MSGID: 1:317/3 64781f01   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Can we learn to think further ahead?    
    Cognitive scientists' computational model shows how expertise improves   
   planning depth    
      
     Date:   
         May 31, 2023   
     Source:   
         New York University   
     Summary:   
         Chess grandmasters are often held up as the epitome of thinking   
         far ahead. But can others, with a modest amount of practice,   
         learn to think further ahead? In addressing this question, a team   
         of cognitive scientists has created a computational model that   
         reveals our ability to plan for future events. The work enhances   
         our understanding of the factors that affect decision-making and   
         shows how we can boost our planning skills through practice.   
      
      
         Facebook Twitter Pinterest LinkedIN Email   
      
   ==========================================================================   
   FULL STORY   
   ==========================================================================   
   Chess grandmasters are often held up as the epitome of thinking far   
   ahead. But can others, with a modest amount of practice, learn to   
   think further ahead?  In addressing this question, a team of cognitive   
   scientists has created a computational model that reveals our ability   
   to plan for future events. The work enhances our understanding of the   
   factors that affect decision-making and shows how we can boost our   
   planning skills through practice.   
      
   The research, conducted by scientists in New York University's Center for   
   Neural Science and reported in the journal Nature, centers on the role of   
   "planning depth" -- the number of steps that an individual thinks ahead --   
   in decision-making.   
      
   "While artificial intelligence has made impressive progress in solving   
   complex planning problems, much less is understood about the nature   
   and depth of planning in people," explains Wei Ji Ma, a professor of   
   neuroscience and psychology at NYU and the paper's senior author. "Our   
   work adds to this body of knowledge by showing that even a relatively   
   modest amount of practice can improve depth of planning."  It's been long   
   established that a hallmark of human intelligence is the ability to plan   
   multiple steps into the future. However, it's less clear whether or not   
   skilled decision makers plan more steps ahead than do novices. This is   
   because methods for measuring this aptitude (e.g., experiments involving   
   board games) have notable shortcomings -- in part, because they don't   
   reliably estimate planning depth.   
      
   The Naturepaper's authors had people play a relatively simple game --   
   a more sophisticated version of tic-tac-toe -- that still required   
   players to plan deeply (i.e., multiple steps ahead). Then, to understand   
   precisely what goes on in people's minds as they are thinking of their   
   next move in this game, the authors designed a computer model based on   
   AI principles. The model allows them to describe and subsequently predict   
   the moves that people make when faced with new situations in the game.   
      
   "In this computational model, players build a 'decision tree' in their   
   heads the same way that you might plan for multiple possible scenarios   
   for a complex travel itinerary," Ma explains.   
      
   Here, their calculations showed that human behavior can be captured using   
   a computational cognitive model based on a heuristic search algorithm --   
   one that maps out a sequence of promising moves for both players.   
      
   To validate the model, the researchers conducted a series of behavioral   
   experiments with human participants. Specifically, they tracked how   
   players planned their moves under different scenarios while also testing   
   their memory and their ability to learn from and reconstruct their   
   game-playing experiences.   
      
   In addition, the team conducted a Turing test experiment in which   
   observers, who had played the game before, were asked to determine whether   
   sequences of moves they witnessed were generated by the model or by human   
   players. These observers were able to make the correct distinction only   
   about half the time, suggesting that the model makes similar decisions   
   that a human would make.   
      
   Several of these experiments may be played online by going to Ma's   
   laboratory website.   
      
   Overall, their results showed that better planning is driven by the   
   ability to recognize patterns more accurately and in less time --   
   outcomes that point to the benefits of practice and experience.   
      
   "It is known that cognitive abilities can improve in adulthood through   
   practice," observes Ma. "These findings show that even a relatively   
   modest amount of practice can improve one's depth of planning. This   
   opens up new avenues of research. For example, we can use these methods   
   to study the development of planning abilities in children, or test   
   whether planning abilities can be retained in old age. Of course, it is   
   also crucial that we connect planning in the laboratory to planning in   
   real life."  The paper's other authors are: Bas van Opheusden, an NYU   
   doctoral student at the time of the study and now a research scientist at   
   Generally Intelligent; Ionatan Kuperwajs, an NYU doctoral student; Gianni   
   Galbiati, an NYU researcher at the time of the study and now director of   
   research and development at Vidrovr; Zahy Bnaya, a postdoctoral researcher   
   at NYU's Center for Neural Science; and Yunqi Li, an NYU researcher at   
   the time of the study and now a doctoral student at Stanford University.   
      
   The research was supported by grants from the National Science Foundation   
   (IIS- 1344256, DGE1839302).   
      
       * RELATED_TOPICS   
             o Mind_&_Brain   
                   # Intelligence # Numeracy # Behavior #   
                   Language_Acquisition # Child_Development # Psychology #   
                   Multiple_Sclerosis # Brain-Computer_Interfaces   
       * RELATED_TERMS   
             o Intellectual_giftedness o Cognitive_bias o Attention   
             o Thought o Mental_confusion o Experimental_economics o   
             Cognitive_neuroscience o Psycholinguistics   
      
   ==========================================================================   
   Story Source: Materials provided by New_York_University. Note: Content   
   may be edited for style and length.   
      
      
   ==========================================================================   
   Journal Reference:   
      1. van Opheusden, B., Kuperwajs, I., Galbiati, G. et al. Expertise   
      increases   
         planning depth in human gameplay. Nature, 2023 DOI:   
         10.1038/s41586-023- 06124-2   
   ==========================================================================   
      
   Link to news story:   
   https://www.sciencedaily.com/releases/2023/05/230531150112.htm   
      
   --- up 1 year, 13 weeks, 2 days, 10 hours, 50 minutes   
    * Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)   
   SEEN-BY: 15/0 106/201 114/705 123/120 153/7715 218/700 226/30 227/114   
   SEEN-BY: 229/110 112 113 307 317 400 426 428 470 664 700 291/111 292/854   
   SEEN-BY: 298/25 305/3 317/3 320/219 396/45   
   PATH: 317/3 229/426