Chemosensory event-related potentials (CSERP) as a key to the psychology of odors

Pause, Bettina M.; Krauel, Kerstin

doi:10.1016/s0167-8760(99)00105-1

Cited by 134 publications

(99 citation statements)

References 55 publications

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“…MMN is resistant to manipulations of attention and states of wakefulness (Sculthorpe et al, 2009) even though these parameters can modulate its amplitude. An analog of MMN was described in visual (Tales et al, 1999;Pazo-Alvarez et al, 2003), olfactive (Krauel et al, 1999;Pause and Krauel, 2000), and somatosensory (Kekoni et al, 1997;Shinozaki et al, 1998) modalities, supporting a broad computational significance of MMN as a shared and automatic brain mechanism responsive to stimulus novelty.…”

Section: Introductionmentioning

confidence: 81%

“…As noted by Friston et al (2005), the closely similar neuronal architecture of cortical layers throughout the cerebral cortex supports the view that a similar computational principle of predictive coding may apply to the multiple hierarchical levels of the cortical areas of the brain. Thus, our model may be used to account for higher-order instances of mismatch responses, such as the distinct MMNs evoked by a change in phoneme versus speaker (Giard et al, 1995;Dehaene-Lambertz, 1997), or the mismatch responses observed outside the auditory modality, either in visual (Tales et al, 1999;Pazo-Alvarez et al, 2003), olfactive (Krauel et al, 1999;Pause and Krauel, 2000), and somatosensory (Kekoni et al, 1997;Shinozaki et al, 1998) modalities or even in a crossmodal context (Arnal et al, 2011).…”

Section: Extensions and Limits Of The Modelmentioning

confidence: 99%

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A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Wacongne¹,

Changeux²,

Dehaene³

2012

J. Neurosci.

476

423

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The mismatch negativity (MMN) is thought to index the activation of specialized neural networks for active prediction and deviance detection. However, a detailed neuronal model of the neurobiological mechanisms underlying the MMN is still lacking, and its computational foundations remain debated. We propose here a detailed neuronal model of auditory cortex, based on predictive coding, that accounts for the critical features of MMN. The model is entirely composed of spiking excitatory and inhibitory neurons interconnected in a layered cortical architecture with distinct input, predictive, and prediction error units. A spike-timing dependent learning rule, relying upon NMDA receptor synaptic transmission, allows the network to adjust its internal predictions and use a memory of the recent past inputs to anticipate on future stimuli based on transition statistics. We demonstrate that this simple architecture can account for the major empirical properties of the MMN. These include a frequency-dependent response to rare deviants, a response to unexpected repeats in alternating sequences (ABABAA. . . ), a lack of consideration of the global sequence context, a response to sound omission, and a sensitivity of the MMN to NMDA receptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: the MMN results from active cortical prediction rather than passive synaptic habituation.

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Section: Introductionmentioning

confidence: 81%

Section: Extensions and Limits Of The Modelmentioning

confidence: 99%

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Wacongne¹,

Changeux²,

Dehaene³

2012

J. Neurosci.

476

423

View full text Add to dashboard Cite

show abstract

“…Pleasantness is the primary perceptual aspect humans use to discriminate odorants (Schiffman, 1974;Godinot and Sicard, 1995) or combine them into groups (Berglund et al, 1973;Schiffman et al, 1977). Pleasant and unpleasant odorants are evaluated at different speeds (Bensafi et al, 2002) and by dissociable neural substrates, as evidenced in both electrophysiological recordings (Kobal et al, 1992;Pause and Krauel, 2000;Masago et al, 2001) and functional neuroimaging studies (Zald and Pardo, 1997;Royet et al, 2000;Gottfried et al, 2002;Anderson et al, 2003;Rolls et al, 2003). Finally, studies with newborns suggest that at least some aspects of olfactory pleasantness may be innate (Steiner, 1979;Soussignan et al, 1997).…”

Section: Discussionmentioning

confidence: 99%

Predicting Odor Pleasantness from Odorant Structure: Pleasantness as a Reflection of the Physical World

Khan

Luk²,

Flinker³

et al. 2007

J. Neurosci.

337

296

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Although it is agreed that physicochemical features of molecules determine their perceived odor, the rules governing this relationship remain unknown. A significant obstacle to such understanding is the high dimensionality of features describing both percepts and molecules. We applied a statistical method to reduce dimensionality in both odor percepts and physicochemical descriptors for a large set of molecules. We found that the primary axis of perception was odor pleasantness, and critically, that the primary axis of physicochemical properties reflected the primary axis of olfactory perception. This allowed us to predict the pleasantness of novel molecules by their physicochemical properties alone. Olfactory perception is strongly shaped by experience and learning. However, our findings suggest that olfactory pleasantness is also partially innate, corresponding to a natural axis of maximal discriminability among biologically relevant molecules.

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“…The early, exogenous components, of the OERP reflect the external stimulus and various studies have demonstrated that the N1P2 amplitude is dependent upon the stimulus concentration (Kobal and Hummel, 1991;Tateyama et al, 1998;Wang et al, 2002). The later positive components (1000-1500 ms poststimulus) are thought to reflect cognitive operations (Rösler et al, 1986; Vergeler, Learning to smell steroids N Boulkroune et al 1988; Pause and Krauel, 2000). We show that there are increases in both the early (P1N1) and late (N1P2) components of the OERP (Figure 1e and d), with a pronounced increase in the first positive peak (P1).…”

Section: Discussionmentioning

confidence: 99%

Repetitive Olfactory Exposure to the Biologically Significant Steroid Androstadienone Causes a Hedonic Shift and Gender Dimorphic Changes in Olfactory-Evoked Potentials

Boulkroune

Wang

March

et al. 2007

Neuropsychopharmacol

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The function of a sensory system is to transduce and relay sensory information in a constant and reproducible manner. However, in the olfactory processing of certain steroids this precept of sensory constancy does not appear to apply. Using threshold testing, psychometrics, and electrophysiological techniques, we investigated the effects of a repetitive exposure protocol on the response to androstadienone. Androstadienone is a steroid found in human secretions that has been widely proposed as a candidate for a human pheromone. The detection threshold, hedonic perception, and evoked potential response all changed following repetitive exposure to androstadienone and not to a control odorant, benzaldehyde. Furthermore, the exposure-dependent changes in evoked potentials exhibited a gender dimorphism in which there were changes in the later components of the evoked potentials specific to women. These components have been associated with cognitive and perceptual operations. This 'learning' to smell a compound found in sweat may be related to biological signaling.

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Chemosensory event-related potentials (CSERP) as a key to the psychology of odors

Cited by 134 publications

References 55 publications

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Predicting Odor Pleasantness from Odorant Structure: Pleasantness as a Reflection of the Physical World

Repetitive Olfactory Exposure to the Biologically Significant Steroid Androstadienone Causes a Hedonic Shift and Gender Dimorphic Changes in Olfactory-Evoked Potentials

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