Stimulus Reliability Automatically Biases Temporal Integration of Discrete Perceptual Targets in the Human Brain

Abstract

Many decisions, from crossing a busy street to choosing a profession, require integration of discrete sensory events. Previous studies have shown that integrative decision-making favors more reliable stimuli, mimicking statistically optimal integration. It remains unclear, however, whether reliability biases operate even when they lead to suboptimal performance. To address this issue, we asked human observers to reproduce the average motion direction of two suprathreshold coherent motion signals presented successively and with varying levels of reliability, while simultaneously recording whole-brain activity using electroencephalography. By definition, the averaging task should engender equal weighting of the two component motion signals, but instead we found robust behavioral biases in participants' average decisions that favored the more reliable stimulus. Using population-tuning modeling of brain activity we characterized tuning to the average motion direction. In keeping with the behavioral biases, the neural tuning profiles also exhibited reliability biases. A control experiment revealed that observers were able to reproduce motion directions of low and high reliability with equal precision, suggesting that unbiased integration in this task was not only theoretically optimal but demonstrably possible. Our findings reveal that temporal integration of discrete sensory events in the brain is automatically and suboptimally weighted according to stimulus reliability.

Integrated Perceptual Decisions Rely on Parallel Evidence Accumulation

“…Direction-specific responses started at about .2 s after motion onset and remained strong for the duration of the epoch. The onset time is comparably late 44 for the directionspecific activity to reflect early, sensory responses (e.g., C1/P1), while it closely matches other well-documented correlates of decision-making such as the central-parietal positivity (i.e., CPP) [3][4][5]29,30 . Interestingly, tuning to the derived, average motion direction -which was never actually presented in the displays -was stronger than tuning to either of the two component motion stimuli displayed in the left and right hemifields.…”

“…Continuous EEG data were pre-processed offline using the FASTER 38 pipeline and segmented into 1 s epochs, time-locked to the onset of coherent motion. We chose to measure neural responses using EEG because this method affords millisecond-level temporal resolution, and because we and others have shown that timeresolved direction-selective responses to brief dot-motion stimuli can be reliably decoded from EEG recordings using multivariate decoding [3][4][5]39,40 (Figure 2A EEG analyses. For each trial (i.e., test), the EEG epoch was compared with the average (i.e., the ERP) of other, training trials using multivariate Mahalanobis distance 41 ("-" symbol).…”

“…Decision-making has been widely studied in the laboratory by psychologists [1][2][3][4][5][6] , economists [7][8][9][10][11] and neuroscientists [12][13][14][15][16][17][18][19] over several decades. Significant progress has been made in understanding the computational and neural processes involved, particularly in the realm of simple perceptual decisions on elementary sensory stimuli 1,12,15,20 .…”

“…Significant progress has been made in understanding the computational and neural processes involved, particularly in the realm of simple perceptual decisions on elementary sensory stimuli 1,12,15,20 . A characteristic shared by almost all variants of these simple decision-making paradigms is that just a single taskrelevant stimulus -a patch of moving dots 4 , a static grating 5 or a face 18 -are displayed at a time. In the real world, however, perceptual decisions typically require integration of discrete sensory events over time and space.…”

“…When deciding to cross the road, for example, the locations and speeds of several cars must be considered. Much less is known about these more complex, 'integrative' perceptual decisions [3][4][5][21][22][23][24] . Here we used psychophysics combined with brain imaging and computational modelling to understand how human observers combine visual motion signals in different spatial locations in the service of integrated perceptual decisions.…”