A Comparison of Lateral and Medial Intraparietal Areas during a Visual Categorization Task

Swaminathan, Sruthi K.; Masse, Nicolas Y.; Freedman, David J.

doi:10.1523/jneurosci.5723-12.2013

Cited by 31 publications

(46 citation statements)

References 39 publications

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“…Previous studies showed that firing rates of LIP and PFC neurons are markedly tuned to the learned stimulus categories, although the strengths and latencies of categorical signals may differ across areas (Freedman and Assad, 2006; Swaminathan and Freedman, 2012; Swaminathan et al, 2013). The broad similarity in category-related responses suggest that they play overlapping roles in solving the DMC task (Goodwin et al, 2012; Merchant et al, 2011).…”

Section: Resultsmentioning

confidence: 99%

Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions

Chaisangmongkon

Swaminathan

Freedman

et al. 2017

Neuron

169

192

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Summary Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a ‘neural landscape’, consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally-relevant circuit motifs and generalize the framework to solve other categorization tasks.

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Section: Resultsmentioning

confidence: 99%

Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions

Chaisangmongkon

Swaminathan

Freedman

et al. 2017

Neuron

169

192

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“…As in a previous study, we employed two specialized SVM classifiers 22 designed to independently assess direction and category selectivity (see Methods and Fig. 4a,b).…”

Section: Resultsmentioning

confidence: 99%

“…The detailed methods used in this study have been described in previously 22,28 , and are briefly described below.…”

Section: Methodsmentioning

confidence: 99%

“…We trained and tested linear classifiers on surrogate populations of neurons constructed from the individual neuron recordings 22,47 . When we compared classification accuracy between two populations, we always made sure to use an equal number of neurons when classifying each population.…”

Section: Methodsmentioning

confidence: 99%

“…Category selectivity was quantified for individual neurons using a receiver operating characteristic (ROC)-based category tuning index (rCTI) that compared differences in the distributions of firing rates across trials within and between categories 22,28 . We defined the between-category discrimination (BCD) as the average absolute value of the area under the ROC curve subtracted by 0.5 for the distributions of firing rates for all pairs of sample directions where directions were in different categories.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices

et al. 2015

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Our ability to learn a wide range of behavioral tasks is essential for responding appropriately to sensory stimuli according to behavioral demands, but the underlying neural mechanism has been rarely examined by neurophysiological recordings in the same subjects across learning. To understand how learning new behavioral tasks impacts underlying neuronal representations, we recorded from posterior parietal cortex (PPC) before and after training on a visual motion categorization task. Here we show that categorization training influenced cognitive encoding in PPC, with a marked enhancement of memory-related delay-period encoding during the categorization task which was absent during a motion discrimination task prior to categorization training. In contrast, the prefrontal cortex (PFC) exhibited strong delay-period encoding during both discrimination and categorization tasks. This reveals a dissociation between PFC’s and PPC’s roles in working memory, with general engagement of PFC across multiple tasks, in contrast with more task-specific mnemonic encoding in PPC.

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Acute psychosocial stress modulates neural and behavioral substrates of cognitive control

Spencer,

Mill,

Bhanji

et al. 2024

Human Brain Mapping

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Acute psychosocial stress affects learning, memory, and attention, but the evidence for the influence of stress on the neural processes supporting cognitive control remains mixed. We investigated how acute psychosocial stress influences performance and neural processing during the Go/NoGo task—an established cognitive control task. The experimental group underwent the Trier Social Stress Test (TSST) acute stress induction, whereas the control group completed personality questionnaires. Then, participants completed a functional magnetic resonance imaging (fMRI) Go/NoGo task, with self‐report, blood pressure and salivary cortisol measurements of induced stress taken intermittently throughout the experimental session. The TSST was successful in eliciting a stress response, as indicated by significant Stress > Control between‐group differences in subjective stress ratings and systolic blood pressure. We did not identify significant differences in cortisol levels, however. The stress induction also impacted subsequent Go/NoGo task performance, with participants who underwent the TSST making fewer commission errors on trials requiring the most inhibitory control (NoGo Green) relative to the control group, suggesting increased vigilance. Univariate analysis of fMRI task‐evoked brain activity revealed no differences between stress and control groups for any region. However, using multivariate pattern analysis, stress and control groups were reliably differentiated by activation patterns contrasting the most demanding NoGo trials (i.e., NoGo Green trials) versus baseline in the medial intraparietal area (mIPA, affiliated with the dorsal attention network) and subregions of the cerebellum (affiliated with the default mode network). These results align with prior reports linking the mIPA and the cerebellum to visuomotor coordination, a function central to cognitive control processes underlying goal‐directed behavior. This suggests that stressor‐induced hypervigilance may produce a facilitative effect on response inhibition which is represented neurally by the activation patterns of cognitive control regions.

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A Comparison of Lateral and Medial Intraparietal Areas during a Visual Categorization Task

Cited by 31 publications

References 39 publications

Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions

Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions

Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices

Acute psychosocial stress modulates neural and behavioral substrates of cognitive control

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