Evidence against perfect integration of sensory information during perceptual decision making

Abstract

Perceptual decision making is often modeled as perfect integration of sequential sensory samples until the accumulated total reaches a fixed decision bound. In that view, the buildup of neural activity during perceptual decision making is attributed to temporal integration. However, an alternative explanation is that sensory estimates are computed quickly with a low-pass filter and combined with a growing signal reflecting the urgency to respond and it is the latter that is primarily responsible for neural activity buildup. These models are difficult to distinguish empirically because they make similar predictions for tasks in which sensory information is constant within a trial, as in most previous studies. Here we presented subjects with a variant of the classic constant-coherence motion discrimination (CMD) task in which we inserted brief motion pulses. We examined the effect of these pulses on reaction times (RTs) in two conditions: 1) when the CMD trials were blocked and subjects responded quickly and 2) when the same CMD trials were interleaved among trials of a variable-motion coherence task that motivated slower decisions. In the blocked condition, early pulses had a strong effect on RTs but late pulses did not, consistent with both models. However, when subjects slowed their decision policy in the interleaved condition, later pulses now became effective while early pulses lost their efficacy. This last result contradicts models based on perfect integration of sensory evidence and implies that motion signals are processed with a strong leak, equivalent to a low-pass filter with a short time constant.

Modulation of Premotor and Primary Motor Cortical Activity during Volitional Adjustments of Speed-Accuracy Trade-Offs

“…These include the driftdiffusion model (Ratcliff, 1978;Gold and Shadlen, 2007), which assumes perfect integration of sensory evidence to a fixed accuracy criterion, as well as variations in which the accuracy criterion decreases over time (Drugowitsch et al, 2012) possibly due to a rising urgency signal (Ditterich, 2006; or in which integration is "leaky" (Usher and McClelland, 2001). One can conceive of each of these models as lying within a space defined by different assumptions about parameter settings (Thura, 2015). For example, the drift-diffusion model lies at a corner corresponding to zero leak and zero urgency, the leaky competing accumulator (Usher and McClelland, 2001) assumes leak but no urgency, the bounded accumulator with urgency (Drugowitsch et al, 2012) assumes no leak but a growing urgency, and the UGM assumes both a large leak and growing urgency (Cisek et al, 2009;.…”

“…However, the UGM provides a good fit to such data as well; and indeed, Hawkins et al (2015a) showed that it fits better than perfect accumulator models without urgency to the classic data from Roitman and Shadlen (2002). Furthermore, Carland et al (2015) showed that, even during random-dot motion discrimination tasks, the motion signal is not integrated with the long time constant usually assumed by EAMs, but with a time constant on the order of 200 ms. This is consistent with the UGM and with the proposal that neural activity buildup is primarily caused by an urgency signal.…”

“…In previous publications, we suggested that the brain only integrates that novel information (Cisek et al, 2009;. For example, we have recently shown that, during random-dot motion discrimination, the motion signal is not integrated over time, as usually assumed (Carland et al, 2015), and that what is instead integrated are the changes in the motion signal .All arm and eye movement data were analyzed off-line using MATLAB (The MathWorks). Reaching characteristics were assessed using monkeys' movement kinematics.…”

The Role of the Lateral Intraparietal Area in (the Study of) Decision Making

“…This pattern of residuals has been cast in decision-related terms as an added urgency signal that serves to push the accumulated evidence closer to the bound (Carland et al 2016, Churchland et al 2008, Cisek et al 2009, Hanks et al 2014). In LIP experiments, this urgency signal was originally defined as the residuals between the observed LIP response and the predicted time course based on symmetric drift-diffusion.…”

Strategic and Dynamic Temporal Weighting for Perceptual Decisions in Humans and Macaques

“…In many instances, subjects have exhibited “early weighting,” where sensory evidence presented in early epochs contributes more to choices than that in late (Huk and Shadlen, 2005; Kiani et al, 2008, Nienborg and Cumming, 2009; Yates et al, 2017). In other instances, however, “late weighting” has been observed, where choices were primarily influenced by sensory evidence presented in late stimulus epochs (Tsetsos et al, 2012; Cheadle et al, 2014; Bronfman et al, 2016; Carland et al, 2016). In rodents, a mixture of either early or flat weighting profiles has been reported (Erlich et al, 2015; Scott et al, 2015; Pinto et al, 2017; Licata et al, 2017).…”