Abstract
Decisions are faster and less accurate when conditions favor speed, and are slower and more accurate when they favor accuracy. This speed-accuracy trade-off (SAT) can be explained by the principles of bounded integration, where noisy evidence is integrated until it reaches a bound. Higher bounds reduce the impact of noise by increasing integration times, supporting higher accuracy (vice versa for speed). These computations are hypothesized to be implemented by feedback inhibition between neural populations selective for the decision alternatives, each of which corresponds to an attractor in the space of network states. Since decision-correlated neural activity typically reaches a fixed rate at the time of commitment to a choice, it has been hypothesized that the neural implementation of the bound is fixed, and that the SAT is supported by a common input to the populations integrating evidence. According to this hypothesis, a stronger common input reduces the difference between a baseline firing rate and a threshold rate for enacting a choice. In simulations of a two-choice decision task, we use a reduced version of a biophysically-based network model (Wong and Wang, 2006) to show that a common input can control the SAT, but that changes to the threshold-baseline difference are epiphenomenal. Rather, the SAT is controlled by changes to network dynamics. A stronger common input decreases the model's effective time constant of integration and changes the shape of the attractor landscape, so the initial state is in a more error-prone position. Thus, a stronger common input reduces decision time and lowers accuracy. The change in dynamics also renders firing rates higher under speed conditions at the time that an ideal observer can make a decision from network activity. The difference between this rate and the baseline rate is actually greater under speed conditions than accuracy conditions, suggesting that the bound is not implemented by firing rates per se.
Highlights
In decision making experiments, subjects make faster, less accurate decisions when conditions favor speed, and make slower, more accurate decisions when conditions favor accuracy (e.g., Bogacz et al, 2010a; Heitz and Schall, 2012)
We demonstrate that the threshold-baseline difference cannot account for the speed-accuracy trade-off (SAT) in the model, since raising the threshold to compensate for the higher baseline activity under the speed condition does not “untrade” speed and accuracy, i.e., reinstating the threshold-baseline difference of the neutral condition does not recover the neutral behavior of the model
Our results conflict with the threshold-baseline hypothesis because the changes in network dynamics engendered by a uniform input dwarf the corresponding changes to the threshold-baseline difference
Summary
Subjects make faster, less accurate decisions when conditions favor speed, and make slower, more accurate decisions when conditions favor accuracy (e.g., Bogacz et al, 2010a; Heitz and Schall, 2012). We use a neurally-derived model (Wong and Wang, 2006) to demonstrate that adjusting the strength of spatially nonselective excitation can control the SAT (Furman and Wang, 2008; Roxin and Ledberg, 2008) We demonstrate that this signal raises (lowers) the baseline activity of integrator populations, consistent with higher (lower) baseline activity under speed (accuracy, neutral) conditions in SAT experiments (Forstmann et al, 2008; Ivanoff et al, 2008; van Veen et al, 2008; Wenzlaff et al, 2011; Heitz and Schall, 2012; Hanks et al, 2014). These results demonstrate that the threshold-baseline hypothesis does not account for the SAT under the principles of the attractor framework
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