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1. Electroencephalogram Experimentation to Understand Creativity of Mechanical Engineering Students

   https://doi.org/10.1115/1.4056473  

   Ahad, Md Tanvir; Hartog, Tess; Alhashim, Amin G.; Marshall, Megan; Siddique, Zahed (January 2023, ASME Open Journal of Engineering)

   Abstract Electroencephalogram (EEG) alpha power (8–13 Hz) is a characteristic of various creative task conditions and is involved in creative ideation. Alpha power varies as a function of creativity-related task demands. This study investigated the event-related potentials (ERPs), alpha power activation, and potential machine learning (ML) to classify the neural responses of engineering students involved with creativity task. All participants performed a modified alternate uses task (AUT), in which participants categorized functions (or uses) for everyday objects as either creative, nonsense, or common. At first, this study investigated the fundamental ERPs over central and parietooccipital temporal areas. The bio-responses to understand creativity in engineering students demonstrates that nonsensical and creative stimuli elicit larger N400 amplitudes (−1.107 mV and −0.755 mV, respectively) than common uses (0.0859 mV) on the 300–500 ms window. N400 effect was observed on 300–500 ms window from the grand average waveforms of each electrode of interest. ANOVA analysis identified a significant main effect: decreased alpha power during creative ideation, especially over (O1/2, P7/8) parietooccipital temporal area. Machine learning is used to classify the specific temporal area data’s neural responses (creative, nonsense, and common). A k-nearest neighbors (kNN) classifier was used, and results were evaluated in terms of accuracy, precision, recall, and F1- score using the collected datasets from the participants. With an overall 99.92% accuracy and area under the curve at 0.9995, the kNN classifier successfully classified the participants’ neural responses. These results have great potential for broader adaptation of machine learning techniques in creativity research. 

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2. Throw me a lifeline: Articulating mobile social network dispersion and the social construction of risk in rescue communication

   https://doi.org/10.1177/2050157919846522  

   Stephens, Keri_K; Robertson, Brett_W; Murthy, Dhiraj (June 2019, Mobile Media & Communication)

   This research develops a model of mobile social network dispersion in rescue communication, and illustrates how people use a combination of mobile and social media, along with real-time communication, in their decision-making process. Guided by established research on smartphones, social media, and affordances, we used a qualitative approach and conducted field interviews that included photo-elicitation interview (PEI) techniques to examine participants’ private social media data. Our analysis of these rescue decisions reveals why so few people used the official 9-1-1 system. We show how rescue communication often occurs through a socially constructed assessment of risk that involves persuasion by trusted others in their network, regardless of professional qualifications. Furthermore, trusted others can function as proxies and can draw upon mobile social network affordances, helping to compensate for material limitations. The affordances people drew from can be organized into two sets: foundational and amplification. Hierarchical relationships exist among these sets of affordances, and materiality plays a pivotal role in rescue communication. Ultimately, our analysis uncovers the multimodality around people’s decisions to ask for help. 

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3. Shared spatiotemporal category representations in biological and artificial deep neural networks

   https://doi.org/https://doi.org/10.1371/journal.pcbi.1006327  

   Greene, Michelle R.; Hansen, Bruce C. (October 2018, PLOS computational biology)

   Visual scene category representations emerge very rapidly, yet the computational transformations that enable such invariant categorizations remain elusive. Deep convolutional neural networks (CNNs) perform visual categorization at near human-level accuracy using a feedforward architecture, providing neuroscientists with the opportunity to assess one successful series of representational transformations that enable categorization in silico. The goal of the current study is to assess the extent to which sequential scene category representations built by a CNN map onto those built in the human brain as assessed by high-density, time-resolved event-related potentials (ERPs). We found correspondence both over time and across the scalp: earlier (0–200 ms) ERP activity was best explained by early CNN layers at all electrodes. Although later activity at most electrode sites corresponded to earlier CNN layers, activity in right occipito-temporal electrodes was best explained by the later, fully-connected layers of the CNN around 225 ms post-stimulus, along with similar patterns in frontal electrodes. Taken together, these results suggest that the emergence of scene category representations develop through a dynamic interplay between early activity over occipital electrodes as well as later activity over temporal and frontal electrodes. 

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4. What the Flip? What the P-N Flip Can Tell Us about Proactive Suppression

   https://doi.org/10.1162/jocn_a_01901  

   Tam, Joyce; Callahan-Flintoft, Chloe; Wyble, Brad (January 2022, Journal of Cognitive Neuroscience)

   Abstract It has been debated whether salient distractors in visual search can be proactively suppressed to completely prevent attentional capture, as the occurrence of proactive suppression implies that the initial shift of attention is not entirely driven by physical salience. While the presence of a Pd component in the EEG (associated with suppression) without a preceding N2pc component (associated with selection) has been used as evidence for proactive suppression, the link between these ERPs and the underlying mechanisms is not always clear. This is exemplified in two recent articles that observed the same waveform pattern, where an early Pd-like component flipped to a N2pc-like component, but provided vastly different interpretations (Drisdelle, B. L., & Eimer, E. PD components and distractor inhibition in visual search: New evidence for the signal suppression hypothesis. Psychophysiology, 58, e13898, 2021; Kerzel, D., & Burra, N. Capture by context elements, not attentional suppression of distractors, explains the PD with small search displays. Journal of Cognitive Neuroscience, 32, 1170–1183, 2020). Using RAGNAROC (Wyble et al., Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition. Psychological Review, 127, 1163–1198, 2020), a computational model of reflexive attention, we successfully simulated this ERP pattern with minimal changes to its existing architecture, providing a parsimonious and mechanistic explanation for this flip in the EEG that is unique from both of the previous interpretations. Our account supports the occurrence of proactive suppression and demonstrates the benefits of incorporating computational modeling into theory building. 

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5. Personality similarity predicts synchronous neural responses in fMRI and EEG data

   https://doi.org/10.1038/s41598-022-18237-1  

   Matz, Sandra C.; Hyon, Ryan; Baek, Elisa C.; Parkinson, Carolyn; Cerf, Moran (August 2022, Scientific Reports)

   Abstract Successful communication and cooperation among different members of society depends, in part, on a consistent understanding of the physical and social world. What drives this alignment in perspectives? We present evidence from two neuroimaging studies using functional magnetic resonance imaging (fMRI;N = 66 with 2145 dyadic comparisons) and electroencephalography (EEG;N = 225 with 25,200 dyadic comparisons) to show that: (1) the extent to which people’s neural responses are synchronized when viewing naturalistic stimuli is related to their personality profiles, and (2) that this effect is stronger than that of similarity in gender, ethnicity and political affiliation. The localization of the fMRI results in combination with the additional eye tracking analyses suggest that the relationship between personality similarity and neural synchrony likely reflects alignment in the interpretation of stimuli and not alignment in overt visual attention. Together, the findings suggest that similarity in psychological dispositions aligns people’s reality via shared interpretations of the external world. 

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