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Reward Value-Based Gain Control: Divisive Normalization in Parietal Cortex
Open accessAbstract
The representation of value is a critical component of decision making. Rational choice theory assumes that options are assigned absolute values, independent of the value or existence of other alternatives. However, context-dependent choice behavior in both animals and humans violates this assumption, suggesting that biological decision processes rely on comparative evaluation. Here we show that neurons in the monkey lateral intraparietal cortex encode a relative form of saccadic value, explicitly dependent on the values of the other available alternatives. Analogous to extra-classical receptive field effects in visual cortex, this relative representation incorporates target values outside the response field and is observed in both stimulus-driven activity and baseline firing rates. This context-dependent modulation is precisely described by divisive normalization, indicating that this standard form of sensory gain control may be a general mechanism of cortical computation. Such normalization in decision circuits effectively implements an adaptive gain control for value coding and provides a possible mechanistic basis for behavioral context-dependent violations of rationality.
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TL;DR
Neurons in monkey lateral intraparietal cortex (LIP) encode saccadic value relative to the values of other available alternatives, not as absolute values. Full divisive normalization best describes this context-dependent modulation, suggesting a canonical cortical gain-control mechanism for value coding.
Key findings
- In a two-target task, LIP activity increased with reward inside the response field (Vin) and decreased with reward outside it (Vout): 12/16 neurons (75.0%) were positively correlated with Vin, and 18/27 (66.7%) were negatively correlated with Vout; population mean correlations were 0.33 and −0.21, respectively.
- In a three-target task, stimulus-driven activity was suppressed by summed extra-RF value: 33/62 neurons (53.2%) were modulated, with 30/33 showing suppression; population mean correlation was −0.155 (p = 2.6×10−6). Activity in the absence of an RF target was also suppressed: 27/62 (43.6%) modulated, 21/27 suppressed; mean correlation −0.087 (p = 0.0019).
- Full divisive normalization best fit individual neurons (MED-RF: 53/62, 85.5%; HIGH-RF: 16/17, 94.1%; LOW-RF: 26/41, 61.9%) and the combined population dataset (R² = 0.961) versus fractional (0.730), difference (0.601), and simple normalization (0.219); it also had the lowest cross-validated mean squared error (0.0393).
- Suppression depended on extra-RF target value but not relative target distance (ANCOVA: value p = 0.004; distance p = 0.058; interaction p = 0.793), and behavioral measures such as fixation breaks, error rates, and reaction times showed no consistent dependence on Vout.
Methods
- Three male rhesus monkeys performed two-target and three-target saccade tasks with water reward magnitudes assigned to targets inside or outside the LIP neuron’s response field. Two-target task: 27 LIP neurons (monkey D n = 17; monkey W n = 10), with a subset (n = 16) also tested with RF value varied. Three-target task: 62 neurons under MED-RF (monkey W n = 31; monkey B n = 31), plus 45 neurons under HIGH-RF or LOW-RF conditions.
- Single-unit electrophysiology in LIP; activity analyzed mainly 0–1000 ms after peripheral target presentation and normalized to peak firing in single-target RF mapping trials.
- Analyses included Pearson correlations, multiple regression with Vout and saccade latency, ANCOVA on a suppression index, and model fitting of fractional, difference, simple normalization, and full normalization models using AIC and leave-one-out cross-validation.
Limitations
- Stated: the circuit mechanisms generating divisive normalization remain unknown, the biophysical implementation of the semisaturation term σ is unknown, and the causal role of normalized value coding in context-dependent choice remains to be explored.
- Stated: visuospatial parameters may influence suppression when alternative targets are very close to the RF (distance effect p = 0.058, not significant here), and attention cannot be fully excluded as an alternative explanation, though the authors argue the parametric value effects are not easily reconciled with simple attentional modulation.
- Evident: findings are restricted to macaque LIP, specific saccade tasks, and water reward magnitudes; some neurons were recorded under multiple value conditions, so across-condition analyses were restricted to population responses rather than individual-neuron comparisons.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Examination of the AIC values confirmed that the full divisive normalization model was the most likely of these underlying value representations in the vast majority of LIP neurons (MED-RF condition: n = 53/62, 85.5%; HIGH-RF condition: n = 16/17, 94.1%; LOW-RF conditions: n = 26/41, 61.9%).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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In the paper
“As seen in Figure 7, the full normalization equation (Rmax = 3.86, σ = 1541 µl, β = 87.6 µl) characterized LIP responses in both conditions, which is evident when the best-fit normalization model (black lines) is compared to the population responses (red and blue points).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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In the paper
“Examination of AIC values confirmed that the full normalization model best explained the MED-RF condition data (AICfract = −31.8, AICdiff = −26.5, AICnorm_simp = −21.9, AICnorm_full = −70.9).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, the average correlation coefficient in RF target-absent trials remained significantly negative.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consistent with previous reports, many LIP neurons in this task possessed motor-related activity: a presaccadic increase in firing rate selective for saccades into the RF.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Figure 5, population activity in the three-target task also depended on the value of the RF target, with progressively higher firing rates at larger levels of Vin.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Across the population of neurons, the average correlation coefficient was significantly negative for most of the cue presentation interval in both RF target-present and RF target-absent conditions (p < 0.05, t-test).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown for the example neuron in Figure 2B, activity in trials without a target in the RF also varied as a function of Vout (r = −0.161, p = 0.037).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, reaction times for saccades towards the RF target across the different value contexts did not differ across different Vout conditions in either monkey (Monkey D: p = 0.97; Monkey W: p = 0.20; one-way ANOVA).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
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In the paper
“We observed that stimulus-driven activity, elicited by the appearance of the target in the RF, was a consistent function of the total amount of reward available, despite the fact that RF target value remained constant.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As in the trials where a target appeared in the RF, multiple regression analyses showed that activity in the absence of a RF target was primarily dependent on value and not latency to saccade (significant for value in 23 neurons, for value and latency in 3 neurons, and for latency in 6 neurons).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, this increase was small and coexists with neurons suppressed by Vout; as a result, the average correlation coefficient across the population was not significant for any timepoint in the presaccadic interval (Figure 3, top; p > 0.05, t-test).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared to the fractional (Figure 6A) and difference (Figure 6B) models, the divisive normalization models (Figure 6C,D) more accurately predicted activity when a target appeared inside the RF.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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In the paper
“Furthermore, of the neurons significantly modulated by total value, the large majority was suppressed by increasing extra-RF value in both conditions across the cue interval (Figure 3; at 300 ms, target-present: 95.7%, 22/23 neurons; target absent: 78.3%, 18/23 neurons).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For both animals, saccade errors rates to the medium and large targets were independent of target configuration (p > 0.1, chi-square test).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consistent with previous reports, we found that LIP activity was strongly modulated by RF target value, as shown for an example neuron and the population response (Figure 1B).”
- Also stated in: § Two-alternative neural data · plain
“Activity in the majority of studied neurons was significantly positively correlated to Vin (p < 0.05, Pearson’s correlation; n = 12/16, 75.0%), an effect that was significant across the population (mean correlation 0.33; p = 1.6×10−5, t-test) and evident in the mean firing rates (Figure 1C).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
- Also stated in: § Two-alternative neural data · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that when the value of the target outside the RF was explicitly varied (with RF target value fixed), LIP activity was negatively modulated by value, shown for both an example neuron and the population activity (Figure 1D).”
- Also stated in: § Two-alternative neural data · plain
“Correlation analysis showed that this suppressive modulation was significantly negative for the majority of LIP neurons (p < 0.05, Pearson’s correlation; n = 18/27, 66.7%) and significant across the population (mean correlation −0.21; p = 1.2×10−5, t-test).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
- Also stated in: § Two-alternative neural data · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the recorded neurons, firing rate was significantly dependent on only value in 16 neurons, and on only latency in 1 neuron, suggesting that the observed modulation of LIP activity was primarily driven by the value context and not motivational or motor factors.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Examination of the population-averaged firing rates shows that suppression below baseline levels follows a similar time-course to value modulation of stimulus-driven activity, emerging soon after target onset and persisting until the saccadic instruction signal (Figure 2C).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, examination of fixation break frequencies, saccade error rates, and saccadic reaction times revealed no consistent dependence of behavior on Vout.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
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In the paper
“the mean correlation coefficient in both these windows is significant and negative for both RF target-present (early visual: mean r = −0.157, p = 6.0×10−11; late visual: mean r = −0.092, p = 1.4×10−4) and RF target-absent (early visual: mean r = −0.090, p = 0.001; late visual: mean r = −0.045, p = 0.047) conditions.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These effects can be quantified together by examining the correlation between trial-by-trial spike rates (0–1000 ms after target onset) and the summed values of extra-RF targets presented (r = −0.142, p = 0.026).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a two-target task explicitly manipulating Vin or Vout, LIP activity encoded a relative rather than absolute function of saccade value; in a three-target task spanning a more complete sample of value space, model comparison revealed an underlying divisive normalization computation.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
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In the paper
“As shown in Figure 7, the full divisive normalization model (gray lines; Rmax = 2.96, σ = 1161 µl, β = 92.6 µl) accurately predicted population activity changes induced by both changing context outside the RF (changes along the curves) and changing value within the RF (changes between the curves).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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In the paper
“We show here that LIP encodes value in a context-dependent manner, incorporating the values of both a saccade to the RF and other alternative saccades.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a two-target task explicitly manipulating Vin or Vout, LIP activity encoded a relative rather than absolute function of saccade value; in a three-target task spanning a more complete sample of value space, model comparison revealed an underlying divisive normalization computation.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
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In the paper
“In the simple normalization model, the constraint requiring zero activity in RF target-absent conditions prevented the model from accurately predicting responses at other Vin magnitudes.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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In the paper
“Examination of AIC values confirmed that, of the examined models, the full normalization model best characterized the population data, even when accounting for the additional parameter (AICfract = −131.3, AICdiff = −120.4, AICnorm_simp = −101.5, AICnorm_full = −183.0).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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In the paper
“Finally, in cross-validation analysis of the combined neural data, the full divisive normalization model performed with the highest accuracy in out-of-sample prediction (MSEdiff = 0.0482, MSEfract = 0.0440, MSEnorm_simp = 0.0593, MSEnorm_full = 0.0393).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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In the paper
“Second, we find that LIP activity in the absence of a RF target is differentially suppressed according to the value of a single extra-RF target, despite the fact that spatial attention is presumably localized to the extra-RF target in both cases.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In our spatial analysis, alternative targets closer to the RF yielded slightly higher suppression effects that did not reach significance (p = 0.058), leaving open the possibility that such interactions may become important at nearer distances than those examined here.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
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In the paper
“Many normative models of choice, such as expected utility theory and foraging theory, assume that each option is evaluated in an absolute manner, without regard to the other alternatives (Von Neumann and Morgenstern, 1944; Luce, 1959; Stephens and Krebs, 1986).”
§ Value-based gain control: divisive normalization in parietal cortex > INTRODUCTION
What the sources say
- Not checked yetcites Von Neumann and Morgenstern, 1944
- Not checked yetcites Luce, 1959
- Not checked yetcites Stephens and Krebs, 1986
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In the paper
“Examined directly, both animals and humans display context-dependent preferences that vary as additional alternatives are added to the choice set (Tversky and Simonson, 1993; Shafir et al., 2002; Bateson et al., 2003).”
§ Value-based gain control: divisive normalization in parietal cortex > INTRODUCTION
What the sources say
- Not checked yetcites Tversky and Simonson, 1993
- Not checked yetcites Shafir et al., 2002
- Not checked yetcites Bateson et al., 2003
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In the paper
“In monkey area LIP, a parietal region responsive to both visual stimuli and saccadic eye movements, neurons are strongly modulated by the value associated with a saccade.”
§ Value-based gain control: divisive normalization in parietal cortex > INTRODUCTION
What the sources say
- Not checked yetcites Platt and Glimcher, 1999
- Not checked yetcites Sugrue et al., 2004
- Not checked yetcites Dorris and Glimcher, 2004
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This context-dependent modulation is precisely described by divisive normalization, indicating that this standard form of sensory gain control may be a general mechanism of cortical computation.”
§ Value-based gain control: divisive normalization in parietal cortex > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here we show that neurons in the monkey lateral intraparietal cortex (LIP) encode a relative form of saccadic value, explicitly dependent on the values of the other available alternatives.”
- Also stated in: § Three-alternative neural data · plain
“These data identify a relative rather than absolute representation of value in area LIP, combining value information about a saccade into the RF with information about an alternative, extra-RF eye movement.”
§ Value-based gain control: divisive normalization in parietal cortex > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
- Also stated in: § Three-alternative neural data · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results suggest that, as in the two-target data, the suppressive modulation observed in the three-target task was primarily controlled by the value of the alternative, extra-RF targets.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Because neurons are normalized to activity in identical trial types, this method of normalization allows comparison of relative levels of activity across neurons recorded under different value conditions; note that activity normalized in this manner is not constrained to be between 0 and 1.”
§ Value-based gain control: divisive normalization in parietal cortex > MATERIALS AND METHODS > Data analysis
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This context-dependent modulation is precisely described by divisive normalization, indicating that this standard form of sensory gain control may be a general mechanism of cortical computation.”
§ Value-based gain control: divisive normalization in parietal cortex > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Together, these measures indicate that behavioral factors such as motivation or vigilance remained relatively constant across different value context conditions.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Across the network of neurons in LIP, suppression of RF-target activity by extra-RF targets implements a value-based scaling of the most active neurons, a form of adaptive gain control.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, these results link value coding in parietal cortex to a well-characterized phenomenon in visual cortex.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, divisive normalization implements an adaptive form of gain control, adjusting the input-output function from absolute to relative value as total value increases.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unlike previous electrophysiological studies, these findings support a normalization based not on stimulus characteristics, such as contrast or orientation, but on value.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“These results suggest that LIP activity, rather than mirroring the level of spatial attention, serves as a priority map to guide the attentional allocation process itself (Bisley and Goldberg, 2010).”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
What the sources say
- Not checked yetcites Bisley and Goldberg, 2010
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, several aspects of the current results are not easily reconcilable with a simple attentional effect.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At much larger values (V ≫ σ), the divisive normalization model approaches pure fractional value coding as σ becomes negligible.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A normalized value representation provides a single integrated mechanism for the dependence of decision-related neural activity on both relative value and set size.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, like the individual neuron data, the population LIP response was best characterized by the full divisive normalization model.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, individual parietal neurons integrate information from RF-driven activity with that involving surrounding regions of visual space, a contextual effect analogous to phenomena in early visual cortex associated with extra-classical receptive field effects.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a normalized value system, the mean firing rates representing option values decrease as the number of alternatives (or the value of an irrelevant option) increases.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By applying a specific algorithmic model to value coding, our results extend previous findings about the influence of both relative value and number of choice options on parietal activity.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Specifically, the normalization model predicts that activity will primarily reflect RF reward when total value (Vin+Vout) is small (V ≪ σ) and the term σ dominates the denominator. This coding approximates an absolute value code, where the value of extra-RF targets exerts little control over activity (vertical shading and vertical contour lines).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While both the fractional and normalization models predict saturating responses as RF value increases, under normalization the system shifts between representing absolute and relative value due to the presence of the semisaturation term σ in the denominator.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Though the rank ordering of option desirability would not change, uncompensated variability (for example, additive noise or correlated activity) could decrease the discriminability between the options as the distributions are normalized, leading to stochastic choice behavior that is dependent on the composition of the choice set (Tversky and Simonson, 1993; Shafir et al., 2002; Bateson et al., 200”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
What the sources say
- Not checked yetcites Tversky and Simonson, 1993
- Not checked yetcites Shafir et al., 2002
- Not checked yetcites Bateson et al., 2003
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, a divisive normalization representation produces a value coding that is functionally distinct from fractional value (Figure 9).”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 37
- Probably not a substantial claim. 11
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the simple normalization model failed to predict the suppression below baseline levels observed in the absence of RF stimulation; only the full normalization model fully predicted the modulation of both RF-driven and baseline activity.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, examination of the relative target positions (distance from RF target) of small and large value alternative targets showed no discernible difference in the average relative position (distancesmall = 35.43 deg, distancelarge = 35.10 deg; p = 0.77, t-test) or the relative position distributions (p = 0.98, two-sample Kolmogorov-Smirnov test; Figure 4 inset).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The majority of LIP neurons were modulated by value context, with almost all neurons showing suppression (modulation: n = 33/62, 53.2%; suppression n = 30/33, 90.9%); furthermore, suppression was significant across the population of neurons (mean correlation −0.155; p = 2.6×10−6, t-test) and evident in the mean population activity (Figure 2C).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Figure 3, the population of LIP neurons displayed Vout-dependent suppression in both RF target-present and RF target-absent conditions across much of the trial, with peak modulation occurring approximately 200–300 ms after peripheral target onset.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the recorded neurons, firing rate was significantly dependent on only value in 29 neurons, on both value and latency in 3 neurons, and on only latency in 1 neuron, suggesting that the observed modulation of LIP activity was primarily driven by the value context.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, the difference model predicted suppression even in RF target-absent conditions; however, it could not adequately model both the RF target-driven and RF target-absent effects simultaneously.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For saccades to the small target, error rates varied by target configuration (Monkey B, χ2(3) = 13.4, p = 0.004; Monkey W, χ2(3) = 17.2, p = 0.0006) but were correlated with total value in only one monkey (Monkey B: r = 0.83, p = 0.167; Monkey W: r = 0.95, p = 0.047) and uncorrelated across the combined data (r = 0.35, p = 0.401).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This value modulation in the absence of RF stimulation was significant for a large fraction of LIP neurons (modulation n = 27/62, 43.6%; suppression 21/27, 77.8%) and significant across the population (mean correlation −0.087; p = 0.0019, t-test).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, the rate of incorrect saccadic choices did not vary consistently with Vout; both monkeys exhibited high success rates (> 90%), with error rates independent of Vout condition in Monkey W (χ2(4) = 4.5, p = 0.34) and dependent but not significantly related to Vout in Monkey D (χ2(4) = 14.4, p = 0.006; r = −0.40, p = 0.51).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite constant Vin levels, activity in trials with a target in the RF (solid red symbols) was suppressed as Vout increased. Similarly, in trials without a target in the RF (solid blue symbols), activity decreased as a function of alternative target values.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both monkeys had low fixation break frequencies during cue presentation (Monkey W: 2.8%; Monkey B: 4.0%) that were uncorrelated with total value in both monkeys individually (Monkey W: r = −0.36, p = 0.42; Monkey B: r = 0.28, p = 0.54) and combined across animals (r = 0.04, p = 0.90).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“consistent with the timepoint analysis, average LIP activity is suppressed by Vout in RF target-absent conditions (mean r = −.0.071, p = 3.7×10−4) and unmodulated in RF target-present (mean r = 0.024, p = 0.302) conditions.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consistent with the results described above, increasing the value of the target outside the RF in the two-target conditions decreased LIP firing rates (red, orange). Furthermore, activity was also a function of target number, decreasing from single target (black) to double target (red, orange) to triple target (yellow) conditions.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fixation break frequencies were low in both monkeys (Monkey D: 9.2%; Monkey W: 1.6%); in Monkey W, these frequencies did not differ across Vout conditions (Monkey W: χ2(4) = 5.6, p = 0.23), while in Monkey D, these frequencies differed across conditions (χ2(4) = 20.6, p = 0.0004) but were not significantly correlated with Vout magnitudes (r = −0.87, p = 0.06).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The fractional and simple normalization models failed to account for suppression in the absence of a RF target; because both models incorporate only the quantity Vin in the numerator, they predicted constant levels of activity in all RF-target absent conditions (simple normalization: R = 0; fractional: R = a).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Across the population, suppression was independent of the relative spatial location of the extra-RF target, supporting a modulation driven primarily by target value rather than spatial location.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Targets at these locations did not activate the recorded neuron when presented alone, consistent with a contralateral visual bias in LIP (Ben Hamed et al., 2001; Patel et al., 2010).”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
What the sources say
- Not checked yetcites Ben Hamed et al., 2001
- Not checked yetcites Patel et al., 2010
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As noted previously, the fractional model failed to account for background suppression in the absence of a RF target, evident as a horizontal line in predicted activity (Figure 6, top); furthermore, the predicted linear responses correspond poorly to the observed nonlinear activity.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results show that the value of targets situated far from the RF can modulate both stimulus-driven activity and activity in the absence of direct feed-forward RF drive.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The value of σ derived from the present study is ~1000 µl, a quantity large enough at normal volumes of reward to significantly shift value coding away from a strict fractional representation and towards a richer function of value context.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, we find a monotonic relationship between the value of the target in the RF and LIP activity, as well as a parametric suppression related to the value of extra-RF targets.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the three-target task, extra-RF targets were far from the RF center (average 35 deg) and located primarily in the opposite visual hemifield.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When examined across all data conditions, the full divisive normalization model explained the full dataset (R2 = 0.961) far better than the fractional (R2 = 0.730), difference (R2 = 0.601), or simple normalization (R2 = 0.219) models (Figure 6, right column).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The difference model predicted some RF target-absent suppression, but performed particularly poorly in characterizing the range of responses to different Vin conditions.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While such linear models accurately characterized basic visual neuron responses such as orientation and direction selectivity, they could not explain more complicated, nonlinear phenomena such as contrast saturation and nonspecific suppression.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“More recently, motivated by computational models of two-alternative choice behavior, some researchers have suggested that the difference in option values may better describe LIP activity (Corrado et al., 2005).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Corrado et al., 2005
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✗ Asserted: fails✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Together, these results suggest that LIP encodes a context-dependent value representation, where the value configuration of the entire choice set controls both stimulus-driven and target-absent activity early in the valuation process.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, while both the fractional and difference models are consistent with LIP value modulation, neither theoretical proposal is directly based on the structure and function of cortical circuits.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such normalization in decision circuits effectively implements an adaptive gain control for value coding and provides a possible mechanistic basis for behavioral context-dependent violations of rationality.”
§ Value-based gain control: divisive normalization in parietal cortex > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Early models of striate cortex described visual neuron function as a linear operation, producing responses that are weighted sums of relevant inputs (Hubel and Wiesel, 1962, 1968; Movshon et al., 1978).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Hubel and Wiesel, 1962, 1968
- Not checked yetcites Movshon et al., 1978
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Initial studies of value coding in LIP (Platt and Glimcher, 1999; Sugrue et al., 2004) focused on a fractional representation of value due to its association with classic matching law behavior (Herrnstein, 1961).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Platt and Glimcher, 1999
- Not checked yetcites Sugrue et al., 2004
- Not checked yetcites Herrnstein, 1961
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Analogous to extra-classical receptive field effects in visual cortex, this relative representation incorporates target values outside the response field and is observed in both stimulus-driven activity and baseline firing rates.”
§ Value-based gain control: divisive normalization in parietal cortex > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Notably, activity positively related to Vout was absent in the two-target task (Figure 1C), suggesting that such activity may be a salience effect related to the instructional offset of the peripheral targets which only occurred in the three-target task.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This epoch represents the entire period of target array presentation prior to the signal identifying the saccade target; the general results presented here are not sensitive to the exact definition of this epoch.”
§ Value-based gain control: divisive normalization in parietal cortex > MATERIALS AND METHODS > Data analysis
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, activity was both an increasing function of Vin and a decreasing function of Vout, suggesting that LIP value representation is relative rather than absolute; we explore the specific algorithm underlying this relative representation below.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, normalization-style models yield efficient coding of natural signal statistics in both visual and auditory modalities (Schwartz and Simoncelli, 2001; Valerio and Navarro, 2003) and may play a role in attentional processing (Reynolds et al., 1999; Reynolds and Heeger, 2009), suggesting that divisive normalization may serve as a canonical computational algorithm in cortical circuits.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Schwartz and Simoncelli, 2001
- Not checked yetcites Valerio and Navarro, 2003
- Not checked yetcites Reynolds and Heeger, 2009
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In sensory cortices, stimuli outside the classical receptive field can nonetheless significantly modulate neuronal activity. Many of these extra-classical effects are well-described by models where response is specified by the sensory properties of the stimulus inside the receptive field, divided by the weighted sum of the sensory properties of stimuli both outside and inside the receptive field (”
§ Value-based gain control: divisive normalization in parietal cortex > INTRODUCTION
What the sources say
- Not checked yetcites Heeger, 1992
- Not checked yetcites Carandini et al., 1997
- Not checked yetcites Cavanaugh et al., 2002
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the relatively small number of neurons recorded in multiple Vin conditions, we restricted analyses across all value conditions to the population responses.”
§ Value-based gain control: divisive normalization in parietal cortex > MATERIALS AND METHODS > Electrophysiological recording
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To address these issues, Heeger proposed that visual cortical function involves a normalization stage, where a given cell’s response is divided by a quantity representing the pooled activity of a large number of other similar neurons (Heeger, 1992).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Heeger, 1992
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“This divisive normalization model explains a number of nonlinear phenomena in striate and extrastriate cortex (Carandini et al., 1997; Britten and Heuer, 1999; Cavanaugh et al., 2002; Heuer and Britten, 2002) as well as responses in the ventral visual stream (Zoccolan et al., 2005).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Carandini et al., 1997
- Not checked yetcites Britten and Heuer, 1999
- Not checked yetcites Cavanaugh et al., 2002
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For both tasks, the initial ten trials in a block were discarded to avoid learning effects.”
§ Value-based gain control: divisive normalization in parietal cortex > MATERIALS AND METHODS > Data analysis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Together, these results indicate that full divisive normalization outperformed the alternative models in terms of both response characterization and predictive power.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to nonlinearity introduced by the semisaturation term (σ), the divisive normalization representation can produce responses on a continuum between absolute and relative value coding; this form of adaptive gain control depends on the relative balance between total value and σ.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, at intermediate total values (V ~ σ) the denominator in the normalization model depends on both total value and σ, and activity grows increasingly sensitive to choice context, similar to a fractional model (angled shading and angled contour lines).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results provide the first example of normalization outside a strictly sensory area, and the most anterior extension of divisive normalization coding in the dorsal stream.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
The neural basis of context-dependent valuation is unknown.
“However, the neural basis of such context-dependent valuation is unknown.”
The paper relies on this without support.
The specific form of value representation in LIP and how it arises out of cortical circuit interactions remains largely unexplored.
“Current evidence suggests that parietal neurons are modulated by the value of their associated saccade, but the specific form of value representation and how it arises out of cortical circuit interactions remains largely unexplored.”
The paper relies on this without support.
LIP neurons have a circumscribed response field and many have a similarly tuned response to upcoming saccade amplitude and direction.
“LIP neurons, like those in visual cortex, respond to stimulus presentation in a circumscribed region of visual space termed the response field (RF); many of these neurons also possess a similarly tuned response to upcoming saccade amplitude and direction.”
The authors state this as an assumption.
The value of a given target is equated with the associated reward magnitude.
“The value of a given target was equated with the associated reward magnitude.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Similar results are obtained in tasks that manipulate the desirability of a target using different methods, for example by controlling the relative magnitude, probability or delay of its expected reward (Bernacchia et al., 2011; Louie et al., 2011; Sugrue et al., 2004; Yang and Shadlen, 2007).”
From Attention, learning, and the value of information (2012) · cites this paper as Louie et al., 2011
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentχ²(4) = 5.6reported p = .23 · recomputed p = .231
“χ2(4) = 5.6, p = .23”
- Consistentχ²(4) = 20.6reported p = .0004 · recomputed p < .001
“χ2(4) = 20.6, p = .0004”
- Consistentχ²(4) = 4.5reported p = .34 · recomputed p = .343
“χ2(4) = 4.5, p = .34”
- Consistentχ²(4) = 14.4reported p = .006 · recomputed p = .006
“χ2(4) = 14.4, p = .006”
- Consistentχ²(3) = 13.4reported p = .004 · recomputed p = .004
“χ2(3) = 13.4, p = .004”
- Consistentχ²(3) = 17.2reported p = .0006 · recomputed p < .001
“χ2(3) = 17.2, p = .0006”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
LOW-RF neuron percentage does not match its count
MinorThe reported 26 of 41 neurons is 63.4%, not 61.9%.
Reported 61.9 · recomputed 63.414634
“LOW-RF conditions: n = 26/41, 61.9%”
Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · cited 1×
“To quantify the relative performance of the different models, we calculated the AIC for each neuron under every model; the AIC provides an information theoretic basis for model comparison that considers both goodness of fit and parsimony (Akaike, 1974).”
MethodTheory of games and economic behavior
Garcia, S. R., & Miller, S. J. (2019) · American Mathematical Society eBooks · doi:10.1090/mbk/121/32 · Cited by 16,777
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · cited 1×
“Early models of striate cortex described visual neuron function as a linear operation, producing responses that are weighted sums of relevant inputs (Hubel and Wiesel, 1962, 1968; Movshon et al., 1978).”
BackgroundReceptive fields and functional architecture of monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,685 · cited 1×
“Early models of striate cortex described visual neuron function as a linear operation, producing responses that are weighted sums of relevant inputs (Hubel and Wiesel, 1962, 1968; Movshon et al., 1978).”
BackgroundTheory of Games and Economic Behavior
N., E., von Neumann, J., & Morgenstern, O. (1945) · The Journal of Philosophy · doi:10.2307/2019327 · Cited by 4,095
When choice is demotivating: can one desire too much of a good thing?
Iyengar, S. S., & Lepper, M. R. (2000) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.79.6.995 · Cited by 2,878 · cited 1×
“Under larger choice sets, human decision makers report greater difficulty choosing and lower outcome satisfaction, and they often choose the default option or opt not to choose at all (Iyengar and Lepper, 2000).”
Luce, R. (1959) · Econometrica · doi:10.2307/1911299 · Cited by 2,603 · cited 2×
“Reward Value-Based Gain Control: Divisive Normalization in”
BackgroundRELATIVE AND ABSOLUTE STRENGTH OF RESPONSE AS A FUNCTION OF FREQUENCY OF REINFORCEMENT 1, 2
Herrnstein, R. J. (1961) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1961.4-267 · Cited by 2,281 · cited 2×
“Initial studies of value coding in LIP (Platt and Glimcher, 1999; Sugrue et al., 2004) focused on a fractional representation of value because of its association with classic matching law behavior (Herrnstein, 1961).”
BackgroundShadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178 · cited 1×
“Cortical neurons exhibit considerable variability in their spiking activity, and information is likely represented by distributions of neurons and spiking rates (Tolhurst et al., 1983; Shadlen and Newsome, 1998; Pouget et al., 2003).”
BackgroundNormalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835 · cited 4×
“…well described by models in which response is specified by the sensory properties of the stimulus inside the receptive field, divided by the weighted sum of the sensory properties of stimuli both outside and inside the receptive field (Heeger, 1992; Carandini et al., 1997; Cavanaugh et al., 2002).”
BackgroundInfluential
Flagged first, then by citations
5 of the 60 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Stephens DW Krebs JR Foraging theory 1986 Princeton, NJ Princeton University Press
Cited by
Sorted by citations
Bartra, O., McGuire, J. T., & Kable, J. W. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.063 · Cited by 1,927
“We have assumed for simplicity that zero corresponds to no outcome at all, but there is evidence that decision makers actually evaluate outcomes relative to a reference point based either on predictions (Montague et al., 1996) or concurrent context (Louie et al., 2011).”
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906
Top-down versus bottom-up attentional control: a failed theoretical dichotomy
Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.06.010 · Cited by 1,557
Spectral fingerprints of large-scale neuronal interactions
Siegel, M., Donner, T. H., & Engel, A. K. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3137 · Cited by 1,295
Neuronal Reward and Decision Signals: From Theories to Data
Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126
“Divisive normalization from sensory physiology describes well the adaptations to changes in individual values of choice options in reward neurons of parietal cortex (332).”
Background
Metadata
- Authors
- Louie, K., Grattan, L. E., & Glimcher, P. W.
- Affiliations
- New York University (US)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2011-07-20
- Volume
- 31
- Issue
- 29
- Pages
- 10627-10639
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3285508
- OpenAlex
- W2069815761
- Semantic Scholar
- 2c68a342adf5dd25439ef7f3f553afd1e9a0d1d4
- Cited by
- 308
- References
- 60
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 20 · 2023: 22 · 2022: 18 · 2021: 29 · 2020: 19 · 2019: 25 · 2018: 23 · 2017: 22 · 2016: 16 · 2015: 24 · 2014: 23 · 2013: 24 · 2012: 19
- FWCI
- 11.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Eye Institute (R01EY010536)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- divisive normalization, gain control, context-dependent choice, comparative analysis, decision-making, monkey visual cortex
- MeSH terms
- Action Potentials, Analysis of Variance, Animals, Choice Behavior, Macaca mulatta, Male, Models, Neurological, Neurons, Parietal Lobe, Photic Stimulation, Predictive Value of Tests, Reference Values, Reward, Time Factors, Chi-Square Distribution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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