Departures from optimality when pursuing multiple approach or avoidance goals.

Ballard, Timothy; Yeo, Gillian; Neal, Alan C.; Farrell, Simon

doi:10.1037/apl0000082

Cited by 19 publications

(24 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In what follows we derive the process of estimating choice values and subsequent choices based on dynamic programming applied to a finite horizon Markov decision process ([20]; for experimental studies see also [9, 21]).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

Ott

Marković

Strobel

et al. 2019

Preprint

View full text Add to dashboard Cite

27Selecting goals and successfully pursuing them in an uncertain and dynamic environment is an 28 important aspect of human behaviour. In order to decide which goal to pursue at what point in time, 29 one has to evaluate the consequences of one's actions over future time steps by forward planning. 30 However, when the goal is still temporally distant, detailed forward planning can be prohibitively 31 costly. One way to select actions at minimal computational costs is to use heuristics. It is an open 32 question how humans mix heuristics with forward planning to balance computational costs with goal 33 reaching performance. To test a hypothesis about dynamic mixing of heuristics with forward 34 planning, we used a novel stochastic sequential two-goal task. Comparing participants' decisions 35 with an optimal full planning agent, we found that at the early stages of goal-reaching sequences, in 36 which both goals are temporally distant and planning complexity is high, on average 42% (SD = 37 19%) of participants' choices deviated from the agent's optimal choices. Only towards the end of the 38 sequence, participant's behaviour converged to near optimal performance. Subsequent model-based 39 analyses showed that participants used heuristic preferences when the goal was temporally distant 40 and switched to forward planning when the goal was close. 41Author summary 42 When we pursue our goals, there is often a moment when we recognize that we did not make the 43 progress that we hoped for. What should we do now? Persevere to achieve the original goal, or 44 switch to another goal? Two features of real-world goal pursuit make these decisions particularly 45 complex. First, goals can lie far into an unpredictable future and second, there are many potential 46 goals to pursue. When potential goals are temporally distant, human decision makers cannot use an 47 exhaustive planning strategy, rendering simpler rules of thumb more appropriate. An important 48 question is how humans adjust the rule of thumb approach once they get closer to the goal. We 49 addressed this question using a novel sequential two-goal task and analysed the choice data using a 50 computational model which arbitrates between a rule of thumb and accurate planning. We found that 51 participants' decision making progressively improved as the goal came closer and that this 52 improvement was most likely caused by participants starting to plan ahead. 53 Introduction 54 Decisions of which goal to pursue at what point in time are central to everyday life [1-3]. Typically, 55in our dynamic environment, the outcomes of our decisions are stochastic and one cannot predict 56 with certainty whether a preferred goal can be reached. Often, our environment also presents 57 alternative goals that may be less preferred but can be reached with a higher probability than the 58 preferred goal. For example, when working towards a specific dream position in a career, it may turn 59 out after some time that the position is unlikely to be obtained, while another le...

show abstract

Section: Methodsmentioning

confidence: 99%

“…This approach is often used in cognitive neuroscience to model the mechanism of how humans make decisions in temporally extended goal-reaching scenarios, (e.g. [6–9]).…”

Section: Introductionmentioning

confidence: 99%

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

Ott

Marković

Strobel

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Pts A T < 10 or Pts B T � 10). In what follows we derive the process of estimating choice values and subsequent choices based on dynamic programming applied to a finite horizon Markov decision process ( [20]; for experimental studies see also [9,21]).…”

Section: Behavioural Modelmentioning

confidence: 99%

“…This approach is often used in cognitive neuroscience to model the mechanism of how humans make decisions in temporally extended goal-reaching scenarios, (e.g. [6][7][8][9]).…”

Section: Introductionmentioning

confidence: 99%

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

et al. 2020

View full text Add to dashboard Cite

Selecting goals and successfully pursuing them in an uncertain and dynamic environment is an important aspect of human behaviour. In order to decide which goal to pursue at what point in time, one has to evaluate the consequences of one's actions over future time steps by forward planning. However, when the goal is still temporally distant, detailed forward planning can be prohibitively costly. One way to select actions at minimal computational costs is to use heuristics. It is an open question how humans mix heuristics with forward planning to balance computational costs with goal reaching performance. To test a hypothesis about dynamic mixing of heuristics with forward planning, we used a novel stochastic sequential two-goal task. Comparing participants' decisions with an optimal full planning agent, we found that at the early stages of goal-reaching sequences, in which both goals are temporally distant and planning complexity is high, on average 42% (SD = 19%) of participants' choices deviated from the agent's optimal choices. Only towards the end of the sequence, participant's behaviour converged to near optimal performance. Subsequent model-based analyses showed that participants used heuristic preferences when the goal was temporally distant and switched to forward planning when the goal was close.

show abstract

“…Incentives improved team performance when difficult goals were set but not in the case of easy goals. Ballard, Yeo, Neal, and Farrell (2016) investigated goal-framing effects (approach vs. avoidance goals) on the optimality of decisions in the pursuit of two simultaneous goals. Decision makers chose between two actions that would prioritize one of the two simultaneous goals (i.e., achieve or avoid a target score).…”

Section: Motivation and Volition In Decisions Under Riskmentioning

confidence: 99%

The role of motivation and volition in economic decisions: Evidence from eye movements and pupillometry

Ludwig

Jaudas

Achtziger

2019

Behavioral Decision Making

View full text Add to dashboard Cite

Economic decisions usually involve high stakes, real consequences, and some degree of personal risk. This article explores the impact of motivational and volitional states on economic decision processes in an incentivized lottery choice task. We investigated the patterns of decision time, choice, information search, and pupil dilation dependent on an experimental manipulation of motivation and volition, that is, the deliberative and the implemental mindset. The results indicated that choice preferences in economic decisions were robust and remained unaffected by motivational and volitional states, but decision processes were notably impacted. Decision makers in a deliberative state of mind searched for information more extensively and made slower decisions than the baseline. The implemental mindset was associated with more attention paid to the probability attributes of the gambles relative to the deliberative mindset. Furthermore, we observed that gamble outcomes that entailed no win at all (i.e., zero outcomes) played an important role for information search. These outcomes were largely disregarded in terms of predecisional information search but elicited pupillary responses similar to very high outcome lotteries. These results inform the current debate about the zero effect in risky choice. We also discuss the potential of eye‐tracking studies of risky choice to dissolve ambiguities concerning the contributions of effort and arousal to modulating pupillary response. Implications for theoretical advances in decision research are discussed.

show abstract

Departures from optimality when pursuing multiple approach or avoidance goals.

Cited by 19 publications

References 59 publications

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit

The role of motivation and volition in economic decisions: Evidence from eye movements and pupillometry

Contact Info

Product

Resources

About