Optimal temporal resolution for decoding of visual stimuli in inferior temporal cortex

Babolhavaeji, Ali; Karimi, Sajjad; Ghaffari, Aboozar; Hamidinekoo, Azam; Vosoughi-Vahdat, Bijan

doi:10.1109/icbme.2014.7043903

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…Variance of the decoding accuracy was computed and averaged across iterations (over re-selection of trials for each neuron) to get the standard error of the mean decoding accuracy. A separate model was fit for each 150 ms time window (50 ms sliding window), following previous work demonstrating success in using these timing parameters for decoding neural responses in area IT ( Babolhavaeji et al. 2014 ).…”

Section: Methodsmentioning

confidence: 99%

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Burk

Sheinberg

2022

Cerebral Cortex Communications

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Our brains continuously acquire sensory information and make judgments even when visual information is limited. In some circumstances, an ambiguous object can be recognized from how it moves, such as an animal hopping or a plane flying overhead. Yet it remains unclear how movement is processed by brain areas involved in visual object recognition. Here we investigate whether inferior temporal cortex, an area known for its relevance in visual form processing, has access to motion information during recognition. We developed a matching task that required monkeys to recognize moving shapes with variable levels of shape degradation. Neural recordings in area IT showed that, surprisingly, some IT neurons responded stronger to degraded shapes over clear ones. Furthermore, neurons exhibited motion sensitivity at different times during the presentation of the blurry target. Population decoding analyses showed that motion pattern could be decoded from IT neuron pseudo-populations. Contrary to previous findings, these results suggest that neurons in IT can integrate visual motion and shape information, particularly when shape information is degraded, in a way that has been previously overlooked. Our results highlight the importance of using challenging multi-feature recognition tasks to understand the role of area IT in naturalistic visual object recognition.

show abstract

Section: Methodsmentioning

confidence: 99%

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Burk

Sheinberg

2022

Cerebral Cortex Communications

View full text Add to dashboard Cite

show abstract

“…Variance of the decoding accuracy was computed at this phase and then averaged across iterations (over re-selection of trials) to get the standard error of the mean decoding accuracy. A separate model was fit for each 150 ms time window (50 ms sliding window), following previous work demonstrating success in using these timing parameters for decoding neural responses in area IT (Babolhavaeji et al 2014) Shuffled label tests were performed to establish decoding performance at chance level to capture bias in the data. Previous work has shown that comparing performance to only theoretical chance level can lead to misinterpretation of decoder performance, as an increase in sample variance of data values will also increase the chance of rejecting the null hypothesis when compared against theoretical chance (Combrisson & Jerbi 2015).…”

Section: Population Decoding Analysismentioning

confidence: 99%

Section: Population Decoding Analysismentioning

confidence: 99%

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Burk

Sheinberg

2022

Preprint

View full text Add to dashboard Cite

Our brains continuously acquire sensory information and make judgments even when visual information is limited. In some circumstances, an ambiguous object can be recognized from how it moves, such as an animal hopping or a plane flying overhead. Yet it remains unclear how movement is processed by brain areas involved in visual object recognition. Here we investigate whether inferior temporal cortex, an area traditionally known for shape processing, has access to motion information during degraded shape recognition. We developed a matching task that required monkeys to recognize moving shapes with variable levels of shape degradation. Neural recordings in area IT showed that, surprisingly, some IT neurons preferred blurry shapes over clear ones. Further, many of the neurons exhibited motion sensitivity at different times during the presentation of the blurry target. Population decoding analyses showed that motion pattern could be decoded from IT neuron pseudo-populations. Contrary to previous findings, these results suggest that neurons in IT can integrate visual motion and shape information, particularly when shape information is degraded, in a way that has been previously overlooked. Our results highlight the importance of using challenging multi-feature recognition tasks to understand the role of area IT in naturalistic visual object recognition.

show abstract

Optimal temporal resolution for decoding of visual stimuli in inferior temporal cortex

Cited by 2 publications

References 10 publications

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

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