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Computational models of the Posner simple and choice reaction time tasks.

Feher da Silva C, Baldo MV - Front Comput Neurosci (2015)

Bottom Line: In doing so, main findings of experimental research on RT were replicated: the relative frequency effect, suboptimal RTs and significant error rates due to noise and invalid cues, slower RT for choice RT tasks than for simple RT tasks, fastest RTs for valid cues and slowest RTs for invalid cues.Analysis of the optimized systems revealed that the employed mechanisms were consistent with related findings in neurophysiology.Our models, however, reveal that the main problems that must be overcome to perform the Posner task effectively are distinguishing signal from external noise and selecting the appropriate response in the presence of internal noise.

View Article: PubMed Central - PubMed

Affiliation: Department of General Physics, Institute of Physics, University of São Paulo São Paulo, Brazil.

ABSTRACT
The landmark experiments by Posner in the late 1970s have shown that reaction time (RT) is faster when the stimulus appears in an expected location, as indicated by a cue; since then, the so-called Posner task has been considered a "gold standard" test of spatial attention. It is thus fundamental to understand the neural mechanisms involved in performing it. To this end, we have developed a Bayesian detection system and small integrate-and-fire neural networks, which modeled sensory and motor circuits, respectively, and optimized them to perform the Posner task under different cue type proportions and noise levels. In doing so, main findings of experimental research on RT were replicated: the relative frequency effect, suboptimal RTs and significant error rates due to noise and invalid cues, slower RT for choice RT tasks than for simple RT tasks, fastest RTs for valid cues and slowest RTs for invalid cues. Analysis of the optimized systems revealed that the employed mechanisms were consistent with related findings in neurophysiology. Our models predict that (1) the results of a Posner task may be affected by the relative frequency of valid and neutral trials, (2) in simple RT tasks, input from multiple locations are added together to compose a stronger signal, and (3) the cue affects motor circuits more strongly in choice RT tasks than in simple RT tasks. In discussing the computational demands of the Posner task, attention has often been described as a filter that protects the nervous system, whose capacity is limited, from information overload. Our models, however, reveal that the main problems that must be overcome to perform the Posner task effectively are distinguishing signal from external noise and selecting the appropriate response in the presence of internal noise.

No MeSH data available.


Related in: MedlinePlus

Average networks in Experiment 2—Condition C. Average network for SRT (A) and CRT (B) tasks in Condition C of Experiment 2. Excitatory biases and synapses are represented in red and inhibitory ones in blue, with proportional color intensity.
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Figure 8: Average networks in Experiment 2—Condition C. Average network for SRT (A) and CRT (B) tasks in Condition C of Experiment 2. Excitatory biases and synapses are represented in red and inhibitory ones in blue, with proportional color intensity.

Mentions: Average weights and biases are shown for every neuron and synapse in Figure 8. A neural network built from average parameters—an average network—does not necessarily represents well a population of networks. In this case, however, the average network's RTs are similar to the average RTs of the neural networks (39.13, 36.25, and 39.38 for the SRT and 48.14, 59.06, and 95.02 for the CRT, N = 1000). It is therefore useful to examine such networks in order to understand the results of Condition C. It is possible to see that, in the SRT average network, the target neurons are mutually excitatory. Tests indicate that when the target appears on one side, the ipsilateral target neuron fires under the target stimulus's direct stimulation, but the excitatory synapses between target neurons make the contralateral target neuron fire as well, albeit with a lower rate. The output neuron, under stimulation of both target neurons, fires faster than it would under stimulation of only one target neuron. In the CRT average network, the synapses between target and cue neurons and contralateral output neurons are inhibitory, which reduces the probability of incorrect responses, and the synapses between target and cue neurons and ipsilateral output neurons are excitatory, which is necessary for the network to respond correctly.


Computational models of the Posner simple and choice reaction time tasks.

Feher da Silva C, Baldo MV - Front Comput Neurosci (2015)

Average networks in Experiment 2—Condition C. Average network for SRT (A) and CRT (B) tasks in Condition C of Experiment 2. Excitatory biases and synapses are represented in red and inhibitory ones in blue, with proportional color intensity.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4488626&req=5

Figure 8: Average networks in Experiment 2—Condition C. Average network for SRT (A) and CRT (B) tasks in Condition C of Experiment 2. Excitatory biases and synapses are represented in red and inhibitory ones in blue, with proportional color intensity.
Mentions: Average weights and biases are shown for every neuron and synapse in Figure 8. A neural network built from average parameters—an average network—does not necessarily represents well a population of networks. In this case, however, the average network's RTs are similar to the average RTs of the neural networks (39.13, 36.25, and 39.38 for the SRT and 48.14, 59.06, and 95.02 for the CRT, N = 1000). It is therefore useful to examine such networks in order to understand the results of Condition C. It is possible to see that, in the SRT average network, the target neurons are mutually excitatory. Tests indicate that when the target appears on one side, the ipsilateral target neuron fires under the target stimulus's direct stimulation, but the excitatory synapses between target neurons make the contralateral target neuron fire as well, albeit with a lower rate. The output neuron, under stimulation of both target neurons, fires faster than it would under stimulation of only one target neuron. In the CRT average network, the synapses between target and cue neurons and contralateral output neurons are inhibitory, which reduces the probability of incorrect responses, and the synapses between target and cue neurons and ipsilateral output neurons are excitatory, which is necessary for the network to respond correctly.

Bottom Line: In doing so, main findings of experimental research on RT were replicated: the relative frequency effect, suboptimal RTs and significant error rates due to noise and invalid cues, slower RT for choice RT tasks than for simple RT tasks, fastest RTs for valid cues and slowest RTs for invalid cues.Analysis of the optimized systems revealed that the employed mechanisms were consistent with related findings in neurophysiology.Our models, however, reveal that the main problems that must be overcome to perform the Posner task effectively are distinguishing signal from external noise and selecting the appropriate response in the presence of internal noise.

View Article: PubMed Central - PubMed

Affiliation: Department of General Physics, Institute of Physics, University of São Paulo São Paulo, Brazil.

ABSTRACT
The landmark experiments by Posner in the late 1970s have shown that reaction time (RT) is faster when the stimulus appears in an expected location, as indicated by a cue; since then, the so-called Posner task has been considered a "gold standard" test of spatial attention. It is thus fundamental to understand the neural mechanisms involved in performing it. To this end, we have developed a Bayesian detection system and small integrate-and-fire neural networks, which modeled sensory and motor circuits, respectively, and optimized them to perform the Posner task under different cue type proportions and noise levels. In doing so, main findings of experimental research on RT were replicated: the relative frequency effect, suboptimal RTs and significant error rates due to noise and invalid cues, slower RT for choice RT tasks than for simple RT tasks, fastest RTs for valid cues and slowest RTs for invalid cues. Analysis of the optimized systems revealed that the employed mechanisms were consistent with related findings in neurophysiology. Our models predict that (1) the results of a Posner task may be affected by the relative frequency of valid and neutral trials, (2) in simple RT tasks, input from multiple locations are added together to compose a stronger signal, and (3) the cue affects motor circuits more strongly in choice RT tasks than in simple RT tasks. In discussing the computational demands of the Posner task, attention has often been described as a filter that protects the nervous system, whose capacity is limited, from information overload. Our models, however, reveal that the main problems that must be overcome to perform the Posner task effectively are distinguishing signal from external noise and selecting the appropriate response in the presence of internal noise.

No MeSH data available.


Related in: MedlinePlus