Perceptual Learning - A Computational Perspective
Perceptual learning: a
computational perspective
CS786
3
rd
March 2022
Course logistics
•
I have extended the Assignment 2 deadline to
Mar 7 11:59:59PM
–
Please don’t ask me to extend it further
•
Many people have still not sent in their
research paper topics
–
Please do so immediately
–
I will discuss topics shared already with me in our
first in-person discussion hour, scheduled for
tomorrow at 1830 in CGS classroom
Absolute Thresholds
Psychometric function demonstrating the probabilistic (statistical)
nature of the threshold
Perception as inference
Want to know
θ
, get to see
φ
Can perceptual ability improve?
•
Classic psychophysics imagined perception to
be governed by absolute limits
•
Signal detection theory was developed to
measure these limits
•
Visual perception research shows
–
Perception strongly influenced by prior knowledge
–
Question: how does this influence work?
•
Evidence for perceptual learning presents
some hypotheses
Perceptual learning
•
Perceptual discrimination improves with
training
(Guggenmos, Wilbertz, Hebart & Sterzer, 2016)
Practice makes perfect
•
https://www.youtube.com/watch?v=Qzhs1Z8
Rwnk
Hypotheses
•
Attentional weighting
–
Observers learn to attend to discriminative features of
stimuli
•
Stimulus imprinting
–
Detectors developed that are specialized for stimuli
•
Differentiation
–
Perceptual adaptation by the development of
increasingly differentiated object representations
•
Unitization
–
Development of sensory units that are triggered when
a complex configuration occurs
Hypotheses
•
Attentional weighting
–
Observers learn to attend to discriminative features of
stimuli
•
Stimulus imprinting
–
Detectors developed that are specialized for stimuli
•
Differentiation
–
Perceptual adaptation by the development of
increasingly differentiated object representations
•
Unitization
–
Development of sensory units that are triggered when
a complex configuration occurs
(Gold & Ding, 2013)
Perceptual learning as improved
decision-making
The dataset
•
Recorded spiking
response of neurons in
monkey LIP cortex
•
Neurons responsive to
different motion
directions
•
Measured behavioral
and neural data across
multiple sessions (e.g.
165 sessions over 645
days for monkey C)
Lapse rate
Perceptual learning is a fascinating topic exploring how our perceptions improve with training. This computational perspective delves into the probabilistic nature of perception, inference, and the influence of prior knowledge. Hypotheses suggest attentional weighting, stimulus imprinting, perceptual adaptation, and unitization play crucial roles in perceptual improvement. Research in visual perception shows that discriminative features, detectors specialized for stimuli, and development of differentiated object representations contribute to enhancing perceptual discrimination. Practice indeed makes perfect in this field, as evidenced by perceptual abilities improving with training.
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Presentation Transcript
Perceptual learning: a computational perspective CS786 3rdMarch 2022
Course logistics I have extended the Assignment 2 deadline to Mar 7 11:59:59PM Please don t ask me to extend it further Many people have still not sent in their research paper topics Please do so immediately I will discuss topics shared already with me in our first in-person discussion hour, scheduled for tomorrow at 1830 in CGS classroom
Absolute Thresholds Psychometric function demonstrating the probabilistic (statistical) nature of the threshold
Perception as inference Want to know , get to see
Can perceptual ability improve? Classic psychophysics imagined perception to be governed by absolute limits Signal detection theory was developed to measure these limits Visual perception research shows Perception strongly influenced by prior knowledge Question: how does this influence work? Evidence for perceptual learning presents some hypotheses
Perceptual learning Perceptual discrimination improves with training (Guggenmos, Wilbertz, Hebart & Sterzer, 2016)
Practice makes perfect https://www.youtube.com/watch?v=Qzhs1Z8 Rwnk
Hypotheses Attentional weighting Observers learn to attend to discriminative features of stimuli Stimulus imprinting Detectors developed that are specialized for stimuli Differentiation Perceptual adaptation by the development of increasingly differentiated object representations Unitization Development of sensory units that are triggered when a complex configuration occurs
Hypotheses Attentional weighting Observers learn to attend to discriminative features of stimuli Stimulus imprinting Detectors developed that are specialized for stimuli Differentiation Perceptual adaptation by the development of increasingly differentiated object representations Unitization Development of sensory units that are triggered when a complex configuration occurs
Perceptual learning as improved decision-making (Gold & Ding, 2013)
The dataset Recorded spiking response of neurons in monkey LIP cortex Neurons responsive to different motion directions Measured behavioral and neural data across multiple sessions (e.g. 165 sessions over 645 days for monkey C) Lapse rate
