From Salience to Saccades: Multiple-Alternative Gated Stochastic Accumulator Model of Visual Search

Purcell, Braden A.; Schall, Jeffrey D.; Logan, Gordon D.; Palmeri, Thomas J.

doi:10.1523/jneurosci.4622-11.2012

Cited by 168 publications

(268 citation statements)

References 103 publications

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“…Cells with different response properties have been suggested to play different roles during oculomotor behavior (Hanes et al, 1998;Purcell et al, 2012). Hence, it is possible that difficulty and error-related responses are preferentially found in one specific functionally defined subtype.…”

Section: Relation To Functional Specificitymentioning

confidence: 99%

Performance Monitoring in Monkey Frontal Eye Field

Teichert

Ferrera

2014

J. Neurosci.

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The frontal eye fields (FEF) are thought to mediate response selection during oculomotor decision tasks. In addition, many FEF neurons have robust postsaccadic responses, but their role in postchoice evaluative processes (online performance monitoring) is only beginning to become apparent. Here we report error-related neural activity in FEF while monkeys performed a biased speed-categorization task that enticed the animals to make impulsive errors. Twenty-three percent of cells in macaque FEF coded an internally generated error-related signal, and many of the same cells also coded task difficulty. The observed responses are primarily consistent with three related concepts that have been associated with performance monitoring: (1) response conflict; (2) uncertainty; and (3) reward prediction. Overall, our findings suggest a novel role for the FEF as part of the neural network that evaluates the preceding choice to optimize behavior in the future.

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Section: Relation To Functional Specificitymentioning

confidence: 99%

Performance Monitoring in Monkey Frontal Eye Field

Teichert

Ferrera

2014

J. Neurosci.

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“…Directly relating neural signals to computational mechanisms has led to advances in a number of cognitive domains, including visual search (Purcell et al, 2012), reinforcement learn-ing (Daw et al, 2011), object categorization (Nosofsky et al, 2012, and problem solving (Rosenberg-Lee et al, 2009). While a number of recent studies suggest the promise of this approach in the domain of episodic memory (Manning et al, 2011;Turner et al, 2013;Polyn and Sederberg, 2014), our understanding of the cognitive processes engaged in memory search remains tentative.…”

Section: Discussionmentioning

confidence: 99%

Neural Activity in the Medial Temporal Lobe Reveals the Fidelity of Mental Time Travel

Kragel

Morton²,

Polyn³

2015

J. Neurosci.

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Neural circuitry in the medial temporal lobe (MTL) is critically involved in mental time travel, which involves the vivid retrieval of the details of past experience. Neuroscientific theories propose that the MTL supports memory of the past by retrieving previously encoded episodic information, as well as by reactivating a temporal code specifying the position of a particular event within an episode. However, the neural computations supporting these abilities are underspecified. To test hypotheses regarding the computational mechanisms supported by different MTL subregions during mental time travel, we developed a computational model that linked a blood oxygenation level-dependent signal to cognitive operations, allowing us to predict human performance in a memory search task. Activity in the posterior MTL, including parahippocampal cortex, reflected how strongly one reactivates the temporal context of a retrieved memory, allowing the model to predict whether the next memory will correspond to a nearby moment in the study episode. A signal in the anterior MTL, including perirhinal cortex, indicated the successful retrieval of list items, without providing information regarding temporal organization. A hippocampal signal reflected both processes, consistent with theories that this region binds item and context information together to form episodic memories. These findings provide evidence for modern theories that describe complementary roles of the hippocampus and surrounding parahippocampal and perirhinal cortices during the retrieval of episodic memories, shaping how humans revisit the past.

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“…T E and T R were set in accordance with values measured empirically (48) and used in previous neurally constrained stochastic accumulator models (49). If the accumulation process had not met the termination rule within 100 s, it was aborted and no RT was logged.…”

Section: Methodsmentioning

confidence: 99%

Response times from ensembles of accumulators

Zandbelt

Purcell

Palmeri

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Decision-making is explained by psychologists through stochastic accumulator models and by neurophysiologists through the activity of neurons believed to instantiate these models. We investigated an overlooked scaling problem: How does a response time (RT) that can be explained by a single model accumulator arise from numerous, redundant accumulator neurons, each of which individually appears to explain the variability of RT? We explored this scaling problem by developing a unique ensemble model of RT, called e pluribus unum, which embodies the wellknown dictum "out of many, one." We used the e pluribus unum model to analyze the RTs produced by ensembles of redundant, idiosyncratic stochastic accumulators under various termination mechanisms and accumulation rate correlations in computer simulations of ensembles of varying size. We found that predicted RT distributions are largely invariant to ensemble size if the accumulators share at least modestly correlated accumulation rates and RT is not governed by the most extreme accumulators. Under these regimes the termination times of individual accumulators was predictive of ensemble RT. We also found that the threshold measured on individual accumulators, corresponding to the firing rate of neurons measured at RT, can be invariant with RT but is equivalent to the specified model threshold only when the rate correlation is very high.is a core measure of human decisionmaking in experimental psychology (1). The random variation of RT across otherwise identical trials has been a puzzle since the mid-19th century. Since the 1960s, this variation of RT-measured in a wide range of perceptual, cognitive, and economic tasks (1-5)-has been explained through stochastic accumulator models. These models assume that a response is generated when evidence accumulates at a certain rate (v) over time to a threshold (θ) and that the stochastic variation of RTs arises primarily from random fluctuations in accumulation rates (Fig. 1A). Historically, these models were formulated and tested before data on the underlying neural processes were available.Subsequently, neurons exhibiting accumulating discharge rates in various RT tasks have been found in sensory, sensorimotor, and motor brain structures; in premotor circuits for limb and eye movements it is known that the neurons with accumulating activity are necessary and sufficient for initiating movements (6, 7). Movements are initiated when the trial-averaged accumulating spike rate of these neurons reaches a fixed activation level (6) (A RT ) like a threshold, and the distribution of RTs is accounted for by the stochastic variability in the rate of growth of neural activity toward A RT (Fig. 1B). This discovery inspired the conjecture that individual neurons instantiate the evidence accumulation process described by stochastic accumulator models (6). This conjecture has stimulated extensive research replicating the original observation and equating accumulator model parameters with measures of neural dynamics assessed by spike rates (8...

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From Salience to Saccades: Multiple-Alternative Gated Stochastic Accumulator Model of Visual Search

Cited by 168 publications

References 103 publications

Performance Monitoring in Monkey Frontal Eye Field

Performance Monitoring in Monkey Frontal Eye Field

Neural Activity in the Medial Temporal Lobe Reveals the Fidelity of Mental Time Travel

Response times from ensembles of accumulators

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