Jacob L. Yates

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.

V1 is not uniquely identified by polarity reversals of responses to upper and lower visual field stimuli

The cruciform hypothesis states that if a visual evoked potential component originates in V1, then stimuli placed in the upper versus lower visual fields will generate responses with opposite polarity at the scalp. This diagnostic has been used by many studies as a definitive marker of V1 sources. To provide an empirical test of the validity of the cruciform hypothesis, we generated forward models of cortical areas V1, V2 and V3 that were based on realistic estimates of the 3-D shape of these areas and the shape and conductivity of the brain, skull and scalp. Functional MRI was used to identify the location of early visual areas and anatomical MRI data was used to construct detailed cortical surface reconstructions and to generate boundary element method forward models of the electrical conductivity of each participants head. These two data sets for each subject were used to generate simulated scalp activity from the dorsal and ventral subdivisions of each visual area that correspond to the lower and upper visual field representations, respectively. The predicted topographies show that sources in V1 do not fully conform to the cruciform sign-reversal. Moreover, contrary to the model, retinotopic visual areas V2 and V3 show polarity reversals for upper and lower field stimuli. The presence of a response polarity inversion for upper versus lower field stimuli is therefore an insufficient criterion for identifying responses as having originated in V1.

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.

On determining the intracranial sources of visual evoked potentials from scalp topography: A reply to Kelly et al. (this issue)

The cruciform model posits that if a Visual Evoked Potential component originates in cortical area V1, then stimuli placed in the upper versus lower visual field will generate responses with opposite polarity at the scalp. In our original paper (Ales et al., 2010b) we showed that the cruciform model provides an insufficient criterion for identifying V1 sources. This conclusion was reached on the basis of simulations that used realistic 3D models of early visual areas to simulate scalp topographies expected for stimuli of different sizes and shapes placed in different field locations. The simulations indicated that stimuli placed in the upper and lower visual field produce polarity inverting scalp topographies for activation of areas V2 and V3, but not for area V1. As a consequence of the non-uniqueness of the polarity inversion criterion, we suggested that past studies using the cruciform model had not adequately excluded contributions from sources outside V1. In their comment on our paper, Kelly et al. (this issue) raise several concerns with this suggestion. They claim that our initial results did not use the proper stimulus locations to constitute a valid test of the cruciform model. Kelly et al., also contend that the cortical source of the initial visually evoked component (C1) can be identified based on latency and polarity criteria derived from intracranial recordings in non-human primates. In our reply we show that simulations using the suggested critical stimulus locations are consistent with our original findings and thus do not change our conclusions regarding the use of the polarity inversion criterion. We further show that the anatomical assumptions underlying the putatively optimal locations are not consistent with available V1 anatomical data. We then address the non-human primate data, describing how differences in stimuli across studies and species confound an effective utilization of the non-human primate data for interpreting human evoked potential responses. We also show that, considered more broadly, the non-human primate literature shows that multiple visual areas onset simultaneously with V1. We suggest several directions for future research that will further clarify how to make the best use of scalp data for inferring cortical sources.

Functional dissection of signal and noise in MT and LIP during decision-making

During perceptual decision-making, responses in the middle temporal (MT) and lateral intraparietal (LIP) areas appear to map onto theoretically defined quantities, with MT representing instantaneous motion evidence and LIP reflecting the accumulated evidence. However, several aspects of the transformation between the two areas have not been empirically tested. We therefore performed multistage systems identification analyses of the simultaneous activity of MT and LIP during individual decisions. We found that monkeys based their choices on evidence presented in early epochs of the motion stimulus and that substantial early weighting of motion was present in MT responses. LIP responses recapitulated MT early weighting and contained a choice-dependent buildup that was distinguishable from motion integration. Furthermore, trial-by-trial variability in LIP did not depend on MT activity. These results identify important deviations from idealizations of MT and LIP and motivate inquiry into sensorimotor computations that may intervene between MT and LIP.

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

Over the past two decades, neurophysiological responses in the lateral intraparietal area (LIP) have received extensive study for insight into decision making. In a parallel manner, inferred cognitive processes have enriched interpretations of LIP activity. Because of this bidirectional interplay between physiology and cognition, LIP has served as fertile ground for developing quantitative models that link neural activity with decision making. These models stand as some of the most important frameworks for linking brain and mind, and they are now mature enough to be evaluated in finer detail and integrated with other lines of investigation of LIP function. Here, we focus on the relationship between LIP responses and known sensory and motor events in perceptual decision-making tasks, as assessed by correlative and causal methods. The resulting sensorimotor-focused approach offers an account of LIP activity as a multiplexed amalgam of sensory, cognitive, and motor-related activity, with a complex and often indirect relationship to decision processes. Our data-driven focus on multiplexing (and de-multiplexing) of various response components can complement decision-focused models and provides more detailed insight into how neural signals might relate to cognitive processes such as decision making.

Response to Comment on “Single-trial spike trains in parietal cortex reveal discrete steps during decision-making”

Shadlen et al.’s Comment focuses on extrapolations of our results that were not implied or asserted in our Report. They discuss alternate analyses of average firing rates in other tasks, the relationship between neural activity and behavior, and possible extensions of the standard models we examined. Although interesting to contemplate, these points are not germane to the findings of our Report: that stepping dynamics provided a better statistical description of lateral intraparietal area spike trains than diffusion-to-bound dynamics for a majority of neurons.

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

Abstract Perceptual decision-making is often modeled as the accumulation of sensory evidence over time. Recent studies using psychophysical reverse correlation have shown that even though the sensory evidence is stationary over time, subjects may exhibit a time-varying weighting strategy, weighting some stimulus epochs more heavily than others. While previous work has explained time-varying weighting as a consequence of static decision mechanisms (e.g., decision bound or leak), here we show that time-varying weighting can reflect strategic adaptation to stimulus statistics, and thus can readily take a number of forms. We characterized the temporal weighting strategies of humans and macaques performing a motion discrimination task in which the amount of information carried by the motion stimulus was manipulated over time. Both species could adapt their temporal weighting strategy to match the time-varying statistics of the sensory stimulus. When early stimulus epochs had higher mean motion strength than late, subjects adopted a pronounced early weighting strategy, where early information was weighted more heavily in guiding perceptual decisions. When the mean motion strength was greater in later stimulus epochs, in contrast, subjects shifted to a marked late weighting strategy. These results demonstrate that perceptual decisions involve a temporally flexible weighting process in both humans and monkeys, and introduce a paradigm with which to manipulate sensory weighting in decision-making tasks.

Continuous Psychophysics: measuring visual sensitivity by dynamic target tracking

We introduce a novel framework for estimating visual sensitivity using a continuous target-tracking task in concert with a dynamic internal model of human visual performance. In our main experiment, observers used a mouse cursor to track the center of a 2D Gaussian luminance target as it moved in a Brownian walk in a field of dynamic Gaussian luminance noise. To estimate visual sensitivity, we fit a Kalman filter to the tracking data assuming that humans behave roughly as Bayesian ideal observers. Such observers optimally combine prior information with noisy observations to produce an estimate of target location at each point in time. We found that estimates of human sensory noise obtained from the Kalman filter model were highly correlated with traditional psychophysical measures of human sensitivity (R2 > 0.97). Because data can be collected at the display frame rate, the amount of time required to measure sensitivity is greatly reduced relative to traditional psychophysics. While our modeling framework provides principled estimates of sensitivity that are directly comparable with those from traditional psychophysics, easily-computed summary statistics based on cross-correlograms of the tracking data also accurately predict relative sensitivity, and are thus good empirical substitutes for the more computationally-intensive Kalman filter fitting. As a second example, we show contrast sensitivity functions quickly determined using target tracking. Finally, we show that psychophysical reverse-correlation can also be quickly done via tracking. We conclude that dynamic target tracking is a viable and faster alternative to traditional psychophysical methods in many situations. Meeting abstract presented at VSS 2015.

Canonical correlations reveal co-variability between spike trains and local field potentials in area MT)

Patterns of neuronal correlations can provide important clues about the structure of the underlying network and how it processes information. Several recent studies have found that neural population activity across a region can be explained in large part by a shared, low-dimensional signal [1-5]. Population-wide correlation is likely to influence the local field potential (LFP) - an epiphenomenon that reflects low-frequency, concerted neural activity from anatomically connected circuits. Here, we show that LFP and spike trains recorded simultaneously from the middle temporal (MT) area of the awake macaque indeed share population-wide correlation. We apply canonical correlation analysis (CCA) to 16 channels of LFP and 16 spike sorted neurons (from 12 channels) acquired at 50 ms temporal resolution during inter-trial intervals (when the monkey was free to make eye movements), as well as during performance of a perceptual decision-making task (when the monkey maintained fixation and discriminated the direction of visual motion). CCA finds instantaneous linear projections of the LFP that maximize the correlation to corresponding projections of the population spike trains. Previous studies have suggested using population spike rate as a proxy for the local network state [3,5]. Applied to our dataset, we obtain a correlation coefficient of -12% between population spike rate and the mean LFP during inter-trial interval segments. In contrast, we obtain pairs of canonical variables with corresponding canonical correlations 29%, 26%, and 21%. We then applied the extracted projections to the task-relevant motion stimulus integration window. We find that the correlation of the projections is maintained for the 1st (31%) and 3rd (18%) components, but drops significantly for the 2nd component (7%)-- indicating a task-specific decoupling of LFP and spikes in a subspace uncovered by CCA. Upon further analysis, each CCA projection showed a distinct stimulus encoding pattern in spike rate and LFP. We hypothesize that CCA projections reveal functional, virtual units of information processing. The LFP is an important source of information when neural activity is correlated. It can indicate the strength of correlations, and the common input giving rise to such correlations. Additionally, the LFP provides increased statistical power to analyses, especially in areas where large-scale recording is anatomically difficult. CCA is a simple technique that can reveal low-dimensional structure in the data, uncovering components which maximize covariability between LFP and spike trains within MT.