Biology subjects

Niv, Y.

Publications and source records attributed to Niv, Y..

5 recordsLinked to original sources

bioRxiv2018

Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degree of similarity between these neural activity patterns in response to different events is used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).\n\nAuthor summaryWe show the severity of the bias introduced when performing representational similarity analysis (RSA) based on neural activity pattern estimated within imaging runs. Our Bayesian RSA method significantly reduces the bias and can learn a shared representational structure across multiple participants. We also demonstrate its extension as a new multi-class decoding tool.

neuroscience↗

bioRxiv2018

Sequential replay of non-spatial task states in the human hippocampus

Neurophysiological research has found that previously experienced sequences of spatial events are reactivated in the hippocampus of rodents during wakeful rest. This phenomenon has become a cornerstone of modern theories of memory and decision making. Yet, whether hippocampal sequence reactivation at rest is of general importance also for humans and non-spatial tasks has remained unclear. Here, we investigated sequences of fMRI BOLD activation patterns in humans during wakeful rest following a sequential non-spatial decision-making task. We found that pattern reactivations within the human hippocampus reflected the order of previous task state sequences, and that the extent of this offline reactivation was related to the on-task representation of task states in the orbitofrontal cortex. Permutation analyses and fMRI signal simulations confirmed that these results reflected underlying BOLD activity, and showed that our novel statistical analyses are, in principle, sensitive to sequential neural events occurring as fast as one hundred milliseconds apart. Our results support the importance of sequential reactivation in the human hippocampus for decision making, and establish the feasibility of investigating such rapid signals with fMRI, despite its substantial temporal limitations.\n\nHighlightsO_LIWe provide fMRI evidence for sequential pattern reactivation in the human hippocampus\nC_LIO_LISequences of patterns reflect states from a sequential, non-spatial decision-making task\nC_LIO_LISimulations show that our novel fMRI analysis is sensitive to fast sequences of sub-second neural events\nC_LIO_LIResults support the importance of sequential reactivation in the human hippocampus for decision making\nC_LI

neuroscience↗

bioRxiv2018

A pupillary index of susceptibility to decision biases

Under what conditions do humans systematically deviate from rational decision making? Here we show that pupillary indices of low neural gain are associated with strong and consistent biases across six different extensively-studied decision making tasks, whereas indices of high gain are associated with weak or absent biases. Lower susceptibility to biases, however, comes at the cost of indecisiveness, or alternatively, prolonged deliberation time. We explain the association between low gain and strong biases as reflecting a broader information integration process that gives greater weight to weak biasing influences. The findings underscore the role of pupil-linked brain states in the generation of decision making biases.\n\nSignificance\"Framing effects\" are demonstrations that peoples decisions can be biased by the way a decision problem is presented, and consequently, people can make decisions that violate the principles of rationality. Using a set of classic decision-making tasks, we show that pupil dilation, previously linked to levels of the neuromodulator norepinephrine and to a tradeoff between narrowly focused and broadly integrative modes of information processing, distinguishes between people who are consistently biased and people who are relatively immune to these effects. Our findings suggest that norepinephrine may drive these individual differences, and that a narrowly focused mode of information processing confers relative immunity to these decision making biases, whereas the integration of a wider range of information results in greater susceptibility.

neuroscience↗

bioRxiv2017

A state representation for reinforcement learning and decision-making in the orbitofrontal cortex

Despite decades of research, the exact ways in which the orbitofrontal cortex (OFC) influences cognitive function have remained mysterious. Anatomically, the OFC is characterized by remarkably broad connectivity to sensory, limbic and subcortical areas, and functional studies have implicated the OFC in a plethora of functions ranging from facial processing to value-guided choice. Notwithstanding such diversity of findings, much research suggests that one important function of the OFC is to support decision making and reinforcement learning. Here, we describe a novel theory that posits that OFCs specific role in decision-making is to provide an up-to-date representation of task-related information, called a state representation. This representation reflects a mapping between distinct task states and sensory as well as unobservable information. We summarize evidence supporting the existence of such state representations in rodent and human OFC and argue that forming these state representations provides a crucial scaffold that allows animals to efficiently perform decision making and reinforcement learning in high-dimensional and partially observable environments. Finally, we argue that our theory offers an integrating framework for linking the diversity of functions ascribed to OFC and is in line with its wide ranging connectivity.

neuroscience↗

bioRxiv2017

Dissociable effects of surprising rewards on learning and memory

The extent to which rewards deviate from learned expectations is tracked by a signal known as a \"reward prediction error\", but it is unclear how this signal interacts with episodic memory. Here, we investigated whether learning in a high-risk environment, with frequent large prediction errors, gives rise to higher fidelity memory traces than learning in a low-risk environment. In Experiment 1, we showed that higher magnitude prediction errors, positive or negative, improved recognition memory for trial-unique items. Participants also increased their learning rate after large prediction errors. In addition, there was an overall higher learning rate in the low-risk environment. Although unsigned prediction errors enhanced memory and increased learning rate, we did not find a relationship between learning rate and memory, suggesting that these two effects were due to separate underlying mechanisms. In Experiment 2, we replicated these results with a longer task that posed stronger memory demands and allowed for more learning. We also showed improved source and sequence memory for high-risk items. In Experiment 3, we controlled for the difficulty of learning in the two risk environments, again replicating the previous results. Moreover, equating the range of prediction errors in the two risk environments revealed that learning in a high-risk context enhanced episodic memory above and beyond the effect of prediction errors to individual items. In summary, our results across three studies showed that (absolute) prediction error magnitude boosted both episodic memory and incremental learning, but the two effects were not correlated, suggesting distinct underlying systems.

animal behavior and cognition↗