Information Processing in the Cortex: The Role of Small Assemblies of Neurons

We discuss the role of linguistic metaphors as a cognitive frame for the understanding of genetic information processing. The essential similarity between language and genetic information processing has been recognized since the very beginning, and many prominent scholars have noted the possibility of considering genes and genomes as texts or languages. Most of the core terms in molecular biology are based on linguistic metaphors. The processing of genetic information is understood as some operations on text – writing, reading and editing and their specification (encoding/decoding, proofreading, transcription, translation, reading frame). The concept of gene reading can be traced from the archaic idea of the equation of Life and Nature with the Book. Thus, the genetics itself can be metaphorically represented as some operations on text (deciphering, understanding, code-breaking, transcribing, editing, etc.), which are performed by scientists. At the same time linguistic metaphors portrayed gene entities also as having the ability of reading. In the case of such “bio-reading” some essential features similar to the processes of human reading can be revealed: this is an ability to identify the biochemical sequences based on their function in an abstract system and distinguish between type and its contextual tokens of the same type. Metaphors seem to be an effective instrument for representation, as they make possible a two-dimensional description: biochemical by its experimental empirical results and textual based on the cognitive models of comprehension. In addition to their heuristic value, linguistic metaphors are based on the essential characteristics of genetic information derived from its dual nature: biochemical by its substance, textual (or quasi-textual) by its formal organization. It can be concluded that linguistic metaphors denoting biochemical objects and processes seem to be a method of description and explanation of these heterogeneous properties.

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Emotion information processing as a new component of emotional intelligence: Theoretical framework and empirical evidence

European Journal of Personality ◽

10.1177/08902070211007672 ◽

2021 ◽

pp. 089020702110076

Author(s):

Marina Fiori ◽

Shagini Udayar ◽

Ashley Vesely Maillefer

Keyword(s):

Emotional Intelligence ◽

Information Processing ◽

Incremental Validity ◽

Stressful Situation ◽

Factorial Structure ◽

Ability Emotional Intelligence ◽

Processing Component ◽

Overall Performance ◽

The Relationship

The relationship between emotional intelligence (EI) and emotion information processing (EIP) has received surprisingly little attention in the literature. The present research addresses these gaps in the literature by introducing a conceptualization of emotional intelligence as composed of two distinct components: (1) EIK or emotion Knowledge component, captured by current ability emotional intelligence tests, related to top-down, higher order reasoning about emotions, and which depends more strongly on acquired and culture-bound knowledge about emotions; (2) EIP or emotion information Processing component, measured with emotion information processing tasks, requires faster processing and is based on bottom-up attention-related responses to emotion information. In Study 1 ( N = 349) we tested the factorial structure of this new EIP component within the nomological network of intelligence and current ability emotional intelligence. In Study 2 ( N =111) we tested the incremental validity of EIP in predicting both overall performance and the charisma of a presenter while presenting in a stressful situation. Results support the importance of acknowledging the role of emotion information processing in the emotional intelligence literature and point to the utility of introducing a new EI measure that would capture stable individual differences in how individuals process emotion information.

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The role of time in information processing

ACM SIGMOD Record ◽

10.1145/984505.984508 ◽

1982 ◽

Vol 12 (3) ◽

pp. 27-50 ◽

Cited By ~ 53

Author(s):

A. Bolour ◽

T. L. Anderson ◽

L. J. Dekeyser ◽

H. K. T. Wong

Keyword(s):

Information Processing

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When Feature Information Comes First! Early Processing of Inverted Faces

Perception ◽

10.1068/p5192 ◽

2005 ◽

Vol 34 (9) ◽

pp. 1117-1134 ◽

Cited By ~ 39

Author(s):

Claus-Christian Carbon ◽

Helmut Leder

Keyword(s):

Information Processing ◽

Face Recognition ◽

Face Processing ◽

Contextual Information ◽

Presentation Time ◽

Early Processing ◽

Feature Information ◽

Short Presentation Time ◽

Short Presentation

We investigated the early stages of face recognition and the role of featural and holistic face information. We exploited the fact that, on inversion, the alienating disorientation of the eyes and mouth in thatcherised faces is hardly detectable. This effect allows featural and holistic information to be dissociated and was used to test specific face-processing hypotheses. In inverted thatcherised faces, the cardinal features are already correctly oriented, whereas in undistorted faces, the whole Gestalt is coherent but all information is disoriented. Experiment 1 and experiment 3 revealed that, for inverted faces, featural information processing precedes holistic information. Moreover, the processing of contextual information is necessary to process local featural information within a short presentation time (26 ms). Furthermore, for upright faces, holistic information seems to be available faster than for inverted faces (experiment 2). These differences in processing inverted and upright faces presumably cause the differential importance of featural and holistic information for inverted and upright faces.

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The role of the spindle in human information processing of high-intensity stimuli during sleep

Journal of Sleep Research ◽

10.1046/j.1365-2869.2000.00188.x ◽

2000 ◽

Vol 9 (1) ◽

pp. 19-26 ◽

Cited By ~ 61

Author(s):

Kimberly A. Cote ◽

TinA. M. Epps ◽

Kenneth B. Campbell

Keyword(s):

Information Processing ◽

High Intensity ◽

Human Information Processing

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AI technologies and their impact on supply chain resilience during COVID-19

International Journal of Physical Distribution & Logistics Management ◽

10.1108/ijpdlm-12-2020-0434 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Sachin Modgil ◽

Shivam Gupta ◽

Rébecca Stekelorum ◽

Issam Laguir

Keyword(s):

Artificial Intelligence ◽

Supply Chain ◽

Information Processing ◽

Supply Chains ◽

Failure Modes ◽

Stress Test ◽

Weak Links ◽

Content Type ◽

Supply Chain Resilience

PurposeCOVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approachWe adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.FindingsAn AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implicationsAs the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implicationsSupply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/valueThe present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

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