Biology subjects

Okazawa, G.

Publications and source records attributed to Okazawa, G..

11 recordsLinked to original sources

bioRxiv2026

Zero-Shot Generalization of Prospective Planning in Macaques

Achieving a goal in natural environments often requires a sequence of actions whose consequences extend beyond their immediate outcomes. Because actions within a sequence can hinder or facilitate subsequent actions, successful behavior often demands prospective planning: selecting current actions based on their consequences for future actions. Although prospective planning is well documented in humans, its generalization to unfamiliar problems in nonhuman animals remains poorly understood. Here, we developed maze-navigation tasks in which macaque monkeys moved a token toward a goal while navigating around obstacles. Monkeys selected routes based on the positions of downstream obstacles, and their choices could not be explained by simple whole-maze visual summaries. In previously unseen, complex branching mazes, monkeys avoided deceptive dead ends--routes that initially approached the goal but ultimately failed--even when those dead ends extended several maze segments downstream. Prospective route selection was evident from the first encounters with multistep mazes and generalized to previously unencountered arrangements of familiar obstacles. These findings demonstrate zero-shot generalization of prospective planning: macaques applied a prospective strategy to unfamiliar multistep problems without learning the appropriate action sequences through repeated experience.

neuroscience↗

bioRxiv2026

Monkeys learn to report their own sensory cortical population activity

How sensory cortical activity is read out by downstream circuits to guide behavior is a fundamental unsolved problem. Substantial work has examined correlations between sensory neural responses and animals perceptual judgments, but their interpretations remained controversial due to many intervening variables, such as responses of unrecorded neurons. Furthermore, stimulus and choice encoding in sensory populations are often not well aligned, and it remains contested whether this misalignment indicates a limitation in sensory readout. Here, we introduce a closed-loop, neurofeedback paradigm that directly interrogates the capacity of sensory readout: the key idea is to train subjects to report specific patterns of population activity in a sensory area recorded online, rather than the actual stimuli presented. To test this, we trained macaque monkeys on a visual change-detection task using shape stimuli, implanted an electrode array in visual area V4, and tested whether they could be further trained to rely on their own V4 activity along specific axes in neural state space. Strikingly, monkeys successfully increased the neuron-choice correlation along trained axes in neural population state space. No detectable changes in stimulus selectivity or noise correlations were found within the recorded population, and further model simulations confirmed that adjustment of sensory readouts best accounted for the results. A control experiment that merely disrupted the stimulus-reward contingency without a closed loop failed to enhance neuron-choice correlation. Together, these results demonstrate that closed-loop neural feedback achieves neuron-choice alignment beyond the ceiling of natural perceptual training, suggesting that the misalignment in perceptual tasks reflects constraints on learning within naturally available training regimes.

neuroscience↗

bioRxiv2026

Discovering Novel Circuit Mechanisms in Higher Cognition through Factor-Centric Recurrent Neural Network Modeling

Recurrent neural networks (RNNs) have transformed how systems neuroscientists generate hypotheses about circuit mechanisms in higher cognition. Yet their promise has been constrained by a fundamental limitation: conventional RNNs are neuron-centric and therefore often difficult to interpret mechanistically. Here we introduce Restricted-RNN, a factor-centric RNN modeling framework developed to uncover interpretable circuit mechanisms. By formalizing factor communication through subpopulations, the dual of neuron communication through subspaces, Restricted-RNN departs fundamentally from standard neuron-centric RNN models and provides a distinct framework for describing circuit mechanisms. Using this approach, we identify novel circuit mechanisms underlying sequence working memory control and the counterintuitive firing-rate reversal observed in perceptual decision-making, with key predictions supported by neurophysiological recordings from monkey frontal and parietal cortex. More importantly, factor-centric RNN modeling reveals a unified low-dimensional neural control state space that links seemingly disparate phenomena across tasks, providing a geometric framework for understanding the pervasive role of control in higher cognition.

neuroscience↗

bioRxiv2025

Inferotemporal cortex encodes formation and termination of perceptual decisions

Visually-guided decisions are often modeled as bounded accumulation of sensory evidence, with "ramping" activity in frontoparietal cortex taken as a canonical signature of integration. Here we show that anterior inferotemporal cortex (TEa) implements an alternative integration code during object-based decisions. In macaques performing stochastic face categorization, TEa neurons tracked momentary feature fluctuations while concurrently encoding an accumulated decision variable as a running average of task-relevant evidence. This running-mean code increased category signal-to-noise without ramps, supported flexible amplification of task-relevant features, and showed a sharp collapse in sensitivity to new evidence after commitment. These signatures emerged concurrently with, or slightly earlier than, those in simultaneously recorded LIP neurons, arguing against inheritance from feedback. A simple subunit model combining momentary evidence with a running-average decision variable accounts for TEa dynamics. Our results identify a previously unrecognized format for evidence integration and reposition TEa as a decision-aware hub within the ventral visual pathway.

neuroscience↗

bioRxiv2025

Object categorization based on abstract concepts by monkeys, humans, and vision models

Humans categorize objects at multiple levels of abstraction--animate versus inanimate, big versus small, and many other attributes. Despite its apparent challenge, the advent of deep neural networks (DNNs) has demonstrated that complex visual processing alone can support such classification without language or human-specific knowledge. This raises a natural question: to what extent can non-human primates, without language, perform such categorization? Although basic object-recognition behavior in monkeys such as similarity judgment has been extensively studied, their ability to classify objects across diverse rules remains poorly characterized. Here, we developed a task paradigm that enabled us to train monkeys on a large battery of binary classification tasks using natural object images, spanning more than 10 rules, such as animate versus inanimate, natural versus man-made objects, and mammalian versus non-mammalian animals. Monkeys acquired each rule in a few days, generalized the learned rules to new images, and exhibited error patterns consistent with human judgments. At the same time, their classification performance correlated more strongly with that of visual DNNs trained without language input, whereas human performance was better explained by language-informed DNNs. These results provide an important benchmark for the capacity of biological neural networks to perform image classification without language and human-specific knowledge.

neuroscience↗

bioRxiv2024

Spatiotemporal evidence accumulation through saccadic sampling for object recognition

Visual object recognition has been extensively studied under fixation conditions, but our natural viewing involves frequent saccadic eye movements that scan multiple local informative features within an object (e.g., eyes and mouth in a face image). These saccades would contribute to object recognition by subserving the integration of sensory information across local features, but mechanistic models that explain human behavior during natural object recognition have yet to be established due to the presumed complexity of the interactions between the visual and oculomotor systems. Here, we employ a framework of perceptual decision making and show that human face and object categorization behavior with saccades can be quantitatively explained by a model that simply accumulates the sensory evidence available at each moment. Our model can fit human object-recognition behavior even under conditions in which people freely make saccades to scan local features, departing from past studies that required controlled eye movements to test trans-saccadic integration. Moreover, further experimental results confirmed that active saccade commands (efference copy) do not substantially contribute to behavioral performance. Therefore, we propose that object recognition with saccades can be approximated by a parsimonious decisionmaking model without assuming complex interactions between the visual and oculomotor systems.

neuroscience↗

bioRxiv2023

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 responsible for this remain controversial. Here, we show that humans exhibit a brief reduction in the efficiency of converting sensory inputs into decision evidence immediately after changing rules in perceptual tasks. Participants performed a flexible face categorization task in which they classified parametrically generated face stimuli based on one of two rules, switching every few trials. Although participants were always informed of a rule switch by a context cue, they showed a specific pattern of increase in reaction times, regardless of the time they were given to prepare for the switch. Psychophysical reverse correlation and computational modeling revealed a reduction in sensory weighting immediately after a rule switch, which recovered within a few hundred milliseconds after stimulus presentation. Furthermore, this cost depends on the sensory features being switched, suggesting a major bottleneck at the stage of adjusting the sensory information flow. We propose that decision-making circuits cannot fully adjust their sensory readout based on an abstract context cue alone, but rather require the presence of an actual stimulus to tune it, leading to a limitation in flexible perceptual decision making.

neuroscience↗

bioRxiv2023

Decision uncertainty as a context for motor memory

The current view of perceptual decision-making suggests that once the decision is made, a single motor program associated with the decision is carried out, irrespective of the degree of uncertainty involved in the decision-making process. As opposed to this view, we show that different levels of decision uncertainty contextualize actions differently, allowing the brain to form different motor memories based on each context. The match between decision uncertainty during learning and retrieval is critical for successful motor memory retrieval. The same movement trajectory can be associated with different motor memories if each memory is linked to a different level of decision uncertainty. Encoding motor memories based on decision contexts may enhance the robustness of control during the varying neural activities induced by different cognitive states.

neuroscience↗

bioRxiv2021

The geometry of the representation of decision variable and stimulus difficulty in the parietal cortex

Lateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, decision variables (DVs) are represented as partially potentiated action plans, where ensembles increase their average responses for stronger evidence supporting their preferred actions. As another consequence, DV representation and readout are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Here, we challenge those core principles using a novel face-discrimination task, where LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on task context, necessitating distinct readouts. We show similar manifolds in lateral and medial prefrontal cortices, suggesting a ubiquitous representational geometry across decision-making circuits.

neuroscience↗

bioRxiv2020

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 multi-feature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that humans 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.

neuroscience↗

bioRxiv2018

Psychophysical reverse correlation reflects both sensory and decision-making processes

Goal directed behavior depends on both sensory mechanisms that gather information from the outside world and decision-making mechanisms that select appropriate behavior based on that sensory information. Psychophysical reverse correlation is commonly used to quantify how fluctuations of sensory stimuli influence behavior and is generally believed to uncover the spatiotemporal weighting functions of sensory processes. Here we show that reverse correlations also reflect decision-making processes and can deviate significantly from the true sensory filters. Specifically, changes of decision bound and mechanisms of evidence integration systematically alter psychophysical reverse correlations. Similarly, trial-to-trial variability of sensory and motor delays and decision times causes systematic distortions in psychophysical kernels that should not be attributed to sensory mechanisms. We show that ignoring details of the decision-making process results in misinterpretation of reverse correlations, but proper use of these details turns reverse correlation into a powerful method for studying both sensory and decision-making mechanisms.

neuroscience↗