The use of response latencies to detect impression-managed responding

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

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Variable effects of click polarity on auditory brain‐stem response latencies: Analyses of narrow‐band ABRs suggest possible explanations

The Journal of the Acoustical Society of America ◽

10.1121/1.415858 ◽

1996 ◽

Vol 100 (1) ◽

pp. 458-466 ◽

Cited By ~ 6

Author(s):

Manuel Don ◽

Andrew J. Vermiglio ◽

Curtis W. Ponton ◽

Jos J. Eggermont ◽

Ann Masuda

Keyword(s):

Brain Stem ◽

Narrow Band ◽

Response Latencies ◽

Auditory Brain Stem Response ◽

Auditory Brain Stem ◽

Brain Stem Response ◽

Click Polarity

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Analog Numerical Representations in Rhesus Monkeys: Evidence for Parallel Processing

Journal of Cognitive Neuroscience ◽

10.1162/089892904970807 ◽

2004 ◽

Vol 16 (5) ◽

pp. 889-901 ◽

Cited By ~ 72

Author(s):

Andreas Nieder ◽

Earl K. Miller

Keyword(s):

Rhesus Monkeys ◽

Neuronal Response ◽

Weber Fraction ◽

Discrimination Performance ◽

Model Organisms ◽

Response Latencies ◽

Numerosity Discrimination ◽

Numerical Information ◽

Match To Sample ◽

Numerical Representations

Monkeys have been introduced as model organisms to study neural correlates of numerical competence, but many of the behavioral characteristics of numerical judgments remain speculative. Thus, we analyzed the behavioral performance of two rhesus monkeys judging the numerosities 1 to 7 during a delayed match-to-sample task. The monkeys showed similar discrimination performance irrespective of the exact physical appearance of the stimuli, confirming that performance was based on numerical information. Performance declined smoothly with larger numerosities, and reached discrimination threshold at numerosity “4.” The nonverbal numerical representations in monkeys were based on analog magnitudes, object tracking process (“subitizing”) could not account for the findings because the continuum of small and large numbers shows a clear Weber fraction signature. The lack of additional scanning eye movements with increasing set sizes, together with indistinguishable neuronal response latencies for neurons with different preferred numerosities, argues for parallel encoding of numerical information. The slight but significant increase in reaction time with increasing numerosities can be explained by task difficulty and consequently time-consuming decision processes. The behavioral results are compared to single-cell recordings from the prefrontal cortex in the same subjects. Models for numerosity discrimination that may account for these results are discussed.

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