetition of the first tone (e.g., C4-F4; G7-F7). The tones in each condition were of equal variability in frequency.
The predictable/unpredictable stimulus streams from different frequency sets were interleaved to allow for the efficient manipulation of attention on the two stimulus sets. To counterbalance the mapping between predictable/unpredictable stimulus streams and high/low frequency sets, half of the participants were presented with a low-frequency predictable stimulus stream interleaved with a high-frequency unpredictable stimulus stream and half of the participants were presented with a high-frequency predictable stimulus stream interleaved with a low-frequency unpredictable stimulus stream. To counterbalance the sequential relation of the predictable/unpredictable stimulus streams, half of the blocks started with the predictable stimulus stream and half of the blocks started with the unpredictable stimulus stream. A stimulus onset asynchrony (SOA) of 500 ms was used (
Participants were presented with a total of eight blocks of 100 pairs of tones via headphones (Sennheiser PX200), with each block including 50 predictable pairs of tones and 50 unpredictable pairs of tones. Unaware of the manipulation of prediction, participants were instructed to pay attention to one of the stimulus streams (i.e., high/low frequency) in different blocks where tones of attenuated loudness may appear. To monitor whether participants followed the instructions correctly, participants were required to press a key when they detected a softer tone, which randomly occurred 10 times in each block. No practice session was provided. Block order was counterbalanced across participants. The experiment was administered conjointly with another EEG experiment which is to be reported elsewhere.
The stimuli of interest were the second tones in each pair of tones, which can be attended/predictable, attended/unpredictable, unattended/predictable, and unattended/unpredictable. Note that the manipulations of attention and prediction were both introduced on the basis of tone frequency. Moreover, all stimuli were presented binaurally. Therefore, there was no spatial effect in the current study.
EEG was recorded with 64 active electrodes (actiCAP, Brain Products GmbH, Germany) conforming to the international 10–10 system. The sampling rate was 500 Hz. No online/offline filter was used. The data was recomputed to average reference. Target stimuli and the first stimuli following target stimuli were removed. Epochs extended from -100 to 500 ms relative to stimulus onset, using a 100 ms pre-stimulus baseline. Ocular artifact correction was conducted with independent component analysis in EEGlab (
Mean and range of trial numbers after artifact rejection in each condition.
Attended predictable | Attended unpredictable | Unattended predictable | Unattended unpredictable | |
---|---|---|---|---|
Mean | 175.38 | 171.38 | 171.81 | 173.50 |
Range | 165–182 | 141–187 | 142–188 | 161–181 |
ERP analysis was based on a temporal principal component analysis (PCA) in SPSS 20. The temporal PCA statistically decomposes the ERP waveforms into constituent building blocks, which affords objective data-driven ERP component measures when compared to the conventional peak-picking methods (
Overall, participants’ performance in the target detection task was at ceiling (Hit: mean = 0.96, SD = 0.03; False alarm: mean < 0.01, SD < 0.01; RT: mean = 539.34, SD = 62.21), confirming that participants followed the instructions correctly. The ceiling performance rendered it unlikely that participants’ attention alternated between the two stimulus streams within blocks, which would have brought down participants’ performance. There was no difference between participants’ performance when they attended to predictable/unpredictable stimulus stream [Hit:
The 2 × 2 repeated measures ANOVA showed a significant main effect of attention with attended stimuli triggering enhanced activity compared to unattended stimuli [
In the 2 × 2 repeated measures ANOVA, neither the main effect of attention [
The orthogonal design in the current study allowed us to evaluate whether attention and prediction are dependent of each other, and if so, how these two top-down mechanisms may interact for the optimization of perception. We found that attention and prediction interacted on the amplitude of the N1 ERP component generated in the auditory cortices. The N1 amplitude in the attended/predictable condition was larger than that in any of the other conditions. The relatively early latency of the interaction between these two variables is in line with the idea that attention and prediction can synergistically enhance perceptual analysis through top-down pathways (
Precious research reported that neurons in the sensory cortices can undergo short-term, task-dependent, and context-specific changes in the receptive field properties (
The prediction-dependent attention effect demonstrates, for the first time, that attention alone may not be able to reshape perceptual inferences. It is unknown whether the prediction participants formed in the current study was implicit or explicit. However, it seems that prediction, be it implicit or explicit, is needed for attention effects to occur. At first glance, this idea seems difficult to reconcile with previous studies showing that attention can selectively modulate the response of neuronal subpopulations that prefer the attended stimulus features (
The attention-dependent prediction effect, on the other hand, sheds light on the dynamic influence of attention on prediction. Notably, using functional magnetic resonance imaging (fMRI) and visual stimuli,
Concerning the manipulation of attention, the discrepancy may be explained by a competition hypothesis borrowed from research on mismatch negativity (MMN). Mismatch negativity is generated to violations of abstract stimulus prediction rules (
We suggest that whether the prediction effect appears in the unattended condition depends on the degree of such competition for cognitive resources. In the current study, attention was manipulated in the “filtering” manner. In this case, participants may inhibit the processing of unattended stimuli stream at an early stage to get rid of unnecessary information. In the study of
Alternatively, whether the prediction effect appears in the unattended condition may depend on the particular manipulation of prediction. In the current study, the unpredictable condition consisted of stimuli randomly selected from a given frequency set. In other words, it is difficult for participants to form a specific prediction about the frequency of the upcoming stimuli in the first place. Therefore, the activation in sensory cortex may reflect exclusively the neuronal signals triggered by the presence of unpredictable stimuli. In the study of
On the other hand, no significant effect was found in the P2 time window. At best, there was a marginally significant interaction between attention and prediction. While caution should be taken in interpreting the results, the pattern of the interaction suggests that attention reversed the direction of prediction effects on the P2. In the attended condition, predictable stimuli suppressed the P2. In the unattended condition, predictable stimuli enhanced the P2. Interestingly, the pattern is opposite to the findings of
Overall, our findings suggest that future research on attention and its relation to prediction needs to differentiate between different manipulations of attention and prediction. Moreover, our results confirm the importance of incorporating the modulatory effect of attention in the predictive coding theory. The predictive coding theory postulates that the prediction effect indexes the difference in neurocomputational demand for predictable/unpredictable information (
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This research was supported by a grant from the Agence Nationale de la Recherche (INTACT ANR-09-BLAN-0318). We thank Trevor Agus for help on stimulus calibration and the Paris Descartes Platform for Sensorimotor Studies (Université Paris Descartes, CNRS, INSERM, Région Ile-de-France) for supporting the experimental work presented here.