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Chapter 4 Interpersonal Neural and Physiological Synchrony
The speaker’s brain and the listener’s
Hasson and colleagues proposed that communication can be seen as coupling between two brains: through the sound signal, the speaker’s brain activity drives the listener’s, so that activity in the two becomes correlated in time (Hasson et al. 2012).
Evidence
Brain-to-brain coupling. Stephens, Silbert and Hasson used functional MRI to record a speaker telling a real personal story, then recorded listeners’ brain activity as they heard the recording. Activity in many of the listeners’ brain areas correlated with activity in the speaker’s corresponding areas, usually lagging by a few seconds; in some areas the listeners’ activity preceded the speaker’s, as if predicting what came next. The stronger the coupling, the better the listeners understood the story; when they heard a language they did not know, the coupling vanished (Stephens et al. 2010).
Evidence
Heart-rate synchrony among listeners. Pérez and colleagues had participants listen individually to the same stories while their heart rate was recorded, and found that different participants’ heart rates were significantly correlated in time; when participants were asked to do mental arithmetic while listening, dividing their attention, this correlation fell clearly; and the degree of correlation predicted participants’ later memory of the stories (Pérez et al. 2021). Synchrony in a ritual. Konvalinka and colleagues recorded the heart rates of fire-walkers and spectators at a fire-walking ritual in a Spanish village and found that the fire-walkers were more synchronised with spectators who were relatives or friends than with unrelated spectators (Konvalinka et al. 2011).
Such studies are often cited to show that “a good speech brings the audience into resonance with the speaker”. But one crucial detail deserves attention: Pérez and colleagues’ participants listened to the stories alone, with no interaction among them. Their synchrony came from the same stimulus driving each of them, not from people influencing one another.
Two kinds of synchrony: common drive and mutual coupling
The Kuramoto model of physics describes how a population of oscillators, each with its own natural frequency, can synchronise (Kuramoto 1984; Strogatz 2000). Extended slightly, it can separate two mechanisms: each listener may be driven by a common external stimulus (the rhythm of the speaker’s voice), or influenced by the other listeners (seeing and hearing one another’s reactions).
Calculation
Thirty listeners, each with a slow rhythm (for example heart rate or the ebb and flow of attention) at a different natural frequency. is how strongly the stimulus drives each listener, how strongly listeners influence each other. Synchrony is measured by the order parameter (0 none, 1 perfect). Two control conditions separate the mechanisms: “tested alone”, where each listener hears the same recording in a separate session, so mutual influence cannot act; and “random-delay story”, where listeners sit together but each hears the recording with its own random delay.
# Listener synchrony: common drive or mutual coupling? Kuramoto phase oscillators (Kuramoto 1984)
# with a common external drive. Each of 30 listeners has a phase theta_i (think of a slow rhythm
# such as heart rate or an attentional cycle) with its own natural frequency:
# d theta_i/dt = w_i + F sin(phi_i(t) - theta_i) + (K/N) sum_j sin(theta_j - theta_i)
# phi_i(t) is the phase of the stimulus as listener i receives it (the story's slow envelope).
# F: how strongly the stimulus entrains a listener; K: how strongly listeners entrain one another.
# Synchrony = order parameter R = |mean_j exp(i theta_j)| (0 = none, 1 = perfect), averaged over the
# last half of the run. Two controls separate the mechanisms:
# 'tested alone' - each listener hears the same stimulus in a separate session (K has no effect)
# 'random-delay story' - listeners sit together but each hears the story with its own random delay
import math, random
def run(F, K, alone=False, shuffled=False, N=30, T=120.0, dt=0.01, seed=5):
rng = random.Random(seed)
w = [2 * math.pi * rng.gauss(1.0, 0.08) for _ in range(N)]
th = [rng.uniform(0, 2 * math.pi) for _ in range(N)]
lag = [rng.uniform(0, 2 * math.pi) if shuffled else 0.0 for _ in range(N)]
Keff = 0.0 if alone else K
acc, cnt, phi = 0.0, 0, 0.0
for step in range(int(T / dt)):
t = step * dt
phi = 2 * math.pi * 1.0 * t + 0.6 * math.sin(0.3 * t) # stimulus phase with slow wander
cx = sum(math.cos(x) for x in th) / N; sx = sum(math.sin(x) for x in th) / N
new = []
for i in range(N):
d = w[i] + F * math.sin(phi + lag[i] - th[i]) + Keff * (sx * math.cos(th[i]) - cx * math.sin(th[i]))
new.append(th[i] + d * dt)
th = new
if t > T / 2:
acc += math.hypot(sum(math.cos(x) for x in th) / N, sum(math.sin(x) for x in th) / N); cnt += 1
return acc / cnt
print("mechanism | together, same story | tested alone | together, random delays")
for label, F, K in (("none", 0.0, 0.0), ("common drive only (F = 1.5)", 1.5, 0.0),
("mutual coupling only (K = 2)", 0.0, 2.0), ("both", 1.5, 2.0)):
print(f"{label:31s} | {run(F, K):20.2f} | {run(F, K, alone=True):12.2f} | {run(F, K, shuffled=True):23.2f}")
# mechanism | together, same story | tested alone | together, random delays
# none | 0.15 | 0.15 | 0.15
# common drive only (F = 1.5) | 0.93 | 0.93 | 0.26
# mutual coupling only (K = 2) | 0.96 | 0.15 | 0.96
# both | 0.99 | 0.93 | 0.75
When listeners sit together and hear the same story, both mechanisms produce high synchrony (0.93 and 0.96), and this condition alone cannot tell them apart. The controls do: synchrony from a common drive survives testing alone in full and almost vanishes when each listener hears the story with a random delay; synchrony from mutual coupling vanishes when listeners are tested alone and survives the random-delay story in full. (The value of 0.15 in the “none” row is the level that a finite sample of 30 random phases shows anyway.)
This is exactly the experimental logic needed to tell “passive synchrony” from “interactive resonance”. The synchrony observed by Pérez and colleagues existed when listeners were tested alone, so it must have come from the common drive of the stimulus; the synchrony observed by Konvalinka and colleagues was linked to family and friendship ties, which suggests a component of mutual influence, though it might also come from relatives attending more closely to the fire-walker, that is, a stronger common drive by the same stimulus.
Testing synchrony statistically
Calculation
Computing and testing inter-subject correlation (ISC) on synthetic heart-rate data. Twenty listeners each hear the same 5-minute story, with heart rate sampled once a second. ISC is the mean of the pairwise correlation coefficients; the null distribution is built by circularly shifting each person’s heart-rate series by a random amount, which keeps each series’ own structure but breaks its alignment with the story.
# Inter-subject correlation (ISC) of heart rate, with a circular-shift surrogate test, on SYNTHETIC
# data. 20 listeners hear the same 5-minute story; heart rate is sampled once per second. Each
# listener's heart rate = a shared, story-driven slow component (strength a) + their own slow
# fluctuations (AR(1) noise). ISC = mean pairwise Pearson correlation. The null distribution comes
# from circularly shifting each listener's series by a random offset, which keeps each series'
# own structure but breaks its alignment with the story.
import math, random
def series(a, n_sub=20, T=300, seed=11):
rng = random.Random(seed)
story, s = [], 0.0
for t in range(T): # slow stimulus-driven signal
s = 0.95 * s + rng.gauss(0, 1); story.append(s)
subs = []
for _ in range(n_sub):
e, x = 0.0, []
for t in range(T):
e = 0.9 * e + rng.gauss(0, 1)
x.append(70 + a * story[t] + 1.5 * e)
subs.append(x)
return subs
def pearson(x, y):
n = len(x); mx = sum(x) / n; my = sum(y) / n
sxy = sum((a - mx) * (b - my) for a, b in zip(x, y))
return sxy / math.sqrt(sum((a - mx) ** 2 for a in x) * sum((b - my) ** 2 for b in y))
def isc(subs):
n = len(subs)
return sum(pearson(subs[i], subs[j]) for i in range(n) for j in range(i + 1, n)) / (n * (n - 1) / 2)
def surrogate_p(subs, n_surr=200, seed=3):
rng = random.Random(seed); obs = isc(subs); T = len(subs[0]); hits = 0
for _ in range(n_surr):
shifted = []
for x in subs:
k = rng.randrange(30, T - 30); shifted.append(x[k:] + x[:k])
hits += isc(shifted) >= obs
return obs, (hits + 1) / (n_surr + 1)
for a, label in ((1.0, "attentive listening (shared component strong)"), (0.3, "distracted listening (shared component weak)"),
(0.0, "no shared component")):
obs, p = surrogate_p(series(a))
print(f"{label:46s}: ISC = {obs:+.3f}, surrogate p = {p:.3f}")
# attentive listening (shared component strong) : ISC = +0.589, surrogate p = 0.005
# distracted listening (shared component weak) : ISC = +0.117, surrogate p = 0.005
# no shared component : ISC = -0.001, surrogate p = 0.443With a strong shared component (attentive listening) the ISC is about 0.59; with a weak one (distracted listening) it falls to about 0.12, still significant against 200 circular-shift surrogates; with no shared component the ISC is near 0 and not significant. The circular-shift test is one of the standard methods in this field, because physiological signals are strongly autocorrelated and ordinary significance tests would badly overstate significance.
Note
Synchrony is not “presence”, nor persuasion. First, synchrony of heart rate or brain activity among listeners mainly reflects that they attended to the same stimulus at the same moments; a thrilling film clip can produce strong synchrony without persuading anyone of anything. Second, in existing research synchrony is linked to memory and understanding; its relation to attitude change or persuasion has rarely been studied directly. Third, the claim that “the speaker’s presence brings the audience onto his wavelength” conflates common drive with mutual coupling, and attention with agreement.
Hypothesis · not established
The prosody-drives-synchrony hypothesis (not yet established). With content held identical, a telling with clear prosodic structure (pauses before key information, final movements matching content) produces higher heart-rate ISC among listeners tested alone than a monotonous or rushed telling, and this difference partly explains differences in listeners’ memory. If the two tellings do not differ in ISC, or the ISC difference is unrelated to memory, the hypothesis is weakened.
Summary
There is measurable coupling between the brains of speaker and listener, and its strength is linked to understanding; listeners who hear the same story alone also synchronise in heart rate, less so when their attention is divided, and the degree of synchrony predicts memory. The Kuramoto model shows that “common drive” and “mutual coupling” can produce equally high synchrony, distinguishable only through controls such as testing alone and giving each listener a random delay. Synchrony reflects shared attention; it is not persuasion. The next chapter discusses another vocal cue: roughness, and why it puts listeners on alert.
References
Fan, Y. (2026). Acoustic Persuasion, Prosodic Information Theory and Interpersonal Physiological Synchrony: Pitch Contours, Pause Entropy and Listener Autonomic Coupling in Public Speaking and Leadership [声音说服力、韵律信息论与人际生理同步] (Version 1.0.0). 5D Library. https://doi.org/10.5281/zenodo.23753267 · Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).