Vividness of Visual Imagery Questionnaire

The Vividness of Visual Imagery Questionnaire (VVIQ) was developed in 1973 by the British psychologist David Marks (Marks, 1973). The VVIQ consists of 16 items in four groups of 4 items in which the participant is invited to consider the image formed in thinking about specific scenes and situations. The vividness of the image is rated along a 5-point scale. The questionnaire has been widely used as a measure of individual differences in vividness of visual imagery. The large body of evidence confirms that the VVIQ is a valid and reliable psychometric measure of visual image vividness.

VVIQ2

Marks (1995) published a new version of the VVIQ, the VVIQ2. This questionnaire consists of twice the number of items and reverses the rating scale so that higher scores reflect higher vividness. Campos and Pérez-Fabello (2009) evaluated the reliability and construct validity of the VVIQ and the VVIQ2. Cronbach a reliabilities for both the VVIQ and the VVIQ-2 were found to be high. Estimates of internal consistency reliability and construct validity were found to be similar for the two versions.

Validation

The VVIQ has proved an essential tool in the scientific investigation of mental imagery as a phenomenological, behavioral and neurological construct. Marks' (1973) paper has been cited in more than 1000 studies of mental imagery in a variety of fields including cognitive psychology, clinical psychology and neuropsychology. The VVIQ and VVIQ2 are both available on the Internet[1] and also on YouTube.[2]

The procedure can be carried out with eyes closed and/or with eyes open. Total score on the VVIQ is a predictor of the person's performance in a variety of cognitive, motor, and creative tasks. For example, Marks (1973) reported that high vividness scores correlate with the accuracy of recall of coloured photographs.

Rodway, Gillies and Schepman (2006) used a novel long-term change detection task to determine whether participants with low and high vividness scores on the VVIQ2 showed any performance differences. Rodway et al. (2006) found that high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. This replicated an earlier study by Gur and Hilgard (1975).

fMRI studies

Recent studies have found that individual differences in VVIQ scores can be used to predict changes in a person's brain while visualizing different activities. For example, Amedi, Malach and Pascual-Leone (2005) predicted that VVIQ scores might be correlated with the degree of deactivation of the auditory cortex in individual subjects in functional magnetic resonance imaging (fMRI). These investigators found a significant positive correlation between the magnitude of A1 deactivation (negative blood-oxygen-level-dependent -BOLD- signal in auditory cortex) and the subjective vividness of visual imagery (Spearman r = 0.73, p < 0.05).

In looking for possible correlations across the entire brain, Amedi et al. ran a correlation analysis between the VVIQ and BOLD on a voxel-by-voxel exploratory basis. Significant negative correlations between VI BOLD and VVIQ were located in auditory or somatosensory cortex, including left HG, STG bilaterally, and right STS. Positive correlations were found between VVIQ and BOLD signal in visual and inferior prefrontal cortex but also in parahippocampal areas bilaterally and left parieto-occipital sulcus. Amedi et al. (2005) concluded that "...pure visual imagery is characterized by an isolated activation of visual cortical areas with concurrent deactivation of sensory inputs that could potentially disrupt the image created by our mind’s eye" (p. 867).

Cui et al. (2007) used fMRI to study the association between early visual cortex activity relative to the whole brain while participants visualised themselves or another person bench pressing or stair climbing. The investigators found that reported image vividness correlates with the relative fMRI signal in visual corex. Thus individual differences in the vividness of visual imagery can be measured objectively. The subjective experience of forming a mental image and objective measurement of visual cortical activity show a strong and significant relationship.

Logie, Pernet, Buonocore and Della Sala (2011) used behavioural and fMRI data for mental rotation from individuals reporting vivid and poor imagery on the VVIQ. Groups differed in brain activation patterns suggesting that the groups performed the same tasks in different ways. These findings help to explain the lack of association previously reported between VVIQ scores and mental rotation performance.

Lee, Kravitz and Baker (2012) used fMRI and multi-voxel pattern analysis to investigate the specificity, distribution, and similarity of information for individual seen and imagined objects. Participants either viewed or imagined individual named object images on which they had been trained prior to the scan. Lee et al. found that the identity of both seen and imagined objects could be decoded from the pattern of activity throughout the ventral visual processing stream. Further, there was enough correspondence between imagery and perception to allow discrimination of individual imagined objects based on the response during perception.

However, the distribution of object information across visual areas was strikingly different during imagery and perception. While there was an obvious posterior–anterior gradient along the ventral visual stream for seen objects, there was an opposite gradient for imagined objects. Correlation between fMRI and VVIQ scores showed that, in both object-selective and early visual cortex, Lee et al.'s (2012) measure of discrimination across imagery and perception correlated with the vividness of imagery. These results suggest that, while imagery and perception have similar neural substrates, they involve different network dynamics.

References

  1. ↑ "Softwares - Adnan Niazi". Retrieved 16 October 2016.
  2. ↑ analyze4d (16 October 2012). "vividness of imagery questionnaire (VVIQ)". Retrieved 16 October 2016 – via YouTube.
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