Designing and Interpreting Psychophysical Investigations of Cognition

The Protocol for Active Movement Extent Discrimination Assessment (AMEDA) is Reliable When Shortened From 50 to 25 Stimuli to Reduce Testing Fatigue

Active movement extent discrimination assessment (AMEDA) is a psychophysical task that evaluates proprioception and tactile acuity of the lower limbs, and it is a method of determining sensorimotor ability. Sensorimotor ability is the ability to judge small differences in movement tasks through the process of receiving sensory messages (sensory input) and producing a response (motor output). Participant attention lapses in prior psychophysical studies have been implicated as a cause for increased measurement variance thresholds in these types of assessments. Since minimizing the time needed for the AMEDA may help to reduce attention lapses, we compared the reliability of the 50-repetition AMEDA protocol (Group 1) with that of a 25-repetition protocol (Group 2). We assessed the split half reliability of these two approaches, using the Spearman-Brown Adjusted Pearson correlation (r). For each method, we calculated Bland-Altman Plots and Intra Class Correlation Coefficients to compare the reliability of the two data sets and determine the 95% confidence intervals. Split-half test re-test Spearman-Brown Adjusted Pearson r (rfull) was Group 1 rfull = 0.83 and Group 2 rfull = 0.85. The Bland-Altman Plots indicated only a small degree of bias from the zero-difference line, with 95% of the difference points lying within the limits of agreement. For Group 1, the intraclass correlation coefficient (ICC) two-way, agreement was 0.83 (95% CI 0.54-0.93) and for Group 2, the ICC, two-way, agreement, was 0.85 (95% CI 0.66-0.93). The MDC90 for Group 1 was 0.082 AUC units and for Group 2, it was 0.086 AUC units. The combined data for Group 1 plus Group 2 Bland-Altman Plot indicated only a small degree of bias from the zero-difference line, with 95% of the difference points lying within the limits of agreement. The MDC90 for the combined groups was 0.08 AUC units. The multiple methods from previous research assessing test re-test reliability that we applied to our two data sets indicate that the 25-response AMEDA was a reliable system for evaluating sensorimotor function in the lower limbs and may be an alternative for the more traditional 50-response protocol in which lapses in participant attention from fatigue or other biases may be a concern. There are also practical advantages in time restricted athletic screenings to a shorter administration of this assessment.

Limitation of switching sensory information flow in flexible perceptual decision making

Humans can flexibly change rules to categorize sensory stimuli, but their performance degrades immediately after a task switch. This switch cost is believed to reflect a limitation in cognitive control, although the bottlenecks remain controversial. Here, we show that humans exhibit a brief reduction in the efficiency of using sensory inputs to form a decision after a rule change. Participants classified face stimuli based on one of two rules, switching every few trials. Psychophysical reverse correlation and computational modeling reveal a reduction in sensory weighting, which recovers within a few hundred milliseconds after stimulus presentation. This reduction depends on the sensory features being switched, suggesting a constraint in routing the sensory information flow. We propose that decision-making circuits cannot fully adjust their sensory readout based on a context cue alone, but require the presence of an actual stimulus to tune it, leading to a limitation in flexible perceptual decision making.

Reward Reinforcement Creates Enduring Facilitation of Goal-directed Behavior

Stimulus-response habits benefit behavior by automatizing the selection of rewarding actions. However, this automaticity can come at the cost of reduced flexibility to adapt behavior when circumstances change. The goal-directed system is thought to counteract the habit system by providing the flexibility to pursue context-appropriate behaviors. The dichotomy between habitual action selection and flexible goal-directed behavior has recently been challenged by findings showing that rewards bias both action and goal selection. Here, we test whether reward reinforcement can give rise to habitual goal selection much as it gives rise to habitual action selection. We designed a rewarded, context-based perceptual discrimination task in which performance on one rule was reinforced. Using drift-diffusion models and psychometric analyses, we found that reward facilitates the initiation and execution of rules. Strikingly, we found that these biases persisted in a test phase in which rewards were no longer available. Although this facilitation is consistent with the habitual goal selection hypothesis, we did not find evidence that reward reinforcement reduced cognitive flexibility to implement alternative rules. Together, the findings suggest that reward creates a lasting impact on the selection and execution of goals but may not lead to the inflexibility characteristic of habits. Our findings demonstrate the role of the reward learning system in influencing how the goal-directed system selects and implements goals.

Task-irrelevant stimuli reliably boost phasic pupil-linked arousal but do not affect decision formation

The arousal systems of the brainstem, specifically the locus coeruleus-noradrenaline system, respond "phasically" during decisions. These central arousal transients are accompanied by dilations of the pupil. Mechanistic attempts to understand the impact of phasic arousal on cognition would benefit from temporally precise experimental manipulations. Here, we evaluated a non-invasive candidate approach to manipulate arousal in humans: presenting task-irrelevant auditory stimuli at different latencies during the execution of a challenging task. Task-irrelevant auditory stimuli drive responses of brainstem nuclei involved in the control of pupil size, but it is unknown whether such sound-evoked responses mimic the central arousal transients evoked during cognitive computations. A large body of evidence has implicated central arousal transients in reducing bias during challenging perceptual decisions. We thus used challenging visual decisions as a testbed, combining them with task-irrelevant sounds of varying onset latency or duration. Across three experiments, the sounds consistently elicited well-controlled pupil responses that superimposed onto task-evoked responses. While we replicated a negative correlation between task-evoked pupil responses and bias, the task-irrelevant sounds had no behavioral effect. This dissociation suggests that cognitive task engagement and task-irrelevant sounds may recruit distinct neural systems contributing to the control of pupil size.

Individual differences in belief updating and phasic arousal are related to psychosis proneness

Many decisions entail the updating of beliefs about the state of the environment by accumulating noisy sensory evidence. This form of probabilistic reasoning may go awry in psychosis. Computational theory shows that optimal belief updating in environments subject to hidden changes in their state requires a dynamic modulation of the evidence accumulation process. Recent empirical findings implicate transient responses of pupil-linked central arousal systems to individual evidence samples in this modulation. Here, we analyzed behavior and pupil responses during evidence accumulation in a changing environment in a community sample of human participants. We also assessed their subclinical psychotic experiences (psychosis proneness). Participants most prone to psychosis showed overall less flexible belief updating profiles, with diminished behavioral impact of evidence samples occurring late during decision formation. These same individuals also exhibited overall smaller pupil responses and less reliable pupil encoding of computational variables governing the dynamic belief updating. Our findings provide insights into the cognitive and physiological bases of psychosis proneness and open paths to unraveling the pathophysiology of psychotic disorders.

Confirmation Bias through Selective Use of Evidence in Human Cortex

Decision-makers often process new evidence selectively, depending on their current beliefs about the world. We asked whether such confirmation biases result from biases in the encoding of sensory evidence in the brain, or alternatively in the utilization of encoded evidence for behavior. Human participants estimated the source of a sequence of visual-spatial evidence samples while we measured cortical population activity with magnetoencephalography (MEG). Halfway through the sequence, participants were prompted to judge the more likely source category. Their processing of subsequent evidence depended on its consistency with the previously chosen category, but the encoding of evidence in cortical activity did not. Instead, the encoded evidence in parietal and primary visual cortex contributed less to the estimation report when that evidence was inconsistent with the previous choice. We conclude that confirmation bias originates from the way in which decision-makers utilize information encoded in the brain. This provides room for deliberative control.

Graded decisions in the human brain

Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.

Recurrent Neural Circuits Overcome Partial Inactivation by Compensation and Re-learning

Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments.

Spectral-temporal processing of naturalistic sounds in monkeys and humans

Human speech and vocalizations in animals are rich in joint spectrotemporal (S-T) modulations, wherein acoustic changes in both frequency and time are functionally related. In principle, the primate auditory system could process these complex dynamic sounds based on either an inseparable representation of S-T features or, alternatively, a separable representation. The separability hypothesis implies an independent processing of spectral and temporal modulations. We collected comparative data on the S-T hearing sensitivity in humans and macaque monkeys to a wide range of broadband dynamic spectrotemporal ripple stimuli employing a yes-no signal-detection task. Ripples were systematically varied, as a function of density (spectral modulation frequency), velocity (temporal modulation frequency), or modulation depth, to cover a listener's full S-T modulation sensitivity, derived from a total of 87 psychometric ripple detection curves. Audiograms were measured to control for normal hearing. Determined were hearing thresholds, reaction time distributions, and S-T modulation transfer functions (MTFs), both at the ripple detection thresholds and at suprathreshold modulation depths. Our psychophysically derived MTFs are consistent with the hypothesis that both monkeys and humans employ analogous perceptual strategies: S-T acoustic information is primarily processed separable. Singular value decomposition (SVD), however, revealed a small, but consistent, inseparable spectral-temporal interaction. Finally, SVD analysis of the known visual spatiotemporal contrast sensitivity function (CSF) highlights that human vision is space-time inseparable to a much larger extent than is the case for S-T sensitivity in hearing. Thus, the specificity with which the primate brain encodes natural sounds appears to be less strict than is required to adequately deal with natural images.NEW & NOTEWORTHY We provide comparative data on primate audition of naturalistic sounds comprising hearing thresholds, reaction time distributions, and spectral-temporal modulation transfer functions. Our psychophysical experiments demonstrate that auditory information is primarily processed in a spectral-temporal-independent manner by both monkeys and humans. Singular value decomposition of known visual spatiotemporal contrast sensitivity, in comparison to our auditory spectral-temporal sensitivity, revealed a striking contrast in how the brain encodes natural sounds as opposed to natural images, as vision appears to be space-time inseparable.

Introspective inference counteracts perceptual distortion

Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also known as insight, is critically required for reality testing and is impaired in psychosis, yet little is known about its cognitive underpinnings. We develop a Bayesian modeling framework and a psychophysics paradigm to quantitatively characterize this type of insight while people experience a motion after-effect illusion. People can incorporate knowledge about the illusion into their decisions when judging the actual direction of a motion stimulus, compensating for the illusion (and often overcompensating). Furthermore, confidence, reaction-time, and pupil-dilation data all show signatures consistent with inferential adjustments in the Bayesian insight model. Our results suggest that people can question the veracity of what they see by making insightful inferences that incorporate introspective knowledge about internal distortions.

Tasks and their role in visual neuroscience

Vision is widely used as a model system to gain insights into how sensory inputs are processed and interpreted by the brain. Historically, careful quantification and control of visual stimuli have served as the backbone of visual neuroscience. There has been less emphasis, however, on how an observer's task influences the processing of sensory inputs. Motivated by diverse observations of task-dependent activity in the visual system, we propose a framework for thinking about tasks, their role in sensory processing, and how we might formally incorporate tasks into our models of vision.

Dynamical latent state computation in the male macaque posterior parietal cortex

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkey's displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Toward an Atlas of Canonical Cognitive Mechanisms

A central goal in Cognitive Science is understanding the mechanisms that underlie cognition. Here, we contend that Cognitive Science, despite intense multidisciplinary efforts, has furnished surprisingly few mechanistic insights. We attribute this slow mechanistic progress to the fact that cognitive scientists insist on performing underdetermined exercises, deriving overparametrized mechanistic theories of complex behaviors and seeking validation of these theories to the elusive notions of optimality and biological plausibility. We propose that mechanistic progress in Cognitive Science will accelerate once cognitive scientists start focusing on simpler explananda that will enable them to chart an atlas of elementary cognitive operations. Looking forward, the next challenge for Cognitive Science will be to understand how these elementary cognitive processes are pieced together to explain complex behavior.

A practical guide for studying human behavior in the lab

In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics, and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.

Group-based, autonomous, individualized training and testing of long-tailed macaques (Macaca fascicularis) in their home enclosure to a visuo-acoustic discrimination task

In recent years, the utility and efficiency of automated procedures for cognitive assessment in psychology and neuroscience have been demonstrated in non-human primates (NHP). This approach mimics conventional shaping principles of breaking down a final desired behavior into smaller components that can be trained in a staircase manner. When combined with home-cage-based approaches, this could lead to a reduction in human workload, enhancement in data quality, and improvement in animal welfare. However, to our knowledge, there are no reported attempts to develop automated training and testing protocols for long-tailed macaques (Macaca fascicularis), a ubiquitous NHP model in neuroscience and pharmaceutical research. In the current work, we present the results from 6 long-tailed macaques that were trained using an automated unsupervised training (AUT) protocol for introducing the animals to the basics of a two-alternative choice (2 AC) task where they had to discriminate a conspecific vocalization from a pure tone relying on images presented on a touchscreen to report their response. We found that animals (1) consistently engaged with the device across several months; (2) interacted in bouts of high engagement; (3) alternated peacefully to interact with the device; and (4) smoothly ascended from step to step in the visually guided section of the procedure, in line with previous results from other NHPs. However, we also found (5) that animals' performance remained at chance level as soon as the acoustically guided steps were reached; and (6) that the engagement level decreased significantly with decreasing performance during the transition from visual to acoustic-guided sections. We conclude that with an autonomous approach, it is possible to train long-tailed macaques in their social group using computer vision techniques and without dietary restriction to solve a visually guided discrimination task but not an acoustically guided task. We provide suggestions on what future attempts could take into consideration to instruct acoustically guided discrimination tasks successfully.

Attention network modulation via tRNS correlates with attention gain

Transcranial random noise stimulation (tRNS) can enhance vision in the healthy and diseased brain. Yet, the impact of multi-day tRNS on large-scale cortical networks is still unknown. We investigated the impact of tRNS coupled with behavioral training on resting-state functional connectivity and attention. We trained human subjects for 4 consecutive days on two attention tasks, while receiving tRNS over the intraparietal sulci, the middle temporal areas, or Sham stimulation. We measured resting-state functional connectivity of nodes of the dorsal and ventral attention network (DVAN) before and after training. We found a strong behavioral improvement and increased connectivity within the DVAN after parietal stimulation only. Crucially, behavioral improvement positively correlated with connectivity measures. We conclude changes in connectivity are a marker for the enduring effect of tRNS upon behavior. Our results suggest that tRNS has strong potential to augment cognitive capacity in healthy individuals and promote recovery in the neurological population.

Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy

In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.

Linear Integration of Sensory Evidence over Space and Time Underlies Face Categorization

Visual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multifeature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that human subjects (five males, eight females) categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process over space and time bridges past studies on simple perceptual decisions to complex object recognition behavior.SIGNIFICANCE STATEMENT Although simple perceptual decision-making such as discrimination of random dot motion has been successfully explained as accumulation of sensory evidence, we lack rigorous experimental paradigms to study the mechanisms underlying complex perceptual decision-making such as discrimination of naturalistic faces. We develop a stochastic multifeature face categorization task as a systematic approach to quantify the properties and potential limitations of the decision-making processes during object recognition. We show that human face categorization could be modeled as a linear integration of sensory evidence over space and time. Our framework to study object recognition as a spatiotemporal integration process is broadly applicable to other object categories and bridges past studies of object recognition and perceptual decision-making.

Perceptual timing precision with vibrotactile, auditory, and multisensory stimuli

The growing use of vibrotactile signaling devices makes it important to understand the perceptual limits on vibrotactile information processing. To promote that understanding, we carried out a pair of experiments on vibrotactile, auditory, and bimodal (synchronous vibrotactile and auditory) temporal acuity. On each trial, subjects experienced a set of isochronous, standard intervals (400 ms each), followed by one interval of variable duration (400 ± 1-80 ms). Intervals were demarcated by short vibrotactile, auditory, or bimodal pulses. Subjects categorized the timing of the last interval by describing the final pulse as either "early" or "late" relative to its predecessors. In Experiment 1, each trial contained three isochronous standard intervals, followed by an interval of variable length. In Experiment 2, the number of isochronous standard intervals per trial varied, from one to four. Psychometric modeling revealed that vibrotactile stimulation produced poorer temporal discrimination than either auditory or bimodal stimulation. Moreover, auditory signals dominated bimodal sensitivity, and inter-individual differences in temporal discriminability were reduced with bimodal stimulation. Additionally, varying the number of isochronous intervals in a trial failed to improve temporal sensitivity in either modality, suggesting that memory played a key role in judgments of interval duration.

Getting to the heart of it: Multi-method exploration of nonconscious prioritization processes

Understanding the determinants of consciousness is crucial for theories that see it as functionally adaptive, and for explaining how consciousness affects higher-level cognition. The invention of continuous flash suppression (CFS), a long-duration suppression technique, resulted in a proliferation of research into the process of prioritization for consciousness. We developed a new technique, repeated masked suppression (RMS), that facilitates the measurement of long suppression times, but relies on different visual principles. RMS enables a theoretical leap: It allows scientists to examine the central process of prioritization across different suppression methods. In five experiments (n = 282) we collected chronometric RMS and CFS data, finding that the previously reported face inversion effect and the face priority-dimension generalize beyond CFS. Our results validate the use of multi-method designs in the study of prioritization for consciousness. Furthermore, we show how RMS could be used online to reach diverse samples, previously beyond the reach of consciousness science.