Enaction Research

Enacted mood & multiscale research


Perception under influence

What we see depends on the state we are in

Visual perception is usually described as the recording of a world that is simply there, the same for everyone. My work, like the enactive tradition it belongs to, describes something else: a perceptual system continuously tuned to the state of the organism, to its possibilities for action, and to the contexts with which it is coupled, up to the social context.

Several factors modulate that tuning; I have studied a number of them experimentally:

These influences are not biases to be corrected, nor noise around some "true" perception. They are constitutive: this is how an organism brings forth a world on its own scale. The Multiscale Enaction Model is an attempt to formalise it.


The approach

An enactive, multiscale approach to the ecology of cognition

The Multiscale Enaction Model (MEM) holds that vision is coupled with systems whose scales range at least from the cellular to the social-economical. Its distinctive claim is not to fix a list of levels, but to make the relevant scale flexible: it depends on the coupling under study.

The model thus departs from Gibson's ecological approach — systemic, yet confined to a single scale, that of the perception–action coupling. To it, MEM adds an embodied teleology: the needs of the organism, from cellular demands to motivation, exert pressure on what the eye seeks out.

This work draws on mixed methods — eye-tracking, electrophysiological recording, self-report measures, psychometric studies and systematic reviews — to grasp behavioural, phenomenological and physiological processes as coordinated patterns rather than independent layers.

81peer-reviewed articles
14book chapters
2edited books
3special issues

Selected work

Interdisciplinary approaches to emotion, mood & depression

  1. ·Kuzinas, A., Noiret, N., Bianchi, R., & Laurent, E. (2016). The effects of image hue and semantic content on viewer's emotional self-reports, pupil size, eye movements, and skin conductance response. Psychology of Aesthetics, Creativity and the Arts, 10(3), 360–371.
  2. ·Laurent, E., Bianchi, R., Schonfeld, I. S., & Vandel, P. (Eds.) (2016). Depression, burnout, and other mood disorders: Interdisciplinary approaches. Frontiers in Psychology / Frontiers in Psychiatry.
  3. ·Laurent, E., & Vandel, P. (Eds.) (2016). De l'humeur normale à la dépression en psychologie cognitive, neurosciences et psychiatrie. De Boeck Supérieur.

Multiscale analysis of cognition

  1. ·Laurent, E. (2014). Multiscale Enaction Model (MEM): The case of complexity and "context-sensitivity" in vision. Frontiers in Psychology, 5(1425).

Theory, data & health policy

  1. ·Laurent, E., & Bianchi, R. (2018). Humeur, « burnout » et dépression : enjeux sociétaux, constats scientifiques et stratégies managériales et politiques. In Burnout, droit et cognition (pp. 61–92). Éditions du Borrego.

Analyses of published research on mood disorders

  1. ·Bianchi, R., Schonfeld, I. S., & Laurent, E. (2017). Burnout or depression: both individual and social issue. The Lancet, 390(10091), 230.
  2. ·Laurent, E., & Bianchi, R. (2017). Assessing depression among new fathers. JAMA Psychiatry, 74(8), 855.

Experimental approaches to mood, cognition & health

  1. ·Noiret, N., Carvalho, N., Laurent, E., Chopard, G., Binetruy, M., Nicolier, M., Monnin, J., Magnin, E., & Vandel, P. (2018). Saccadic eye movements and attentional control in Alzheimer's disease. Archives of Clinical Neuropsychology, 33(1), 1–13.
  2. ·Noiret, N., Carvalho, N., Laurent, E., Vulliez, L., Bennabi, D., Chopard, G., Haffen, E., Nicolier, M., Monnin, J., & Vandel, P. (2015). Visual scanning behavior during processing of emotional faces in older adults with depression. Aging and Mental Health, 19(3), 264–273.

Psychometric approaches to mood & health

  1. ·Bianchi, R., Mayor, E., Schonfeld, I. S., & Laurent, E. (2018). Burnout and depressive symptoms are not primarily linked to perceived organizational problems. Psychology, Health & Medicine, 23(9), 1094–1105.
  2. ·Verkuilen, J., Bianchi, R., Schonfeld, I. S., & Laurent, E. (2021). Burnout–depression overlap: exploratory structural equation modeling bifactor analysis and network analysis. Assessment, 28(6), 1583–1600.

See all publications