Differentiating between Models of Perceptual Decision Making Using Pupil Size Inferred Confidence

From pre-processing to advanced dynamic modeling of pupil data

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.

Sensory and Choice Responses in MT Distinct from Motion Encoding

The macaque middle temporal (MT) area is well known for its visual motion selectivity and relevance to motion perception, but the possibility of it also reflecting higher-level cognitive functions has largely been ignored. We tested for effects of task performance distinct from sensory encoding by manipulating subjects' temporal evidence-weighting strategy during a direction discrimination task while performing electrophysiological recordings from groups of MT neurons in rhesus macaques (one male, one female). This revealed multiple components of MT responses that were, surprisingly, not interpretable as behaviorally relevant modulations of motion encoding, or as bottom-up consequences of the readout of motion direction from MT. The time-varying motion-driven responses of MT were strongly affected by our strategic manipulation-but with time courses opposite the subjects' temporal weighting strategies. Furthermore, large choice-correlated signals were represented in population activity distinct from its motion responses, with multiple phases that lagged psychophysical readout and even continued after the stimulus (but which preceded motor responses). In summary, a novel experimental manipulation of strategy allowed us to control the time course of readout to challenge the correlation between sensory responses and choices, and population-level analyses of simultaneously recorded ensembles allowed us to identify strong signals that were so distinct from direction encoding that conventional, single-neuron-centric analyses could not have revealed or properly characterized them. Together, these approaches revealed multiple cognitive contributions to MT responses that are task related but not functionally relevant to encoding or decoding of motion for psychophysical direction discrimination, providing a new perspective on the assumed status of MT as a simple sensory area.SIGNIFICANCE STATEMENT This study extends understanding of the middle temporal (MT) area beyond its representation of visual motion. Combining multineuron recordings, population-level analyses, and controlled manipulation of task strategy, we exposed signals that depended on changes in temporal weighting strategy, but did not manifest as feedforward effects on behavior. This was demonstrated by (1) an inverse relationship between temporal dynamics of behavioral readout and sensory encoding, (2) a choice-correlated signal that always lagged the stimulus time points most correlated with decisions, and (3) a distinct choice-correlated signal after the stimulus. These findings invite re-evaluation of MT for functions outside of its established sensory role and highlight the power of experimenter-controlled changes in temporal strategy, coupled with recording and analysis approaches that transcend the single-neuron perspective.

Audiovisual task switching rapidly modulates sound encoding in mouse auditory cortex

In everyday behavior, sensory systems are in constant competition for attentional resources, but the cellular and circuit-level mechanisms of modality-selective attention remain largely uninvestigated. We conducted translaminar recordings in mouse auditory cortex (AC) during an audiovisual (AV) attention shifting task. Attending to sound elements in an AV stream reduced both pre-stimulus and stimulus-evoked spiking activity, primarily in deep-layer neurons and neurons without spectrotemporal tuning. Despite reduced spiking, stimulus decoder accuracy was preserved, suggesting improved sound encoding efficiency. Similarly, task-irrelevant mapping stimuli during inter-trial intervals evoked fewer spikes without impairing stimulus encoding, indicating that attentional modulation generalized beyond training stimuli. Importantly, spiking reductions predicted trial-to-trial behavioral accuracy during auditory attention, but not visual attention. Together, these findings suggest auditory attention facilitates sound discrimination by filtering sound-irrelevant background activity in AC, and that the deepest cortical layers serve as a hub for integrating extramodal contextual information.

Eye Movements as a Window into Decision-Making

For over 100 years, eye movements have been studied and used as indicators of human sensory and cognitive functions. This review evaluates how eye movements contribute to our understanding of the processes that underlie decision-making. Eye movement metrics signify the visual and task contexts in which information is accumulated and weighed. They indicate the efficiency with which we evaluate the instructions for decision tasks, the timing and duration of decision formation, the expected reward associated with a decision, the accuracy of the decision outcome, and our ability to predict and feel confident about a decision. Because of their continuous nature, eye movements provide an exciting opportunity to probe decision processes noninvasively in real time. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Comparison of Pupil Dilation Responses to Unexpected Sounds in Monkeys and Humans

Pupil dilation in response to unexpected stimuli has been well documented in human as well as in non-human primates; however, this phenomenon has not been systematically compared between the species. This analogy is also crucial for the role of non-human primates as an animal model to investigate neural mechanisms underlying the processing of unexpected stimuli and their evoked pupil dilation response. To assess this qualitatively, we used an auditory oddball paradigm in which we presented subjects a sequence of the same sounds followed by occasional deviants while we measured their evoked pupil dilation response (PDR). We used deviants (a frequency deviant, a pink noise burst, a monkey vocalization and a whistle sound) which differed in the spectral composition and in their ability to induce arousal from the standard. Most deviants elicited a significant pupil dilation in both species with decreased peak latency and increased peak amplitude in monkeys compared to humans. A temporal Principal Component Analysis (PCA) revealed two components underlying the PDRs in both species. The early component is likely associated to the parasympathetic nervous system and the late component to the sympathetic nervous system, respectively. Taken together, the present study demonstrates a qualitative similarity between PDRs to unexpected auditory stimuli in macaque and human subjects suggesting that macaques can be a suitable model for investigating the neuronal bases of pupil dilation. However, the quantitative differences in PDRs between species need to be investigated in further comparative studies.

A confirmation bias in perceptual decision-making due to hierarchical approximate inference

Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observer's temporal bias changed as the result of changing the key stimulus statistics identified by our model. The key dynamic that leads to a primacy bias in our model is an overweighting of new sensory information that agrees with the observer's existing belief-a type of 'confirmation bias'. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.

Decision-related feedback in visual cortex lacks spatial selectivity

Feedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity.

Feedback modulates visual neurons, thought to help achieve flexible task performance. Here, the authors show decision-related feedback is not only relayed to task-relevant neurons, suggesting a broader mechanism and supporting a previously hypothesized link to feature-based attention.

Flexible categorization in perceptual decision making

Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

Phasic Activation of Dorsal Raphe Serotonergic Neurons Increases Pupil Size

Transient variations in pupil size (PS) under constant luminance are coupled to rapid changes in arousal state,1-3 which have been interpreted as vigilance,4 salience,5 or a surprise signal.6-8 Neural control of such fluctuations presumably involves multiple brain regions5,9-11 and neuromodulatory systems,3,12,13 but it is often associated with phasic activity of the noradrenergic system.9,12,14,15 Serotonin (5-HT), a neuromodulator also implicated in aspects of arousal16 such as sleep-wake transitions,17 motivational state regulation,18 and signaling of unexpected events,19 seems to affect PS,20-24 but these effects have not been investigated in detail. Here we show that phasic 5-HT neuron stimulation causes transient PS changes. We used optogenetic activation of 5-HT neurons in the dorsal raphe nucleus (DRN) of head-fixed mice performing a foraging task. 5-HT-driven modulations of PS were maintained throughout the photostimulation period and sustained for a few seconds after the end of stimulation. We found no evidence that the increase in PS with activation of 5-HT neurons resulted from interactions of photostimulation with behavioral variables, such as locomotion or licking. Furthermore, we observed that the effect of 5-HT on PS depended on the level of environmental uncertainty, consistent with the idea that 5-HT could report a surprise signal.19 These results advance our understanding of the neuromodulatory control of PS, revealing a tight relationship between phasic activation of 5-HT neurons and changes in PS.

Binocular disparity-based learning is retinotopically specific and independent of sleep

Sleep supports the consolidation of recently encoded declarative and procedural memories. An important component of this effect is the repeated reactivation of neuronal ensemble activity elicited during memory encoding. For perceptual learning, however, sleep benefits have only been reported for specific tasks and it is not clear whether sleep targets low-level perceptual, higher-order temporal or attentional aspects of performance. Here, we employed a coarse binocular disparity discrimination task, known to rely on low-level stereoscopic vision. We show that human subjects improve over training and retain the same performance level across a 12-h retention period. Improvements do not generalize to other parts of the visual field and are unaffected by whether the retention period contains sleep or not. These results are compatible with the notion that behavioural improvements in binocular disparity discrimination do not additionally benefit from sleep when compared with the same time spent awake. We hypothesize that this might generalize to other strictly low-level perceptual tasks. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Choice (-history) correlations in sensory cortex: cause or consequence?

One challenge in neuroscience, as in other areas of science, is to make inferences about the underlying causal structure from correlational data. Here, we discuss this challenge in the context of choice correlations in sensory neurons, that is, trial-by-trial correlations, unexplained by the stimulus, between the activity of sensory neurons and an animal's perceptual choice. Do these choice-correlations reflect feedforward, feedback signalling, both, or neither? We highlight recent results of correlational and causal examinations of choice and choice-history signals in sensory, and in part sensorimotor, cortex and address formal statistical frameworks to infer causal interactions from data.

The Neurobiology of Confidence: From Beliefs to Neurons

How confident are you? As humans, aware of our subjective sense of confidence, we can readily answer. Knowing your level of confidence helps to optimize both routine decisions such as whether to go back and check if the front door was locked and momentous ones like finding a partner for life. Yet the inherently subjective nature of confidence has limited investigations by neurobiologists. Here, we provide an overview of recent advances in this field and lay out a conceptual framework that lets us translate psychological questions about subjective confidence into the language of neuroscience. We show how statistical notions of confidence provide a bridge between our subjective sense of confidence and confidence-guided behaviors in nonhuman animals, thus enabling the study of the underlying neurobiology. We discuss confidence as a core cognitive process that enables organisms to optimize behavior such as learning or resource allocation and that serves as the basis of metacognitive reasoning. These approaches place confidence on a solid footing and pave the way for a mechanistic understanding of how the brain implements confidence-based algorithms to guide behavior.

Human-level saccade detection performance using deep neural networks

Saccades are ballistic eye movements that rapidly shift gaze from one location of visual space to another. Detecting saccades in eye movement recordings is important not only for studying the neural mechanisms underlying sensory, motor, and cognitive processes, but also as a clinical and diagnostic tool. However, automatically detecting saccades can be difficult, particularly when such saccades are generated in coordination with other tracking eye movements, like smooth pursuits, or when the saccade amplitude is close to eye tracker noise levels, like with microsaccades. In such cases, labeling by human experts is required, but this is a tedious task prone to variability and error. We developed a convolutional neural network to automatically detect saccades at human-level accuracy and with minimal training examples. Our algorithm surpasses state of the art according to common performance metrics and could facilitate studies of neurophysiological processes underlying saccade generation and visual processing. NEW & NOTEWORTHY Detecting saccades in eye movement recordings can be a difficult task, but it is a necessary first step in many applications. We present a convolutional neural network that can automatically identify saccades with human-level accuracy and with minimal training examples. We show that our algorithm performs better than other available algorithms, by comparing performance on a wide range of data sets. We offer an open-source implementation of the algorithm as well as a web service.

Engagement of Pulvino-cortical Feedforward and Feedback Pathways in Cognitive Computations

Computational modeling of brain mechanisms of cognition has largely focused on the cortex, but recent experiments have shown that higher-order nuclei of the thalamus participate in major cognitive functions and are implicated in psychiatric disorders. Here, we show that a pulvino-cortical circuit model, composed of the pulvinar and two cortical areas, captures several physiological and behavioral observations related to the macaque pulvinar. Effective connections between the two cortical areas are gated by the pulvinar, allowing the pulvinar to shift the operation regime of these areas during attentional processing and working memory and resolve conflict in decision making. Furthermore, cortico-pulvinar projections that engage the thalamic reticular nucleus enable the pulvinar to estimate decision confidence. Finally, feedforward and feedback pulvino-cortical pathways participate in frequency-dependent inter-areal interactions that modify the relative hierarchical positions of cortical areas. Overall, our model suggests that the pulvinar provides crucial contextual modulation to cortical computations associated with cognition.