Which of the following top-down effects on recognition cannot be explained by feature nets alone?

• A. The word superiority effect
• B. The detection of simple visual features such as lines and curves
• C. The influence of context on ambiguous letter recognition
• D. The impact of expectations based on prior knowledge

Answer: (D)

Verified step by step guidance

1. Understand what feature nets are: Feature nets are models of visual recognition that process stimuli by detecting simple features (like lines and curves) and combining them to recognize letters and words. They operate primarily in a bottom-up manner, starting from basic features to build up to complex patterns.
2. Identify the top-down effects listed: The word superiority effect, detection of simple visual features, influence of context on ambiguous letter recognition, and the impact of expectations based on prior knowledge.
3. Analyze which effects can be explained by feature nets: Feature nets can explain the detection of simple visual features because they are designed to detect lines and curves. They can also explain the word superiority effect to some extent, as recognizing words can facilitate letter recognition through feedback loops within the network.
4. Consider the influence of context on ambiguous letter recognition: While feature nets can incorporate some contextual information through feedback, this effect often requires higher-level cognitive processes beyond simple feature detection, involving semantic or syntactic context.
5. Recognize that the impact of expectations based on prior knowledge involves top-down processing that goes beyond feature nets, as it requires the integration of prior knowledge, beliefs, or expectations that influence perception, which feature nets alone cannot fully account for.