Epistemic Curiosity and its Role in Knowledge Seeking and Problem Solving

Jordan LitmanInstitute for Human and Machine Cognition, USA

ihmc Ocala Afternoon Lecture Series, 2013

Cognitive Science:A Field That Emphasizes Understanding

Information Processing

The study of cognition has primarily focused on studying general systems involved in how information is processed (e.g., engage in cognitive work like thinking, memory, reasoning, problem solving).

This approach has typically emphasized trying to understand the factors that lead individuals to arrive at either accurate or erroneous cognitive outcomes (i.e., performance concerns).

Common lines of inquiry include focusing on biases in reasoning and errors in thinking (e.g., heuristics, memory distortions) or the role of expertise (e.g., past experience, skill level).

While lines of research guided by this approach have been fruitful, they treat cognition as a sequence of “mechanistic” events (input, series of mental processes, output).

Consequently, cognitive science has tended to emphasize “what” or “how” type questions while paying less attention to questions about “why” and “when”.

Cognitive Science: A Field That Emphasizes Understanding

Information Processing

“Why” and “When” types of questions about cognition orient cognitive scientists differently…

Such questions lead us to examine cognition in terms of the underlying factors that differentially energize or direct (motivate) cognitive activity.

Such questions also lead us to inquire about individual differences (i.e., personality characteristics, varying tendencies) to experience or express these underlying motives.

A consideration of individual differences in factors that guide cognitive processing may be critical to fully understanding why and when individuals approach thinking and reasoning in the “real world.”

This begs the question: Which underlying factors (individual differences, motives) in particular are most relevant to the study of cognition?

Presumably these factors would correspond to individual differences in motives to seek out and make use of knowledge…

The Nature and Measurement of Epistemic Curiosity (EC)

Epistemic Curiosity (EC): An innate motive that underlies thinking, reasoning, inquisitiveness, seeking out new knowledge, and solving intellectual problems. EC reflects individual differences in approaching and using information in order to either...

Stimulate affectively positive states of intellectual interest (I-type)

or

Relieve negative affective states of feeling deprived of knowledge (D-type).

The Nature of Individual Differences in Interest (I) and Deprivation (D) types of

Epistemic Curiosity (EC) The I/D EC Distinction– related but distinct emotional-motivational

aspects of trait curiosity that guide cognitive processes in different ways…

I-type EC D-type EC

.

.

When individuals have little or no prior knowledge (generate information by novelty seeking, brainstorming).

When individuals have some prior knowledge or feel close to solving a problem (persistent, determined to obtain or arrive at precise and correct answers).

Qualitatively Less intense, “learning is fun” Weaker, but pure positive affect.

Qualitatively uncomfortably intense “need to know”. Stronger, but involves some initial negative affect.

To enjoy a new discovery. To accurately solve problems, complete knowledge-sets, and improve the understanding of something in particular.

The anticipated enjoyment of thinking about new ideas (the lukewarm, “Oh, that’s neat” reaction).

The anticipated relief from dispelling an unknown (the intense “Ah-ha!” moment).

Optimally Activated

Subjective Experience

Learning Goals

Expected Reward

The Measurement of Individual Differences

in Epistemic Curiosity (EC)

The EC traits are assessed by two, brief 5-item self-report scales (Alpha >.80).

Excellent simple structure in EFA’s; excellent model fit in CFA’s.

Validated in large samples students and workers in America, Germany, and China (language translations) suggesting cross-cultural stability of the model.

Recently, 2-factor I/D model also found valid for teacher and parent-reports of young children (aged 3-7) and for self-reports of adolescents across cultures as well.

Sample ItemsI-type: I enjoy exploring new ideas

When I learn something new, I would like to find out more about it

D-type: I can spend hours on a single problem because I just can’t rest without knowing the answer.

I work like a fiend at problems that I feel must be solved.

Diagram of the I/D EC Model (from Litman, 2008)

The I/D Distinction in EC – correlated but meaningfully different traits

The 10- item Epistemic Curiosity measurecomprising 5-item I-type and D-type EC scales

Key Findings: Correlates of the I-type and D-type EC Scales in Empirical Studies

Both the I- and D-type EC scales correlate with other measures of intellectual curiosity (e.g. Need For Cognition) showing convergence, while having weak relations to measures of sensation or thrill seeking, demonstrating divergence.

Importantly, consistent with the hypothesized I/D distinction, each scale corresponds to very different correlates and behaviors in laboratory and applied settings…

I- type EC (Fun, Relaxed, Positive, Novelty Oriented)

D-type EC (Intense & Persistent, Problem-Solving Oriented)

Laboratory Studies

Academic and Workplace Settings

Positively correlated with Desire for Novelty, Ambiguity Tolerance, Openness, Agreeableness and Extraversion; negatively correlated with Neuroticism and Depression.

Associated with lower levels of state curiosity and predicts less actual information-seeking behavior.

Associated with “Don’t Know” metacognitions and predicts poorer actual recall performance (no prior knowledge).

Positively correlated with Intellectual Absorption, Conscientiousness, Neuroticism, Anger; negatively correlated with Agreeableness and Ambiguity Tolerance

Associated with higher levels of state-curiosity and predicts more actual information-seeking behavior.

Associated with “TOT” and “FOK” metacognitions and predicts better actual recall performance (incomplete prior knowledge).

Predicts adopting Mastery oriented learning goals (learning for personal fun) in college students.

On the job, predicts searching for optimal challenges, placing importance on learning new ideas and on how much fun working with new ideas will be.

Predicts developing study motives aimed at intrinsic interest and personal satisfaction in Med Students.

Also correlated with Mastery goals but stronger predictor of adopting Performance and Failure Avoidant goals.

Predicts being concerned with how understandable new information will be and how easily it can be applied to solving problems on the job.

Predicts using study strategies aimed at expending time and effort to fully understand material in Med students.

New Research at IHMC – The Role of I-type and D-type EC in Reasoning about the

Causes of Complex Real World Events

IHMC Senior Scientist, Dr. Robert Hoffman has been examining how both experts at reasoning (e.g., social scientists, economists, intelligence analysts) and novices approach and attempt to understand the causes of complex real world events.

“Why did the stock market crash?”

“Why did the favored team lose the big game?”

“Why did the military invasion fail to achieve its end-goals in the region?

Reasoning about the causes of such events is especially complex because they typically have multiple causes, the scope of the problem (and thus the solution sought) may change over time, and they are, in general, rooted in human behavior. Therefore causation is always indeterminate.

Consequently, traditional cognitive reasoning processes (e,g, deductive or inductive, step model) fall short in trying to make sense of problems of indeterminate casuation.

New Research at IHMC – The Role of I-type and D-type EC in Reasoning about the

Causes of Indeterminate Real World Events

Dr. Hoffman’s work has focused on observing and empirically classifying the different kinds of processes experts vs. novice reasoners engage in when faced with problems of indeterminate causation, and has identified a number of different kinds of Causal Attributions reasoners make…

Examples of Kinds of Causal Attributions Specific Examples

Counter-cause: A cause that might have made the to-be-explained effect not occur .

Abstraction: A generalization over contributing causes.

“The SEC got multiple warnings but failed to uncover Madoff’s activities.”

“The Patriots won the Super Bowl because their receivers kept catching Manning’s last-ditch passes.”

List: Multiple causes led to an effect, in this case, the Industrial Revolution in Britain.

"Steam engines had been designed to pump water, watchmakers provided high-quality gears... coal was mined in greater quantities...”

What kind of solution-strategy will best suit a problem will vary depending on the problem.

However, the underlying factors that predict whether a reasoner will apply any given method is unclear, and varies considerably from person to person.

Swarm: Multiple causes all converge on a single effect.

"Managers miscalculated risk….created perverse incentives, which in turn brought the global financial system down.".

Onion: A cause is itself unpacked into a cause-effect relation

"The industrial revolution occurred in Britain because it was profitable. But why did Britain have such cheap energy in the first place?

This is where a consideration of individual differences in I-type and D-type EC come in to play!

New Research at IHMC – The Role of I-type and D-type EC in Reasoning about the

Causes of Indeterminate Real World Events

We will ask experts and non-experts to reason about indeterminate causation for complex real-world events. Participants will read actual news reports and will underline statements that they identify as causal assertions about why something happened.

Additionally, participants will be instructed to generate their own causal attributions based on inferences they may develop, which are not explicit statements in the articles, which will be content analyzed.

New Research at IHMC – The Role of I-type and D-type EC in Reasoning about the Causes

of Indeterminate Real World Events

Major Hypotheses I-type EC D-type EC

Strength Of Motive To Engage In Cognitive Work

Preferred Method Of Problem Solving

Lower

Tolerance Of Uncertainty

Hypothetical Reasoning

Information Search Strategy

Higher

Value Placed On Information That Builds On What is Known

Broader in scope

Higher

Rigorous evaluation

Lower

Narrower in scope

Value Placed On Information That Leads To New Ideas

Kinds of causal questions asked

Major Aims of This Research

To reliably predict and explain use of problem solving strategies: Why and when do reasoners lean towards innovation (value new ideas) vs. engaging in narrow and specific searches for answers (value data based on expected fit).

Accordingly, we have a number of key hypotheses we will examine in this study, building on I/D EC theory…

Lesser Greater

Greater Lesser

Broader in scope Narrower in scope

New Research at IHMC – The Role of I-type and D-type EC in Reasoning about the

Causes of Complex Real World Events

Secondary Aims

To examine and understand interactions between expertise and I- and D-type EC: This may be critically important to success or failure in adopting a reasoning strategy that best suits a given problem.

To examine consequences when problem vs. EC types match or mismatch: Innovative solutions needed vs. focused problems solving.

Additionally, learning how people approach problems of indeterminate causation in real world settings should guide us to develop methods of training individuals to become better reasoners by applying their I- and D-type EC tendencies more effectively!

This is the ultimate goal of our work – to predict reasoners’ tendencies, but also to train and improve the ways in which experts and novices make sense of these sorts of tough real-world problems!

Whether broadly interested and wish to explore further…

or

Quite intrigued with specific questions you need answered…

Want more information about the I/D Model of EC and/or Reasoning about Indeterminate

Causation?

Please email drjlitman@gmail.com for reprints, details, etc.

Thank you for your time!