PMID- 37145388
OWN - NLM
STAT- MEDLINE
DCOM- 20231216
LR  - 20231216
IS  - 1531-5320 (Electronic)
IS  - 1069-9384 (Linking)
VI  - 30
IP  - 5
DP  - 2023 Oct
TI  - Value-driven attention and associative learning models: a computational 
      simulation analysis.
PG  - 1689-1706
LID - 10.3758/s13423-023-02296-0 [doi]
AB  - Value-driven attentional capture (VDAC) refers to a phenomenon by which stimulus 
      features associated with greater reward value attract more attention than those 
      associated with smaller reward value. To date, the majority of VDAC research has 
      revealed that the relationship between reward history and attentional allocation 
      follows associative learning rules. Accordingly, a mathematical implementation of 
      associative learning models and multiple comparison between them can elucidate 
      the underlying process and properties of VDAC. In this study, we implemented the 
      Rescorla-Wagner, Mackintosh (Mac), Schumajuk-Pearce-Hall (SPH), and 
      Esber-Haselgrove (EH) models to determine whether different models predict 
      different outcomes when critical parameters in VDAC were adjusted. Simulation 
      results were compared with experimental data from a series of VDAC studies by 
      fitting two key model parameters, associative strength (V) and associability (alpha), 
      using the Bayesian information criterion as a loss function. The results showed 
      that SPH-V and EH- alpha outperformed other implementations of phenomena related to 
      VDAC, such as expected value, training session, switching (or inertia), and 
      uncertainty. Although V of models were sufficient to simulate VDAC when the 
      expected value was the main manipulation of the experiment, alpha of models could 
      predict additional aspects of VDAC, including uncertainty and resistance to 
      extinction. In summary, associative learning models concur with the crucial 
      aspects of behavioral data from VDAC experiments and elucidate underlying 
      dynamics including novel predictions that need to be verified.
CI  - (c) 2023. The Psychonomic Society, Inc.
FAU - Jeong, Ji Hoon
AU  - Jeong JH
AD  - School of Psychology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, 
      Korea.
FAU - Ju, Jangkyu
AU  - Ju J
AD  - School of Psychology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, 
      Korea.
FAU - Kim, Sunghyun
AU  - Kim S
AD  - School of Psychology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, 
      Korea.
FAU - Choi, June-Seek
AU  - Choi JS
AD  - School of Psychology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, 
      Korea.
FAU - Cho, Yang Seok
AU  - Cho YS
AUID- ORCID: 0000-0002-8481-3740
AD  - School of Psychology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, 
      Korea. yscho_psych@korea.ac.kr.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230505
PL  - United States
TA  - Psychon Bull Rev
JT  - Psychonomic bulletin & review
JID - 9502924
SB  - IM
MH  - Humans
MH  - Bayes Theorem
MH  - *Conditioning, Classical
MH  - *Reward
MH  - Computer Simulation
MH  - Uncertainty
MH  - Association Learning
OTO - NOTNLM
OT  - Associative learning
OT  - Computational simulation
OT  - Mathematical implementation
OT  - Model comparison
OT  - Value-driven attentional capture
EDAT- 2023/05/05 12:42
MHDA- 2023/12/17 09:45
CRDT- 2023/05/05 11:17
PHST- 2023/04/16 00:00 [accepted]
PHST- 2023/12/17 09:45 [medline]
PHST- 2023/05/05 12:42 [pubmed]
PHST- 2023/05/05 11:17 [entrez]
AID - 10.3758/s13423-023-02296-0 [pii]
AID - 10.3758/s13423-023-02296-0 [doi]
PST - ppublish
SO  - Psychon Bull Rev. 2023 Oct;30(5):1689-1706. doi: 10.3758/s13423-023-02296-0. Epub 
      2023 May 5.