Biology subjects

Malalasekera, W. M. N.

Publications and source records attributed to Malalasekera, W. M. N..

5 recordsLinked to original sources

bioRxiv2026

Rapid value learning reveals generalized and context-dependent codes in frontal cortex

Studying how the brain represents value spans distinct methods and training histories, from neuroimaging in task-naive humans to single-neuron recordings in extensively trained non-human primates. Similar findings across fields have encouraged the untested assumption that rapidly emerging and overtrained value representations are equivalent. Here we recorded single-neuron activity in anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC) as macaques learned novel cue values and chose between novel and overtrained cues. Value responses emerged within 4-7 cue presentations, matching behavioural adaptation. Yet ACC and OFC used distinct codes. ACC encoded value in a common format that generalised across training history. OFC coding was more context-dependent, with distinct subpopulations recruited by choice experience. Choice novelty was also encoded independently of value before chosen value signals emerged. During learning, ACC responses shifted from overtrained secondary reinforcers to newly predictive cues. These findings establish the necessary (rapid acquisition) and sufficient (generalised format) conditions for comparing value representations across methods, species, and training histories.

neuroscience↗

bioRxiv2025

Neural signatures of model-based and model-free reinforcement learning across prefrontal cortex and striatum

Animals integrate knowledge about how the state of the environment evolves to choose actions that maximise reward. Such goal-directed behaviour - or model-based (MB) reinforcement learning (RL) - can flexibly adapt choice to changes, being thus distinct from simpler habitual - or model-free (MF) RL - strategies. Previous inactivation and neuroimaging work implicates prefrontal cortex (PFC) and the caudate striatal region in MB-RL; however, details are scarce about its implementation at the single-neuron level. Here, we recorded from two PFC regions - the dorsal anterior cingulate cortex (ACC) and dorsolateral PFC (DLPFC), and two striatal regions, caudate and putamen - while two rhesus macaques performed a sequential decision-making (two-step) task in which MB-RL involves knowledge about the statistics of reward and state transitions. All four regions, but particularly the ACC, encoded the rewards received and tracked the probabilistic state transitions that occurred. However, ACC (and to a lesser extent caudate) encoded the key variables of the task - namely the interaction between reward, transition and choice - which underlies MB decision-making. ACC and caudate neurons also encoded MB-derived estimates of choice values. Moreover, caudate value estimates of the choice options flipped when a rare transition occurred, demonstrating value update based on structural knowledge of the task. The striatal regions were unique (relative to PFC) in encoding the current and previous rewards with opposing polarities, reminiscent of dopaminergic neurons, and indicative of a MF prediction error. Our findings provide a deeper understanding of selective and temporally dissociable neural mechanisms underlying goal-directed behaviour.

neuroscience↗

bioRxiv2021

Covert valuation for information sampling and choice

We use our eyes to assess the value of objects around us and carefully fixate options that we are about to choose. Neurons in the prefrontal cortex reliably encode the value of fixated options, which is essential for decision making. Yet as a decision unfolds, it remains unclear how prefrontal regions determine which option should be fixated next. Here we show that anterior cingulate cortex (ACC) encodes the value of options in the periphery to guide subsequent fixations during economic choice. In an economic decision-making task involving four simultaneously presented cues, we found rhesus macaques evaluated cues using their peripheral vision. This served two distinct purposes: subjects were more likely to fixate valuable peripheral cues, and more likely to choose valuable options whose cues were never even fixated. ACC, orbitofrontal cortex, dorsolateral prefrontal cortex, and ventromedial prefrontal cortex neurons all encoded cue value post-fixation. ACC was unique, however, in also encoding the value of cues before fixation and even cues that were never fixated. This pre-saccadic value encoding by ACC predicted which cue was next fixated during the decision process. ACC therefore conducts simultaneous processing of peripheral information to guide information sampling and choice during decision making.

neuroscience↗

bioRxiv2019

Combined model-free and model-sensitive reinforcement learning in non-human primates

Contemporary reinforcement learning (RL) theory suggests that potential choices can be evaluated by strategies that may or may not be sensitive to the computational structure of tasks. A paradigmatic model-free (MF) strategy simply repeats actions that have been rewarded in the past; by contrast, model-sensitive (MS) strategies exploit richer information associated with knowledge of task dynamics. MF and MS strategies should typically be combined, because they have complementary statistical and computational strengths; however, this tradeoff between MF/MS RL has mostly only been demonstrated in humans, often with only modest numbers of trials. We trained rhesus monkeys to perform a two-stage decision task designed to elicit and discriminate the use of MF and MS methods. A descriptive analysis of choice behaviour revealed directly that the structure of the task (of MS importance) and the reward history (of MF and MS importance) significantly influenced both choice and response vigour. A detailed, trial-by-trial computational analysis confirmed that choices were made according to a combination of strategies, with a dominant influence of a particular form of model sensitivity that persisted over weeks of testing. The residuals from this model necessitated development of a new combined RL model which incorporates a particular credit assignment weighting procedure. Finally, response vigor exhibited a subtly different collection of MF and MS influences. These results provide new illumination onto RL behavioural processes in non-human primates.

neuroscience↗

bioRxiv2017

Triple dissociation of attention and decision computations across prefrontal cortex

Anatomical, neuroimaging and lesion studies indicate that prefrontal cortex (PFC) can be subdivided into different subregions supporting distinct aspects of decision making. However, explanations of neuronal computations within these subregions varies widely across studies. An integrated and mechanistic account of PFC function therefore remains elusive. Resolving these debates demands a rich dataset that directly contrasts neuronal activity across multiple PFC subregions within a single paradigm, whilst experimentally controlling factors such as the order, duration and frequency in which choice options are attended and compared. Here, we contrast neuronal population responses between macaque orbitofrontal (OFC), anterior cingulate (ACC) and dorsolateral prefrontal cortices (DLPFC) during sequential value-guided information search and choice. From the first fixation of choice-related stimuli, a strong triple dissociation of information encoding emerges in parallel across these PFC subregions. As further information is gathered, population responses in OFC reflect an attention-guided value comparison process. Meanwhile, parallel signals in ACC reflect belief updating in light of new evidence, integration of that evidence to a decision bound, and an emerging action plan for which option should be chosen. Our findings demonstrate the co-existence of multiple, distributed decision-related computations across PFC subregions during value-guided choice. They provide a synthesis of several competing accounts of PFC function.

neuroscience↗