C9087 - LINGUISTICA COGNITIVA (LM)

Learning outcomes

The course offers advanced insights into the relationship between language and cognitive processes, with particular emphasis on how multilingualism shapes the mental representation of concepts and semantic categorisation. Students will acquire the skills to analyse how language is represented and processed in the mind and to explore models of the cognitive mechanisms underlying its functioning. In addition, the course provides both theoretical foundations and practical tools, with applications that extend to areas such as language teaching.

Course contents

This course introduces the main theoretical models of contemporary Cognitive Linguistics, exploring the relationship between language, experience, and conceptual representation. The course approaches language as a dynamic system of meaning-making, analyzing its role in relation to phenomena such as abstraction and categorization, embodiment, metaphorization, and pragmatic inference.
Part of the course will be devoted to the epistemological positioning of Cognitive Linguistics in relation to classical cognitivism. The main points of divergence between symbolic approaches and usage-based and experientialist approaches will be discussed, with particular attention to the topics of the modularity of the mind, the nature of grammar, the role of linguistic experience, and the relationship between language and general cognition.
Special attention will also be given to the contemporary dialogue between Cognitive Linguistics, computational cognitive science, and Artificial Intelligence. The course will critically examine the role of distributional models of meaning, neural networks, and deep learning as possible models for the representation and construction of linguistic meaning. In this context, the following topics will be addressed:

• the distributional hypothesis and vector semantics;
• statistical learning and cross-situational learning;
• indirect learning and second-language vocabulary acquisition;
• relationships between linguistic experience and conceptual organization;
• language, cognition, and embodied experience;
• categorization, prototypes, and conceptual structure;
• polysemy, semantic frames, and lexical organization;
• metaphor and conceptual metonymy;
• applications of cognitive linguistics to language teaching and discourse analysis.
• cognitive plausibility of Large Language Models and contemporary neural networks.

The course will alternate between theoretical aspects and laboratory activities based on the analysis of authentic linguistic data, examples drawn from multiple languages, critical discussion of the scientific literature, and an introduction to simple corpus-based and distributional analysis tools.

Readings/Bibliography

Main Reference Texts

• William Croft e D. Alan Cruse (2010), Linguistica cognitiva. Edizione italiana a cura di Silvia Luraghi, Carocci Editore, Roma
• Vyv Evans (2019). The Crucible of Language: How Language and Mind Create Meaning. Cambridge: Cambridge University Press.

Supplementary readings and materials

  Additional scholarly articles, book chapters, and teaching materials will be made available during the course via the course’s online learning platform.

Teaching methods

Lectures supplemented by:

• guided discussion of scientific articles;
• case study analysis;
• seminar activities and group work;
• exercises on linguistic data and semantic-cognitive phenomena;
• an introduction to simple examples of distributional representations of meaning and semantic vector spaces;
• student presentations on specific topics;
• possible use of digital tools and corpus-based resources for linguistic analysis.

The course encourages active and critical participation by students, with particular attention to the ability to connect theoretical models with observable linguistic phenomena.

Assessment methods

The assessment will consist of a written exam with 5 open-ended questions, designed to evaluate:

• the knowledge acquired during the course,
• mastery of specialized terminology,
• the ability to contextualize theoretical concepts,
• and to analyze concrete case studies in a critical and well-reasoned manner.

Assessment Criteria

 The final assessment will take into account:

• the clarity of presentation and formal correctness of the answers;
• the comprehensiveness of the content covered;
• the ability to argue a point and engage in critical analysis demonstrated in the answers;
• the oral presentation of the scientific article (group activity).

Non-attending students will be asked to answer an open-ended question related to the article chosen as supplementary material.
Excellent grades will be awarded to papers that demonstrate a solid and comprehensive understanding of the topics, precise and appropriate language, and effective use of analytical tools.
Good or fair grades will correspond to a generally solid grasp of the material, but with minor uncertainties in the use of terminology or analytical ability.

Satisfactory or barely satisfactory grades will reflect limited and fragmented knowledge, language that is not always appropriate, and weak or underdeveloped analytical skills.
In the presence of serious theoretical and practical gaps, inadequate presentation, and imprecise use of disciplinary language, the paper will be graded as a fail.

Use of Artificial Intelligence

 AI can be a useful tool to support independent study by providing in-depth analysis, summaries, and self-assessment exercises. With regard to assessment, the use of AI is prohibited during in-person exams. Any use constitutes a violation of academic integrity.

Teaching tools

During in-person classes, slides and/or printed materials will be used to support the lecture.

All course materials (slides, diagrams, examples, etc.) will be uploaded weekly to the online platform.

For attending students, these materials are an integral part of the exam syllabus. Non-attending students are also strongly encouraged to study them, as they provide essential support for exam preparation.

Students with specific learning disabilities (SLD) or temporary or permanent disabilities:

Any requests for accommodations must be submitted within 15 days of the exam date by sending an email to the instructor and copying disabilita@unibo.it (in the case of disabilities) or dsa@unibo.it (for students with SLD) in the CC field.

Office hours

See the website of Marianna Marcella Bolognesi