Author response: A neural mechanism of speed-accuracy tradeoff in macaque area LIP (2014)

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods References Decision letter Author response Article and author information Metrics Abstract Decision making often involves a tradeoff between speed and accuracy. Previous studies indicate that neural activity in the lateral intraparietal area (LIP) represents the gradual accumulation of evidence toward a threshold level, or evidence bound, which terminates the decision process. The level of this bound is hypothesized to mediate the speed-accuracy tradeoff. To test this, we recorded from LIP while monkeys performed a motion discrimination task in two speed-accuracy regimes. Surprisingly, the terminating threshold levels of neural activity were similar in both regimes. However, neurons recorded in the faster regime exhibited stronger evidence-independent activation from the beginning of decision formation, effectively reducing the evidence-dependent neural modulation needed for choice commitment. Our results suggest that control of speed vs accuracy may be exerted through changes in decision-related neural activity itself rather than through changes in the threshold applied to such neural activity to terminate a decision. https://doi.org/10.7554/eLife.02260.001 eLife digest Many actions involve a trade-off between speed and accuracy, with typing being a good example: the faster you try to type a sentence, the more mistakes you are likely to make. Mathematical models have successfully reproduced the speed-accuracy trade-off, but it is not clear how the brain represents and weighs up these two factors. Now, Hanks et al. have shown how single neurons in a region of the brain called the lateral intraparietal cortex vary their firing rate to optimize the balance between speed and accuracy. Two macaque monkeys were trained to fixate on a single dot on a screen and then move their eyes in one of two directions in response to movies of random dots on a video screen. Initially, the monkeys received a reward immediately after every correct response, whereas incorrect responses were punished with a very short time-out. Under these conditions, the optimal strategy is to respond quickly at the expense of accuracy. In a separate block of trials, the monkeys were again rewarded for correct responses, but this time their reward was delayed if they responded too quickly. The most effective strategy now is to respond accurately, but more slowly. In both the ‘high speed’ and ‘high accuracy’ conditions, the firing of neurons in lateral intraparietal cortex increased while the dots were on the screen. As soon as the firing rate reached a threshold—representing the point at which the monkey had accumulated enough evidence to make a decision about the direction of movement—the monkey moved its eyes. Previous theories had suggested that when speed was the priority, the level of activity required to trigger a decision would be lower than when accuracy was emphasized. Surprisingly, however, the threshold did not differ between the ‘high speed’ and ‘high accuracy’ conditions. Instead, neurons displayed a higher initial firing rate whenever speed was prioritized, enabling the monkey to make a decision on the basis of less evidence. This finding is consistent with human brain imaging studies that have shown increased baseline activity in decision-making circuitry when speed is prioritized over accuracy. Studying these mechanisms could help to reveal why some individuals are more impulsive decision-makers than others. https://doi.org/10.7554/eLife.02260.002 Introduction It is well known that the accuracy of many types of decisions suffers when there is pressure to decide quickly. Conversely, decision accuracy often improves if temporal demands permit longer deliberation. This balance between decision time and performance is referred to as the speed-accuracy tradeoff (SAT). Examples of SAT are prevalent, with demonstrations for visual, auditory, olfactory, and memory tasks (Green and Luce, 1973; Reed, 1973; Wickelgren, 1977; Luce, 1986; Reddi and Carpenter, 2000; Reddi et al., 2003; Palmer et al., 2005; Rinberg et al., 2006; Ings and Chittka, 2008; Bogacz et al., 2010). Models based on the stochastic accumulation of evidence to a bound provide a unifying behavioral framework that can explain both decisions and the time taken to reach those decisions (Stone, 1960; Laming, 1968; Link, 1992; Ratcliff and Rouder, 1998; Ratcliff and Smith, 2004; Palmer et al., 2005). In this class of models, the decision bound establishes a policy on the amount of evidence needed for decision commitment and thus determines the tradeoff between speed and accuracy. Providing confirmation of this idea, multiple studies have found that human SAT in a variety of decision tasks can be explained by changes in decision bound (Reddi and Carpenter, 2000; Reddi et al., 2003; Palmer et al., 2005). While providing insight into computation, these mathematical models of behavior leave open the question of neural implementation. Neuroimaging studies suggest that SAT may be implemented by changes in baseline neural activity in decision-related areas, with higher baseline responses when speed is given precedence over accuracy (Forstmann et al., 2008; Ivanoff et al., 2008; van Veen et al., 2008; Forstmann et al., 2010; Wenzlaff et al., 2011). This suggests the possibility that a reduction of decision bound in behavioral models is implemented in the brain by increasing the starting level of neural activity in regions that accumulate evidence rather than by reducing the bound level required to terminate a decision. The only neurophysiological study of SAT to date reported that during a visual search task with different response deadlines, SAT was associated with a variety of effects in FEF, including changes in the baseline and gain of visual neurons, the duration of perceptual processing, and the rate of ramping responses of movement neurons (Heitz and Schall, 2012). Here we investigate the neural processes that may underlie SAT by recording from the lateral intraparietal area (LIP) of the rhesus monkey during a visual motion discrimination task. We focus on LIP because numerous studies suggest that neurons in this area exhibit the signatures of both evidence accumulation and a decision bound that leads to commitment for saccadic decisions (Shadlen and Newsome, 2001; Roitman and Shadlen, 2002; Hanks et al., 2006; Yang and Shadlen, 2007; Churchland et al., 2008; Kiani and Shadlen, 2009; Rorie et al., 2010; Churchland et al., 2011; Bollimunta et al., 2012). We found that SAT was implemented in LIP by an evidence-independent signal that was added to responses during the same period that the neurons represent the accumulating evidence leading to a choice. The addition of this time-dependent, evidence-independent signal serves to reduce the amount of evidence needed to terminate a decision in the high speed regime. Results Two rhesus monkeys (Macaca mulatta) were trained to perform a reaction time (RT) motion discrimination task in two speed-accuracy regimes (Figure 1). The stimulus consisted of a patch of dynamic random dots, with different motion strengths randomly interleaved on a trial-by-trial basis (‘Materials and methods’). The task of the subjects was to determine the direction of the coherent motion and to indicate a choice by making an eye movement to a corresponding target. Since this was a RT task, the viewing epoch was not preset, and the monkey controlled when to make a choice on each trial. Figure 1 Download asset Open asset Choice-reaction time task. Monkeys were required to identify the net direction of motion in a dynamic random dot display. Several levels of difficulty were randomly interleaved. The monkeys indicated their choice by making a saccadic eye movement to the appropriate choice target. They could do so at any time after onset of the random dot motion stimulus. Reaction time (RT) was measured from the time of motion onset until initiation of the saccade. The monkeys learned to perform the task in two different speed-accuracy regimes. For neural recordings, one choice target was placed in the response field of a single LIP neuron, as illustrated, and the other target was placed in the opposite hemifield. The circle showing the RF is for illustration purposes and was not displayed to the monkey. The target in the neuron's RF is referred to as Tin; the other target is referred to as Tout. https://doi.org/10.7554/eLife.02260.003 We manipulated the monkeys' SAT primarily by controlling the timing of reward delivery. To encourage speed, we simply rewarded the monkey immediately after each correct response, since, in our experience, monkeys are naturally inclined to make fast responses at the expense of accuracy. To encourage accuracy, reward was delayed relative to motion onset by 600–800 ms, so that fast responses involved a short additional wait until delivery of reward. We refer to the first regime as the ‘speed’ regime and the second as the ‘accuracy’ regime. Each speed-accuracy regime was maintained for many consecutive daily sessions. Neural data were collected when the monkey had achieved a stable set point for each regime. We first characterize the monkeys' behavior in the two speed-accuracy regimes. We show that the change in behavior in the different speed-accuracy regimes is explained by a change in the decision criterion. Second, we turn to a neural correlate of this change. We show that LIP neurons reflect the change in the decision policy as a motion evidence-independent signal that adds to the responses throughout the period of evidence accumulation. Behavior In both regimes, stronger motion was associated with improved choice accuracy and faster RTs, as previously shown (Figure 2A; Roitman and Shadlen, 2002). Behavior during the two regimes revealed a tradeoff between speed and accuracy. In the high accuracy regime, both monkeys exhibited longer RTs and improved performance (Figure 2A). This observation is important because it indicates that the monkeys did not slow down merely by delaying the motor response, but rather, they used the time to accumulate more evidence from the stimulus. Similarly, if the monkeys had failed to pay attention to the early portion of the stimulus, an improvement in accuracy should not have accompanied the longer RTs. Figure 2 Download asset Open asset Decision accuracy and reaction time in two speed-accuracy regimes are explained by bounded evidence accumulation. (A) Data from two monkeys. Black symbols and lines show the high accuracy regime. Red symbols and lines show the high speed regime. The top graph shows the proportion of correct choices plotted as a function of motion strength. Decision accuracy improved with stronger motion in both regimes. The bottom graph shows mean RT (±SEM) plotted as a function of motion strength. RTs decreased for stronger motion in both regimes. In the regime where the monkeys responded more slowly, their performance improved relative to the other regime. N = 3534 and 3307 trials for the high speed and high accuracy regimes, respectively, for monkey D and 4838 and 4733 trials for the same regimes for monkey E. (B and C) Bounded evidence-accumulation models. Noisy evidence is accumulated until it reaches a terminating threshold or bound, which establishes the time and sign of the decision. (B) Traditionally, this is represented as a single accumulation—or drift-diffusion process—between an upper and lower bound. (C) The model can be described equivalently as a race between two anticorrelated drift-diffusion processes, such that negative evidence for one process is positive evidence for the other. The decision is determined by the first accumulation to reach the bound. The traces in (A) show fits to the data using the bounded accumulation model. https://doi.org/10.7554/eLife.02260.004 A bounded accumulation model can explain the behavior in both regimes (Figure 2B). The traditional depiction of this model (Figure 2B) would represent the accumulation of evidence as a random walk between two terminating bounds. We consider a version of this model that is more consistent with neurophysiology (Figure 2C) in which one process is replaced by two competing processes that accumulate moment-by-moment sensory evidence bearing on each alternative (Usher and McClelland, 2001). The choice is dictated by which process first reaches the bound (Figure 2C). This two-process model explains the established neural representations of both saccadic choices in LIP for this task (Mazurek et al., 2003). In the model, RT is determined by a combination of the time taken to reach the bound and a stimulus-independent non-decision time. As explained in ‘Materials and methods’, the models in Figure 2B,C can be treated as mathematically equivalent. For both depictions, the distance from the starting point of evidence accumulation to the bound, termed the ‘excursion’, determines how much evidence must be accumulated in order to commit to a choice. In theory, the excursion may change as a function of decision time, for example, if the bound collapses during the course of decision formation. However, for simplicity, we first consider the restricted set of models with static excursions. Monkeys traded off speed and accuracy by changing the amount of evidence that was needed to commit to a choice. In particular, SAT was explained by the bounded accumulation model with a change in the decision variable excursion. Overall, the excursion was reduced by 43% ± 2% in the high speed regime compared to the high accuracy regime (monkey D: 58% ± 3%; monkey E: 23% ± 4%). Thus, less accumulated evidence was needed to commit to a choice in the high speed regime compared to the high accuracy regime. The change in SAT was not explained in a consistent way by the other model parameters. Overall, we observed a 33.4 ± 5.8 ms shorter non-decision time in the high speed regime compared to the accuracy regime, and this effect was not consistent between the two monkeys (monkey D: 3.1 ± 5.2 ms longer in high speed regime; monkey E: 63.7 ± 9.1 ms shorter in high speed regime). Importantly, the RT elongation in the high accuracy regime exceeded any changes observed in non-decision time by a large margin (mean RT elongation, monkey D: 176 ± 4 ms; monkey E: 159 ± 3 ms). Moreover, if changes in RTs were solely a result of altered non-decision time, there would be no associated changes in accuracy. In principle, changes in the SAT could be associated with changes in the signal-to-noise ratio of the sensory evidence, owing perhaps to a change in attentional state, which would lead to a change of sensitivity to motion strength (parameter k of the model). We cannot rule this out, but the model fits provided inconsistent support for such a mechanism in the two monkeys. In the high speed regime, k changed by a factor of 1.36 ± 0.12 and 0.78 ± 0.07, respectively, for monkeys D and E (1.01 ± 0.06 in combined data). We cannot rule out the possibility that some of the behavioral change was manifested by a competing process in which the monkey simply guessed quickly, so-called ‘fast guesses’ (Ratcliff and Rouder, 1998). A fast guess process could lead to faster and less accurate choices in the high speed regime. However, the level of accuracy for both monkeys at the highest motion strength in the high speed regime suggests that any such fast guess mechanism plays a relatively minor role because fast guesses would reduce accuracy for all stimulus strengths. Together, these considerations show that the change in SAT was achieved primarily via a process that alters the excursion required for the accumulating evidence to support termination. Traditionally, bounded accumulation models implement this change in excursion by altering the bound height. However, the brain is thought to employ competing accumulator processes for decision making (Figure 2C; Usher and McClelland, 2001; Gold and Shadlen, 2007; Churchland et al., 2008). Accordingly, the brain can achieve a reduction in the excursion of accumulated evidence in two ways: (1) by lowering the bounds for both processes, or (2) by adding a signal to the accumulated evidence for both processes. These can be treated as mathematically equivalent: in both cases the effect of the change in the excursion is to alter the amount of accumulated evidence necessary to terminate the process. However, they imply different neural mechanisms. Neurophysiology We recorded neural activity from 70 neurons while the monkeys performed the task (35 in the high speed regime and 35 in the high accuracy regime). In each recording session, we screened neurons based on whether they exhibited spatial selectivity during a delayed saccade task and persistent delay activity during a memory-guided saccade task. For the motion discrimination task, one of the choice targets was placed in the RF of each recorded neuron; the other choice target was placed in the opposite hemifield. In both speed-accuracy regimes, LIP neurons exhibited signatures of evidence accumulation. These features are illustrated in Figure 3, and they are similar to previous observations (Roitman and Shadlen, 2002; Churchland et al., 2008). Initially, the responses were elevated in the presence of the choice targets, one of which was in the neuron's RF. For the first 200 ms after motion onset, there was no relationship between stimulus strength and firing rate (Figure 3A,B; Equation 1, p>0.1 for both regimes in each monkey). Onset of the random dot motion (outside the RF) then induced a transient depression in the firing rate that recovered over a 200-ms period. This pattern was similar for speed and accuracy regimes, but the average firing rate in this epoch was 15% ± 1% lower in the high accuracy regime (monkey D: 24% ± 2% lower in the high accuracy regime; monkey E: 8% ± 2% lower in the high accuracy regime; p<0.01 in all cases), as discussed in detail below. Figure 3 Download asset Open asset Comparison of neural responses accompanying decision formation in the two speed-accuracy regimes. (A and B) Population firing rates plotted as function of time from stimulus onset and sorted by stimulus strength. Lighter green colors correspond to stronger Tin motion; lighter orange colors correspond to stronger Tout motion; darker colors correspond to intermediate stimulus strengths. Averages depict combined data from both monkeys. In both regimes, the responses exhibit characteristic ramping profiles that depend on stimulus strength approximately 200 ms after stimulus onset. Prior to averaging, responses for each neuron were normalized based on the peri-saccadic response (0–50 ms prior to saccade initiation) measured during a block of visually-guided delayed saccade trials. N = 35 neurons in each speed-accuracy regime. Error bars show SEM. For each condition, traces end at the point beyond which fewer than 50% of the trials would contribute to the averages, owing to the different RTs for each trial. (C) Buildup rate plotted as a function of motion coherence for the high accuracy regime (black) and the high speed regime (red). Error bars show SEM for the estimated buildup rates for each data point. Line fits capture the relationship between buildup rate and motion strength. Overall, the buildup rates were shifted to larger values in the high speed regime, but the change in buildup rate per unit motion strength was similar in the two regimes. https://doi.org/10.7554/eLife.02260.005 After the transient depression in the firing rate, the average responses began to display characteristic ramping activity that depended on stimulus strength and direction (Figure 3A,B). To characterize this relationship, we estimated the ‘buildup rate’ for each motion strength (‘Materials and methods’). motion in the Tin direction to larger buildup and stronger motion in the Tout direction to buildup rates (Figure Equation p<0.01 for each monkey in both This is consistent with the bounded accumulation which that the rate of the decision variable should depend on the strength of the evidence. larger buildup rates were observed in the high speed regime (Figure Equation 3, for each monkey). Importantly, however, there was not a between the of the relationship between buildup rate and motion strength between the two regimes 3, p>0.1 for each monkey). This suggests that the sensory evidence the decision variable in the same way in both speed-accuracy regimes, consistent with the from the fits to In both speed-accuracy regimes, LIP neurons a of decision commitment. saccade initiation for Tin rather than stimulus responses a level of activity (Figure Equation 1, for coherence effect ms the saccade for each monkey in both The of Tin responses is when responses are by RT rather than stimulus strength (Figure Equation for RT effect ms the saccade for each monkey in both This pattern is consistent with the that LIP responses support the presence of a decision bound which when terminates and commitment to a choice. Figure 4 Download asset Open asset Comparison of neural responses accompanying decision in the two speed-accuracy regimes. (A) responses plotted as a function of motion coherence for Tin rates were measured in a ms ms the saccade and speed regime in and The is consistent with the that the responses reach a Tin for all motion strengths. is to Figure (B and C) Tin responses plotted as a function of time from the saccade and sorted by Averages depict combined data from both monkeys. In both speed-accuracy regimes, responses are the saccade. N = 35 neurons in each speed-accuracy regime. Error bars show SEM. the level of this decision bound was by the speed-accuracy regime. that the bounded evidence-accumulation model explained the behavioral data with a change in the excursion of the decision variable in the two regimes. The brain could implement a change in excursion by altering the threshold level of LIP firing rate necessary to commit to a choice. However, we did not between the firing rates in the two regimes 200 ms of the saccade for monkey (Figure Equation for each monkey). Figure Download asset Open asset tradeoff alters LIP responses from the beginning of the decision process. (A) Population firing rates plotted for each monkey. Red and traces correspond to the speed and accuracy regimes, the responses are plotted as a function of time from motion onset both directions of motion and all motion strengths as in Figures 3 and contribute data to the average only up to ms the saccade. Averages are shown up to the time when of the 50% coherent motion trials contribute the responses are plotted as a function of time from the saccade. are all trials that in a Tin choice. contribute data to the average only from 200 ms after motion onset shows SEM. N = and neurons for the high speed and high accuracy regimes, respectively, for monkey N = and neurons for the high speed and high accuracy regimes, respectively, for monkey E. (B) The relationship between the of the evidence-independent neural response and mean RT The response the evidence-independent of the firing rate early in the decision as a of the average response from the neuron ms all Tin choices (‘Materials and methods’). values imply that less evidence-dependent signal is required to reach the bound. A of 1 indicates that on average the evidence-independent signal would reach the by from monkey D are shown as neurons from monkey E are shown as Red symbols correspond to neurons recorded in the high speed regime; symbols correspond to the high accuracy regime. are to the 50% region based on the for each speed-accuracy regime. The in each regime were the excursion could be by of an evidence-independent signal that adds to the accumulated evidence. We for such a signal by responses in each speed-accuracy regime all stimulus strengths and both directions to the of the response that was not on the evidence. This evidence-independent was elevated in the high speed regime relative to the high accuracy regime (Figure In one this began from motion onset and throughout the period of evidence accumulation For the other a ms after motion onset and at all time Thus, for both LIP neurons a excursion in firing rate from the to the end of the decision in the high speed regime compared to the high accuracy regime. It that an evidence-independent signal effectively the neural of accumulated evidence to a This additional support from an of all recorded neurons, there was a negative between the estimated evidence-independent neural response and the mean RTs for the corresponding behavioral (Figure = this is to a large by the changes in the mean values between the two regimes. there is each regime, and for neurons in each regime the was = and for high speed and high accuracy regimes, in both these changes in the evidence-independent neural signal explained the changes observed in the behavior between the two speed-accuracy regimes. way to this is to the behavior in both regimes with the bounded accumulation model such that the only between the two regimes is established by the measured evidence-independent neural that in both regimes, the evidence-independent signal increased over time (Figure consistent with reported previously 2006; Churchland et al., 2008; et al., For each we the evidence-independent in each regime with (Figure and used these to control the excursion for each regime. Importantly, the change in excursion between the two regimes is not to the behavioral it is measured from the neural In this the only in the model are the that are between the two regimes (‘Materials and methods’). This model provided a of the of the changes in performance and RT for the two regimes = for monkey = for monkey Figure Figure Download asset Open asset The signal explains the of the behavioral change between speed-accuracy regimes. (A) to the neural responses from 200 ms after motion onset time = to the point in time beyond which fewer than 50% of the trials contribute to the for the highest motion strength in each regime (Figure Equation in ‘Materials and methods’). is for monkey is for monkey E. (B) fits to show behavioral data from Figure The are model fits to the model are to be between speed-accuracy regimes for the in which is measured from the neural In traditional models, the one in Figure the evidence-independent signal would be represented by a change in the of the bound 2006; et al., 2012). In the high speed regime, the starting point of the bound is to the and the rate of is more In the (Figure a change in bound is to a change in the starting point of both and a