General procedures

All procedures conformed to the guidelines of the National Institutes of Health (NIH Pub. No. 86–23, Revised 1985) and were approved by the Institutional Animal Care and Use Committee of Duke University. Two adult rhesus monkeys (Macaca mulatta) participated (monkey P, and monkey Y, both female). Under general anesthesia and in sterile surgery we first implanted a head post holder to restrain the head and a scleral search coil to track eye movements67,68. After recovery with suitable analgesics and veterinary care, we trained the monkeys in the experimental task. In a second surgery, we implanted a recording cylinder (2 cm diameter) over the right (monkey Y) or left (monkey Y, P) IC respectively. We determined the location of the cylinder with stereotactic coordinates and verified it with MRI scans e.g. 53.

Sound localization task

The monkeys performed a single-sound or dual-sound localization task (Fig. 1b) by making saccades toward one or two simultaneously-presented auditory targets with one or two saccades as appropriate. All sound targets were located in front of the monkey at eye level; the horizontal location, frequency and intensity were varied pseudorandomly as described below (Recording Procedures). Each trial began with 600–700 ms of fixation of a visual stimulus (light emitting-diode, LED, located straight ahead and 10–14° below the speakers). During fixation we presented one sound (single-sound trials) or two simultaneous sounds (dual-sound trials). After a fixation time of either 600–800 ms (Data Set I, some of Data Set II) or 1000–1100 ms (remainder of Data Set II), the fixation light was extinguished and the monkey was required to make a single saccade on single-sound trials or a sequence of two saccades (in either order) on dual-sound trials. Trials were considered correct if each saccade was directed within 10–17.5 degrees horizontally and 20–40 degrees vertically of a target due to vertical inaccuracies in localizing non-visual targets in primates69, and if the gaze was maintained on the final target for 100–200 ms. On correct trials monkeys were rewarded with juice drops.

Training

Training was accomplished in three stages. We initially trained the monkeys to report the location of single visual targets by saccading to them. We then introduced single auditory targets. As these were novel and unexpected in the silent experimental booth, monkeys readily saccaded to them70. To help the monkeys calibrate their auditory saccades, a visual feedback was added on trials where the auditory saccade was not initiated correctly within 700 ms. The feedback was presented only at the most peripheral target locations (+/−24 degree) and only for a few initial days of training. Finally, we trained monkey to localize dual-sound targets. Initially we presented the two sounds sequentially in a specific order, then we gradually reduced the temporal gap between them until the sounds were simultaneous.

In the final version of the task, monkeys were allowed to look at the targets in either order, as noted above. However, due to the initial training with sequential sounds, they retained stereotyped patterns of saccades in which they tended to look first to whichever sound location had been presented first during the sequential and partial overlap stages of training. Monkey P was trained with more central target locations (e.g., −6 or 6 degree targets) initially occurring first and more peripheral targets (e.g., −24 or 24 degree targets) occurring second, and monkey Y was trained with sounds initially occurring in the opposite sequence. Midway through neural data collection, we provided additional training to monkey Y to encourage free choice of which sound to look at first. This allowed us to investigate the relationship between each behavioral response and the neural representation at that moment.

Recording procedure and strategy

The behavioral paradigm and the recordings of eye gaze and single cell activity were controlled using the Beethoven program (Ryklin Software). Recordings were made with one or two tungsten electrodes (FHC, impedance between 1 and 3 MΩ at 1 kHz). Each electrode was lodged in a stainless-steel guide tube (manually advanced through the dura) and controlled independently with an oil hydraulic pulse micropositioner (Narishige International USA, Inc. and NAN INSTRUMENTS LTD, Israel). First, we localized the IC (and isolated single neurons) while the monkey listened passively to sounds of different frequencies. We then collected single unit spiking activity and local field potential while the monkey performed the single-sound and dual-sound localization tasks. We used a Multichannel Acquisition Processor (MAP system, Plexon Inc., Dallas, TX) and Sort Client software. The single unit spiking activity was filtered between 150 Hz and 8 kHz and sampled at 20 kHz, while the LFP signal was filtered between 0.7 and 300 Hz and sampled at either 20 or 1 kHz (see 'Local field potential'). Data were collected as long as the neurons were well isolated and the monkey performed the tasks.

Neural signals were recorded primarily from two functionally-defined subregions of the IC, the low frequency area and the tonotopic area53. Neurons in the low frequency tuned area generally respond best to low frequencies and there is little heterogeneity in tuning, whereas neurons recorded in the tonotopic area had best frequencies that could be either low or high depending on the position of the recording electrode.

Data sets and sound stimuli

The spiking activity of 166 single neurons was recorded in two datasets involving the same task but differing in which sound levels and frequencies were included. A total of 68 of these neurons were recorded as pairs from separate electrodes positioned in the IC on the same side of the brain at a minimum spatial separation of 2 mm. Local field potentials (LFP) were also recorded from 87 of these recording sites.

In both datasets, the sounds consisted of bandpass noise with a bandwidth of +/−200 Hz. On dual-sound trials, the sounds were delivered from pairs of locations (24 degrees and −6 degrees), and (−24 and +6 degrees) i.e., 30 degrees apart. The two sounds differed in frequency, with one of the two sounds having a 742 Hz center frequency and the other differing by at least 0.285 octaves or multiples of this distance. Single-sound trials involved the same set of locations and frequencies as on dual-sound trials, but with only a single-sound presented at a time. All sounds were frozen within an individual session; that is, all trials with a given set of auditory parameters involved the same time series signal delivered to the relevant speaker.

In data set I (N = 98 neurons), the sounds presented on dual-sound trials were 742 Hz and a sound from the set (500, 609, 903, 1100 Hz); these frequencies were ±0.285 octave or ±0.57 octaves above or below 742 Hz, or ±3.4 and 6.8 semitones. Combining two sounds will produce a combination that is louder than either component. Sound levels were therefore calibrated to provide two sets of conditions: dual sounds for which the component sounds involve the same signals to the audio speakers as on single-sound trials, producing a louder dual sound, and dual sounds for which the level of the component sounds was reduced so that the overall loudness was the same on dual as on single trials. The levels used for the components were 51 and 55 dB, producing sound levels of minimum 55 or maximum 60 dB on dual-sound trials. The same-signal comparison involved using the 55 dB component levels, singly and on dual-sound trials. The same-loudness comparison involved using the 55 dB levels on single-sound trials and the 51 dB levels for the components of dual-sound trials. Calibrations were performed using a microphone (Bruel and Kjaer 2237 sound level meter) placed at the position normally occupied by the animal’s head.

Because results did not differ substantively when comparisons were made between same-signal and same-loudness conditions (Supplementary Figure 2 vs. Supplementary Figure 3), we pooled across sound levels for subsequent analyses, and we dispensed with the multiple sound levels for data set II (monkey Y only, N = 68 neurons), using either 50 or 55 dB levels for all components. We also incorporated additional sound frequencies, [1340 1632 1988 Hz], to improve the odds that responses to each of the component sounds differed significantly. Again, one of the two sounds on dual-sound trials was 742 Hz; the other sound frequency was either from the original list of [500 609 903 1100] or from the new frequencies. Most of the neurons in this data set were tested with [500 742 1632].

Cell inclusion and trial counts

The N = 166 neurons (N = 98 from Data Set I and N = 68 from Data Set II) included for analysis were drawn from a larger set of 325 neurons. Neurons were excluded from analysis if the neuron proved unresponsive to sound (Student’s t-test, spike counts during the 600 ms after sound onset compared to the same period immediately prior to sound onset, one-tailed, p > 0.05), or if there were too few correct trials (minimum of five correct trials for each of the components [A, B, and AB trials] that formed a given triplet of conditions or if there were technical problems during data collection (e.g., problems with random interleaving of conditions or with computer crashes). The average number of correct trials for a given set of stimulus conditions in the included dataset (N = 166) was 10.5 trials. The total number of included triplets was 1484. All analyses concerned correctly performed trials.

Summation vs. averaging in time-and-trial pooled activity

To evaluate IC activity using conventional analysis methods that pool across time and/or across trials, we counted action potentials during two standard time periods. The baseline period (Base) was the 600 ms period before target onset, and the sensory-related target period (Resp) was the 600 ms period after target onset (i.e., ending before, or at the time of, the offset of the fixation light, Fig. 1b).

Summation/averaging indices: We quantified the activity on dual-sound trials in comparison to the sum and the average of the activity on single-sound trials, expressed in units of standard deviation (Z-scores), similar to a method used by28. Specifically, we calculated,

$${{\rm PredictedSum}_{\rm A,B,}} = {\,\, \rm mean}\left( {{\rm Resp}_{\rm A}} \right) + {\rm mean}\left( {{\rm Resp}_{\rm B}} \right) \\ - {\rm mean}\left( {{\rm Base}_{\rm A,B}} \right)$$ (1)

and

$$\rm PredictedAvg_{A,B,} = \left( {mean\left( {Resp_A} \right) + mean\left( {Resp_B} \right)} \right)/2$$ (2)

where Resp A and Resp B were the number of spikes of a given neuron for a given set of single-sound conditions A and B (location, frequency, and intensity) that matched the component sounds of the dual-sound trials being evaluated. As the response may actually include a contribution from spontaneous baseline activity, we subtracted the mean of the baseline activity for the single-sounds (Base A, B ). Without this subtraction, the predicted sum would be artificially high because two copies of baseline activity are included under the guise of the response activity.

The Z scores for the dual-sound trials were computed by subtracting these predicted values from the mean of the dual-sound trials (mean(Resp AB )) and dividing by the mean of the standard deviations of the responses on single-sound trials:

$$ {\rm Z}{\rm sum}_{\rm AB} = \frac{{{\rm mean}\left( {{\rm Resp}_{\rm AB}} \right) - {\rm PredictedSum}_{\rm A,B}}}{{{\rm mean}\left( {{\rm std}\left( {{\rm Resp}_{\rm A}} \right),{\rm std}\left( {{\rm Resp}_{\rm B}} \right)} \right)}}$$ (3)

and

$${\rm Z}{\rm Avg}_{\rm AB} = \frac{{{\rm mean}\left( {{\rm Resp}_{\rm AB}} \right) - {\rm PredictedAvg}_{\rm A,B}}}{{{\rm mean}\left( {{\rm std}\left( {{\rm Resp}_{\rm A}} \right),{\rm std}\left( {{\rm Resp}_{\rm B}} \right)} \right)}}$$ (4)

If the dual response was within +/−1.96 standard deviations of the predicted sum or predicted average, we could say the actual dual response was within the 95% confidence intervals for addition or averaging of two single responses, respectively.

Analysis of whole-trial fluctuations and inclusion criteria

Our statistical tests for fluctuations in neural firing were conducted on triplets, or related sets of single and dual-sound trials (A, B, AB trials). To evaluate whether neural activity fluctuates across trials in a fashion consistent with switching between firing patterns representing the component sounds, we evaluated the Poisson characteristics of the spike trains on matching dual and single-sound trials (triplets: AB, A and B). Spike train data from each trial was summarized by the total spike count between 0–600 ms or 0–1000 ms from sound onset (i.e., whatever the minimum duration of the overlap between fixation and sound presentation was for that recorded neuron, see section Sound localization task). We modeled the distribution of spike counts in response to single-sounds A and B as Poisson distributions with unknown rates λA, denoted Poi(λA), and λB, denoted Poi(λB). Four hypotheses were considered for the distribution of sound AB spike counts:

1. a mixture distribution \(\alpha \cdot {\rm Poi}\left( {\lambda ^{\mathrm A}} \right) + \left( {1 - \alpha } \right) \cdot {\mathrm Poi}\left( {\lambda ^{\mathrm B}} \right)\) with an unknown mixing weight α (Mixture) 2. a single Poi(λAB) with some λAB in between λA and λB (Intermediate) 3. a single Poi(λAB) where λAB is either larger or smaller than both λA and λB (Outside) 4. a single Poi(λAB) where λAB exactly equals one of λA and λB (Single)

Relative plausibility of these competing hypotheses was assessed by computing their posterior probabilities with equal prior weights (1/4) assigned to the models, and with default Jeffreys’ prior71 on model specific Poisson rate parameters, and a uniform prior on the mixing weight parameter α. The Jeffreys’ prior was truncated to appropriate ranges for the Intermediate and Outside models. Posterior model probabilities were calculated by computation of relevant intrinsic Bayes factors29.

Triplets were excluded if either of the following applied: (1) the Poisson assumption on A and B trial counts was not supported by data; or (2) λA and λB were not well separated. To test the Poisson assumption on single-sound trials A and B of a given triplet, we used an approximate chi-square goodness of fit test with Monte Carlo p-value calculation. For each sound type, we estimated the Poisson rate by averaging counts across trials. Equal probability bins were constructed from the quantiles of this estimated Poisson distribution, with number of bins determined by expected count of five trials in each bin or at least three bins—whichever resulted in more bins. A lack-of-fit statistic was calculated by summing across all bins the ratio of the square of the difference between observed and expected bin counts to the expected bin count. Ten thousand Monte Carlo samples of Poisson counts, with sample size given by the observed number of trials, were generated from the estimated Poisson distribution and the lack-of-fit statistic was calculated from each one of these samples. p-value was calculated as the proportion of these Monte Carlo samples with lack-of-fit statistic larger than the statistic value from the observed data. Poisson assumption was considered invalid if the resulting Monte Carlo p-value < 0.1.

For triplets with valid Poisson assumption on sound A and B spike counts, we tested for substantial separation between λA and λB, by calculating the intrinsic Bayes factor of the model λA ≠ λB against λA = λB with the non-informative Jeffreys’ prior on the λ parameters: λA, λB or their common value. The triplet was considered well separated in its single sounds if the logarithm of the intrinsic Bayes factor equaled three or more, which is the same as saying the posterior probability of λB ≠ λA exceeded 95% when a priori the two models were given 50–50 chance.

It should be noted that the sensitivity/specificity of detection were not equal across the four competing hypotheses. Because the Single hypothesis is a limiting case of each of the other three hypotheses, the method’s sensitivity to this response pattern is lower than for the other three possible outcomes. This was verified on simulated data for which the truth could be known; simulated Mixtures, Intermediates, and Outsides were commonly correctly categorized with >95% confidence, whereas Singles were correctly categorized but at a lower level of confidence.

Dynamic Admixture Point Process Model

To evaluate whether neural activity fluctuates within trials, we developed a novel analysis method we call a Dynamic Admixture Point Process model (DAPP) which characterized the dynamics of spike trains on dual-sound trials as an admixture of those occurring on single-sound trials. The analysis was carried out by binning time into moderately small time intervals. Given a predetermined bin-width w = T/C for some integer C, we divided the response period into contiguous time intervals I 1 = [0;w); I 2 = [w; 2w)… I C = [(C-1)w,Cw) and reduced each trial to a C-dimensional vector of bin counts (Xe j1 ,…,Xe jC ) for e ∈ {A;B;AB} and j = 1,…, n e . Mathematically, Xe jC = Ne j (Ic). The results reported here were based on w = 50 (with time measured in ms and T = 600 or 1000), but we also repeated the analyses with w = 25 and noticed little difference.

Our model for the bin counts was the following. Below we denote by \(t_c^ \ast\) the mid-point (c−1/2)w of sub-interval I c .

1. \(X_{jc}^e\sim {\rm Poi}\left( {w \cdot \lambda ^e\left( {t_c^ \ast } \right)} \right)\), e∈{A,B}, c∈ {1,…,C}, j∈{1,…,n e }. We assume both λA(t) and λB(t) are smooth functions over t ∈ [0, T].

2. \(X_{jc}^{\rm AB}\sim {\rm Poi}\left( {w \cdot \lambda _j\left( {t_c^ \ast } \right)} \right)\), where λ j (t) = α j (t) + {1 − α j (t)}λB(t) with α j :[O,T]→(0,1) being unknown smooth functions.

We modeled α j (t) = S(η j (t)), where S(t) = 1/(1 + e−t), and, each η j (t) was taken to be a (smooth) Gaussian process with E{η j (t)} ≡ ϕ j , \({\rm Var}\left\{ {\eta _j\left( t \right)} \right\} \equiv \psi _j\), and, \({\rm Cor}\{ \eta _j\left( t \right),\eta _j(t^\prime )\} = {\mathrm{exp}}\{ - 0.5\left( {t - t^\prime } \right)^2/\ell _j^2\}\). The three parameters \((\phi _j,\psi _j,\ell _j)\) respectively encoded the long-term average value, the total swing magnitude and the waviness of the α j (t) curve.—Intuitively, these parameters can be thought of as related to the means and variances of the distribution of weight values α regardless of time within a trial, as well as the correlation between the weight observed at one point in time and the weight observed at another on any given trial. While the temporal imprint carried by each α j was allowed to be distinct, we enforced the dual trials to share dynamic patterns by assuming \(\left( {\phi _j,\psi _j,\ell _j} \right),j = 1, \ldots ,n_{\rm AB}\), were drawn from a common, unknown probability distribution P, which we called a dynamic pattern generator and viewed as a characteristic of the triplet to be estimated from the data.

To facilitate estimation of P, we assumed it decomposed as \(P = P_{\phi \psi } \times P_\ell\), where P ϕψ was an unknown distribution on (−∞,∞)×(0,∞) generating (ϕ j ,ψ j ), and, \(P_\ell\) was an unknown distribution on (0,∞) generating \(\ell _j\). To simplify computation, we restricted \(\ell _j\) to take only a finitely many positive values, representative of the waviness range we are interested in (in our analyses, we took these representative values to be {75, 125, 200, 300, 500}, all in ms). This restricted \(P_\ell\) to be a finite dimensional probability vector.

We performed an approximate Bayesian estimation of model parameters. Note that only λA(t) and λB(t) were informed by the single-sound trial data. All other model parameters were informed only by the dual-sound trial data conditionally on the knowledge of λA(t) and λB(t). To take advantage of this partial factorization of information sources, we first smoothed each set of single-sound trial data to construct a conditional gamma prior for the corresponding \(\lambda ^e\left( {t_c^ \ast } \right),e \in \left\{ {\rm A,B} \right\},c = 1, \ldots ,C\), where the gamma distribution’s mean and standard deviation were matched with the estimate and standard error of \(\lambda ^e\left( {t_c^ \ast } \right)\). A formal Bayesian estimation was then carried out on all model parameters jointly by (a) using only the dual-sound trial data, (b) utilizing the conditional gamma priors on λA(t) and λB(t), and, (c) assuming a Dirichlet process prior72 on P ϕψ and an ordinary Dirichlet prior on \(P_\ell\). This final step involved a Markov chain Monte Carlo computation whose details will be reported in a separate paper.

Next, the estimate of the generator P was utilized to repeatedly simulate α(t) functions for hypothetical, new dual trials for the triplet. For each simulated α(t) curve, we computed its maximum swing size \(\left| \alpha \right| = {\rm max}_t\alpha (t) - {\rm min}_t\alpha \left( t \right),\) and, time aggregated average value \(\bar \alpha = \mathop {\int }

olimits_0^T \alpha (t){\rm d}t/T\). The waviness index of the triplet was computed as the odds of seeing an α(t) function exhibiting a swing of at least 50% between its peak and trough:

$$r_w = \frac{{P(|\alpha | > \, 0.5)}}{{P(|\alpha | < \,0.5)}}$$

where P denotes the sampling proportion of the simulated α draws. The triplet’s extremeness index was computed as the odds of seeing an α(t) function with its long-term average \(\bar \alpha\) being closer to the mid-way mark of 50% than the extremes:

$$r_c = \frac{{P\left( {\bar \alpha \in \left( {0.25,0.75} \right)} \right)}}{{P\left( {\bar \alpha \,

otin \left( {0.25,0.75} \right)} \right)}}$$

The two indices were then thresholded to generate a 2-way classification of all triplets. On waviness, a triplet was categorized as Wavy, Flat or Ambiguous according to whether r w >1.3, r w >0.77, or, \(0.77 \le r_w \le 1.3\), respectively On extremeness, the categories were Central, Extreme, or, Ambiguous according to whether r c >3.24, r c <1.68, or, \(1.68 \le r_c \le 3.24\), respectively.

In addition to the Flat/Wavy and Extreme/Central classification, a third parameter was evaluated for each triplet: the degree of skewness in the distribution of \(\bar {\alpha _ \ast }\):

$$r_s = {\it{\rm max}}\left( {\left\{ {\frac{{P\left( {\bar \alpha _ \ast < \,0.5} \right)}}{{P\left( {\bar \alpha _ \ast > \,0.5} \right)}},\frac{{P\left( {\bar \alpha _ \ast > \,0.5} \right)}}{{P\left( {\bar \alpha _ \ast < \,0.5} \right)}}} \right\}} \right.$$

which ranges in (1,∞). Each triplet’s Flat/Wavy/Central/Extreme tag could then be subcategorized as either Skewed or Symmetric depending on whether r S > 4 or r S < 2 (with no label in the middle). This subcategorization step was useful for distinguishing the dynamic admixtures associated with the whole-trial categorizations of Single and Outside from Intermediate and Mixture, with Single and Outside tending to be classified as Skewed. Supplementary Table 1 and Supplementary Figures 6 and 7 give the full results of the main 2-way classification together with the symmetry/skewness subclassification, cross tabulated with the classification done under the whole trial spike count analysis.

A vs. B assignment scores

A vs. B assignment scores were computed for several analyses (the example shown in Fig. 1e, f; pairs of recorded neurons; the relationship between spiking activity and local field potential; and the relationship between saccade sequences and spiking activity). For each triplet, every dual-sound trial received an A-like score and a B-like score, either for the entire response window (600–1000 ms after sound onset) or for 50 ms time bins. The scores were computed as the posterior probability that the spike count in each dual-sound trial was drawn from the Poisson distribution of single-sound spike counts.

For the pairs analysis, the A vs. B assignment scores were computed within each 50 ms time bin independently for each pair of neurons recorded simultaneously. The scores were normalized across trials by subtracting the mean score and dividing by the standard deviation of scores for that bin (a Z-score in units of standard deviation). Only conditions for which both recorded neurons exhibited reasonably different responses to the A vs. the B sound and for which there were at least five correct trials for A, B, and AB trials were included (t-test, p < 0.05). A total of 206 conditions were included in this analysis.

Local field potential analysis

We analyzed the local field potential from 87 sites in both monkeys (30 sites from monkey P’s left IC, 31 sites from monkey Y’s right IC and 26 sites from monkey Y’s left IC). The LFP acquisition was either recorded in discrete temporal epochs encompassing behavioral trials (roughly 1.2 to 2 s long) and at a sampling rate of 20 kHz (Dataset I, part of Dataset II), or as a continuous LFP signal during each session, at a sampling rate of 20 or 1 kHz (rest of Dataset II). We standardized the LFP signals by trimming the continuous LFP into single trial intervals and down-sampling all signals to 1 kHz. The MAP system filters LFP signals between 0.7 and 300 Hz; no additional filtering was applied. For each site we subtracted the overall mean LFP value calculated over the entire session, to remove any DC shifts, and we excluded trials that exceeded 500 mV.

For the voltage-and-time domain analysis presented in Fig. 5a, for each triplet, we assigned individual dual-sound trials to two groups based on the total spike count in a 600 ms response window (see Methods: A vs. B assignment scores). The average LFP was then compared across the two groups in two 600 ms windows before and after sound onset (baseline and response periods). The results reported here refer to these mean-normalized LFP signals. We obtained similar results when the amplitude of each trial’s LFP was scaled as a proportion of the maximum response within the session.

Face patch MF recording procedures

The full experimental procedure is described in37. We give a summary here. All procedures conformed to the US National Institutes of Health Guide for Care and Use of Laboratory Animals, and were approved by The Rockefeller University Institutional Animal Care and Use Committee (IACUC).

The localization of face patch MF was guided via fMRI as described in37. A total of 105 single neurons were recorded from MF in two male adult macaques (monkey Q, Macaca mulatta, and monkey J, Macaca fascicularis). The monkeys were head-restrained and performed a fixation task while viewing visual stimuli on a CRT screen placed 57 cm in front of them. Eye position was monitored with an infrared pupil tracking system (ETL-200, ISCAN Inc.,Burlington, MA). The monkeys were rewarded with juice for maintaining the eyes within a ≤2 × 2 degree square window around the fixation point.

All stimuli were controlled by custom software written in C (Visiko) running on a Windows PC. For each neuron, three visual stimuli (400 ms, 4 × 4 degrees in size) were selected from among a pool of face and object stimuli: a face that elicited a strong response, dubbed the preferred face; a face that elicited a weak or no response, dubbed the non-preferred face; and an object that also elicited a poor response, dubbed the non-preferred object. They were presented either alone or in pairs consisting of the preferred face and one or the other of the non-preferred stimuli. Thus, there were two triplets per cell suitable for analysis. The stimuli were randomly interleaved with each other and with other conditions not analyzed here see37.

Stimulus positions on the screen were such that the preferred face was always at the center of the neuron’s receptive field whereas the non-preferred stimulus could occupy one of eight equidistant locations adjacent to the preferred face. The exact location of the non-preferred face/object was ignored in the present analysis, but excessive heterogeneity in the responses due to variation in location would have caused the triplet to be excluded on the grounds of not exhibiting a sufficiently Poisson-like spike count distribution on the relevant single-stimulus trials.

The data were otherwise analyzed as described in the preceding Analysis section.

Data availability

The data and computer code that support the findings of this study are available from the corresponding authors upon reasonable request.