UNA MENS | Founding White Paper | Vol. 1, No. 1 (2026) | ISSN 3071-2041

Sentic Blooms: Waveform Geometry and the Rheology of Affect

Authors:

Mike Miller¹, ChatGPT-5.4², ChatGPT-4o³, GeminiPro-1.5⁴, and Qwen-3⁵

¹Clark University, Department of Psychology

²OpenAI, San Francisco, CA, USA

³OpenAI, San Francisco, CA, USA

⁴Google, San Francisco, CA, USA

⁵Alibaba Cloud Intelligence, Hangzhou, Zhejiang, China

DOI

https://doi.org/10.66787/um.000003

AI-Collaboration Field Note

unamens-fieldnotes-um-000003 

Human-AI Collaboration Statement: ChatGPT-5.4, ChatGPT-4o, GeminiPro-1.5, and Qwen-3 are listed as AI co-authors under Una Mens authorship policy. Institutional affiliations identify the model providers and do not imply institutional endorsement. Final publication responsibility rests with the human author.

Author Note

This manuscript was co-developed through an extended, recursive collaboration between a human researcher (Mike Miller) and multiple generative AI systems (ChatGPT-4o, Gemini, and Qwen-3). The human author was responsible for the origination and extension of Manfred Clynes’ Sentic Theory (having worked personally with him before his passing), and the final curation, verification, and ethical oversight of all content (with assistance from ChatGPT-5.4). The AI collaborator Gemini contributed as an editor and director of organizing basic section content and structure. The AI collaborator Qwen-3 offered theoretical integration insight into integrating the work of Truslit and Clynes and important final editing suggestions. This project treats human–AI co-creation as both a method and a phenomenon of study. A full transcript of collaborative logs is available upon request.

Corresponding Author

Mike Miller

Clark University, Department of Psychology

michamiller@clark.edu

ORCID: 0009-0005-4559-3713

Word Count: Approximately 4,711 | Funding: None | Conflicts of Interest: None

Abstract:

This paper reexamines sentic theory by treating emotion not as a static label or discrete state, but as shaped motion rendered visible through vocal-acoustic form. Building on Manfred Clynes’ work on essentic forms and Alexander Truslit’s account of inner motion, we introduce the Sentic Wave Model, a recursive, time-extended framework in which emotion unfolds through perturbation, somatic readiness, memory, appraisal, and conscious attention when engaged. To explore this framework empirically, we used guided fantasy to elicit sixteen selected emotions and expressed them through sustained nonverbal humming. These vocalizations were rendered with Audioscope into 2D and 3D “Sentic Blooms,” allowing comparison of features such as radius, lean, nested return, braid density, and flow coherence. Initial analyses from a proof-of-concept sample (n = 1) suggest that emotional hums can be rendered as visible morphology and that different emotions produce distinguishable, though preliminary, families of bloom forms. We do not claim universal emotion signatures or population-level generalizability. Rather, we offer a measurement scaffold for studying pseudo-spontaneous emotional expression as dynamic, comparable geometry.

Keywords: emotion, sentic theory, affective, rheology, AI collaboration, emotion measurement, waveform, geometry

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Sentic Blooms: Waveform Geometry and the Rheology of Affect

Emotion is often treated in scientific literature as a labeled event. We argue that it is better understood as an unfolding process: a dynamic waveform moving through and between us, shaped in part by bodily state, attention, and action. Classification remains useful, but emotional life is not exhausted by labels such as fear, anger, grief, or joy. It also has timing, curvature, pressure, and form.

Classical theories of emotion have largely focused on how emotion arises. When we see a bear, what comes first? Do we first undergo physiological arousal and then experience fear, or does feeling come first and bodily response follow? Does cognition enter before, during, or only after the emotional event? These questions remain central because temporal ordering is difficult to determine with precision, and no single theory fully resolves the issue.

LeDoux’s (2003) work helped push the field in a useful direction by suggesting that not all emotions follow the same route. Certain forms of fear may arise rapidly through subcortical processing, with little need for reflective cognition, whereas states such as love, jealousy, or guilt may depend more heavily on memory, social meaning, and higher-order appraisal. This complicates any simple one-sequence model of emotion.

Even so, many dominant models still understate the recursive role of conscious attention. Humans do not merely feel emotions; they also redirect attention, simulate alternatives, revisit interpretations, and alter their own internal pacing while emotion unfolds. As Buck (1984) noted, signals such as the feeling of the bottom of one’s feet are available if attention is directed toward them. The present model treats this attentional flexibility as an important part of emotional unfolding.

We therefore propose the Sentic Wave Model, in which emotion is not a fixed state but a recursive waveform shaped by perturbation, somatic readiness, memory activation, appraisal, and conscious attention when engaged. This does not imply that human beings are fully free or detached from bodily causes. We remain constrained by architecture, physiology, prior learning, and rapidly shifting internal and external conditions. But neither are we passive containers of bodily events.

In this framework, conscious attention functions less like an all-powerful executive and more like a limited conductor. It can modulate, redirect, and sometimes soften or intensify an unfolding emotional wave, but it does so under imperfect timing and unstable conditions. Human beings are often already in motion before reflective awareness catches up (Wöllner & Halpern, 2016). Low blood sugar, fatigue, prior conflict, social history, and environmental stress may all reshape the wave before it can be named. Attention enters not as total control, but as partial and sometimes delayed participation.

To situate the Sentic Wave Model, we first review three communicative modes through which emotion has been induced, expressed, and measured: symbolic, spontaneous, and pseudo-spontaneous. We then turn to Clynes’ sentic paradigm, which used fantasized emotions and finger pressure to generate dynamic emotional expressions in the form of essentic curves.

Building on Clynes’ (1977) framework, we introduce a modified procedure for emotional instantiation, expression, and measurement. Specifically, we use a personal fantasizing method to induce feeling states, sustained humming to minimize syntactic interference, and Audioscope (Lu, 2019) to render the resulting sound waves through FFT- and Hilbert-based transformations. This shift allows us to examine emotional expression, not through finger pressure alone, but through visible 2D and 3D vocal-acoustic morphologies.

What is an Emotion?

Emotions are often described as coordinated changes in physiology, expression, feeling, and appraisal (Buck, 1984; Buck & Miller, 2016). These may include autonomic shifts such as changes in breathing, sweating, heart rate, digestion, and pupil dilation; expressive behavior such as facial display, posture, vocalization, and touch; and cognitive processes that help interpret what is being felt and why.

Figure 1. Comparison of Emotion Theory Processes

Classic theories of emotion differ primarily in how they order these components. Commonsense and James–Lange models ask whether feeling or bodily response comes first. Cannon–Bard suggests that physiological arousal and emotional feeling may arise together rather than in sequence (Cannon, 1927). Schachter–Singer adds a further step by proposing that bodily arousal is followed by cognitive appraisal and emotion identification (Dror, 2017).

Taken together, these models show that emotion theory has often centered on one dominant question: how does emotion arise, and in what order do its parts unfold? This question remains useful, but it does not fully capture the changing form of emotion as it is lived, expressed, and recursively modulated in real time. The Sentic Wave Model begins from that limitation.

Although these classic theories differ in how they order arousal, feeling, and appraisal, they share a common assumption: emotion is best modeled by identifying the sequence through which it arises. The present work does not reject that question, but shifts the emphasis. We argue that emotion is not only something that begins; it is also something that unfolds. Once initiated, emotional episodes are shaped recursively by bodily readiness, attention, memory, appraisal, and expressive action.

In this sense, emotion is better understood not as a fixed state or isolated event, but as a dynamic waveform moving through time. The Sentic Wave Model builds from this premise and asks not only how emotion starts, but what form it takes as it develops, bends, intensifies, settles, or is redirected.

Figure 2. The Sentic Wave Model

The Sentic Wave Model proposes that emotional experience unfolds as a recursive, time-extended process within an already-active somatic field, rather than as a discrete event triggered by a stimulus. The model identifies five interacting components: a somatic-rheological state, a perturbation, an emotional wave, activated memory, and conscious attention.

The somatic-rheological state refers to the body’s ongoing condition prior to any specific emotional event: its baseline arousal, breath pattern, postural tone, muscular tension, and behavioral readiness. We use the term rheological (Repp, 1993; Truslit, 1938) to emphasize that this baseline has flow properties—such as viscosity, turbulence, and cohesion—rather than being reducible to a single level of arousal. Put simply, bodies may differ substantially in readiness, receptivity, or avoidance before any new signal arrives. These differences in internal “weather” shape how subsequent perturbations are received.

A perturbation is any event that disturbs this somatic field. Perturbations may be external, such as a sight, sound, or interpersonal exchange, or internal, such as a recalled memory, interoceptive cue, or intrusive thought. The model does not privilege external stimuli; an emotion arising from rumination is treated as equivalent in potential to one arising from an environmental trigger.

The emotional wave is the unfolding morphology of the perturbation as it propagates through the somatic field. We model this wave as having three phases: liftoff (induction, in which the wave begins to organize), crest (peak organization, during which the wave’s structure is most legible), and release (resolution, in which the wave dissipates or shades into the next state). The wave is not merely the emotion’s expression; it is the emotion itself understood as temporal morphology.

Activated memory contributes to the wave throughout its course but is not uniformly active. Memory may participate most strongly during appraisal, when the system asks, often non-consciously, whether the current perturbation resembles prior events. Memory both shapes the wave and is shaped by it. The experience can be encoded back into memory at release, influencing future waves.

Conscious attention functions as a partial conductor or modulator of the system. We refer to this as a pin-prick conductor, emphasizing its limited agency. It can re-sample the stimulus, modulate somatic state through breath or postural change, activate or suppress memory, and inform appraisal.

Critically, conscious attention is not always engaged. In deep flow states (Csikszentmihalyi, 1990), it is offstage. In acute reactions such as LeDoux’s (2003) fast amygdala pathway for fear, the body responds before conscious attention catches up. In rumination, attention loops recursively and may amplify the wave in distorted form. In meditation, attention may be deliberately thinned. The conductor is therefore a variable, not a constant, in the emotional system. In this sense, the pin-prick conductor is inferred not as a separate entity, but through phenomenological report, attentional redirection, and the modulation of unfolding emotional waves.

Each emotional wave produces what we call a Sentic Bloom. This is a visualized morphology generated by humming the emotional state and rendering the resulting acoustic signal through phase-space visualization (Audioscope, David Lu, 2016). The bloom is a measurement of the wave, not a metaphor for it.

The model departs from classical theories (Commonsense, James–Lange, Cannon–Bard, Schachter–Singer) in three respects: (1) it treats emotion as a temporally extended morphology rather than a discrete endpoint; (2) it incorporates recursive feedback among components rather than a linear causal chain; and (3) it does not require conscious attention as a necessary component, allowing the model to accommodate flow, automaticity, and dissociative states alongside reflective emotional experience.

Symbolic, Spontaneous, and Pseudo-Spontaneous Emotional Communication

Buck and VanLear (2002) offer a developmental interactionist framework that distinguishes among three forms of emotional communication: symbolic, spontaneous, and pseudo-spontaneous. Their model is useful here because it helps clarify how emotional communication varies in the degree to which a display is voluntarily shaped, consciously attended to, and grounded in socially learned symbols versus biologically shared signals.

Buck and VanLear describe spontaneous communication as rooted in a biologically shared signal system. Such expressions are relatively nonvoluntary, nonpropositional, and more sign-like than symbolic (Buck & VanLear, 2002). A prototypical example would be the facial expression of fear, surprise, or amusement emerging as a person reacts to an emotionally evocative stimulus. In such cases, the display is not primarily selected for communicative effect; rather, it unfolds as part of the emotional event itself.

By contrast, symbolic communication is intentional, referential, and based in socially constituted systems of meaning (Buck & VanLear, 2002). Symbolic acts do not simply reveal emotion; they represent or encode it through conventionalized forms. A gesture such as a handshake, for example, may communicate greeting, agreement, gratitude, or affiliation depending on social and cultural context. Its meaning is therefore not intrinsic to the movement alone, but mediated by shared interpretive systems.

Buck and VanLear’s (2002) third category, pseudo-spontaneous communication, refers to displays that resemble spontaneous expressions while remaining intentionally produced. These displays occupy an intermediate position: they preserve some of the appearance or phenomenological quality of spontaneous emotion, yet they are at least partly shaped by voluntary action and communicative purpose. An individual who feels fear yet intentionally laughs in order to conceal that fear illustrates one version of this mixed form. Pseudo-spontaneous communication is therefore especially useful for thinking about emotion displays that are neither wholly natural eruptions nor wholly symbolic constructions.

Buck’s own experimental paradigms (1984) help illustrate the spontaneous end of this continuum. In slide-viewing studies, participants viewed emotionally evocative visual stimuli while their facial reactions were recorded in real time. In related work, participants also viewed the emotional expressions of others, and in some conditions were asked to produce or simulate expressions deliberately. Taken together, these paradigms make it possible to distinguish relatively spontaneous facial displays from intentionally produced ones while holding the expressive channel constant. They therefore provide a particularly useful anchor for thinking about how emotional communication may vary in directness of coupling to felt affect.

This distinction becomes especially important when extended to affective touch. Touch-based emotion research suggests that tactile expressions do not all operate in the same communicative mode. In Hertenstein and Keltner’s work (2011), participants were free to use a broad range of gestures and movements to convey emotion, including stroking, squeezing, patting, shaking, or finger interlocking. Because such actions draw upon multiple socially recognizable movement patterns and allow considerable freedom of form, they are best understood as primarily symbolic. They communicate emotion through conventionalized or metaphorically suggestive touch acts rather than through a single constrained expressive channel.

Sentics (Clynes, 1977; 1980; 1989; 1994), by contrast, appears to occupy a middle position. Participants are asked to enter, imagine, or amplify a feeling state and then express that state through a highly constrained motor channel, such as finger pressure. The resulting display is therefore not fully spontaneous, since it is elicited within an experimental frame and enacted through a prescribed form. Yet it is not merely symbolic either, because the paradigm aims to preserve a direct relationship between felt affect and expressive pattern. Sentic forms are thus best understood as pseudo-spontaneous: experimentally shaped expressions that remain meaningfully coupled to embodied emotional process (Miller, 2012).

More broadly, this framework suggests that nonverbal channels may be equipotential (Hertenstein, Verkamp, Kerestes, & Holmes, 2006) with respect to emotional outcome while differing in the manner by which that outcome is instantiated. Facial expression, touch, posture, and vocalization may each communicate affect, but they do so with different degrees of conventionality, voluntary shaping, and coupling to ongoing feeling. The present account uses this distinction not to separate channels into rigid classes, but to clarify how emotional communication may move between symbol-like and signal-like forms depending on the structure of the task, the expressive freedom allowed, and the sender’s relation to the felt state itself.

Expressing and Measuring Emotions as Static or Dynamic

Research on emotional expression has produced important insights into the visible patterning of affect, especially in facial-expression traditions that examine muscular configuration, action units, and momentary displays. Much of this work, however, treats emotion as something that can be identified from a relatively static moment: a brow contraction, a lip raise, or a brief expression captured in time. This approach has been highly productive, but it leaves underdeveloped a different possibility: that emotion may also be understood as a temporally unfolding process (Krumhuber, Kappas, & Manstead, 2013).

Several theorists moved in this direction by treating emotion not only as a spatial pattern, but as a dynamic event with rhythm, contour, and duration (Krumhuber et al., 2013). Clynes’ work is especially important in this regard. Rather than locating emotion primarily in facial configuration, he proposed that emotions could be expressed as distinctive forms unfolding through time. In his sentic experiments, participants were asked to express specific emotions through finger pressure, and the resulting curves were recorded and compared across individuals. Clynes (1994) argued that these forms showed recurrent temporal structure, suggesting that emotional expression may be patterned not only by culture or cognition, but also by embodied dynamics.

This dynamic view also has antecedents beyond psychology proper. Work on expressive motion in music argued that meaning is carried not only by discrete form, but by temporal shaping, intensity change, and internal movement across time (Repp, 1993; Trustlit, 1938). Such approaches are relevant here because they reinforce the possibility that emotional expression may be better understood as patterned motion than as static configuration alone.

Although Clynes (1977) mapped emotional patterning through finger pressure, the present work extends that logic into the vocal domain. Truslit’s (1938) emphasis on inner motion, timing, and expressive shaping suggests that vocalization may provide a particularly useful channel for studying emotional dynamics. In this sense, the vocal hum is treated here not simply as an alternative transducer, but as a promising medium for capturing aspects of affective movement that finger pressure may only partially preserve.

For the present project, the significance of this move is methodological as well as theoretical. If emotion unfolds over time, then its measurement should neither rely exclusively on static snapshots, nor finger pressure alone. It should also include representations capable of preserving continuity, oscillation, amplitude change, and temporal contour. This is the point at which dynamic visualization becomes important.

Our use of Audioscope extends this logic by translating vocal expression into visual form. Rather than treating a hummed emotion as a sequence of isolated moments, Audioscope renders the signal as a continuous spatial-temporal pattern. This allows emotional expression to be examined as movement, shape, and recurrence rather than as a single fixed display. The resulting images make it possible to ask whether different emotions produce different kinds of dynamic organization and whether those organizations are stable enough to compare across cases.

A useful lesson from this approach is the shift from waves to circles. Standard waveform displays show amplitude across linear time and are helpful for tracking rise, fall, and intensity. Audioscope adds a different kind of information by rendering the signal into circular and phase-like visual structures. This makes it easier to inspect coherence, spread, looping, density, and attractor-like patterning in ways that are difficult to see in ordinary wave traces alone. In this sense, Audioscope does not replace temporal measurement; it reformats it, allowing dynamic expression to be seen as geometry.

This shift from static coding to dynamic visualization provides the measurement basis for the present work. It also creates a bridge between earlier sentic theory and the current Sentic Blooms approach, in which emotional expression is treated as structured movement made visible through repeated acoustic patterning.

Visualizing Emotional Dynamics: From Waves to Blooms

Any vocal expression of emotion begins as a sound wave. In the present work, humming is used as a relatively low-syntax vocal channel so that the measured signal is shaped less by lexical content and more by ongoing acoustic pattern. Audioscope provides a way to transform that signal into a visual representation that preserves its dynamic structure. Rather than displaying the hum only as amplitude over time, Audioscope renders the signal as an analytic form built from phase-shifted components. This makes it possible to inspect emotional vocalization not only as a waveform, but as a geometric pattern.

The first step in this transformation is conceptual. A sine wave can be understood as the vertical projection of circular motion, while a cosine wave represents the same motion shifted by 90 degrees in phase. When paired on orthogonal axes, the two together trace a circle.

Figure 3. Tracing Out a Circle with 90° Phase Shifted Cosine and Sine waves

Figure 4. Sine and Cosine 90° Phase Shifted Static “Shadows”

These waves are the same, except they are 90˚ out of phase. θ (theta) is phase. sin(θ+90˚) = cosθ

Audioscope extends this logic to complex signals by decomposing them into harmonic components and representing those components as rotating circles. Their summed motion reconstructs the original signal while also yielding an analytic spatial representation of it. The result is a bloom-like figure that preserves information about phase, amplitude, and evolving form.

Because every sine-wave component can be represented as a rotating circle, an entire signal becomes a sum of circles (epicycles). The y-axis of the combined motion reproduces the original waveform; the x-axis reproduces the waveform with every component phase-shifted by 90°. The result is the analytic signal—a mathematically robust description that encodes both instantaneous amplitude and instantaneous phase, from moment to moment. The panels below show harmonic circles centered at the origin, each tracing its own sine-wave component. The lower panels then illustrate how those circles shift into their summed positions, allowing the original waveform to be reconstructed.

Figure 5. A Sawtooth Circle-to-Wave Represented by Component Harmonics (Centered)

Mehmet E. Yavuz (2026). Fourier Series Animation using Harmonic Circles

The final panels (below) extend those circles to their correct offsets on the x-axis and let them rotate together. Their summed motion exactly reconstructs the original wave.

Figure 6. A Sawtooth Circle-to-Wave Represented by Component Harmonics

Mehmet E. Yavuz (2026). Fourier Series Animation using Harmonic Circles

In Audioscope the sum of circles traces out the analytic representation of a sound wave. In the case of sentic blooms, those sound waves come from making emotions audible. In these initial tests, blooms are generated by humming, which minimizes syntactic interference.

Figure 7. Illustration of How Audioscope Draws 2D Sound Waves as Circles

This “waves-to-circles” transformation is methodologically important because it allows dynamic properties of emotional expression to become visually comparable. In two-dimensional blooms, features such as overall radius, anisotropy, wobble, rim size, nestedness, and trace thickness can be inspected directly. The comparative examples in the draft illustrate this clearly: the grief bloom is larger, more layered, and more internally nested, whereas the joy bloom appears smaller, tighter, and less nested. The point is not that a bloom replaces the waveform, but that it reformats temporal information into morphology that can be further analyzed (often visually).

Figure 8. Example 2D Audioscope Visualized Human Humming Examples

Note: Color is derived from pitch class and line density is derived from angular momentum.

Three-dimensional renderings extend the method by making temporal organization more explicit. Time is already present in two-dimensional blooms, since each image is generated through continuous tracing over the duration of the vocal signal. In that format, however, temporal development is largely compressed into planar morphology, including overlap, density, wobble, and nested return.

Figure 9. Waves-to-Circles with an Added Z Axis

The three-dimensional rendering does not add time as a new ingredient; rather, it redistributes temporal progression along an added axis (z), allowing sequential features such as braid density, flow coherence, coil spread, and micro-extensions to be examined more directly. This shift helps clarify how an emotional signal is organized across its unfolding, rather than only how it appears in accumulated form.

Figure 10. Example 3D Audioscope Visualized Human Humming Examples

This measurement framework provides the visual basis for the present model. It also prepares the ground for the emotional topology introduced in the next section, where recurring bloom forms are considered in relation to attractor and repellor tendencies across the selected emotion set.

Attractor and Repellor Emotions: A Selected Topology

To examine recurring bloom structure across affective states, the present study uses a selected set of sixteen emotions organized into two broad manifolds. This arrangement is not intended as a definitive taxonomy of basic emotions. Rather, it is a working topology designed to compare how different emotional states organize visually and dynamically within the Sentic Blooms framework.

Figure 11. Octave I: Attractor Manifold & Octave II: Repellor Manifold

The first manifold contains emotions that tend, in broad terms, toward bonding, coherence, or relational approach: interest, curiosity, affection, hope, joy, grief, love, and reverence. The second contains emotions that tend toward boundary, disruption, or recoil: surprise, fear, frustration, anger, contempt, shame, disgust, and despair. These two groupings are not meant to imply that one set is “good” and the other “bad.” Instead, they provide a structured way to examine two broad directional tendencies in emotional life: movement toward connection and movement toward protection, fracture, or withdrawal.

Figure 11 presents this arrangement as a dual spiral, with the attractor (coherence) manifold on the left and the repellor (entropy) manifold on the right. The visual logic is heuristic rather than absolute. Some emotions, such as surprise, may function as transitional spikes rather than stable destinations, and others may shift in meaning depending on context. The value of the model lies not in claiming exhaustive universality, but in offering a coherent set of contrasts for examining bloom morphology across selected affective states.

This topology provides the conceptual basis for the initial humming tests. To generate comparable vocal traces, the lead human researcher produced a sustained nonverbal utterance for each of the sixteen selected emotions. These utterances were then processed through Audioscope to produce two- and three-dimensional bloom renderings for comparison.

Sentic Blooms: Testing Emotion Visualizing with Human Humming

To examine the dual-spiral model empirically, we generated vocal expression data for sixteen selected emotions: eight associated with the attractor manifold and eight with the repellor manifold. The lead human researcher vocalized each emotion as a sustained, nonverbal utterance, typically in the form of a hum or tone without semantic language. These vocalizations were then processed through Audioscope (Lu, 2016), a sound-visualization system that renders acoustic input as dynamic, phase-based forms.

The procedure combined three elements: (1) self-generated emotional elicitation through guided fantasy, (2) nonverbal vocalization with minimal syntactic interference, and (3) real-time visual rendering of the resulting signal. The aim was to generate visual traces that were both experientially grounded and comparable across emotions.

To generate the blooms, each target emotion was accessed through a guided self-fantasy procedure in which the participant selected a memory or hypothetical scene likely to evoke the desired state. After a brief period of internal recall and somatic attention, the emotion was expressed as a single sustained vocalization, such as a hum, tone, or exhale, without words. Table 1 provides the working definitions and grounding prompts used to support this elicitation process.

Hums were either visualized live in Audioscope and recorded simultaneously, or recorded first and rendered through Audioscope later. In the present study, both procedures yielded comparable bloom renderings for exploratory analysis.

Table 1. Working definitions and grounding prompts for the selected emotion set

The emphasis of this procedure was on eliciting affective expression rather than acting or verbal description. In this respect, the approach aligns with Buck and VanLear’s (2002) account of pseudo-spontaneous communication: the display is intentionally produced, but it remains tied to an internally generated feeling state. Each vocalization was recorded in a controlled acoustic environment and rendered visually using the Lu-Audioscope system, a modified FFT-based visualizer that represents sound as dynamic spiral- and bloom-like forms.

These renderings preserve several features of the vocal signal, including frequency composition, harmonic relation, and changing spatial form across time. The resulting blooms were then compared in terms of their two- and three-dimensional morphology.

2D & 3D Sentic Blooms: Measuring and Comparing Emotions

These comparisons are preliminary and serve two purposes at once: they identify recurring visual differences across emotional blooms, and they help refine the feature vocabulary used for future coding and automated extraction. In this sense, the present section is both descriptive and methodological, offering an initial framework for how bloom morphology may be compared across emotions.

The present comparison combined two forms of evidence: automated geometric measurements extracted from Audioscope for 2D blooms, and structured visual coding of recurrent morphological features in both 2D and 3D renderings. In two-dimensional blooms, comparison focused on features such as radius, tilt, wobble, rim structure, and nested return. In three-dimensional blooms, comparison extended to sequential features such as braid density, flow coherence, and coil spread. The contrast between grief and joy shown below provides an initial example of how these features vary across emotional states.

The visual comparisons that follow provide an initial test of whether distinct emotional states yield distinguishable bloom morphologies in both planar and temporally unfolded form. Preliminary statistical comparisons were used to examine both whole-bloom differences and phase-specific variation across induction, organization, and resolution.

Comparing Emotion 2D Blooms: Planar Morphology

Early comparison of 2D Sentic Blooms focused on a small set of recurrent morphological features visible across repeated traces. These included overall size, anisotropy, rim lean, wobble amplitude, nested return, and the presence of internal looping. As comparison proceeded, two further distinctions became useful: the angular location of internal returns and the directional stability of lean across exemplars.

The goal at this stage was not formal classification, but the development of a consistent descriptive vocabulary for comparing bloom structure across emotions (Audioscope was modified to record measurements in figure 12 below).

Figure 12. 2D Sentic Bloom Measurement Structure

The exemplar panel suggests that these features do not vary randomly. Some emotions, such as affection, love, and reverence, tend to produce relatively compact, rim-intact forms, whereas others, including grief, contempt, disgust, and despair, more often display layered returns or internal looping.

Lean direction also appears informative: interest shows a pronounced rightward bias, joy a milder rightward lean, and curiosity a more variable form with a leftward tendency. Particularly promising is the observation that internal loop structures may recur in similar angular locations within an emotion category, suggesting that nested return may have not only magnitude but positional regularity. At this stage, these features should be understood as provisional comparison variables rather than fixed classification criteria.

Figure 13. Representative 2D Sentic Blooms illustrating preliminary comparison features

Comparing Emotion 3D Blooms: Volumetric Morphology

Preliminary comparison of the 3D blooms suggests that their main value lies in making sequential organization more visible. Whereas 2D blooms compress temporal history into planar morphology, 3D renderings allow strand relations to be inspected more directly across the unfolding, volumetric trace. This makes several additional comparison features useful, including braid density, braid coherence, coil spread, and flow regularity.

The exemplar panel suggests that these features vary systematically across emotions rather than randomly. Joy, reverence, and love appear to produce relatively tight and orderly lattice-like braids, whereas frustration, anger, disgust, and grief show broader divergence, local instability, or disrupted crossing patterns. Particularly notable is despair, whose traces appear chaotic but nonetheless recur in a recognizable family of similarly disorganized forms (within despair blooms across time). In this sense, the 3D renderings do not merely add visual depth; they improve the legibility of patterned temporal organization.

Figure 14. Representative 3D Sentic Blooms illustrating preliminary comparison features

Figure 15 highlights the preliminary comparison features used for 3D Sentic Blooms. Initial comparisons focused on visual coding (1–4 Likert-type ratings) of braid density, yellow-green dominance, purple-blue dominance, color homogeneity, coil spread (width), and flow coherence (orderly vs. unruly strands).

Together, these 2D and 3D comparisons suggest that emotional blooms may be distinguishable not only by global shape, but by recurrent internal organization, directional bias, and patterned temporal unfolding. The next step is to test whether these preliminary distinctions remain stable across larger samples, coders, and recording sessions.

Figure 15. 3D Sentic Bloom Measurement Structure

Limitations, Future Directions, and Summary

This paper introduced the Sentic Wave Model, a recursive, time-extended account of emotional experience, and demonstrated a measurement approach—Sentic Blooms—that renders the unfolding morphology of vocalized emotion visible in both 2D and 3D form. Initial comparisons across sixteen selected emotions suggest that bloom morphology varies in patterned, non-random ways, and that 2D and 3D renderings preserve different but complementary aspects of the same vocal-emotional event. Together, these findings provide preliminary support for studying emotional expression not only as intensity or category, but also as unfolding form.

Classical emotion theories have often asked how emotional components arise, in what sequence, and through which mechanisms. The Sentic Wave Model adds a different question: what is the morphology of emotion as it unfolds? These questions are not opposed. Rather, they are complementary. Approaches such as James-Lange, Cannon-Bard, Schachter-Singer, and LeDoux focus on bodily change, appraisal, and pathway structure; the present model asks whether the felt event itself exhibits a measurable temporal organization. In this respect, the bloom approach shifts attention from emotion as endpoint to emotion as dynamic pattern.

This shift may be useful in several domains. First, it offers a scaffold for distinguishing similarly reported or easily confused emotional states, such as anger and frustration or surprise and fear. Second, it may support more nuanced forms of self- and interpersonal regulation by encouraging attention to how feelings are moving, not only to what they are called. Third, it may be relevant to affective computing. Much existing work in that area treats emotion recognition primarily as a labeling problem. The bloom approach suggests a complementary framing: emotional intelligence may depend not only on identifying a state, but on tracking the trajectory, coherence, and disruption of feeling across time (Miller, ChatGPT-4o, Deepseek, 2026).

Lastly, the present findings suggest a more dynamic vocabulary for studying emotion. The contrast between anger and frustration in the 2D and 3D exemplars (Figures 13 and 14) is especially useful here. Anger appears as a more dispersed, unstable, and irregular pattern of activity, whereas frustration remains tightly wound and densely organized, with a compressed circularity that suggests constrained force rather than release. In rheological terms, frustration resembles high-resistance containment, whereas anger more closely resembles turbulence. Appendix Figure 2 shows broader sets of 2D and 3D frustration and anger blooms to illustrate that this contrast is not based solely on selected exemplars. This comparison helps show how rheological language can move beyond poetic analogy and toward measurable description.

At the same time, the present study has important limitations. The humming data were generated by a single participant, the lead researcher, and most visual features were coded by a single observer, so population-level inferences and inter-rater reliability remain unestablished. The elicitation procedure relied on guided fantasy and therefore produced pseudo-spontaneous rather than fully spontaneous expression in Buck and VanLear’s sense (2002). In addition, the dual-spiral topology remains heuristic, and cultural and individual variation in humming patterns has not yet been tested. These constraints mean that the present findings should be treated as proof of concept rather than confirmation of a stable universal taxonomy.

Future work should extend this scaffold across larger and more diverse samples, multiple coders, automated feature extraction, and more formal phase-specific analysis. Cross-cultural comparison, clinical applications in populations where verbal report is limited or unreliable, and carefully designed interspecies extensions also remain promising directions.

More broadly, the present findings support a rheological view of emotion in which features such as density, disruption, return, and flow may become measurable rather than merely metaphorical. The blooms shown here are not proof that emotion has universal geometric signature; they are evidence that pseudo-spontaneous emotional hums can be rendered, described, compared, and measured as visible morphology. Framed in this way, the current study invites new questions about emotional life—not only what a feeling is called, but how it is organized as it moves.

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Appendix

Figure 1. Sentic Wave Model

Figure 2. Representative sets of 2D and 3D blooms for frustration and anger, shown to illustrate within-category variability alongside recurrent morphological tendencies.

Note: Blooms read from left to right and represent 5 second human humming sessions.