Relating normalization to neuronal populations across cortical areas

Ruff, Douglas A.; Alberts, Joshua J.; Cohen, Marlene R.

doi:10.1152/jn.00017.2016

Cited by 31 publications

(69 citation statements)

References 53 publications

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“…We therefore developed a simple leaky competing accumulator network model (Usher and McClelland, 2001) to test a new hypothesis of how these computations might be implemented. This model extends previous work to incorporate a known property of perceptual circuitry: tuned normalization Ruff et al, 2016;Verhoef and Maunsell, 2017), meaning each neuron is characterized by the specific degree to which it is normalized (i.e., inhibited) by surrounding network activity (Reynolds and Heeger, 2009;Carandini and Heeger, 2012), and specifically by units with opposing tuning preferences. We hypothesized that each neuron's degree of tuned normalization dictates how it differentially participates in discrimination decisions versus confidence judgments.…”

mentioning

confidence: 59%

“…Further, it was recently reported that neurons in primary sensory areas exhibit tuned normalization, i.e. that each neuron possesses a unique, consistent degree of normalization: some neurons are very sensitive to activity of other units in the network (especially those which have different tuning preferences), while others operate more independently Ruff et al, 2016;Verhoef and Maunsell, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…However, we now know that a range of normalization tuning exists, at least in sensory cortices. We hypothesized that these neuron-by-neuron variations in normalization may reflect not noise or measurement error, but meaningful properties of the perceptual decision-making circuitry Ruff et al, 2016;Verhoef and Maunsell, 2017). We refer to this model as the Differential Tuned Normalization model, or the Tuned Normalization model for short.…”

Section: Methodsmentioning

confidence: 99%

“…We examined this hypothesis by incorporating tuned normalization Ruff et al, 2016) into a leaky competing accumulator network (Usher and McClelland, 2001) (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…Drawing from previous work Ruff et al, 2016;Verhoef and Maunsell, 2017), we calculated the normalization tuning Modulation Index (MI) as:…”

Section: Calculating Normalization Tuning Via the Modulation Indexmentioning

confidence: 99%

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Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

Maniscalco

Odegaard

Grimaldi

et al. 2019

Preprint

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of the model; M.A.K.P. and B.M. formally specified the model and conducted model simulations; P.G., S.H.C., and M.A.B. collected the monkey data; M.A.K.P., B.O., P.G. and S.H.C. analyzed the monkey data; M.A.K.P., B.M., and H.L. wrote the manuscript. AbstractCurrent dominant views hold that perceptual confidence reflects the probability that a decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple leaky competing accumulator neural network model incorporating a known property of sensory neurons: tuned normalization. The key idea of the model is that each accumulator neuron's normalization 'tuning' dictates its contribution to perceptual decisions versus confidence judgments. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature, where confidence and decision accuracy were shown to dissociate --and that the differential contribution a neuron makes to decisions versus confidence judgments based on its normalization tuning is vital to capturing some of these effects. One critical prediction of the model is that systematic variability in normalization tuning exists not only in sensory cortices but also in the decision-making circuitry. We tested and validated this prediction in macaque superior colliculus (SC; a region implicated in decisionmaking). The confirmation of this novel prediction provides direct support for our model. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by capitalizing on the diversity of neural resources available. Samaha et al., 2016Samaha et al., , 2017. In other words, to compute confidence the system uses a suboptimal heuristic that overly relies on decision-congruent evidence magnitude rather than optimal computations. Indeed, a recent study reported evidence for these decision-congruent evidence confidence computations using human intracranial electrocorticography (Peters et al., 2017b).However, to date no biologically plausible mechanism has been proposed that might explain these dissociations between confidence and performance, or the decision-congruent confidence computations on which they seem to depend. We therefore developed a simple leaky competing accumulator network model (Usher and McClelland, 2001) to test a new hypothesis of how these computations might be implemented. This model extends previous work to incorporate a known property of perceptual circuitry: tuned normalization Ruff et al., 2016;Verhoef and Maunsell, 2017), meaning each neuron is characterized by the specific degree to which i...

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mentioning

confidence: 59%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…We examined this hypothesis by incorporating tuned normalization Ruff et al, 2016) into a leaky competing accumulator network (Usher and McClelland, 2001) (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…Drawing from previous work Ruff et al, 2016;Verhoef and Maunsell, 2017), we calculated the normalization tuning Modulation Index (MI) as:…”

Section: Calculating Normalization Tuning Via the Modulation Indexmentioning

confidence: 99%

See 3 more Smart Citations