Plan docente

Attention and Prediction in Cognitive Neuroscience

Index

General Information

Estimated learning time

Recommendations

Competences / Learning outcomes

Learning objectives

Teaching blocks

Teaching methods and general organization

Official assessment of learning outcomes

Reading and study resources

General Information

Course unit name

Attention and Prediction in Cognitive Neuroscience

Course unit code

573616

Academic year

2024/2025

Coordinator

Iria San Miguel Insua

Department

Department of Clinical Psychology and Psychobiology

Credits

2.5

Single program

Estimated learning time

Total number of hours : 62.5 Hours

Activities	Type of training	Hours
Face-to-face and/or online activities		25
- Lecture with practical component	Face-to-face	7.5
- Group tutorial	Face-to-face	7
- Student presentation and discussion	Face-to-face	10.5
Supervised project		20
Independent learning		17.5

Recommendations

Students are encouraged to bring a laptop or tablet to class to work on their group assignments.

Competences / Learning outcomes to be gained during study

BASIC
Knowledge forming the basis of original thinking in the development or application of ideas, typically in a research context.

Capacity to apply the acquired knowledge to problem-solving in new or relatively unknown environments within broader (or multidisciplinary) contexts related to the field of study.

Capacity to integrate knowledge and tackle the complexity of formulating judgments based on incomplete or limited information, taking due consideration of the social and ethical responsibilities involved in applying knowledge and making judgments.

Capacity to communicate conclusions, judgments and the grounds on which they have been reached to specialist and non-specialist audiences in a clear and unambiguous manner.

Skills to enable lifelong self-directed and independent learning.

GENERAL

Capacity to speak in public.

Interpersonal skills and capacity to build professional relationships.

Capacity to use information and communication technologies for different purposes (communicating with other professionals, acquiring information, disseminating knowledge, etc.).

SPECIFIC

Capacity to identify and apply different data analysis techniques in behaviour and cognition.

Skills in evaluating scientific research in behaviour and cognition.

Capacity to use style guides to write scientific papers and books on different subjects related to research in behaviour and cognition, particularly APA Style, and know the steps required to publish such papers or books.

Learning objectives

Referring to knowledge

Prediction and attention are theoretical constructs with a long tradition in psychology and biology. Both have the joint function of aiding perception, yet they are rarely considered together and, in separate research fields, these two constructs have often been operationalised using very similar empirical manipulations.

Despite there being ample consensus that attention is one of the main cognitive functions of the human brain, its mere conceptual definition has proven elusive. Nevertheless, decades of research have yielded a solid body of evidence demonstrating clear behavioural and neurophysiological effects of what are traditionally considered attentional manipulations.

Similarly, a plethora of empirical findings have proven that the brain makes use of prior information to guide and modulate the processing of upcoming information. Therefore, prediction is regarded as a pervasive property of neural function and is placed at the centre of current forefront theoretical approaches to the understanding of brain function.

In recent years, partly due to the popularisation of predictive coding models of brain function, the clash between these two research lines has been brought to the spotlight. Interest has risen in arriving to a consensual definition of attention and prediction and understanding their interrelation. Questions such as whether these two constructs are the same, and, if not, whether and how they interact, are now hot topics in cognitive neuroscience.

In this course, an overview of the topics of attention and prediction and their interaction is provided, emphasizing the most recent empirical and theoretical advances in the field and attempting a reinterpretation of classic theories and findings from a modern cognitive neuroscience perspective.

Learning objectives

Referring to knowledge

Teaching blocks

1 Prediction

2 Attention

3 Prediction and attention mix-up

Teaching methods and general organization

The contents of the course are presented through a combination of theoretical lectures by the teacher, journal club presentations from the students on scientific articles related to the course contents, and exercises in small groups. The timetable for the activities and required readings and the deadlines for the submission of exercises is published on the Virtual Campus.

Official assessment of learning outcomes

Assessment of the learning outcomes is based on the following three activities:

Journal club presentation (30% of the final grade, compulsory): each student gives a short individual presentation on a research article. Students receive feedback from the lecturer and the other students to improve their presentation. The improved version must be submitted as a video and is graded by the teacher following the assessment rubric shared via the Virtual Campus.

Group work in class (10% of the final grade, compulsory): during class sessions, students work in small groups on solving small exercises and performing peer assessment of the journal club presentations.

Final exam (60% of the final grade, compulsory): individual written examination with open-answer questions covering the contents of the fundamental bibliography, the lectures, and the journal club presentations and discussions. It takes place on the last session of the course.

A minimum final grade of 5 is required to pass the subject. A minimum mark of 5 (out of 10) in the final exam is required so that the other marks may be incorporated in calculating the final grade. Participation is required in at least 80% of the group activities to pass the subject.

Students who fail the final exam are eligible to repeat assessment through another examination with the same characteristics, worth a maximum of 60% of the final grade. The marks obtained in the journal club presentation and in the group work are kept, and the weighted sum is added to the mark obtained in the repeat assessment exam to calculate the final grade. Students with a grade of Absent are not eligible for repeat assessment.

Final grades are rated as follows: Absent, Pass (>= 5), Merit (>= 7.5), Excellent (>= 9). The Excellent with honours distinction is assigned to 5% of students who obtain the highest grades, as long as they have obtained a final grade of Excellent.

Reading and study resources

Article

1. Prediction
Bendixen, A., SanMiguel, I. & Schröger, E. (2012). Early electrophysiological indicators for predictive processing in audition: a review. International Journal of Psychophysiology, 83, 120-131.

Heilbron, M., & Chait, M. (2017). Great expectations: Is there evidence for predictive coding in auditory cortex? Neuroscience.

2. Attention

Fries, P. (2005). A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences, 9, 474-480.

Schroeder, C. E., Wilson, D. A., Radman, T., Scharfman, H., & Lakatos, P. (2010). Dynamics of Active Sensing and perceptual selection. Current Opinion in Neurobiology, 20(2), 172–6.

Meehan TP, Bressler SL (2012). Neurocognitive networks: findings, models, and theory. Neuroscience and Biobehavioral Reviews 36(10):2232-47.

3. Prediction and attention mixup

Schröger, E., Marzecová, A., & SanMiguel, I. (2015). Attention and prediction in human audition: a lesson from cognitive psychophysiology. The European Journal of Neuroscience, 41(5), 641–64.

Summerfield, C., Egner, T. (2009). Expectation (and attention) in visual cognition. Trends in Cognitive Sciences, 13(9), 403–9.

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