Spatial Frequency Information Modulates Response Inhibition and Decision-Making Processes

<div><p>We interact with the world through the assessment of available, but sometimes imperfect, sensory information. However, little is known about how variance in the quality of sensory information affects the regulation of controlled actions. In a series of three experiments, comprising a total of seven behavioral studies, we examined how different types of spatial frequency information affect underlying processes of response inhibition and selection. Participants underwent a stop-signal task, a two choice speed/accuracy balance experiment, and a variant of both these tasks where prior information was given about the nature of stimuli. In all experiments, stimuli were either intact, or contained only high-, or low- spatial frequencies. Overall, drift diffusion model analysis showed a decreased rate of information processing when spatial frequencies were removed, whereas the criterion for information accumulation was lowered. When spatial frequency information was intact, the cost of response inhibition increased (longer SSRT), while a correct response was produced faster (shorter reaction times) and with more certainty (decreased errors). When we manipulated the motivation to respond with a deadline (i.e., be fast or accurate), removal of spatial frequency information slowed response times only when instructions emphasized accuracy. However, the slowing of response times did not improve error rates, when compared to fast instruction trials. These behavioral studies suggest that the removal of spatial frequency information differentially affects the speed of response initiation, inhibition, and the efficiency to balance fast or accurate responses. More generally, the present results indicate a task-independent influence of basic sensory information on strategic adjustments in action control.</p></div

Table showing the quantitative results of the cluster-to-factor matching procedure at facet level.

<p>Standard, confirmatory, exploratory: type of factor analysis. N, E, O, A, C: NEUROTICISM, EXTRAVERSION, OPENNESS, AGREEABLENESS, CONSCIENTIOUSNESS. F1–F6: factors of the corresponding factor analysis. F1–F6 correspond to N, E, O, A,C, and a 6th factor that is the product of the exploratory factor analysis of the current dataset. Best overall match: best match between overall cluster contents and overall FS. r and p: r and p values at which the best overall match is found. Best cluster-to-factor match: best match between individual factors and network clusters, which may occur at different r and p-values per cluster. % mismatch denotes the percentage of normalized mismatch between factor and network cluster contents. Note that FSs and NCSs at facet level generally show high degrees of correspondence (96.2%), with a best match occurring with the confirmatory 5-FS at r = 0.271, p = 4.89E-09.</p

Scree-plots of facet level (A) and item level (B) principal component analyses.

<p><b>A.</b> At facet level, a 6-factor structure is suggested by the screeplot. <b>B.</b> At item level, a 10-factor structure is found without any resemblance to either a 5-factor structure or a 30-facet structure. Such item level decompositions are known to be unreliable in smaller-than-standard datasets such as the present dataset. Hence, both 5-factor (‘confirmatory’) and 6-factor (‘exploratory’) PCAs were performed, to force item level results to more plausible solutions. The factor structures of these PCAs, rather than the 10-factor structure, served as templates in the NCS-to-FS matching procedure. See text for further details.</p

Behavioral overview speed-accuracy experiments.

<p>Values are median RT given in ms, and errors in percentages () SDs.Cue refers to the spatial frequency information that was (or was not) provided prior to the stimulus.</p

Experiment 1: Effects of spatial frequency information during the visual stop task.

<p>A) Both Go RT (left panel) and choice errors (middle panel) increased when spatial frequency information was degraded. There right panel shows how the efficiency to withdraw a response (SSRT) improves, when categorization is more difficult. B) HDDM individual subject parameter estimates. When spatial frequency information was removed, the rate of information accumulation, “drift rate” () decreased (left panel), whereas the decision “boundary” () to categorize the face stimuli on time was lowered (middle panel). The right panel illustrates how lower drift rates, and decision boundaries together can result in prolonged RT and more errors.</p

The Personality Web at facet level: network graph of correlational relationships between the 30 facets of the NEO-PI-R.

<p>The community structure of this graph has an overall best fit with the confirmatory 5-FS, occurring at r>0.271, p<4.89 E-09. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-t002" target="_blank">Table 2</a> for significances and correlation coefficients. Node = facet, link = significant correlation. Red links: positive correlations. Blue links: negative correlations. The thickness of the links represents the strength of the correlation. For further information, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-t004" target="_blank">Table 4B</a>. n = neuroticism, e = extraversion, o = openness, a = agreeableness, c = conscientiousness. Numbers refer to facet number. Nodes are positioned in clusters according to their factor membership (standard 5-FS). The color of nodes denotes their network cluster membership. Only two facets show a mismatch with the standard 5-FS (n5 and n2). Both mismatches involve the neuroticism dimension depicted below in red. These facets have strong correlations with facets from the conscientiousness cluster (blue) and the agreeableness cluster (green), as can be observed by the thickness of the corresponding links. As a result, n5 is “drawn” into the conscientiousness cluster and n2 into the agreeableness cluster.</p

Visual stop-task. Each trial started with a white fixation-cross followed by a male or female face stimulus, indicating a left or right response.

<p>During stop trials, a tone was played at some delay (SSD) and instructed participants to suppress the indicated response. The presented face stimulus contained allSF, only LSF, or only HSF information. Prints underestimate the contrasts used in the current experiment, especially for the LSF and HSF pictures. The letters displayed on each face are only included here for clarity and were not on top of the faces during the experiment.</p

Results of the network community structure to factor structure matching procedure at facet level.

<p><b>A.</b> Results of the NCS-to-FS matching procedure for standard, confirmatory and exploratory FSs. X-axis shows the correlation coefficient r as a threshold for significance of a link in the network graph (as r increases to the right, more links are pruned from the network). Y-axis shows normalized dissimilarity (mismatch) scores. Blue: standard 5-FS, red: confirmatory 5-FS, green: exploratory 6-FS. The confirmatory 5-FS shows the best match with NCS at r = 0.271, p = 4.89E-09. For details, see text and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-t002" target="_blank">2</a>. <b>B.</b> Results of the NCS-to-FS matching procedure for the specific case of the winning confirmatory 5-FS (red line in Fig A), with a subspecification of the matching results per factor. F1–F5: confirmatory factors resembling Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness, respectively. For details, see text and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051558#pone-0051558-t002" target="_blank">2</a>.</p

The results of standard 5-factor, confirmatory 5-factor and exploratory 6-factor PCAs of our dataset.

<p>Factor loadings >0.4 are shown. A forced-choice filter was performed on factor loadings to create the final matching templates (see Materials and Methods). The highest factor loadings determined the final factor membership of individual items or factors in the templates. These highest loadings are shown in bold.</p

The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure

<div><h3>Introduction</h3><p>Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods.</p> <h3>Aim</h3><p>To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R).</p> <h3>Methods</h3><p>434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis.</p> <h3>Results</h3><p>At facet level, NCS showed a best match (96.2%) with a ‘confirmatory’ 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with ‘confirmatory’ 5-FS and ‘exploratory’ 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks.</p> <h3>Conclusion</h3><p>We present the first optimized network graph of personality traits according to the NEO-PI-R: a ‘Personality Web’. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.</p> </div