Hansjörg Neth Stephen J. Payne School of Psychology, Cardiff University Digital Calculations 1 0011 2 Digit Addition as Interactive Problem Solving.

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Presentation transcript:

Hansjörg Neth Stephen J. Payne School of Psychology, Cardiff University Digital Calculations Digit Addition as Interactive Problem Solving

Interactive Addition, CogSci /17 Introduction: Feedback Loops - Evolution of the genotype - Development and learning of the individual - Interactive problem solving Cognitive Agent Perception Environment Action

Interactive Addition, CogSci /17 Interaction: internal external External representations provide constraints on problem solving (e.g. Larkin & Simon, 1987; Zhang and Norman, 1994) Cost of operator application affects planning and learning (e.g. O’Hara & Payne, 1998) Information displays as a resource in human-computer interaction (e.g. Payne, 1991; Gray & Fu, 2001)

Interactive Addition, CogSci /17 ‘Complementary’ actions Kirsh (1995): Counting coins with or w/o hands - spontaneous ‘organizing activities’ - increased speed and accuracy But: - hands can serve multiple functions - coins ‘afford’ to be manipulated

Interactive Addition, CogSci /17 Experiment: Interaction in Addition Please add these numbers: (a) Linear: Sum: (b) Pairs: Sum: (c) Complements: Sum: Three strategies: Task: Environmental Arithmetic

Interactive Addition, CogSci /17 P N Materials: Lists of single digit numbers }Same ingredients, e.g. R 1 { } R 2 Different structures: Pair lists: Σ=63 C Complement list: = Neutral list: = Structuring the Environment

Interactive Addition, CogSci /17 Design & Procedure Experimental factors: 1.List type: linear (P, C, N) vs. spatial (S) 2.List length: 4, 8, 12 single-digit numbers 3.Interactive mode: look only, point, mark, move Mixed design: within/between-subjects; 44 undergraduates, each participant correctly added 36 lists in ~25 min.

Interactive Addition, CogSci /17 Questions & Hypotheses What do people do when they think? Spontaneous complementary actions? If so, why and how? Other factors? Predictions move > look only mark > point interactive features enable ‘smart’ strategies

Interactive Addition, CogSci /17 Performance: Accuracy and Latency

Interactive Addition, CogSci /17 Moderating factors? Moderating factor 1: List length

Interactive Addition, CogSci /17 Moderating factor 2: List type

Interactive Addition, CogSci /17 Strategies? Assessing ‘moves’ between two addends → Distance? Type? Sum of move distances per trial: More activity on longer lists Group differences only for short lists: move < [mark = point] Strategies: Mouse moves

Interactive Addition, CogSci /17 Move types: Choice of next addend

Interactive Addition, CogSci /17 Move distance x type Selection of nearest neighbours: - point group: 71% of all moves, - mark group: 61% ‘Attractiveness’ of move types? - neutral move: 0.53 nearest neighbours skipped - complement: pair: 2.20

Interactive Addition, CogSci /17 How did the movers move? Economy: No movements for short lists Strategies: - move to mark added numbers - move to group (e.g. pairs, triples…)

Interactive Addition, CogSci /17 Conclusions Spontaneous exploitation of interactive resources reliable differences in performance modulated by task characteristics explained by strategy differences Problems solving depends on the interactions between agent, task, and task environment.

Interactive Addition, CogSci /17 Implications Methodological: studying problem solving at a higher resolution Practical: costs and benefits of minimal interactions Conceptual: ‘action’ ~ ‘cognition’ ~ ‘perception’ What do we do when we think?