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Types of Selection

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...

The Representativeness Heuristic

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

Schemas

Schemas

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

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Related Experiment Video

Updated: Apr 22, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

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The hierarchical sparse selection model of visual crowding.

Wesley Chaney¹, Jason Fischer², David Whitney³

¹Vision Science Graduate Group, University of California, Berkeley Berkeley, CA, USA.

Frontiers in Integrative Neuroscience

|October 14, 2014

Summary

Visual crowding limits object recognition in cluttered scenes. A new hierarchical sparse selection (HSS) model explains this by impoverished sampling, not degraded representations, unifying diverse crowding findings.

Keywords:

attention coarse coding ensemble coding neural network perception summary statistics visual attention

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Visual Perception

Background:

Objects in cluttered environments require attentional selection.
Visual crowding impairs object identification in peripheral vision when surrounded by other objects.

Purpose of the Study:

To propose a new model of visual crowding that accounts for object-level crowding.
To explain how crowding operates at multiple visual processing levels.
To challenge existing theories by suggesting impoverished sampling rather than degraded representations.

Main Methods:

Development of the hierarchical sparse selection (HSS) model.
Analysis of existing visual crowding literature.
Formulation of testable predictions for crowded scene information access.

Main Results:

The HSS model explains object-level crowding and puzzling findings in recent literature.
Crowding can occur at high-level, configural object representations.
Crowded objects can still inform "gist" judgments even when unrecognizable.

Conclusions:

Visual crowding arises from impoverished sampling of visual representations for perception, not degraded neural representations.
The HSS model unifies disparate visual crowding findings.
The HSS model offers testable predictions for understanding information access in cluttered visual scenes.