Act first, think later: The presence and absence of inferential planning in problem solving

Abstract

Planning is fundamental to successful problem solving, yet individuals sometimes fail to plan even one step ahead when it lies within their competence to do so. In this article, we report two experiments in which we explored variants of a ball-weighing puzzle, a problem that has only two steps, yet nonetheless yields performance consistent with a failure to plan. The results fit a computational model in which a solver's attempts are determined by two heuristics: maximization of the apparent progress made toward the problem goal and minimization of the problem space in which attempts are sought. The effectiveness of these heuristics was determined by lookahead, defined operationally as the number of steps evaluated in a planned move. Where move outcomes cannot be visualized but must be inferred, planning is constrained to the point where some individuals apply zero lookahead, which with n-ball problems yields seemingly irrational unequal weighs. Applying general-purpose heuristics with or without lookahead accounts for a range of rational and irrational phenomena found with insight and noninsight problems.

A Self‐Report Measure of Productive Thinking in Solving Insight Problems

“…For example, Representational Change Theory (Kn€ oblich, Ohlsson, Haider, & Rhenius, 1999) proposes several mechanisms by which a problem-solver may restructure an initial faulty representation and thereby break an impasse and make progress toward a solution. The Criterion of Satisfactory Progress Theory (MacGregor, Ormerod, & Chronicle, 2001;Ormerod, MacGregor, Chronicle, Dewald, & Chu, 2013) has focused on conditions that create and sustain perseveration in a faulty problem representation, thereby preventing or delaying the restructuring required to reach a solution.…”

When distraction helps: Evidence that concurrent articulation and irrelevant speech can facilitate insight problem solving

“…A good example of a business-as-usual approach is the "progress-monitoring theory" developed by MacGregor, Ormerod, and Chronicle (2001;see also Chronicle, Ormerod, & MacGregor, 2004;Ormerod, MacGregor, Chronicle, Dewald, & Chu, 2013), which proposes that solvers aim to minimize the difference between their current "problem state" and the "goal state" regardless of whether a problem is an insight or non-insight task. Accordingly, insight is simply the realisation that a goal state is not achievable with the remaining available moves such that new moves must be devised to attain the solution.…”

Enabling spontaneous analogy through heuristic change

“…Previously we proposed a theory of insight to explain why initial misrepresentations occur and how restructuring may be initiated via heuristic change (MacGregor, Ormerod & Chronicle, 2001;Ormerod, MacGregor, & Chronicle, 2002;Ormerod, MacGregor, Chronicle, Dewald & Chu, 2013). The theory proposed two heuristics that guide the selection of moves in problem solving, move maximization and search minimization, and a mechanism for evaluating candidate moves, progress monitoring.…”

“…While the move maximization heuristic helps to explain why the nine-dot problem is so difficult, people do occasionally solve it (Weisberg & Alba, 1981;MacGregor et al, 2001). CSPT explains these occasional successes through search minimization, a second heuristic that operates to create and change the mental representation of a problem (Ormerod et al, 2013). Under search minimization, individuals limit the initial representation and subsequent expansion of the problem space to the minimum necessary to permit a search for moves that might meet the criterion.…”

“…This happens when the problem solver employs insufficient cognitive resources to both mentally sample a move and then evaluate it. In the absence of any lookahead at all ("zero" lookahead), a maximizing move is considered to be selected Spontaneous analogy 6 automatically without being evaluated against the criterion of progress (Ormerod et al, 2013). We found it necessary to make this distinction between sampling and evaluating moves when extending CSPT to the n-ball problem, described below, which forms the focus of empirical work reported here.…”