Heuristics and Controlled Problem Solving

1. Heuristics and Controlled Problem Solving “Forging the Link”

2. Forging the Link • How Motivation to Control interfaces with the implicit and explicit cognitive processes that guide the organisms behavioral engagement of the ecology. • How implicit cognitive processes are linked to Heuristics • How explicit cognitive processes are linked to Controlled Problem Solving

3. MOTIVATION TO CONTROL • The evolved function of the brain, cognitive, affective, and other systems is to attend to, process, and respond to the types of information that co-varied with survival or reproductive outcomes during the species evolutionary history. • Combinations of these systems as they are linked to behavioral engagement of the environment represent a functional model. • Purpose of evolved behaviors is to recreate outcomes that facilitated survival or reproduction

4. Implicit cognitive process which are features of the modular systems that guided behavioral engagement of the environment . Explicit cognitive processes that support problem solving about potentially important outcomes or relations in the environment.

5. Cost Benefit Trade-Offs • Most basic trade off involves balancing the speed of behaviorally responding to the ecology against the accuracy of identifying ecological information • These brain and cognitive systems are likely to evolve to capture information that has been reliably associated with survival or reproductive outcomes in evolutionary history.

6. Variance- Invariance Evolved Cognitive Mechanisms he Heuristics: Fast, Frugal, simple, and implicit mechanisms The evolution of implicit and explicit cognitive systems can be tied to directly to evolutionarily significant information patterns that have tended toward the invariant and variant ends of the continuum Controlled Problem -Solving: Slow, effortful, complex, and explicit/conscious mechanisms Variant Invariant Information Patterns

7. Bounded Rationality • Simon(1955,1956) argued that behaviors, cognitions, and brain functions are fully understandable only when placed in the type of ecological context that drove their evolution, • Relation between exoskeleton systems and information in the ecology • Timberlake (1994) argues the relation can be understood in terms of Pavlovian conditioning. • There is a relation between the here-and-now mechanisms that guide behavioral decision making and the ecologies in which these mechanism have evolved.

8. Satisficing and Aspiration level • Cognitive mechanisms that support decision making and behavioral engagement are not considered to be optimal • Combined cost of limited ecological information, limited cognitive resources and delayed behavioral responding to the ecology make the evolution of the brain and cognitive systems that optimize unlikely • Evolution favors Satisficing and Aspiration level • Satisficing- motivational and behavioral systems will be directed toward achieving an ecological goal that satisfies a basic need. Results in outcome that is “good enough” • Aspiration level- organism my continually adjust the definition of what is “good enough” on the basis of most immediate success and failure Megalomania

9. Mate Choice • Pairs of Barnacle Geese bond for many breeding seasons and choose mates according to size and age • Creates conditions that will favor the evolution of brain, cognitive, and behavioral mechanisms that will guide mate choice decisions • Black et al. (1996) found that individuals that sampled potential mates and switched mates if a marginally better one was found had 50% reduction in the probability of successfully breeding during the season. • Cost of mate switching and comparing potential mates on one or more dimensions place serious limits on the potential for the evolution of socially and cognitively sophisticated optimizing mechanisms Barnacle Geese

10. Heuristics • The combination of mechanisms that enable information to be identified and processed quickly and that enable fast and frugal decision making • Decision making rules of thumb • Tversky and Kahneman et. (1974,19820) • Availability heuristics- people tend to judge things or make inferences about the likely hood of a situation occurring based on how easily they can recall the occurrence from long term memory • They often lead to near optimal decision making in many real world situations, that is, the type of situations in which these decision making mechanisms likely evolved. • When a bounded rationality match between internal systems and ecological conditions is achieved heuristic-based decisions and behavioral responses are executed.

11. Complex Social Dynamics • Comides(1989) - Humans have evolved heuristics that guide reciprocal social exchanges. • Brosnan and de Waal(2003)- demonstrated that capuchin monkeys have a sense of fair play • Comides, Tooby, and Knight (2002)-areas of the Prefrontal neocortex (theory of the mind) and the amygdala (affect-eliciting social information) are the neural systems behind our “fairness” heuristic Dorsolateral prefrontal cortex Anterior cingulate Cortex

12. Controlled Problem Solving • Operators- rules that define how people can change the initial state into the successive intermediate states that ultimately lead to the desired goal. • Problem Space- defined by state representations • Goal of problem solving is to change the initial state to the desired end state • Effortful processing that is most evident in conditions that deviate from expectations • Variant, explicit in nature, abstract • Knowledge lean domains –little prior knowledge or exposure to problem or situation

13. Knowledge –Lean Domains Initial State Newell and Simon (1972) discovered that common approach to solving similar problems involves a means-end analysis 1.Current state compared to desired end state 2. Memory is searched to identify operations that can be used to reduce the difference 3. Operation chosen, executed and resulting state compared to desired state End State Container 3 3 ounces Container 2 5 ounces Container 1 8 ounces

14. Means-End Analysis With experience solving a particular type of problem , people can develop heuristics that simplify movement through problem space. These learned heuristics are formed during a individuals life time

15. Knowledge-Rich Domains Goal Definition Assumptions Constrain Problem Space Most problem solving in the real world occurs for domains in which individuals have varying degrees of experience knowledge Ill structured Influenced by nature and extent of the individual’s declarative knowledge Schemata –memory systems of linked operations and the sequence in which they were executed in previous problem-solving. Goal Relevant Knowledge Legal Operators Schemata Problem Representation and Transformation General Problem Solving Strategies Reasoning: Analogy, Induction, Deduction

16. Reasoning and Mental Models • Ability to engage in rational analysis and reason through difficult problems captures aspects of general intelligence. • Have the ability to mentally generate, maintain and manipulate abstract representations but it is limited by limitations in attentional and working memory resources. • Mental models - pattern of representations used as an analog simulation of a specific situation or state of affairs in the world. • Constructed through language or images. • Proposition- the important language component , most basic unit of meaning. • Images- can be vivid and perceptual-like or composed of nonvisual spatial representations.

17. Inhibition • Most people’s decision making and inference drawing often results from bounded rationality and heurists as well as acquired tasks-relevant knowledge • John- Laird (1983) argued that in most situations , people’s reasoning is based not on the formal rules of logic, but rather on the construction of a mental model of the information presented. • Most humans have the crucial ability to inhibit heuristic –based responses and draw inferences on the basis of explicit processes • The ability to modify heuristics-based processes is most likely to evolve in situations in which two species are in competition or with intense social competition. Syllogism All people taller than 6ft 5in are basketball players Joe is 6ft 7inch tall Therefore, Joe is a basketball player What conclusion would you draw?