Introducing Cognitive Neuroscience
This chapter begins by placing a number of philosophical and scientific approaches to the mind and brain in a historical perspective. Cognitive neuroscience is a bridging discipline between cognitive science and cognitive psychology. The term cognition collectively refers to a variety of higher mental processes such as thinking, perceiving, imagining, speaking, acting, and planning. The distinction between recording methods and stimulation methods is crucial in cognitive neuroscience. Another distinction that has been used to contrast cognitive psychology and cognitive neuroscience is that between software and hardware, respectively. Different regions of the brain are specialized for different functions. The modern foundations of cognitive psychology lie in the computer metaphor of the brain and the information-processing approach, popular from the 1950s onwards. The chapter concludes with contemporary methodological concerns around reproducibility and replication in cognitive neuroscience.
Multiple Choice Questions
Flashcards
A variety of higher mental processes such as thinking, perceiving, imagining, speaking, acting, and planning
Aims to explain cognitive processes in terms of brain-based mechanisms
The problem of how a physical substance (the brain) can give rise to our sensations, thoughts, and emotions (our mind)
The belief that mind and brain are made up of different kinds of substance
The belief that mind and brain are two levels of description of the same thing
The belief that mind-based concepts will eventually be replaced by neuroscientific concepts
The failed idea that individual differences in cognition can be mapped on to differences in skull shape
Different regions of the brain are specialized for different functions
Functional specialization
The study of brain-damaged patients to inform theories of normal cognition
Cognitive neuropsychology
An approach in which behavior is described in terms of a sequence of cognitive stages
Later stages of processing can begin before earlier stages are complete
The influence of later stages on the processing of earlier ones (e.g. memory influences on perception)
The passage of information from simpler (e.g. edges) to more complex (e.g. objects)
Different information is processed at the same time (i.e. in parallel)
The notion that certain cognitive processes (or regions of the brain) are restricted in the type of information they process
The idea that a cognitive process (or brain region) is dedicated solely to one particular type of information (e.g. colors, faces, words)
Computational models in which information processing occurs using many interconnected nodes
The basic units of neural network models that are activated in response to activity in other parts of the network
The accuracy with which one can measure when an event (e.g. a physiological change) occurs
The accuracy with which one can measure where an event (e.g. a physiological change) is occurring
A comprehensive map of neural connections in the brain that may be thought of as its "wiring diagram"
A mathematical technique for computing the pattern of connectivity (or " wiring diagram" ) from a set of correlations
A neural network model containing multiple layers, typically producing a simple-to-complex hierarchy of information processing.
Systemic difficulties in being able to independently reproduce published results that have been documented in many scientific fields
Performing multiple analyses of the same dataset across all reasonable options for excluding, transforming, and coding data.
Hypothesizing after the results are known.
An open set of hypotheses and analysis plan, that is posted prior to conducting the analysis.
Peer-reviewed scientific paper in which hypotheses, methods and analysis are reviewed prior to data collection.
Analysing the data in multiple ways and chosen to publish a single favorable analysis.
The tendency for non-significant results to be unpublished.
A statistical method for determining a required sample size given a likely effect size (whether a variable is strong or weak) and the probability of detecting it (due to sample variability).
A statistical method for pooling across equivalent datasets (based on a weighted average of effect sizes).
Ability to examine and validate an existing set of analyses
