Stimulus reliability automatically biases temporal integration of discrete perceptual targets

Abstract

Decision making is a ubiquitous cognitive process that determines choice behaviour. In recent years there has been increased interest in how information about multiple discrete sensory events are combined in support of single, integrated decisions. Previous studies have shown that integrative decision-making is biased in favour of more reliable stimuli. As reliability-weighted integration typically mimics statistically optimal integration, it remains unclear whether reliability biases are automatic or strategic. To dissociate reliability-weighting and optimal decisions, we developed a task that required participants to monitor two successive epochs containing brief, suprathreshold coherent motion signals which varied in their reliability. Rather than judging the individual target motion directions, however, participants had to reproduce the average motion direction of the two targets. Using mixture distribution modelling and linear regression to model behavioural data, we found robust biases in favour of the more reliable stimulus, despite the fact that unbiased responses were optimal in our paradigm. Using population-tuning modelling to characterise feature specific brain activity recorded using electroencephalography, we observed robust and sustained feature-specific responses to target signals in both epochs. Using the same method, we were able to capture the temporal dynamics of integrated decision-making by characterising tuning to the average motion direction. Critically, the tuning profiles to the average motion direction exhibited biases in favour of the more reliable signal, in keeping with the modelled behavioural responses. Taken together, our findings reveal that temporal integration of discrete sensory events is automatically and suboptimally weighted according to stimulus reliability.

Implicit Neurofeedback Training of Feature-Based Attention Promotes Biased Sensory Processing during Integrative Decision-Making

“…where W reflects the decision weight for the trained and untrained motion direction, x reflects response error relative to the average motion direction on each trial (D°), and Y reflects the distance of the trained and untrained motion directions from the average motion direction on each trial (D°). To account for the circular nature of the data, both x and Y were expressed as complex numbers (i.e., as Cartesian coordinates) (Brouwer and Heeger, 2009;Rangelov et al, 2020).…”

“…Studies of decision-making based on single stimuli have shown that factors, such as the rate of evidence accumulation, or the decision threshold, vary for different decision alternatives (Salzman et al, 1990;Gao et al, 2011;John-Saaltink et al, 2016). There is also evidence to suggest that integrative decisions are biased toward more reliable stimuli (Raposo et al, 2012;Brunton et al, 2013), even when this leads to suboptimal decisions (Rangelov et al, 2020). We set out to investigate the temporal dynamics of evidence accumulation during integrative decision-making by using an implicit neurofeedback protocol to unconsciously reward prioritized processing of one stimulus over another.…”