Psychology and Artificial Intelligence (PSY 104)

These flashcards cover the definition of AI, its subfields (Machine Learning, Deep Learning), the relationship between cognitive psychology and AI, mental health applications, research studies, and ethical considerations.

What is the definition of Artificial Intelligence (AI) in the context of cognitive functions?

The broad field of creating computer systems that can perform tasks normally requiring human intelligence, such as reasoning, problem-solving, understanding language, perception, and decision-making by simulating aspects of human cognitive functions.

How is Machine Learning (ML) distinguished from standard AI?

It is a subset of AI that enables systems to learn from data and improve performance over time without being explicitly programmed, using algorithms to identify patterns for predictions.

What are the common approaches used in Machine Learning?

Supervised, unsupervised, and reinforcement learning.

What is Deep Learning and what is it particularly effective at?

A specialized subfield of Machine Learning using artificial neural networks with multiple layers; it is effective in image recognition, speech processing, and natural language understanding.

How does technology influence human behavior according to the lecture?

It shapes how people think, communicate, and form relationships, influencing attention, identity development, and emotional experiences.

What historical shift occurred during the cognitive revolution of the 1950s–1960s?

A shift from behaviorism to studying internal mental processes, such as memory, attention, perception, and problem-solving, which laid the groundwork for AI.

How were early AI models like symbolic AI and rule-based systems inspired by psychology?

They were inspired by cognitive psychology, assuming intelligence could be replicated by formal rules, logical operations, and step-by-step reasoning simulations.

How does the 'attention' mechanism in AI models differ from human attention?

AI attention is purely mathematical and pattern-based (focusing on relevant words in a sequence), while human attention is influenced by emotions, motivation, and context.

In what form is 'memory' represented in AI systems?

As stored data, parameters, or learned patterns (such as adjusted weights in neural networks), though it lacks conscious experience or episodic memory.

What is the difference between human and AI decision-making?

Humans use reasoning, emotions, and past experiences (sometimes irrationally), while AI uses algorithms, probabilities, and optimization based solely on training data.

How can AI be used in automated psychological assessment?

By analyzing text, speech patterns, questionnaire responses, or behavioral data (like sleep and activity levels) to detect indicators of depression or anxiety.

What did the research by Walsh et al. (2017) demonstrate regarding suicide risk prediction?

That AI models could predict suicide attempts more accurately than clinicians in some cases by identifying complex patterns in large datasets.

What are the primary ethical concerns regarding AI risk prediction in psychology?

False positives (causing stigma or unnecessary stress), false negatives (fatal consequences), privacy, and informed consent.

What are two examples of AI-based chatbots used for emotional support mentioned in the lecture?

Woebot and Wysa.

What were the findings of Fitzpatrick et al. (2017) regarding Woebot?

Users showed reduced depressive symptoms after short use of the CBT-based chatbot.

What is anthropomorphism in the context of human–AI interaction?

Attributing human-like qualities—such as emotions, intentions, or personality—to non-human systems like AI.

Why is AI empathy characterized as 'simulated empathy'?

Because AI does not actually feel emotions; it follows learned patterns to produce appropriate responses (like "I understand how you feel") to create an illusion of empathy.

What is algorithmic bias and how does it occur?

Systematic errors in AI systems leading to unfair outcomes, occurring when training data is incomplete or reflects existing social inequalities like gender or race.

What is automation bias in clinical settings?

The tendency of clinicians to over-rely on automated systems and trust AI recommendations without critically evaluating them, which can reduce professional judgment.