Neural dynamics implement a flexible decision bound with a fixed firing rate for choice: a model-based hypothesis

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.

Categorical evidence, confidence, and urgency during probabilistic categorization

“…The final function we review is urgency, a monotonically-increasing signal hypothesized to represent an effective mechanism to modulate decision thresholds within trials (Cisek et al, 2009; Standage, Blohm, & Dorris, 2014; Standage, Wang, & Blohm, 2014; Standage, You, Wang, & Dorris, 2011; Thura, Beauregard-Racine, Fradet, & Cisek, 2012). Previous groups have modeled urgency in two different ways.…”

Characterizing the role of the pre‐SMA in the control of speed/accuracy trade‐off with directed functional connectivity mapping and multiple solution reduction

“…Finally, as our analysis focused heavily on explanations for SAT that adopt an accumulate‐to‐threshold framework, it may be difficult to extend our inferences to theories of SAT rooted in attractor models (Furman & Wang, ; Roxin & Ledberg, ; Standage, Blohm, & Dorris, ; Standage, Wang, & Blohm, ). Relatedly, our results do not address another leading theory of SAT, which posits that the subthalamic nucleus (STN), in response to a control signal from the ACC or pre‐SMA, raises response thresholds under accuracy‐emphasis by inhibiting motor circuitry (Bogacz et al, ; Frank et al, ; Frank, Scheres, & Sherman, ).…”

“…Furthermore, other theoretical accounts challenge the assumption that distance‐to‐threshold changes are the primary driver of SAT. Specifically, work using attractor network models, which replicate features of neural circuits putatively responsible for decision making, has suggested that a common excitatory input to decision circuit controls SAT by altering the strength of network dynamics, rather than through the modulation of thresholding (Furman & Wang, ; Roxin & Ledberg, ; Standage, Wang, & Blohm, ). However, Standage, Blohm, and Dorris () proposed that a “unifying” account, in which top‐down excitatory signals project to both decision‐making attractor networks and to thresholding circuitry, is plausible given the current state of evidence in the SAT literature.…”

“…Hence, although current theories disagree about how specific decision processes change in SAT, most share the assumption that regions widely believed to be involved in sensory evidence accumulation or motor thresholding receive an excitatory input under speed emphasis, and that this input is provided by regions involved in the top‐down control of strategy adjustment (Standage, Blohm, & Dorris, ; Standage, Wang, & Blohm, ). Prior human neuroimaging work aimed at testing the “cortical” and “striatal” accounts of SAT has suggested that several regions previously found to be involved in cognitive control processes, including the dorsolateral prefrontal cortex (DLPFC: van Veen et al, ), the pre‐SMA (Forstmann et al, , ), and the anterior cingulate cortex (ACC; van Maanen et al, ), may be the source of such control signals.…”

Disinhibition as a canonical neural mechanism for flexible behavior

“…Thus, disinhibition not only offers a plausible mechanism by which generic cortical circuitry can be modulated to support DM, but further offers a mechanism for controlling decision processing according to task conditions. Such flexible cognitive control is fundamental to choice behaviour [see Standage, Wang, and Blohm (2014)].…”