Alexander C. Huk

The effect of stimulus strength on the speed and accuracy of a perceptual decision

Both the speed and the accuracy of a perceptual judgment depend on the strength of the sensory stimulation. When stimulus strength is high, accuracy is high and response time is fast; when stimulus strength is low, accuracy is low and response time is slow. Although the psychometric function is well established as a tool for analyzing the relationship between accuracy and stimulus strength, the corresponding chronometric function for the relationship between response time and stimulus strength has not received as much consideration. In this article, we describe a theory of perceptual decision making based on a diffusion model. In it, a decision is based on the additive accumulation of sensory evidence over time to a bound. Combined with simple scaling assumptions, the proportional-rate and power-rate diffusion models predict simple analytic expressions for both the chronometric and psychometric functions. In a series of psychophysical experiments, we show that this theory accounts for response time and accuracy as a function of both stimulus strength and speed-accuracy instructions. In particular, the results demonstrate a close coupling between response time and accuracy. The theory is also shown to subsume the predictions of Piérons Law, a power function dependence of response time on stimulus strength. The theorys analytic chronometric function allows one to extend theories of accuracy to response time.

Spikes versus BOLD: what does neuroimaging tell us about neuronal activity?

By demonstrating that fMRI responses in human MT+ increase linearly with motion coherence and comparing these responses with slopes of single-neuron firing rates in monkey MT, a new paper provides the best evidence so far that fMRI responses are proportional to firing rates.

Neuronal Basis of the Motion Aftereffect Reconsidered

Several fMRI studies have reported MT+ response increases correlated with perception of the motion aftereffect (MAE). However, attention can strongly affect MT+ responses, and subjects may naturally attend more to the MAE than control trials without MAE. We found that requiring subjects to attend to motion on both MAE and control trials produced equal levels of MT+ response, suggesting that attention may have confounded the interpretation of previous experiments; in our data, attention accounts for the entire effect. After eliminating this confound, we observed that direction-selective motion adaptation produced a direction-selective imbalance in MT+ responses (and earlier visual areas), and yielded a corresponding asymmetry in speed discrimination thresholds. These findings provide physiological evidence that population level response imbalances underlie the MAE, and quantify the relative proportions of direction-selective neurons across human visual areas.

Neural circuit dynamics underlying accumulation of time-varying evidence during perceptual decision making

How do neurons in a decision circuit integrate time-varying signals, in favor of or against alternative choice options? To address this question, we used a recurrent neural circuit model to simulate an experiment in which monkeys performed a direction-discrimination task on a visual motion stimulus. In a recent study, it was found that brief pulses of motion perturbed neural activity in the lateral intraparietal area (LIP), and exerted corresponding effects on the monkeys choices and response times. Our model reproduces the behavioral observations and replicates LIP activity which, depending on whether the direction of the pulse is the same or opposite to that of a preferred motion stimulus, increases or decreases persistently over a few hundred milliseconds. Furthermore, our model accounts for the observation that the pulse exerts a weaker influence on LIP neuronal responses when the pulse is late relative to motion stimulus onset. We show that this violation of time-shift invariance (TSI) is consistent with a recurrent circuit mechanism of time integration. We further examine time integration using two consecutive pulses of the same or opposite motion directions. The induced changes in the performance are not additive, and the second of the paired pulses is less effective than its standalone impact, a prediction that is experimentally testable. Taken together, these findings lend further support for an attractor network model of time integration in perceptual decision making.

Single-trial spike trains in parietal cortex reveal discrete steps during decision-making

A better way to explain neuronal activity A brain region called the lateral intraparietal (LIP) area is involved in primate decision-making. The dominant model to explain neuronal firing in LIP assumes that neurons slowly accumulate sensory evidence in favor of one choice or another. Latimer et al. hypothesized that neurons instead exhibit rapid steps or jumps in their firing rate, reflecting discrete changes in the animals decision state. They recorded from LIP neurons in macaque monkeys performing a motion-discrimination task. LIP spike trains in most cells involved discrete stepping dynamics rather than slow evidence integration dynamics. Science, this issue p. 184 Macaque cortical neurons exhibit rapid “jumps” in spike rate, reflecting discrete changes in the animal’s decision state. Neurons in the macaque lateral intraparietal (LIP) area exhibit firing rates that appear to ramp upward or downward during decision-making. These ramps are commonly assumed to reflect the gradual accumulation of evidence toward a decision threshold. However, the ramping in trial-averaged responses could instead arise from instantaneous jumps at different times on different trials. We examined single-trial responses in LIP using statistical methods for fitting and comparing latent dynamical spike-train models. We compared models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete “stepping” dynamics. Roughly three-quarters of the choice-selective neurons we recorded were better described by the stepping model. Moreover, the inferred steps carried more information about the animal’s choice than spike counts.

Disparity- and velocity-based signals for three-dimensional motion perception in human MT+

How does the primate visual system encode three-dimensional motion? The macaque middle temporal area (MT) and the human MT complex (MT+) have well-established sensitivity to two-dimensional frontoparallel motion and static disparity. However, evidence for sensitivity to three-dimensional motion has remained elusive. We found that human MT+ encodes two binocular cues to three-dimensional motion: changing disparities over time and interocular comparisons of retinal velocities. By varying important properties of moving dot displays, we distinguished these three-dimensional motion signals from their constituents, instantaneous binocular disparity and monocular retinal motion. An adaptation experiment confirmed direction selectivity for three-dimensional motion. Our results indicate that MT+ carries critical binocular signals for three-dimensional motion processing, revealing an important and previously overlooked role for this well-studied brain area.

Encoding and decoding in parietal cortex during sensorimotor decision-making

It has been suggested that the lateral intraparietal area (LIP) of macaques plays a fundamental role in sensorimotor decision-making. We examined the neural code in LIP at the level of individual spike trains using a statistical approach based on generalized linear models. We found that LIP responses reflected a combination of temporally overlapping task- and decision-related signals. Our model accounts for the detailed statistics of LIP spike trains and accurately predicts spike trains from task events on single trials. Moreover, we derived an optimal decoder for heterogeneous, multiplexed LIP responses that could be implemented in biologically plausible circuits. In contrast with interpretations of LIP as providing an instantaneous code for decision variables, we found that optimal decoding requires integrating LIP spikes over two distinct timescales. These analyses provide a detailed understanding of neural representations in LIP and a framework for studying the coding of multiplexed signals in higher brain areas.

Signal Multiplexing and Single-Neuron Computations in Lateral Intraparietal Area During Decision-Making

Previous work has revealed a remarkably direct neural correlate of decisions in the lateral intraparietal area (LIP). Specifically, firing rate has been observed to ramp up or down in a manner resembling the accumulation of evidence for a perceptual decision reported by making a saccade into (or away from) the neurons response field (RF). However, this link between LIP response and decision formation emerged from studies where a saccadic target was always stimulating the RF during decisions, and where the neural correlate was the averaged activity of a restricted sample of neurons. Because LIP cells are (1) highly responsive to the presence of a visual stimulus in the RF, (2) heterogeneous, and (3) not clearly anatomically segregated from large numbers of neurons that fail selection criteria, the underlying neuronal computations are potentially obscured. To address this, we recorded single neuron spiking activity in LIP during a well-studied moving-dot direction–discrimination task and manipulated whether a saccade target was present in the RF during decision-making. We also recorded from a broad sample of LIP neurons, including ones conventionally excluded in prior studies. Our results show that cells multiplex decision signals with decision-irrelevant visual signals. We also observed disparate, repeating response “motifs” across neurons that, when averaged together, resemble traditional ramping decision signals. In sum, neural responses in LIP simultaneously carry decision signals and decision-irrelevant sensory signals while exhibiting diverse dynamics that reveal a broader range of neural computations than previously entertained.

Dissociated functional significance of decision-related activity in the primate dorsal stream

During decision making, neurons in multiple brain regions exhibit responses that are correlated with decisions. However, it remains uncertain whether or not various forms of decision-related activity are causally related to decision making. Here we address this question by recording and reversibly inactivating the lateral intraparietal (LIP) and middle temporal (MT) areas of rhesus macaques performing a motion direction discrimination task. Neurons in area LIP exhibited firing rate patterns that directly resembled the evidence accumulation process posited to govern decision making, with strong correlations between their response fluctuations and the animal’s choices. Neurons in area MT, in contrast, exhibited weak correlations between their response fluctuations and choices, and had firing rate patterns consistent with their sensory role in motion encoding. The behavioural impact of pharmacological inactivation of each area was inversely related to their degree of decision-related activity: while inactivation of neurons in MT profoundly impaired psychophysical performance, inactivation in LIP had no measurable impact on decision-making performance, despite having silenced the very clusters that exhibited strong decision-related activity. Although LIP inactivation did not impair psychophysical behaviour, it did influence spatial selection and oculomotor metrics in a free-choice control task. The absence of an effect on perceptual decision making was stable over trials and sessions and was robust to changes in stimulus type and task geometry, arguing against several forms of compensation. Thus, decision-related signals in LIP do not appear to be critical for computing perceptual decisions, and may instead reflect secondary processes. Our findings highlight a dissociation between decision correlation and causation, showing that strong neuron-decision correlations do not necessarily offer direct access to the neural computations underlying decisions.

A Motion Aftereffect From Still Photographs Depicting Motion

A photograph of an action can convey a vivid sense of motion. Does the inference of motion from viewing a photograph involve the same neural and psychological representations used when one views physical motion? In this study, we tested whether implied motion is represented by the same direction-selective signals involved in the perception of real motion. We made use of the motion aftereffect, a visual motion illusion. Three experiments showed that viewing a series of static photographs with implied motion in a particular direction produced motion aftereffects in the opposite direction, as assessed with real-motion test probes. The transfer of adaptation from motion depicted in photographs to real motion demonstrates that the perception of implied motion activates direction-selective circuits that are also involved in processing real motion.