Two different mechanisms support selective attention at different phases of training

Abstract

Selective attention supports the prioritized processing of relevant sensory information to facilitate goal-directed behavior. Studies in human subjects demonstrate that attentional gain of cortical responses can sufficiently account for attention-related improvements in behavior. On the other hand, studies using highly trained nonhuman primates suggest that reductions in neural noise can better explain attentional facilitation of behavior. Given the importance of selective information processing in nearly all domains of cognition, we sought to reconcile these competing accounts by testing the hypothesis that extensive behavioral training alters the neural mechanisms that support selective attention. We tested this hypothesis using electroencephalography (EEG) to measure stimulus-evoked visual responses from human subjects while they performed a selective spatial attention task over the course of~1 month. Early in training, spatial attention led to an increase in the gain of stimulusevoked visual responses. Gain was apparent within~100 ms of stimulus onset, and a quantitative model based on signal detection theory (SDT) successfully linked the magnitude of this gain modulation to attention-related improvements in behavior. However, after extensive training, this early attentional gain was eliminated even though there were still substantial attention-related improvements in behavior. Accordingly, the SDT-based model required noise reduction to account for the link between the stimulus-evoked visual responses and attentional modulations of behavior. These findings suggest that training can lead to fundamental changes in the way attention alters the early cortical responses that support selective information processing. Moreover, these data facilitate the translation of results across different species and across experimental procedures that employ different behavioral training regimes. Author summarySelective attention can enhance processing of sensory information via sensory gain and neural noise reduction. However, the extent to which these 2 mechanisms contribute to improvement in perceptual performance during attention is still debated. We hypothesized PLOS Biology | https://doi.org/10.1371/journal.pbio

Reconciling fMRI and EEG indices of attentional modulations in human visual cortex

“…Thus, there was no main effect of stimulus contrast (F(5, 30) = 2.46, p = 0.056), attention (F(1, 6) = 0.16, p = 0.70), or measurement modality (F(1, 6) = 0.00, p = 0.96), nor an interaction between any combination of these three factors (F(5, 30) = 0.67, F(5, 30) = 1.40, F(1, 6) = 0.36, and F(5, 30) = 0.91 for the interactions between contrast and attention, between contrast and modality, between attention and modality, and between all three factors, respectively, with all p's ≥ 0.25). The contrast detection thresholds increased as a function of stimulus contrast consistent with many past studies (a significant main effect of contrast: F(5, 30) = 233.40, p <0.001) (Legge and Foley, 1980;Ross et al, 1993;Boynton et al, 1999;Gorea and Sagi, 2001;Huang and Dobkins, 2005;Pestilli et al, 2011;Itthipuripat et al, 2014aItthipuripat et al, , 2017. Moreover, behavioral performance did not differ significantly across fMRI and EEG sessions ( Figure 1d) (no main effect of measurement modality and no interaction between contrast and measurement modality: F(1, 6)=0.61 and F(5, 30) = 2.46 with p = 0.464 and p = 0.055, respectively).…”

“…Moreover, fMRI and EEG are both population-level measures that aggregate information across large populations of responsive neurons, yet they still exhibit substantially different patterns of modulation with changes in cognitive demands. This is especially important because recent fMRI and EEG studies using quantitative modeling to link attentional modulations of neural CRFs and psychophysical performance have reached very different conclusions about neural mechanisms underlying attentional selection (Pestilli et al, 2011;Itthipuripat et al, 2014aItthipuripat et al, , 2017. Specifically, fMRI data suggested that sensory gain (i.e., the increase in CRF slopes) and noise reduction (i.e., the decrease in trial-by-trial variability in neural responses) had limited roles in supporting attention-induced changes in behavioral performance (Pestilli et al, 2011;.…”

“…Specifically, fMRI data suggested that sensory gain (i.e., the increase in CRF slopes) and noise reduction (i.e., the decrease in trial-by-trial variability in neural responses) had limited roles in supporting attention-induced changes in behavioral performance (Pestilli et al, 2011;. In contrast, EEG data suggested that sensory gain modulations and in some cases noise reduction could sufficiently account for attentional benefits in behavior (Mangun and Hillyard, 1990;Störmer et al, 2009;Itthipuripat et al, 2014aItthipuripat et al, , 2017. The results from the present study help to reconcile these different conclusions and point to differences in the physiological sensitivity of neural recording techniques rather than other task and stimulus parameters.…”

“…Results from fMRI unanimously support a mechanism whereby attention increases the evoked response to all stimuli equally, regardless of their contrast, while results from EEG support a contrast-dependent response modulation (either a horizontal shift of the CRF, called 'contrast gain', or a multiplicative scaling of the CRF, called 'response gain' (Figure 1a; reviewed in (Lee and Maunsell, 2009;Reynolds and Heeger, 2009;Itthipuripat and Serences, 2015)). While these different types of attention effects could be due to differences in task designs, stimulus properties, recording sites, training duration, and subjects' attentional strategy and expertise (Reynolds and Heeger, 2009;Herrmann et al, 2010;Itthipuripat et al, 2014bItthipuripat et al, , 2017Zhang et al, 2016), another reasonable source of divergence is the neural response properties to which each measurement is most sensitive (Boynton, 2011;Itthipuripat and Serences, 2015).…”

Categorical Biases in Human Occipitoparietal Cortex

“…For example, our participants learned to categorize stimuli based on performance feedback after approximately 10 minutes of training. In contrast, macaque monkeys typically require six months or more of training using a similar feedback scheme to reach a similar level of performance, and this extensive amount of training may influence how neural circuits code information (e.g., Itthipurripat et al, 2017;Birman & Gardner, 2015). Moreover, there is growing recognition that the contribution(s) of sensory cortical areas to performance on a visual task are highly susceptible to recent history and training effects (Itthipurripiat et al, 2017, Chen et al, 2016Liu & Pack, 2017).…”