Biology subjects

Ma, W. J.

Publications and source records attributed to Ma, W. J..

At least 19 recordsLinked to original sources

bioRxiv2025

Drift-like dynamics and information flow across the cortical hierarchy during working memory

Working memory is supported by widespread and distributed brain regions spanning across the cortical hierarchy. However, how working memory content evolves and is transmitted across cortical regions remains largely unknown. Here, we investigated the flow of working memory information across the cortex using time-resolved fMRI decoding. Across multiple regions in visual and parietal cortex, we found that decoded working memory content drifted over time from the memoranda towards the later errors in memory reports, consistent with drift-like dynamics predicted by attractor models. These behaviorally predictive neural memory errors emerged earliest in higher-order dorsal visual area (V3AB) and intraparietal sulcus (IPS0), and then later in early visual cortex (V1), suggesting a propagation of mnemonic information originated in higher-level visual area. Inter-areal correlation analyses revealed that during memory maintenance, information flowed predominantly in a top-down manner--from higher to lower visual areas whereas during passive viewing feedforward dynamics prevailed. Together, our findings demonstrate that working memory maintenance involves temporally structured drift dynamics and feedback-dominated information flow across cortical hierarchy, providing a mechanistic link between neural population dynamics and the formation of memory errors.

neuroscience↗

bioRxiv2024

Neural mechanisms of resource allocation in working memory

To mitigate capacity limits of working memory, people allocate resources according to an items relevance. However, the neural mechanisms supporting such a critical operation remain unknown. Here, we developed computational neuroimaging methods to decode and demix neural responses associated with multiple items in working memory with different priorities. In striate and extrastriate cortex, the gain of neural responses tracked the priority of memoranda. Higher-priority memoranda were decoded with smaller error and lower uncertainty. Moreover, these neural differences predicted behavioral differences in memory prioritization. Remarkably, trialwise variability in the magnitude of delay activity in frontal cortex predicted differences in decoded precision between low and high-priority items in visual cortex. These results suggest a model in which feedback signals broadcast from frontal cortex sculpt the gain of memory representations in visual cortex according to behavioral relevance, thus, identifying a neural mechanism for resource allocation.

neuroscience↗

bioRxiv2022

Reward prediction error neurons implement an efficient code for reward

We use efficient coding principles borrowed from sensory neuroscience to derive the optimal population of neurons to encode rewards from a probability distribution. We find that the response properties of dopaminergic reward prediction error neurons in a rodent and a primate data set are similar to those of the efficient code in many ways: the neurons have a broad distribution of midpoints covering the reward distribution; neurons with higher thresholds have higher gains, more convex tuning functions, and lower slopes; moreover, their slope is higher when the reward distribution is narrower. Furthermore, we derive learning rules that converge to this efficient code; the learning rule for the position of the neuron on the reward axis closely resembles the learning rule of distributional reinforcement learning. Thus, reward prediction error neuron responses may be optimized to broadcast an efficient reward signal, forming a connection between efficient coding and reinforcement learning, two of the most successful theories in computational neuroscience.

neuroscience↗

bioRxiv2021

Normalization by orientation anisotropy in human V1-V3

An influential account of neuronal responses in primary visual cortex is the normalized energy model. This model is often implemented as a two-stage computation. The first stage is the extraction of contrast energy, whereby a complex cell computes the squared and summed outputs of a pair of linear filters in quadrature phase. The second stage is normalization, in which a local population of complex cells mutually inhibit one another. Because the population includes cells tuned to a range of orientations and spatial frequencies, the result is that the responses are effectively normalized by the local stimulus contrast. Here, using evidence from human functional MRI, we show that the classical model fails to account for the relative responses to two classes of stimuli: straight, parallel, band-passed contours (gratings), and curved, band-passed contours (snakes). The snakes elicit fMRI responses that are about twice as large as the gratings, yet a traditional divisive normalization model predicts responses that are about the same. Here, motivated by these observations and others from the literature, we implement a divisive normalization model, in which there is a preferential inhibition between cells matched in orientation tuning ("tuned normalization"). We first show that this model accounts for differential responses to these two classes of stimuli. We then show that the model successfully generalizes to other band-pass textures, both in V1 and in extrastriate cortex (V2 and V3). We conclude that even in primary visual cortex, complex features of images such as the degree of heterogeneity, can have large effects on neural responses.

neuroscience↗

bioRxiv2021

Point-estimating observer models for latent cause detection

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several "point-estimating" observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by "committing" to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.

neuroscience↗

bioRxiv2021

Joint representation of working memory and uncertainty in human cortex

Neural representations of visual working memory (VWM) are noisy, and thus, decisions based on VWM are inevitably subject to uncertainty. However, the mechanisms by which the brain simultaneously represents the content and uncertainty of memory remain largely unknown. Here, inspired by the theory of probabilistic population codes, we test the hypothesis that the human brain represents an item maintained in VWM as a probability distribution over stimulus feature space, thereby capturing both its content and uncertainty. We used a neural generative model to decode probability distributions over memorized locations from fMRI activation patterns. We found that the mean of the probability distribution decoded from retinotopic cortical areas predicted memory reports on a trial-by-trial basis. Moreover, in several of the same mid-dorsal stream areas the spread of the distribution predicted subjective trial-by-trial uncertainty judgments. These results provide evidence that VWM content and uncertainty are jointly represented by probabilistic neural codes.

neuroscience↗

bioRxiv2020

Uncertainty is Maintained and Used in Working Memory

1What are the contents of working memory? In both behavioral and neural computational models, a working memory representation is typically described by a single number, namely a point estimate of a stimulus. Here, we asked if people also maintain the uncertainty associated with a memory, and if people use this uncertainty in subsequent decisions. We collected data in a two-condition orientation change detection task; while both conditions measured whether people used memory uncertainty, only one required maintaining it. For each condition, we compared an optimal Bayesian observer model, in which the observer uses an accurate representation of uncertainty in their decision, to one in which the observer does not. We find that this "Use Uncertainty" model fits better for all participants in both conditions. In the first condition, this result suggests that people use uncertainty optimally in a working memory task when that uncertainty information is available at the time of decision, confirming earlier results. Critically, the results of the second condition suggest that this uncertainty information was maintained in working memory. We test model variants and find that our conclusions do not depend on our assumptions about the observers encoding process, inference process, or decision rule. Our results provide evidence that people have uncertainty that reflects their memory precision on an item-specific level, maintain this information over a working memory delay, and use it implicitly in a way consistent with an optimal observer. These results challenge existing computational models of working memory to update their frameworks to represent uncertainty.

neuroscience↗

bioRxiv2020

The psychophysics of visual search with heterogeneous distractors

Visual search is one of the most ecologically important perceptual task domains. One research tradition has studied visual search using simple, parametric stimuli and a signal detection theory or Bayesian modeling framework. However, this tradition has mostly focused on homogeneous distractors (identical to each other), which are not very realistic. In a different tradition, Duncan and Humphreys (1989) conducted a landmark study on visual search with heterogeneous distractors. However, they used complex stimuli, making modeling and dissociation of component processes difficult. Here, we attempt to unify these research traditions by systematically examining visual search with heterogeneous distractors using simple, parametric stimuli and Bayesian modeling. Our experiment varied multiple factors that could influence performance: set size, task (N-AFC localization vs detection), whether the target was revealed before or after the search array (perception versus memory), and stimulus spacing. We found that performance robustly decreased with increasing set size. When examining within-trial summary statistics, we found that the minimum target-to-distractor feature difference was a stronger predictor of behavior than the mean target-to-distractor difference and than distractor variance. To obtain process-level understanding, we formulated a Bayesian optimal-observer model. This model accounted for all summary statistics, including when fitted jointly to localization and detection. We replicated these results in a separate experiment with reduced stimulus spacing. Together, our results represent a critique of Duncan and Humphreys descriptive approach, bring visual search with heterogeneous distractors firmly within the reach of quantitative process models, and affirm the "unreasonable effectiveness" of Bayesian models in explaining visual search.

neuroscience↗

bioRxiv2020

Explaining the effects of distractor statistics in visual search

Visual search, the task of detecting or locating target items amongst distractor items in a visual scene, is an important function for animals and humans. Different theoretical accounts make differing predictions for the effects of distractor statistics. Here we use a task in which we parametrically vary distractor items, allowing for a simultaneously fine-grained and comprehensive study of distractor statistics. We found effects of target-distractor similarity, distractor variability, and an interaction between the two, although the effect of the interaction on performance differed from the one expected. To explain these findings, we constructed computational process models that make trial-by-trial predictions for behaviour based on the full set of stimuli in a trial. These models, including a Bayesian observer model, provided excellent accounts of both the qualitative and quantitative effects of distractor statistics, as well as of the effect of changing the statistics of the environment (in the form of distractors being drawn from a different distribution). We conclude with a broader discussion of the role of computational process models in the understanding of visual search.

animal behavior and cognition↗

bioRxiv2019

Does monetary reward increase visual working memory performance?

Previous work has shown that humans distribute their visual working memory (VWM) resources flexibly across items: the higher the importance of an item, the better it is remembered. A related, but much less studied question is whether people also have control over the total amount of VWM resource allocated to a task. Here, we approach this question by testing whether increasing monetary incentives results in better overall VWM performance. In three experiments, subjects performed a delayed-estimation task on the Amazon Turk platform. In the first two experiments, four groups of subjects received a bonus payment based on their performance, with the maximum bonus ranging from $0 to $10 between groups. We found no effect of the amount of bonus on intrinsic motivation or on VWM performance in either experiment. In the third experiment, reward was manipulated on a trial-by-trial basis using a within-subjects design. Again, no evidence was found that VWM performance depended on the magnitude of potential reward. These results suggest that encoding quality in visual working memory is insensitive to monetary reward, which has implications for resource-rational theories of VWM.

animal behavior and cognition↗

bioRxiv2019

Confidence reports in decision-making with multiple alternatives violate the Bayesian confidence hypothesis

Decision confidence reflects our ability to evaluate the quality of decisions and guides subsequent behaviors. Experiments on confidence reports have almost exclusively focused on two-alternative decision-making. In this realm, the leading theory is that confidence reflects the probability that a decision is correct (the posterior probability of the chosen option). There is, however, another possibility, namely that people are less confident if the best two options are closer to each other in posterior probability, regardless of how probable they are in absolute terms. This possibility has not previously been considered because in two-alternative decisions, it reduces to the leading theory. Here, we test this alternative theory in a three alternative visual categorization task. We found that confidence reports are best explained by the difference between the posterior probabilities of the best and the next-best options, rather than by the posterior probability of the chosen (best) option alone, or by the overall uncertainty (entropy) of the posterior distribution. Our results upend the leading notion of decision confidence and instead suggest that confidence reflects the observers subjective probability that they made the best possible decision.

neuroscience↗

bioRxiv2018

The Cost of Appearing Suspicious? Information Gathering Costs in Trust Decisions

Trust decisions are inherently uncertain, as people have incomplete information about the trustworthiness of the other prior to their decision. Therefore, it is beneficial to gather information about a trustees past behaviour before deciding whether or not to trust them. However, elaborate inquiries about a trustees behavior may change the trustees willingness to reciprocate, causing either a decrease due to the investor appearing suspicious, or an increase because the investor appears to be highly betrayal-averse. Such a change could cause the investor to gather less or more information, respectively. We examine how information acquisition is modulated by social context, monetary cost, and the trustees trustworthiness. Participants had the opportunity to sequentially sample information about a trustees reciprocation history before they decided whether or not to invest. On some trials, we induced a social context by telling the participant that the trustee would later learn how much the participant sampled (\"overt sampling\"). Participants sampled less when there was a monetary cost and when the reciprocation history was more conclusive. Crucially, when sampling was free and overt, participants sampled less, suggesting negative consequences of appearing suspicious. In post-experiment questionnaires, participants indeed reported a belief that the reciprocation probability would decrease when information was overtly sampled. The findings replicated in a second experiment and were well accounted for by a utility-maximizing model in which overt sampling induces a decrease in reciprocation probability. This study opens the door to broader applications of the tools and models of information sampling to social decision-making.\n\nSignificance StatementTrust and reciprocity are essential for establishing and maintaining beneficial cooperative interactions. However, not everyone can be trusted. Here, we focus on a fundamental question in the study of social interaction: how people gather and use information to make a decision to trust or not trust. While more information seems better, participants gathered less information about trustworthiness when the trustee would learn about the inquiries, as if they avoided appearing suspicious. Indeed, participants reported that they believed that sampling elaborately would make the trustee less willing to reciprocate trust. Using a mathematical model of information gathering, we show that this belief indeed reduces the value of information. Our findings contribute to a deeper understanding of information gathering in social contexts.

neuroscience↗

bioRxiv2018

Human online adaptation to changes in prior probability

Optimal sensory decision-making requires the combination of uncertain sensory signals with prior expectations. The effect of prior probability is often described as a shift in the decision criterion. Can observers track sudden changes in probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category probability was updated using a sample-and-hold procedure. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category probability. We compared this model to various alternative models that correspond to different strategies - from approximately Bayesian to simple heuristics - that the observers may have adopted to update their beliefs about probabilities. We find that probability is estimated following an exponential averaging model with a bias towards equal priors, consistent with a conservative bias. The mechanism underlying change of decision criterion is a combination of on-line estimation of prior probability and a stable, long-term equal-probability prior, thus operating at two very different timescales.\n\nAuthor summaryWe demonstrate how people learn and adapt to changes to the probability of occurrence of one of two categories on decision-making under uncertainty. The study combined psychophysical behavioral tasks with computational modeling. We used two behavioral tasks: a typical forced-choice categorization task as well as one in which the observer specified the decision criterion to use on each trial before the stimulus was displayed. We formulated an ideal Bayesian change-point detection model and compared it to several alternative models. We found that the data are best fit by a model that estimates category probability based on recently observed exemplars with a bias towards equal probability. Our results suggest that the brain takes multiple relevant time scales into account when setting category expectations.

neuroscience↗

bioRxiv2018

A neural basis of probabilistic computation in visual cortex

Bayesian models of behavior suggest that organisms represent uncertainty associated with sensory variables. However, the neural code of uncertainty remains elusive. A central hypothesis is that uncertainty is encoded in the population activity of cortical neurons in the form of likelihood functions. We studied the neural code of uncertainty by simultaneously recording population activity from the primate visual cortex during a visual categorization task in which trial-to-trial uncertainty about stimulus orientation was relevant for the decision. We decoded the likelihood function from the trial-to-trial population activity and found that it predicted decisions better than a point estimate of orientation. This remained true when we conditioned on the true orientation, suggesting that internal fluctuations in neural activity drive behaviorally meaningful variations in the likelihood function. Our results establish the role of population-encoded likelihood functions in mediating behavior, and provide a neural underpinning for Bayesian models of perception.

neuroscience↗

bioRxiv2018

Sources of suboptimality in a minimalistic explore-exploit task

Balancing exploration and exploitation is a fundamental aspect of decision-making. It remains unknown whether people are close to optimal in striking this balance, and if not, how exactly their behavior deviates from optimality. Many existing paradigms are not ideally suited to answer this question, as they contain complexities such as non-stationary environments, stochasticity under exploitation, and reward distributions that are unknown to participants. Here, we introduce a task without such complexities, in which the optimal policy is to start off exploring and to switch to exploitation at most once in each sequence of decisions. The behavior of 49 laboratory and 143 online participants deviated both qualitatively and quantitatively from the optimal policy, even when allowing for bias and decision noise. Instead, people seem to follow a suboptimal rule in which they switch from exploration to exploitation when the highest reward so far exceeds a certain threshold. Moreover, we show that this threshold decreases approximately linearly with the proportion of the sequence that remains, suggesting a novel temporal ratio law. Finally, we find evidence for \"sequence-level\" variability which is shared across all decisions in the same sequence. Our results provide a new perspective on the explore-exploit dilemma, and emphasize the importance of examining sequence-level strategies and their variability when studying sequential decision-making.

neuroscience↗

bioRxiv2018

The Role of Sensory Uncertainty in Simple Perceptual Organization

Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of contour integration, and more broadly, of perceptual organization, have been re-framed in terms of Bayesian inference, whereby the observer computes the probability that the whole entity is present. Previous studies that proposed a Bayesian interpretation of perceptual organization, however, have ignored sensory uncertainty, despite the fact that accounting for the current level of perceptual uncertainty is one the main signatures of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is a key test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using some manifestly non-Bayesian heuristic. We distinguish between these hypotheses in a simplified form of contour integration, namely judging whether two line segments separated by an occluder are collinear. We manipulate sensory uncertainty by varying retinal eccentricity. A Bayes-optimal observer would take the level of sensory uncertainty into account - in a very specific way - in deciding whether a measured offset between the line segments is due to non-collinearity or to sensory noise. We find that people deviate slightly but systematically from Bayesian optimality, while still performing "probabilistic computation" in the sense that they take into account sensory uncertainty via a heuristic rule. Our work contributes to an understanding of the role of sensory uncertainty in higher-order perception. Author summaryOur percept of the world is governed not only by the sensory information we have access to, but also by the way we interpret this information. When presented with a visual scene, our visual system undergoes a process of grouping visual elements together to form coherent entities so that we can interpret the scene more readily and meaningfully. For example, when looking at a pile of autumn leaves, one can still perceive and identify a whole leaf even when it is partially covered by another leaf. While Gestalt psychologists have long described perceptual organization with a set of qualitative laws, recent studies offered a statistically-optimal - Bayesian, in statistical jargon - interpretation of this process, whereby the observer chooses the scene configuration with the highest probability given the available sensory inputs. However, these studies drew their conclusions without considering a key actor in this kind of statistically-optimal computations, that is the role of sensory uncertainty. One can easily imagine that our decision on whether two contours belong to the same leaf or different leaves is likely going to change when we move from viewing the pile of leaves at a great distance (high sensory uncertainty), to viewing very closely (low sensory uncertainty). Our study examines whether and how people incorporate uncertainty into contour integration, an elementary form of perceptual organization, by varying sensory uncertainty from trial to trial in a simple contour integration task. We found that people indeed take into account sensory uncertainty, however in a way that subtly deviates from optimal behavior.

neuroscience↗

bioRxiv2018

Strategic allocation of working memory resource

Visual working memory (VWM), the brief retention of past visual information, supports a range of cognitive functions (Fukuda, Vogel, Mayr, & Awh, 2010; Johnson et al., 2013). The resource that supports VWM is limited, raising the question of how the brain allocates this limited resource to different objects. This question is even more interesting in ecological settings, in which objects are not equally important. In a psychophysical experiment, participants remembered the location of four targets with different probabilities of being tested after a delay. We then measured their memory accuracy of one of the targets. We found that participants allocated more resource to memoranda with higher priority, but underallocated resource to high- and overallocated to low-priority targets relative to the true probe probabilities. These results are well explained by a computational model in which resource is allocated to minimize expected estimation error. We replicated this finding in a second experiment in which participants bet on their memory fidelity after making the location estimate. The results of this experiment show that people 1) use information about memory quality and 2) minimize error even with an incentivized, alternative resource allocation strategy. Humans may mitigate the behavioral effects of a limited VWM through knowledge of memory fidelity and strategic resource allocation.

neuroscience↗

bioRxiv2018

Humans incorporate trial-to-trial working memory uncertainty into rewarded decisions

In daily life, working memory plays an important role in action planning and decision-making. However, both the informational content of memory and how that information is used in decisions is poorly understood. To investigate this, we conducted a memory experiment where people not only reported an estimate of a remembered stimulus, but also made a rewarded decision designed to reflect memory uncertainty. Reported memory uncertainty is correlated with estimation error, showing people incorporate their trial-to-trial memory quality into rewarded decisions. Moreover, memory uncertainty can be combined with other sources of information; after we induced prior beliefs about stimuli probabilities, we found that estimates shifted towards more frequent colors, with the shift increasing with reported uncertainty. The data is best predicted by models where people incorporate their trial-to-trial memory uncertainty with potential rewards and prior beliefs, highlighting the importance of studying working memory as a process integrated with decision-making.

neuroscience↗